WO2021067844A1 - Closed surface flight pattern generation for unmanned aerial vehicle (uav) flux plane assessment - Google Patents

Closed surface flight pattern generation for unmanned aerial vehicle (uav) flux plane assessment Download PDF

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Publication number
WO2021067844A1
WO2021067844A1 PCT/US2020/054117 US2020054117W WO2021067844A1 WO 2021067844 A1 WO2021067844 A1 WO 2021067844A1 US 2020054117 W US2020054117 W US 2020054117W WO 2021067844 A1 WO2021067844 A1 WO 2021067844A1
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WO
WIPO (PCT)
Prior art keywords
aerial vehicle
processor
continuous surface
flight
gas
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2020/054117
Other languages
French (fr)
Inventor
Victor Alexander MILLER II
Brendan James SMITH
Stuart Buckingham
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Seekops Inc
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Seekops Inc
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Filing date
Publication date
Application filed by Seekops Inc filed Critical Seekops Inc
Priority to US17/766,299 priority Critical patent/US12197233B2/en
Priority to EP20871387.5A priority patent/EP4038357A4/en
Publication of WO2021067844A1 publication Critical patent/WO2021067844A1/en
Anticipated expiration legal-status Critical
Priority to US18/962,544 priority patent/US20250164994A1/en
Ceased legal-status Critical Current

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Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0094Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10Simultaneous control of position or course in three dimensions
    • G05D1/101Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B64AIRCRAFT; AVIATION; COSMONAUTICS
    • B64UUNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00Type of UAV
    • B64U10/10Rotorcrafts
    • B64U10/13Flying platforms
    • B64U10/14Flying platforms with four distinct rotor axes, e.g. quadcopters
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00Investigating fluid-tightness of structures
    • G01M3/02Investigating fluid-tightness of structures by using fluid or vacuum
    • G01M3/04Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00Investigating fluid-tightness of structures
    • G01M3/02Investigating fluid-tightness of structures by using fluid or vacuum
    • G01M3/04Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
    • G01M3/16Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00Investigating fluid-tightness of structures
    • G01M3/02Investigating fluid-tightness of structures by using fluid or vacuum
    • G01M3/04Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
    • G01M3/20Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using special tracer materials, e.g. dye, fluorescent material, radioactive material
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/40Control within particular dimensions
    • G05D1/46Control of position or course in three dimensions [3D]
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft
    • G08G5/20Arrangements for acquiring, generating, sharing or displaying traffic information
    • G08G5/22Arrangements for acquiring, generating, sharing or displaying traffic information located on the ground
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft
    • G08G5/20Arrangements for acquiring, generating, sharing or displaying traffic information
    • G08G5/26Transmission of traffic-related information between aircraft and ground stations
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft
    • G08G5/30Flight plan management
    • G08G5/32Flight plan management for flight plan preparation
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft
    • G08G5/50Navigation or guidance aids
    • G08G5/55Navigation or guidance aids for a single aircraft
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B64AIRCRAFT; AVIATION; COSMONAUTICS
    • B64UUNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00UAVs specially adapted for particular uses or applications
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B64AIRCRAFT; AVIATION; COSMONAUTICS
    • B64UUNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00UAVs specially adapted for particular uses or applications
    • B64U2101/35UAVs specially adapted for particular uses or applications for science, e.g. meteorology
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B64AIRCRAFT; AVIATION; COSMONAUTICS
    • B64UUNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00UAVs characterised by their flight controls
    • B64U2201/10UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft
    • G08G5/50Navigation or guidance aids
    • G08G5/57Navigation or guidance aids for unmanned aircraft
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft
    • G08G5/50Navigation or guidance aids
    • G08G5/59Navigation or guidance aids in accordance with predefined flight zones, e.g. to avoid prohibited zones

Definitions

  • Embodiments relate generally to gas leak detection, and more particularly to gas leak detection of large facilities.
  • a system embodiment may include: a processor having addressable memory, where the processor may be configured to: determine one or more flight paths for an aerial vehicle, where the determined flight path creates a continuous surface about one or more potential gas sources of a survey site; receive a trace gas data from one or more trace gas sensors of the aerial vehicle of the continuous surface as the aerial vehicle flies the determined one or more flight paths; and determine based on the received trace gas data whether a gas leak may be present in the received survey site and a rate of the gas leak if present in the survey site.
  • the continuous surface is a flux plane.
  • the flux plane is a closed flux plane.
  • the continuous surface is formed by any series of turns in the flight path for the aerial vehicle that forms the continuous surface when interpolated.
  • the processor may be further configured to: receive one or more flight platform capabilities of the aerial vehicle, where the determined flight path may be based on the received one or more flight platform capabilities.
  • the processor may be further configured to: receive a wind data for the survey site, where the determined flight path may be further based on the received wind data.
  • the wind data may include instantaneous wind speed measurements in three dimensions from an anemometer.
  • the aerial vehicle may be an unmanned aerial vehicle (UAV).
  • UAV unmanned aerial vehicle
  • the UAV may be configured to fly the determined one or more flight paths autonomously.
  • the UAV may be configured to fly the determined one or more flight paths semi- autonomously.
  • the continuous surface comprises at least one of: a beehive-shaped continuous surface and a cone-shaped continuous surface.
  • the continuous surface comprises a right angle, an arc, and/or a continuous curve in the flight path for the aerial vehicle.
  • the aerial vehicle flies a determined flight path of the one or more flight path two or more times, and where the received trace gas data may be averaged from each of the two or more flights.
  • the processor may be further configured to, for creating the continuous surface: convert a dataset into an altitude-s space; perform a triangulation on the dataset in the altitude s-space; and apply the performed triangulation on a Cartesian X, Y, Z space of the dataset to produce at least one of: a fully closed surface and a semi-closed surface.
  • a method embodiment may include: determining, by the processor, one or more flight paths for an aerial vehicle, where the determined flight path creates a continuous surface about one or more potential gas sources of a survey site; receiving, by the processor, a trace gas data from one or more trace gas sensors of the aerial vehicle of the continuous surface as the aerial vehicle flies the determined one or more flight paths; and determining, by the processor, based on the received gas data whether a gas leak may be present in the received survey site and a rate of the gas leak if present in the survey site.
  • the continuous surface is a flux plane.
  • the flux plane is a closed flux plane.
  • the continuous surface is formed by any series of turns in the flight path for the aerial vehicle that forms the continuous surface when interpolated.
  • Additional method embodiments may include: receiving, by the processor, one or more flight platform capabilities, where the determined flight path may be based the received one or more flight platform capabilities. Additional method embodiments may include: receiving, by the processor, a wind data for the survey site (210, 404), where the determined flight path may be further based the received one or more flight platform capabilities, and the received wind data.
  • the aerial vehicle may be an unmanned aerial vehicle (UAV).
  • UAV unmanned aerial vehicle
  • the UAV may be configured to fly the determined one or more flight paths autonomously.
  • the UAV may be configured to fly the determined one or more flight paths semi- autonomously.
  • creating the continuous surface further comprises: converting, by the processor, a dataset into an altitude-s space; performing, by the processor, a triangulation on the dataset in the altitude s-space; and applying, by the processor, the performed triangulation on a Cartesian X, Y, Z space of the dataset to produce at least one of: a fully closed surface and a semi-closed surface.
  • FIG. 1 depicts an aerial vehicle flying a generally cylindrical flight path to survey a continuous surface in cylindrical coordinates and capture a cross-section of a gas from a survey site, according to one embodiment
  • FIG. 2 depicts an unmanned aerial vehicle (UAV) flying a generally beehive shaped flight path to survey a closed continuous surface and capture a cross-section of a gas from a survey site, according to one embodiment
  • UAV unmanned aerial vehicle
  • FIGS. 3A-3D depict closed continuous surfaces based on UAV flight paths, according to one embodiment
  • FIG. 4 illustrates an example top-level functional block diagram of a flight pattern generation system for creating closed continuous surfaces, according to one embodiment
  • FIG. 5 depicts a high-level flowchart of a method embodiment of generating a flight pattern for creating a closed continuous surface, according to one embodiment
  • FIG. 6 depicts a high-level flowchart of a method embodiment of generating a continuous surface, according to one embodiment
  • FIG. 7 depicts a high-level flowchart of a method embodiment of determining a volumetric flowrate of inflowing gas species concentration, according to one embodiment
  • FIG. 8 illustrates an example top-level functional block diagram of a computing device embodiment
  • FIG. 9 shows a high-level block diagram and process of a computing system for implementing an embodiment of the system and process
  • FIG. 10 shows a block diagram and process of an exemplary system in which an embodiment may be implemented
  • FIG. 11 depicts a cloud computing environment for implementing an embodiment of the system and process disclosed herein.
  • FIG. 12 depicts a system for detecting trace gasses, according to one embodiment.
  • the present system allows for the generation of a flight path for an unmanned aerial vehicle (UAV) having one or more gas sensors to fly around a survey site that may contain one or more potential gas sources.
  • the potential gas sources may include a gas pipeline, an oil rig, and the like.
  • the generated flight path for the UAV may form a closed surface around the potential gas source.
  • the generated flight path may allow the UAV to survey a closed continuous surface and capture a cross-section of gas that may be emitted from one or more potential gas sources in a large facility.
  • Embodiments may include a small, highly maneuverable, and/or remotely piloted UAV capable of detecting, localizing, and/or quantifying gas leaks using novel flight paths.
  • Trace gas sensors may be used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane, sulfur dioxide) in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, such as oil and gas, chemical production, and painting. Detection and quantification of leaks is also of value to environmental regulators for assessing compliance and for mitigating environmental and safety risks.
  • toxic gases e.g., hydrogen disulfide
  • environmentally damaging gases e.g., methane, sulfur dioxide
  • Industrial sites may be surveyed for toxic gas leaks using a manned flight platform, such as a fixed-wing aircraft or a helicopter. If trace gases are detected near or around a survey site, the leak size, i.e., flow rate, may be estimated by flying a continuous surface that captures a cross-section of the gas. This continuous surface relates the flux of trace gas through a plane with the upstream source, under the assumption that all gas flux originating from the source passes through a plane downstream.
  • the specific details of a continuous surface flight pattern may be determined by the capabilities of the flight platform, as well as the details of the survey site itself.
  • a survey site with a linear array of potential leak sources such as a pipeline, may require a different flight pattern than an industrial site that may be essentially an isolated point source.
  • the survey may extend downwind of the site.
  • the generated flight pattern may typically be arcs downwind of the fixed point or aggregate of points.
  • FIG. 1 depicts a system 100 for an aerial vehicle 102 flying a generally cylindrical flight path 104 to survey a continuous surface 106 in cylindrical coordinates and capture a cross-section of a gas 108 from a survey site 110, according to one embodiment.
  • the aerial vehicle 102 may be a fixed-wing flight platform, such as a Cessna aircraft.
  • the aerial vehicle 102 may survey for leaks is the generally cylindrical flight path 104.
  • Laps may be flown around the survey site 110 at different altitudes.
  • the survey site 110 may be a large facility and may include one or more potential gas sources.
  • the generally cylindrical flight path 104 of the aerial vehicle 102 may effectively survey the continuous surface 106 in cylindrical coordinates.
  • the continuous surface 106 is depicted in dashed lines in FIG. 1.
  • This generally cylindrical flight path 104 may minimize the effect of atmospheric turbulence by effectively drawing a line integral around the survey site 110.
  • Such a flight path 104 may be effective in assuring the detection of a leak and quantifying the source leak rate in some embodiments.
  • this flight path 104 is time-consuming and suffers from the inability to assess fluxes in a z-direction 112.
  • the cylindrical flight path 104 fails to measure fluxes flowing out the top 114 of the cylindrical flux path 106.
  • the cylindrical flight path 104 may be ineffective in detecting a gas leak for gasses 108 rising up in the z-direction 108 as these gasses 108 may not pass through the cylindrical continuous surface 106. Extending the flight path 104 and cylindrical continuous surface 106 further in the z-direction may capture additional gasses 108 but only at a greatly increased time and energy cost as the aerial vehicle 102 must fly a much further distance.
  • FIG. 2 depicts a system 200 for an aerial vehicle such as an unmanned aerial vehicle (UAV) 202 flying a generally beehive-shaped flight path 204 to survey a continuous surface 206 and capture a cross-section of a gas 208 from a survey site 210, according to one embodiment.
  • the continuous surface 206 may be a flux plane.
  • the continuous surface 206 may be a closed flux plane.
  • the continuous surface 206 may be formed by any polygon or any series of turns that forms the continuous surface when interpolated.
  • the continuous surface 206 may be formed by a right angle, an arc, and/or a continuous curve in a flight path for the UAV 202.
  • the UAV 202 may be a small, highly maneuverable, and/or remotely piloted airborne platform.
  • the UAV may be used to detect, localize and quantify gas leaks 208 at survey sites 210, such as industrial sites, using novel flight paths 204 that manned aerial platforms, such as shown in FIG. 1, are unable to fly.
  • These manned flight platforms for civilian use may have much lower dynamic performance than unmanned platforms, such as the UAV 202.
  • the UAV 202 may be a multi-rotor platform, such as a quadcopter, which may not be constrained by a stall speed like fixed-wing platforms, enabling the UAV 202 to stop their motion and incorporate acute angles, obtuse angles, and/or right angles into the flight path.
  • the UAV 202 may fly a time- and distance-efficient survey of a gas leak 208 from a survey site 210.
  • the UAV 202 may fly a beehive-shaped flight path 204 around a survey site 210. While the flight path 204 is depicted as forming a generally beehive shape, the flight path 204 may include additional rotations about the survey site 210, variations due to the wind, variations due to survey site conditions, variations due to user settings, variations due to user limitations, and the like.
  • the shape of the flight path 204 may be modified to consider a variety of factors, including possible gas sources, gas plume shapes, and wind velocity. Furthermore, the flight path 204 shape may be generated to account for rules or laws that may require a minimum distance from the source of the leak, such a gas pipeline, offshore oil rig, and the like.
  • the beehive-shaped flight path 204 may have a smaller radius at higher altitudes, effectively closing off the continuous surface 206, which is depicted in dashed lines.
  • Such a flight path 204 is an improvement upon a cylindrical flight path, such as shown in FIG. 1, in that it both assesses fluxes in the z-direction 212, while also reducing the maximum altitude in the flight path 204, thereby reducing the flight time.
  • the flight path 204 may be completed in a shorter amount of time as compared to a cylindrical flight path.
  • the flight path 204 may be expanded to cover a greater area surrounding the survey site 210 in a shorter or same amount of time as compared to a cylindrical flight path.
  • a processor 214 may be in communication 216 with the UAV 202.
  • the processor may generate the flight path 204 and communicate 216 the flight path to the UAV 202.
  • the UAV may then fly the flight path 204 and generate gas data, which may be communicated 216 to the processor 214.
  • the processor 214 may be a ground control station (GCS) used to control the UAV 202.
  • the processor 214 may be a part of the UAV 202.
  • the UAV 202 may fly the flight path 204 autonomously or semi-autonomously.
  • FIGS. 3A-3D depict closed continuous surfaces 300, 304, 308, 312 based on UAV flight paths 302, 306, 310, 314, according to one embodiment.
  • FIG. 3 A depicts a beehive-shaped continuous surface 300 created from a first flight path 302.
  • FIG. 3B depicts a cone-shaped continuous surface 304 created from a second flight path 306.
  • FIG. 3C depicts a box-shaped continuous surface 308 created from a third flight path 310.
  • the third flight path 310 may include right angles if the UAV being used is a multi-rotor vehicle, such as a quadcopter.
  • FIG. 3D depicts a cone-shaped continuous surface 312 created from a fourth flight path 314.
  • the shape of a continuous surface created by a flight path from a UAV may be a closed shape that encapsulates the survey site having one or more potential gas leaks.
  • closed continuous surfaces may be used to detect the presence of a gas leak and/or the rate of a gas leak.
  • These closed continuous surfaces may be any closed shape and may have variations in the shape due to the wind, survey site conditions, user settings, user limitations, laws and regulations, and the like.
  • the flight path of the UAV may be a random flight path creating a closed continuous surface rather than a smooth curve, such as shown in FIGS. 3A, 3B, and 3D.
  • the flight path of the UAV may be flown several times and averaged together to generate a result. For example, a UAV may fly a flight path 2-3 times and the data may be averaged together to generate a more accurate result.
  • the same flight path may be flown each time.
  • different flight paths may be flown.
  • a first flight path may be a smooth curve, such as shown in FIGS. 3A, 3B, or 3D and a second flight path may be a random pattern.
  • the radius of the closed continuous surface may be a function of the gas sensor sensitivity.
  • a sampling rate for the gas sensor in the UAV flight path may be a compromise between a desired spatial sampling resolution and a minimum detection limit.
  • the planning of the flight path and closed continuous surface may be a desired shape, operational constraints, safety from the customer, optimization of a best scenario for time spent in the plume, and the like.
  • FIG. 4 illustrates an example top-level functional block diagram of a flight pattern generation system 400 for creating closed continuous surfaces, according to one embodiment.
  • the system 400 may include a processor 402.
  • the processor 402 may receive information on a survey site 404, which may be an area containing one or more potential gas sources.
  • the one or more potential gas sources may be equipment and/or locations more likely to leak toxic gases, such as hydrogen disulfide, or environmentally damaging gases, such as methane and sulfur dioxide.
  • the survey site information 404 may also include user rules, user preferences, rules, and/or laws relating to the survey site 404. For example, local laws may prohibit an aerial vehicle from being within twenty feet of a pipeline and a user preference may be to remain forty feet away from a pipeline in a survey site.
  • the processor 402 may also receive flight platform capabilities 406 for an aerial vehicle 408.
  • the flight platform capabilities 406 may include battery capacity, payload limits, maximum flight time, operating restrictions, and the like.
  • the flight platform capabilities 406 may also include a maneuverability of the aerial vehicle 408.
  • a quadrotor type aerial vehicle 408 may be able to hover stop, make acute angle turns, make obtuse angle turns, and make right angle turns.
  • a fixed-wing UAV may be limited to a set turn radius and/or minimum flight speed.
  • the aerial vehicle 408 may be an unmanned aerial vehicle (UAV).
  • UAV unmanned aerial vehicle
  • the UAV may be autonomous and/or semi-autonomous.
  • the processor 402 may also receive wind data 410.
  • Wind data 410 may include wind speed and/or wind direction for the survey site 404.
  • wind data 410 may also include predictions as to changes in the wind speed and/or wind direction.
  • the processor 402 may determine one or more flight paths, such as shown in FIGS. 2-3D, for the aerial vehicle 408 based on the received survey site information 404, flight platform capabilities 406, and/or wind data 410.
  • the determined one or more flight paths may create a closed continuous surface, such as shown in FIGS. 2-3D, about one or more potential gas sources of the survey site 404.
  • the aerial vehicle 408 may have at least one trace gas sensor 412 to generate trace gas data based on detected trace gas in the closed continuous surface as the aerial vehicle 408 flies the determined one or more flight paths.
  • the aerial vehicle 408 may have a processor 414 in communication with addressable memory 416, a GPS 418, one or more motors 420, and a power supply 422.
  • the aerial vehicle 408 may receive the flight plan from the processor 402 and communicate gathered gas sensor 412 data to the processor 402.
  • the at least one gas sensor 412 may be configured to detect carbon dioxide.
  • the at least one trace gas sensor 412 may be configured to detect nitrogen oxide.
  • the at least one trace gas sensor 412 may be configured to detect sulfur oxide, such as SO, SO2, SO3, S7O2, S6O2, S2O2, and the like.
  • the GPS 418 may record the location of the aerial vehicle 408 when each gas sensor 412 data is acquired.
  • the GPS 418 may also allow the aerial vehicle 408 to travel the flight path generated by the processor 402.
  • the location of the aerial vehicle 408 may be determined by an onboard avionics 424.
  • the onboard avionics 424 may include a triangulation system, a beacon, a spatial coordinate system, or the like.
  • the onboard avionics 424 may be used with the GPS 418 in some embodiments. In other embodiments, the aerial vehicle 408 may use only one of the GPS 418 and the onboard avionics 424.
  • the location information from the GPS 418 and/or onboard avionics 424 may be combined with the gas sensor 412 data to determine if gas is present through the closed continuous surface created by the flight plan of the aerial vehicle 408.
  • wind data 432 may be measured onboard the aerial vehicle 408, such as via a wind sensor mounted to the aerial vehicle 408.
  • the power supply 422 may be a battery in some embodiments.
  • the power supply 422 may limit the available flight time for the aerial vehicle 408 and so the time- and energy-efficiency flight paths created by the processor 402 allow for the determination as to whether there are any gas leaks through the closed continuous surface.
  • the processor 402 may be a part of the aerial vehicle 408, a cloud computing device, a ground control station (GCS) used to control the aerial vehicle 408, or the like.
  • GCS ground control station
  • a user interface 430 may in communication with the processor 402. The user interface 430 may be used to select the flight path, make changes to the flight path, receive gas data, or the like.
  • the user interface 430 may be a part of the processor 402, the additional processor 428, and/or a GCS.
  • the processor 402 may receive gas data from the one or more trace gas sensors 412 of the aerial vehicle 408. The processor 402 may then determine, based on the received gas data, whether a gas leak is present and/or a rate of the gas leak in the survey site 404. If a gas leak is not detected, no immediate action is needed and further tests may be accomplished in the future to ensure that no gas leaks develop. If a gas leak is detected, then corrective action may be taken to minimize and/or stop the gas leak.
  • the processor 402 may be in communication with addressable memory 426.
  • the memory 426 may store the result of whether a gas leak was detected, historical gas data, the flight platform capabilities 406, wind data 814, and/or data from the aerial vehicle 408.
  • the processor 402 may be in communication with an additional processor 428.
  • the additional processor 428 may be a part of the aerial vehicle 408, a cloud computing device, a GCS used to control the aerial vehicle 408, or the like.
  • FIG. 5 depicts a high-level flowchart of a method embodiment 500 of generating a flight pattern for creating a closed continuous surface, according to one embodiment.
  • the method 500 may include receiving, by a processor having addressable memory, a survey site having one or more potential gas sources (step 502).
  • the method 500 may also include receiving, by the processor, one or more flight platform capabilities (step 504).
  • the method 500 may also include receiving, by the processor, a wind data for the received survey site (step 506).
  • the method 500 may then include determining, by the processor, one or more flight paths for an aerial vehicle based on the received survey site, the received one or more flight platform capabilities, and/or the received wind data (step 508).
  • the determined flight path may create a closed continuous surface about the one or more potential gas sources of the survey site.
  • the method 500 may then include receiving, by the processor, gas data from one or more gas sensors of the closed continuous surface as the aerial vehicle flies the determined one or more flight paths (step 510).
  • the method 500 may then include determining, by the processor, based on the received gas data whether a gas leak is present and/or a rate of the gas leak in the received survey site (step 512).
  • Flying a closed perimeter surface with a UAV may capture all the emissions from a survey site. If a flightpath completely encompasses the survey site, Gauss’ Theorem may be applied directly with a processor having addressable memory to calculate the gas leak rate within the flightpath perimeter using the gas concentration measurements detected by the gas sensor at the perimeter. Due to operational constraints, such as access roads, power lines etc. flying a fully closed flightpath may not always possible; therefore, it is preferred to fly the perimeter with a small gap where flying is not possible. This may be referred to as a “semi-Gauss” flight pattern.
  • Analyzing Gauss and semi-Gauss flight patterns to extract a gas leak rate may require application, with the processor, of computational geometry algorithms.
  • a dataset consisting of point measurements made by the UAV in space may be converted into Cartesian coordinates with the processor.
  • the conversion may be done using the Position Easting, Position Northing coordinates from the Extended Kalman Filter (EKF) as the X and Y components, and the Light Detection and Ranging (LiDAR) altitude from a LiDAR range finder for the Z component.
  • EKF Extended Kalman Filter
  • LiDAR Light Detection and Ranging
  • the individual flux values may have to be integrated over the whole dataset of spatial coordinates.
  • the processor may triangulate the point measurements, thereby providing a continuous surface to determine the surface integral on ( dS ), where S is the surface:
  • FIG. 6 depicts a high-level flowchart of a method embodiment 600 of generating a continuous surface, according to one embodiment.
  • a method embodiment 600 of generating a continuous surface according to one embodiment.
  • Delaunay Triangulation methods create a two-dimensional tri angulation, or surface, from a two-dimensional dataset; however, said methods are not able to create a two-dimensional surface from a three-dimensional dataset. Therefore, before applying a Delaunay Triangulation algorithm, the dataset must first be represented in two-dimensional space.
  • the dataset can be converted into altitude- ⁇ space by a processor having addressable memory, where altitude is the same dimension as in the three- dimensional dataset, and 5 is a new dimension measuring distance along the pass made by the UAV (step 602).
  • the value of 5 monotonically increases along the pass, until the UAV reaches a determined point and then the value of 5 starts again from zero.
  • the value of 5 monotonically increases until the UAV reaches whatever obstacle the UAV must avoid, and then the UAV preforms a U-turn and backtracks along the same path (at a different altitude) while the value of 5 decreases.
  • a Delaunay Triangulation may be performed by the processor (step 604).
  • the resulting triangulation may then be applied by the processor on the dataset’s original Cartesian X, Y, Z space to produce a fully closed or semi-closed surface (step 606), with all the original scalar values (e.g. gas concentrations) intact.
  • FIG. 7 depicts a high-level flowchart of a method embodiment 700 of determining a volumetric flowrate of inflowing gas species concentration, according to one embodiment.
  • an anemometer may be set up on-site at a suitable location to detect unaffected wind measurements. As the UAV is sampling species concentrations, the anemometer may measure instantaneous wind speed in three dimensions (step 702). These measurements may be recorded at synchronized times as the species concentrations are made. An appropriate aerodynamic surface roughness length may be selected to represent the local ground conditions surrounding the measurement survey site.
  • the wind speed measurements taken by the anemometer may then be extrapolated from the anemometer measurement altitude to the altitude of the UAV at that specific point in time (step 704). This may be done by a processor having addressable memory using a log-law boundary condition model for flows near rough boundaries. In one embodiment, the wind direction remains uncorrected, and only the magnitude of the wind speed is scaled using the log-law.
  • the altitude corrected wind vector from the anemometer may then be assumed to be the wind vector at the location the gas concentration measurement is detected and a surface is created (step 706). In moderate wind speed conditions, the stability of the atmospheric boundary layer increases, resulting in less wind variance, therefore giving better correlation between the anemometer measurement and the wind at the sensor.
  • a surface normal unit vector may be calculated by the processor at each point in the dataset; the dot product of the surface normal vector and the wind vector may then be calculated by the processor.
  • a volumetric flux rate of airflow may then by calculated by the processor for every point on the surface (step 708).
  • the volumetric flux rate of airflow represents how many cubic meters of air is entering or leaving the control volume per square meter ( dS ).
  • a high pass filter may be applied over the input gas concentration measurements provide accurate relative gas measurements (step 710).
  • the filter removes any low frequency sensor drift, while still resolving all details from emissions.
  • the time constant chosen for the filter may be based on analysis of the sensor stability.
  • multiplying the above wind flux values from step 708 by the volume fraction of gas (m 3 CH4 or CCk/m 3 air) with the processor provides the volumetric flux rate of gas at each point (m 3 gas / s / m 2 area).
  • control surface does not completely encompass the control volume, and the measurements were not taken concurrently, the standard incompressible equation of continuity may not hold true.
  • the net flux may not simply be calculated by integrating the point gas flux measurements across the entire surface; rather, to calculate the flux of gas entering the control volume, the surface may be kept at a threshold to where wind is only flowing into the control volume (step 712). With this surface, the volumetric flux rate of gas can be integrated by the processor to give the total inward volumetric flux of gas into the control volume (m 3 species / s) (step 714).
  • a total air volumetric rate may be determined by the processor as well by integrating the wind velocity normal component to give a total air volumetric flux rate.
  • the volumetric flowrate averaged inflowing species concentration may be determined by the processor (step 716).
  • volumetric-flow-rate averaged concentration of the inflowing gas may then be subtracted from the concentration values measured where anemometry indicates gas is flowing out of the control volume, providing the enhanced concentrations with the background concentration component removed (step 718).
  • the final net flux rate can be calculated by the processor. Multiplying, with the processor, the component of wind velocity normal to the control surface by the concentration enhancement, the flux rate of the enhanced species concentration per unit area is determined (m 3 species/m 2 /second) (step 720).
  • This approach has the benefit that it satisfies the fundamental fluid dynamics equation for flow of a species through a control volume. Continuity of the species is used to ensure that all of the concentration upstream is subtracted from the concentration downstream, even if the oncoming flow has not got a uniform concentration of gas. Because the control volume is still open at the top, and the discrete sampling process means that there are gaps in the control surface, absolute integrals of continuity of mass and continuity of species cannot be simply applied. In addition, sampling all the points non-simultaneously, and in transient wind conditions, means that flux through any point in the control surface is constantly changing.
  • the disclosed process may utilize a ground weather station that is set up on-site in a location with the least amount of interference from other obstacles as possible. Even in the optimal location, the anemometer is not co-located with the UAV at any time, and so the reading at the anemometer will be different from the actual wind-speed at the UAV. This effect is particularly strong during light wind conditions, as a small change in the wind speed can incur a large change in the concentration values measured. In light wind conditions, the atmospheric boundary layer is also more unstable, resulting in more diffusion of the species plume, and therefore lower concentration values than with a more consistent wind.
  • FIG. 8 illustrates an example of a top-level functional block diagram of a computing device embodiment 800.
  • the example operating environment is shown as a computing device 820 comprising a processor 824, such as a central processing unit (CPU), addressable memory 827, an external device interface 826, e.g., an optional universal serial bus port and related processing, and/or an Ethernet port and related processing, and an optional user interface 829, e.g., an array of status lights and one or more toggle switches, and/or a display, and/or a keyboard and/or a pointer-mouse system and/or a touch screen.
  • the addressable memory may, for example, be: flash memory, eprom, and/or a disk drive or other hard drive.
  • these elements may be in communication with one another via a data bus 828.
  • the processor 824 may be configured to execute steps of a process establishing a communication channel and processing according to the embodiments described above.
  • System embodiments include computing devices such as a server computing device, a buyer computing device, and a seller computing device, each comprising a processor and addressable memory and in electronic communication with each other.
  • the embodiments provide a server computing device that may be configured to: register one or more buyer computing devices and associate each buyer computing device with a buyer profile; register one or more seller computing devices and associate each seller computing device with a seller profile; determine search results of one or more registered buyer computing devices matching one or more buyer criteria via a seller search component.
  • the service computing device may then transmit a message from the registered seller computing device to a registered buyer computing device from the determined search results and provide access to the registered buyer computing device of a property from the one or more properties of the registered seller via a remote access component based on the transmitted message and the associated buyer computing device; and track movement of the registered buyer computing device in the accessed property via a viewer tracking component.
  • the system may facilitate the tracking of buyers by the system and sellers once they are on the property and aid in the seller’s search for finding buyers for their property.
  • the figures described below provide more details about the implementation of the devices and how they may interact with each other using the disclosed technology.
  • FIG. 9 is a high-level block diagram 900 showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein.
  • the computer system includes one or more processors 902, and can further include an electronic display device 904 (e.g., for displaying graphics, text, and other data), a main memory 906 (e.g., random access memory (RAM)), storage device 908, a removable storage device 910 (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data), user interface device 911 (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface 912 (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card).
  • an electronic display device 904 e.g., for displaying graphics, text, and other data
  • main memory 906 e.g.,
  • the communication interface 912 allows software and data to be transferred between the computer system and external devices.
  • the system further includes a communications infrastructure 914 (e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices/modules are connected as shown.
  • a communications infrastructure 914 e.g., a communications bus, cross-over bar, or network
  • Information transferred via communications interface 914 may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface 914, via a communication link 916 that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, an radio frequency (RF) link, and/or other communication channels.
  • Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.
  • Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments.
  • Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions.
  • the computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram.
  • Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc.
  • Computer programs are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface 912. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.
  • FIG. 10 shows a block diagram of an example system 1000 in which an embodiment may be implemented.
  • the system 1000 includes one or more client devices 1001 such as consumer electronics devices, connected to one or more server computing systems 1030.
  • a server 1030 includes a bus 1002 or other communication mechanism for communicating information, and a processor (CPU) 1004 coupled with the bus 1002 for processing information.
  • the server 1030 also includes a main memory 1006, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 1002 for storing information and instructions to be executed by the processor 1004.
  • the main memory 1006 also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor 1004.
  • the server computer system 1030 further includes a read only memory (ROM) 1008 or other static storage device coupled to the bus 1002 for storing static information and instructions for the processor 1004.
  • ROM read only memory
  • a storage device 1010 such as a magnetic disk or optical disk, is provided and coupled to the bus 1002 for storing information and instructions.
  • the bus 1002 may contain, for example, thirty -two address lines for addressing video memory or main memory 1006.
  • the bus 1002 can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU 1004, the main memory 1006, video memory and the storage 1010. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.
  • the server 1030 may be coupled via the bus 1002 to a display 1012 for displaying information to a computer user.
  • An input device 1014 is coupled to the bus 1002 for communicating information and command selections to the processor 1004.
  • cursor control 1016 such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 1004 and for controlling cursor movement on the display 1012.
  • the functions are performed by the processor 1004 executing one or more sequences of one or more instructions contained in the main memory 1006. Such instructions may be read into the main memory 1006 from another computer-readable medium, such as the storage device 1010. Execution of the sequences of instructions contained in the main memory 1006 causes the processor 1004 to perform the process steps described herein.
  • processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory 1006.
  • hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.
  • the terms "computer program medium,” “computer usable medium,” “computer readable medium”, and “computer program product,” are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system.
  • the computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium.
  • the computer readable medium may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems.
  • the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information.
  • Computer programs also called computer control logic
  • main memory and/or secondary memory Computer programs may also be received via a communications interface.
  • Such computer programs when executed, enable the computer system to perform the features of the embodiments as discussed herein.
  • the computer programs when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
  • Non-volatile media includes, for example, optical or magnetic disks, such as the storage device 1010.
  • Volatile media includes dynamic memory, such as the main memory 1006.
  • Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 1002. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
  • Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
  • Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor 1004 for execution.
  • the instructions may initially be carried on a magnetic disk of a remote computer.
  • the remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem.
  • a modem local to the server 1030 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal.
  • An infrared detector coupled to the bus 1002 can receive the data carried in the infrared signal and place the data on the bus 1002.
  • the bus 1002 carries the data to the main memory 1006, from which the processor 1004 retrieves and executes the instructions.
  • the server 1030 also includes a communication interface 1018 coupled to the bus 1002.
  • the communication interface 1018 provides a two-way data communication coupling to a network link 1020 that is connected to the world wide packet data communication network now commonly referred to as the Internet 1028.
  • the Internet 1028 uses electrical, electromagnetic or optical signals that carry digital data streams.
  • the signals through the various networks and the signals on the network link 1020 and through the communication interface 1018, which carry the digital data to and from the server 1030, are exemplary forms or carrier waves transporting the information.
  • interface 1018 is connected to a network 1022 via a communication link 1020.
  • the communication interface 1018 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link 1020.
  • ISDN integrated services digital network
  • the communication interface 1018 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN.
  • LAN local area network
  • Wireless links may also be implemented.
  • the communication interface 1018 sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.
  • the network link 1020 typically provides data communication through one or more networks to other data devices.
  • the network link 1020 may provide a connection through the local network 1022 to a host computer 1024 or to data equipment operated by an Internet Service Provider (ISP).
  • ISP Internet Service Provider
  • the ISP in turn provides data communication services through the Internet 1028.
  • the local network 1022 and the Internet 1028 both use electrical, electromagnetic or optical signals that carry digital data streams.
  • the signals through the various networks and the signals on the network link 1020 and through the communication interface 1018, which carry the digital data to and from the server 1030, are exemplary forms or carrier waves transporting the information.
  • the server 1030 can send/receive messages and data, including e-mail, program code, through the network, the network link 1020 and the communication interface 1018.
  • the communication interface 1018 can comprise a USB/Tuner and the network link 1020 may be an antenna or cable for connecting the server 1030 to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.
  • the example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system 1000 including the servers 1030.
  • the logical operations of the embodiments may be implemented as a sequence of steps executing in the server 1030, and as interconnected machine modules within the system 1000.
  • the implementation is a matter of choice and can depend on performance of the system 1000 implementing the embodiments.
  • the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.
  • a client device 1001 can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 1028, the ISP, or LAN 1022, for communication with the servers 1030.
  • a processor e.g., a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 1028, the ISP, or LAN 1022, for communication with the servers 1030.
  • communication interface e.g., e-mail interface
  • the system 1000 can further include computers (e.g., personal computers, computing nodes) 1005 operating in the same manner as client devices 1001, where a user can utilize one or more computers 1005 to manage data in the server 1030.
  • computers e.g., personal computers, computing nodes
  • cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA), smartphone, smart watch, set-top box, video game system, tablet, mobile computing device, or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or unmanned aerial vehicle (UAV) 54N may communicate.
  • Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof.
  • cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 11 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
  • FIG. 12 depicts a system 1200 for detecting trace gasses, according to one embodiment.
  • the system may include one or more trace gas sensors located in one or more vehicles 1202, 1204, 1206, 1210.
  • the one or more trace gas sensors may detect elevated trace gas concentrations from one or more potential gas sources 1220, 1222, such as a holding tank, pipeline, or the like.
  • the potential gas sources 1220, 1222 may be part of a large facility, a small facility, or any location.
  • the potential gas sources 1220, 1222 may be clustered and/or disposed distal from one another.
  • the one or more trace gas sensors may be used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane, sulfur dioxide) in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, such as oil and gas, chemical production, and painting. Detection and quantification of leaks is also of value to environmental regulators for assessing compliance and for mitigating environmental and safety risks.
  • the at least one trace gas sensor may be configured to detect methane.
  • the at least one trace gas sensor may be configured to detect sulfur oxide, such as SO, S02, S03, S702, S602, S202, and the like.
  • a trace gas leak 1224 may be present in a potential gas source 1220. The one or more trace gas sensors may be used to identify the trace gas leak 1224 and/or the source 1220 of the trace gas leak 1224 so that corrective action may be taken.
  • the one or more vehicles 1202, 1204, 1206, 1210 may include an unmanned aerial vehicle (UAV) 1202, an aerial vehicle 1204, a handheld device 1206, and a ground vehicle 1210.
  • UAV unmanned aerial vehicle
  • the UAV 1202 may be a quadcopter or other device capable of hovering, making sharp turns, and the like.
  • the UAV 1202 may be a winged aerial vehicle capable of extended flight time between missions.
  • the UAV 1202 may be autonomous or semi-autonomous in some embodiments.
  • the UAV 1202 may be manually controlled by a user.
  • the aerial vehicle 1204 may be a manned vehicle in some embodiments.
  • the handheld device 1206 may be any device having one or more trace gas sensors operated by a user 1208.
  • the handheld device 1206 may have an extension for keeping the one or more trace gas sensors at a distance from the user 1208.
  • the ground vehicle 1210 may have wheels and/or treads in one embodiment. In other embodiments, the ground vehicle 1210 may be a legged robot. In some embodiments, the ground vehicle 1210 may be used as a base station for one or more UAVs 1202. In some embodiments, one or more aerial devices, such as the UAV 1202, a balloon, or the like, may be tethered to the ground vehicle 1210. In some embodiments, one or more trace gas sensors may be located in one or more stationary monitoring devices 1226. The one or more stationary monitoring devices may be located proximate one or more potential gas sources 1220, 1222. In some embodiments, the one or more stationary monitoring devices may be relocated.
  • the one or more vehicles 1202, 1204, 1206, 1210 and/or stationary monitoring devices 1226 may transmit data including trace gas data to a ground control station (GCS) 1212.
  • the GCS may include a display 1214 for displaying the trace gas concentrations to a GCS user 1216.
  • the GCS user 1216 may be able to take corrective action if a gas leak 1224 is detected, such as by ordering a repair of the source 1220 of the trace gas leak.
  • the GCS user 1216 may be able to control movement of the one or more vehicles 1202, 1204, 1206, 1210 in order to confirm a presence of a trace gas leak in some embodiments.
  • the GCS 1212 may transmit data to a cloud server 1218.
  • the cloud server 1218 may perform additional processing on the data.
  • the cloud server 1218 may provide third party data to the GCS 1212, such as wind speed, temperature, pressure, weather data, or the like.

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Abstract

Systems, devices, and methods including a processor (402) having addressable memory (426), where the processor is configured to: determine one or more flight paths (204) for an aerial vehicle (202, 408), where the determined flight path (204) creates a continuous surface (206) about one or more potential gas sources of a survey site (210); receive a trace gas data from one or more trace gas sensors (412) of the aerial vehicle of the continuous surface (206) as the aerial vehicle (202, 408) flies the determined one or more flight paths (204); and determine based on the received trace gas data whether a gas leak is present in the received survey site (210, 404) and a rate of the gas leak if present in the survey site (210, 404).

Description

CLOSED SURFACE FLIGHT PATTERN GENERATION FOR UNMANNED AERIAL VEHICLE (UAV) FLUX PLANE ASSESSMENT
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the priority benefit of U.S. Provisional Patent Application Serial Number 62/910,695 filed October 4, 2019, incorporated herein by reference in its entirety.
FIELD OF ENDEAVOR
[0002] Embodiments relate generally to gas leak detection, and more particularly to gas leak detection of large facilities.
BACKGROUND
[0003] Large facilities may have one or more sources of potential gas leaks. Existing systems and methods for detecting these leaks require large sensors and may be inaccurate due to an inability to capture a complete flux plane of gasses being emitted from these one or more sources of potential gas leaks.
SUMMARY
[0004] A system embodiment may include: a processor having addressable memory, where the processor may be configured to: determine one or more flight paths for an aerial vehicle, where the determined flight path creates a continuous surface about one or more potential gas sources of a survey site; receive a trace gas data from one or more trace gas sensors of the aerial vehicle of the continuous surface as the aerial vehicle flies the determined one or more flight paths; and determine based on the received trace gas data whether a gas leak may be present in the received survey site and a rate of the gas leak if present in the survey site.
[0005] In additional system embodiments, the continuous surface is a flux plane. In additional system embodiments, the flux plane is a closed flux plane. In additional system embodiments, the continuous surface is formed by any series of turns in the flight path for the aerial vehicle that forms the continuous surface when interpolated.
[0006] In additional system embodiments, the processor may be further configured to: receive one or more flight platform capabilities of the aerial vehicle, where the determined flight path may be based on the received one or more flight platform capabilities. In additional system embodiments, the processor may be further configured to: receive a wind data for the survey site, where the determined flight path may be further based on the received wind data. In additional system embodiments, the wind data may include instantaneous wind speed measurements in three dimensions from an anemometer.
[0007] In additional system embodiments, the aerial vehicle may be an unmanned aerial vehicle (UAV). In additional system embodiments, the UAV may be configured to fly the determined one or more flight paths autonomously. In additional system embodiments, the UAV may be configured to fly the determined one or more flight paths semi- autonomously.
[0008] In additional system embodiments, the continuous surface comprises at least one of: a beehive-shaped continuous surface and a cone-shaped continuous surface. In additional system embodiments, the continuous surface comprises a right angle, an arc, and/or a continuous curve in the flight path for the aerial vehicle. In additional system embodiments, the aerial vehicle flies a determined flight path of the one or more flight path two or more times, and where the received trace gas data may be averaged from each of the two or more flights. In additional system embodiments, the processor may be further configured to, for creating the continuous surface: convert a dataset into an altitude-s space; perform a triangulation on the dataset in the altitude s-space; and apply the performed triangulation on a Cartesian X, Y, Z space of the dataset to produce at least one of: a fully closed surface and a semi-closed surface.
[0009] A method embodiment may include: determining, by the processor, one or more flight paths for an aerial vehicle, where the determined flight path creates a continuous surface about one or more potential gas sources of a survey site; receiving, by the processor, a trace gas data from one or more trace gas sensors of the aerial vehicle of the continuous surface as the aerial vehicle flies the determined one or more flight paths; and determining, by the processor, based on the received gas data whether a gas leak may be present in the received survey site and a rate of the gas leak if present in the survey site.
[0010] In additional method embodiments, the continuous surface is a flux plane. In additional method embodiments, the flux plane is a closed flux plane. In additional method embodiments, the continuous surface is formed by any series of turns in the flight path for the aerial vehicle that forms the continuous surface when interpolated.
[0011] Additional method embodiments may include: receiving, by the processor, one or more flight platform capabilities, where the determined flight path may be based the received one or more flight platform capabilities. Additional method embodiments may include: receiving, by the processor, a wind data for the survey site (210, 404), where the determined flight path may be further based the received one or more flight platform capabilities, and the received wind data.
[0012] In additional method embodiments, the aerial vehicle may be an unmanned aerial vehicle (UAV). In additional method embodiments, the UAV may be configured to fly the determined one or more flight paths autonomously. In additional method embodiments, the UAV may be configured to fly the determined one or more flight paths semi- autonomously.
[0013] In additional method embodiments, creating the continuous surface further comprises: converting, by the processor, a dataset into an altitude-s space; performing, by the processor, a triangulation on the dataset in the altitude s-space; and applying, by the processor, the performed triangulation on a Cartesian X, Y, Z space of the dataset to produce at least one of: a fully closed surface and a semi-closed surface.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principals of the invention. Like reference numerals designate corresponding parts throughout the different views. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:
[0015] FIG. 1 depicts an aerial vehicle flying a generally cylindrical flight path to survey a continuous surface in cylindrical coordinates and capture a cross-section of a gas from a survey site, according to one embodiment;
[0016] FIG. 2 depicts an unmanned aerial vehicle (UAV) flying a generally beehive shaped flight path to survey a closed continuous surface and capture a cross-section of a gas from a survey site, according to one embodiment;
[0017] FIGS. 3A-3D depict closed continuous surfaces based on UAV flight paths, according to one embodiment;
[0018] FIG. 4 illustrates an example top-level functional block diagram of a flight pattern generation system for creating closed continuous surfaces, according to one embodiment;
[0019] FIG. 5 depicts a high-level flowchart of a method embodiment of generating a flight pattern for creating a closed continuous surface, according to one embodiment;
[0020] FIG. 6 depicts a high-level flowchart of a method embodiment of generating a continuous surface, according to one embodiment; [0021] FIG. 7 depicts a high-level flowchart of a method embodiment of determining a volumetric flowrate of inflowing gas species concentration, according to one embodiment;
[0022] FIG. 8 illustrates an example top-level functional block diagram of a computing device embodiment;
[0023] FIG. 9 shows a high-level block diagram and process of a computing system for implementing an embodiment of the system and process;
[0024] FIG. 10 shows a block diagram and process of an exemplary system in which an embodiment may be implemented;
[0025] FIG. 11 depicts a cloud computing environment for implementing an embodiment of the system and process disclosed herein; and
[0026] FIG. 12 depicts a system for detecting trace gasses, according to one embodiment.
DETAILED DESCRIPTION
[0027] The following description is made for the purpose of illustrating the general principles of the embodiments discloses herein and is not meant to limit the concepts disclosed herein. Further, particular features described herein can be used in combination with other described features in each of the various possible combinations and permutations. Unless otherwise specifically defined herein, all terms are to be given their broadest possible interpretation including meanings implied from the description as well as meanings understood by those skilled in the art and/or as defined in dictionaries, treatises, etc.
[0028] The present system allows for the generation of a flight path for an unmanned aerial vehicle (UAV) having one or more gas sensors to fly around a survey site that may contain one or more potential gas sources. The potential gas sources may include a gas pipeline, an oil rig, and the like. The generated flight path for the UAV may form a closed surface around the potential gas source. The generated flight path may allow the UAV to survey a closed continuous surface and capture a cross-section of gas that may be emitted from one or more potential gas sources in a large facility. Embodiments may include a small, highly maneuverable, and/or remotely piloted UAV capable of detecting, localizing, and/or quantifying gas leaks using novel flight paths.
[0029] Trace gas sensors may be used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane, sulfur dioxide) in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, such as oil and gas, chemical production, and painting. Detection and quantification of leaks is also of value to environmental regulators for assessing compliance and for mitigating environmental and safety risks.
[0030] Industrial sites may be surveyed for toxic gas leaks using a manned flight platform, such as a fixed-wing aircraft or a helicopter. If trace gases are detected near or around a survey site, the leak size, i.e., flow rate, may be estimated by flying a continuous surface that captures a cross-section of the gas. This continuous surface relates the flux of trace gas through a plane with the upstream source, under the assumption that all gas flux originating from the source passes through a plane downstream.
[0031] The specific details of a continuous surface flight pattern may be determined by the capabilities of the flight platform, as well as the details of the survey site itself. For example, a survey site with a linear array of potential leak sources, such as a pipeline, may require a different flight pattern than an industrial site that may be essentially an isolated point source. For a linear array, the survey may extend downwind of the site. For a fixed point, or distribution of fixed points, the generated flight pattern may typically be arcs downwind of the fixed point or aggregate of points.
[0032] FIG. 1 depicts a system 100 for an aerial vehicle 102 flying a generally cylindrical flight path 104 to survey a continuous surface 106 in cylindrical coordinates and capture a cross-section of a gas 108 from a survey site 110, according to one embodiment. The aerial vehicle 102 may be a fixed-wing flight platform, such as a Cessna aircraft. The aerial vehicle 102 may survey for leaks is the generally cylindrical flight path 104. Laps may be flown around the survey site 110 at different altitudes. The survey site 110 may be a large facility and may include one or more potential gas sources. The generally cylindrical flight path 104 of the aerial vehicle 102 may effectively survey the continuous surface 106 in cylindrical coordinates. The continuous surface 106 is depicted in dashed lines in FIG. 1.
[0033] This generally cylindrical flight path 104 may minimize the effect of atmospheric turbulence by effectively drawing a line integral around the survey site 110.
Such a flight path 104 may be effective in assuring the detection of a leak and quantifying the source leak rate in some embodiments. However, this flight path 104 is time-consuming and suffers from the inability to assess fluxes in a z-direction 112. The cylindrical flight path 104 fails to measure fluxes flowing out the top 114 of the cylindrical flux path 106. Depending on wind conditions, available time, and the like, the cylindrical flight path 104 may be ineffective in detecting a gas leak for gasses 108 rising up in the z-direction 108 as these gasses 108 may not pass through the cylindrical continuous surface 106. Extending the flight path 104 and cylindrical continuous surface 106 further in the z-direction may capture additional gasses 108 but only at a greatly increased time and energy cost as the aerial vehicle 102 must fly a much further distance.
[0034] FIG. 2 depicts a system 200 for an aerial vehicle such as an unmanned aerial vehicle (UAV) 202 flying a generally beehive-shaped flight path 204 to survey a continuous surface 206 and capture a cross-section of a gas 208 from a survey site 210, according to one embodiment. In some embodiments, the continuous surface 206 may be a flux plane. In other embodiments, the continuous surface 206 may be a closed flux plane. The continuous surface 206 may be formed by any polygon or any series of turns that forms the continuous surface when interpolated. The continuous surface 206 may be formed by a right angle, an arc, and/or a continuous curve in a flight path for the UAV 202. The UAV 202 may be a small, highly maneuverable, and/or remotely piloted airborne platform. The UAV may be used to detect, localize and quantify gas leaks 208 at survey sites 210, such as industrial sites, using novel flight paths 204 that manned aerial platforms, such as shown in FIG. 1, are unable to fly. These manned flight platforms for civilian use may have much lower dynamic performance than unmanned platforms, such as the UAV 202. In some embodiments, the UAV 202 may be a multi-rotor platform, such as a quadcopter, which may not be constrained by a stall speed like fixed-wing platforms, enabling the UAV 202 to stop their motion and incorporate acute angles, obtuse angles, and/or right angles into the flight path.
[0035] In some embodiments, the UAV 202 may fly a time- and distance-efficient survey of a gas leak 208 from a survey site 210. The UAV 202 may fly a beehive-shaped flight path 204 around a survey site 210. While the flight path 204 is depicted as forming a generally beehive shape, the flight path 204 may include additional rotations about the survey site 210, variations due to the wind, variations due to survey site conditions, variations due to user settings, variations due to user limitations, and the like. The shape of the flight path 204 may be modified to consider a variety of factors, including possible gas sources, gas plume shapes, and wind velocity. Furthermore, the flight path 204 shape may be generated to account for rules or laws that may require a minimum distance from the source of the leak, such a gas pipeline, offshore oil rig, and the like.
[0036] The beehive-shaped flight path 204 may have a smaller radius at higher altitudes, effectively closing off the continuous surface 206, which is depicted in dashed lines. Such a flight path 204 is an improvement upon a cylindrical flight path, such as shown in FIG. 1, in that it both assesses fluxes in the z-direction 212, while also reducing the maximum altitude in the flight path 204, thereby reducing the flight time. The flight path 204 may be completed in a shorter amount of time as compared to a cylindrical flight path. The flight path 204 may be expanded to cover a greater area surrounding the survey site 210 in a shorter or same amount of time as compared to a cylindrical flight path.
[0037] A processor 214 may be in communication 216 with the UAV 202. The processor may generate the flight path 204 and communicate 216 the flight path to the UAV 202. The UAV may then fly the flight path 204 and generate gas data, which may be communicated 216 to the processor 214. In some embodiments, the processor 214 may be a ground control station (GCS) used to control the UAV 202. In other embodiments, the processor 214 may be a part of the UAV 202. In some embodiments, the UAV 202 may fly the flight path 204 autonomously or semi-autonomously.
[0038] FIGS. 3A-3D depict closed continuous surfaces 300, 304, 308, 312 based on UAV flight paths 302, 306, 310, 314, according to one embodiment. FIG. 3 A depicts a beehive-shaped continuous surface 300 created from a first flight path 302. FIG. 3B depicts a cone-shaped continuous surface 304 created from a second flight path 306. FIG. 3C depicts a box-shaped continuous surface 308 created from a third flight path 310. The third flight path 310 may include right angles if the UAV being used is a multi-rotor vehicle, such as a quadcopter. FIG. 3D depicts a cone-shaped continuous surface 312 created from a fourth flight path 314. The shape of a continuous surface created by a flight path from a UAV may be a closed shape that encapsulates the survey site having one or more potential gas leaks.
[0039] These closed continuous surfaces may be used to detect the presence of a gas leak and/or the rate of a gas leak. These closed continuous surfaces may be any closed shape and may have variations in the shape due to the wind, survey site conditions, user settings, user limitations, laws and regulations, and the like. In some embodiments, the flight path of the UAV may be a random flight path creating a closed continuous surface rather than a smooth curve, such as shown in FIGS. 3A, 3B, and 3D. In some embodiments, the flight path of the UAV may be flown several times and averaged together to generate a result. For example, a UAV may fly a flight path 2-3 times and the data may be averaged together to generate a more accurate result. In some embodiments, the same flight path may be flown each time. In other embodiments, different flight paths may be flown. For example, a first flight path may be a smooth curve, such as shown in FIGS. 3A, 3B, or 3D and a second flight path may be a random pattern. The radius of the closed continuous surface may be a function of the gas sensor sensitivity. A sampling rate for the gas sensor in the UAV flight path may be a compromise between a desired spatial sampling resolution and a minimum detection limit. The planning of the flight path and closed continuous surface may be a desired shape, operational constraints, safety from the customer, optimization of a best scenario for time spent in the plume, and the like.
[0040] FIG. 4 illustrates an example top-level functional block diagram of a flight pattern generation system 400 for creating closed continuous surfaces, according to one embodiment. The system 400 may include a processor 402. The processor 402 may receive information on a survey site 404, which may be an area containing one or more potential gas sources. The one or more potential gas sources may be equipment and/or locations more likely to leak toxic gases, such as hydrogen disulfide, or environmentally damaging gases, such as methane and sulfur dioxide. The survey site information 404 may also include user rules, user preferences, rules, and/or laws relating to the survey site 404. For example, local laws may prohibit an aerial vehicle from being within twenty feet of a pipeline and a user preference may be to remain forty feet away from a pipeline in a survey site.
[0041] The processor 402 may also receive flight platform capabilities 406 for an aerial vehicle 408. The flight platform capabilities 406 may include battery capacity, payload limits, maximum flight time, operating restrictions, and the like. The flight platform capabilities 406 may also include a maneuverability of the aerial vehicle 408. For example, a quadrotor type aerial vehicle 408 may be able to hover stop, make acute angle turns, make obtuse angle turns, and make right angle turns. A fixed-wing UAV may be limited to a set turn radius and/or minimum flight speed. The aerial vehicle 408 may be an unmanned aerial vehicle (UAV). The UAV may be autonomous and/or semi-autonomous.
[0042] The processor 402 may also receive wind data 410. Wind data 410 may include wind speed and/or wind direction for the survey site 404. In some embodiments, wind data 410 may also include predictions as to changes in the wind speed and/or wind direction.
[0043] The processor 402 may determine one or more flight paths, such as shown in FIGS. 2-3D, for the aerial vehicle 408 based on the received survey site information 404, flight platform capabilities 406, and/or wind data 410. The determined one or more flight paths may create a closed continuous surface, such as shown in FIGS. 2-3D, about one or more potential gas sources of the survey site 404.
[0044] The aerial vehicle 408 may have at least one trace gas sensor 412 to generate trace gas data based on detected trace gas in the closed continuous surface as the aerial vehicle 408 flies the determined one or more flight paths. The aerial vehicle 408 may have a processor 414 in communication with addressable memory 416, a GPS 418, one or more motors 420, and a power supply 422. The aerial vehicle 408 may receive the flight plan from the processor 402 and communicate gathered gas sensor 412 data to the processor 402. The at least one gas sensor 412 may be configured to detect carbon dioxide. In other embodiments, the at least one trace gas sensor 412 may be configured to detect nitrogen oxide. In other embodiments, the at least one trace gas sensor 412 may be configured to detect sulfur oxide, such as SO, SO2, SO3, S7O2, S6O2, S2O2, and the like.
[0045] The GPS 418 may record the location of the aerial vehicle 408 when each gas sensor 412 data is acquired. The GPS 418 may also allow the aerial vehicle 408 to travel the flight path generated by the processor 402. In some embodiments, the location of the aerial vehicle 408 may be determined by an onboard avionics 424. The onboard avionics 424 may include a triangulation system, a beacon, a spatial coordinate system, or the like. The onboard avionics 424 may be used with the GPS 418 in some embodiments. In other embodiments, the aerial vehicle 408 may use only one of the GPS 418 and the onboard avionics 424. The location information from the GPS 418 and/or onboard avionics 424 may be combined with the gas sensor 412 data to determine if gas is present through the closed continuous surface created by the flight plan of the aerial vehicle 408. In some embodiments, wind data 432 may be measured onboard the aerial vehicle 408, such as via a wind sensor mounted to the aerial vehicle 408.
[0046] The power supply 422 may be a battery in some embodiments. The power supply 422 may limit the available flight time for the aerial vehicle 408 and so the time- and energy-efficiency flight paths created by the processor 402 allow for the determination as to whether there are any gas leaks through the closed continuous surface. In some embodiments, the processor 402 may be a part of the aerial vehicle 408, a cloud computing device, a ground control station (GCS) used to control the aerial vehicle 408, or the like. In some embodiments, a user interface 430 may in communication with the processor 402. The user interface 430 may be used to select the flight path, make changes to the flight path, receive gas data, or the like. In some embodiments, the user interface 430 may be a part of the processor 402, the additional processor 428, and/or a GCS.
[0047] The processor 402 may receive gas data from the one or more trace gas sensors 412 of the aerial vehicle 408. The processor 402 may then determine, based on the received gas data, whether a gas leak is present and/or a rate of the gas leak in the survey site 404. If a gas leak is not detected, no immediate action is needed and further tests may be accomplished in the future to ensure that no gas leaks develop. If a gas leak is detected, then corrective action may be taken to minimize and/or stop the gas leak.
[0048] In some embodiments, the processor 402 may be in communication with addressable memory 426. The memory 426 may store the result of whether a gas leak was detected, historical gas data, the flight platform capabilities 406, wind data 814, and/or data from the aerial vehicle 408. In some embodiments, the processor 402 may be in communication with an additional processor 428. The additional processor 428 may be a part of the aerial vehicle 408, a cloud computing device, a GCS used to control the aerial vehicle 408, or the like.
[0049] FIG. 5 depicts a high-level flowchart of a method embodiment 500 of generating a flight pattern for creating a closed continuous surface, according to one embodiment. The method 500 may include receiving, by a processor having addressable memory, a survey site having one or more potential gas sources (step 502). The method 500 may also include receiving, by the processor, one or more flight platform capabilities (step 504). The method 500 may also include receiving, by the processor, a wind data for the received survey site (step 506). The method 500 may then include determining, by the processor, one or more flight paths for an aerial vehicle based on the received survey site, the received one or more flight platform capabilities, and/or the received wind data (step 508). The determined flight path may create a closed continuous surface about the one or more potential gas sources of the survey site. The method 500 may then include receiving, by the processor, gas data from one or more gas sensors of the closed continuous surface as the aerial vehicle flies the determined one or more flight paths (step 510). The method 500 may then include determining, by the processor, based on the received gas data whether a gas leak is present and/or a rate of the gas leak in the received survey site (step 512).
[0050] Flying a closed perimeter surface with a UAV may capture all the emissions from a survey site. If a flightpath completely encompasses the survey site, Gauss’ Theorem may be applied directly with a processor having addressable memory to calculate the gas leak rate within the flightpath perimeter using the gas concentration measurements detected by the gas sensor at the perimeter. Due to operational constraints, such as access roads, power lines etc. flying a fully closed flightpath may not always possible; therefore, it is preferred to fly the perimeter with a small gap where flying is not possible. This may be referred to as a “semi-Gauss” flight pattern.
[0051] Analyzing Gauss and semi-Gauss flight patterns to extract a gas leak rate may require application, with the processor, of computational geometry algorithms. Firstly, a dataset consisting of point measurements made by the UAV in space may be converted into Cartesian coordinates with the processor. The conversion may be done using the Position Easting, Position Northing coordinates from the Extended Kalman Filter (EKF) as the X and Y components, and the Light Detection and Ranging (LiDAR) altitude from a LiDAR range finder for the Z component. Using the EKF outputs for location provides for an accurate alternative measurement of location to GPS.
[0052] In order to deduce a source rate of gas emission, the individual flux values may have to be integrated over the whole dataset of spatial coordinates. Once the dataset has been converted into Cartesian space, the processor may triangulate the point measurements, thereby providing a continuous surface to determine the surface integral on ( dS ), where S is the surface:
Figure imgf000013_0001
[0053] FIG. 6 depicts a high-level flowchart of a method embodiment 600 of generating a continuous surface, according to one embodiment. Generally speaking,
Delaunay Triangulation methods create a two-dimensional tri angulation, or surface, from a two-dimensional dataset; however, said methods are not able to create a two-dimensional surface from a three-dimensional dataset. Therefore, before applying a Delaunay Triangulation algorithm, the dataset must first be represented in two-dimensional space. For Gauss and semi-Gauss flightpaths, the dataset can be converted into altitude-^ space by a processor having addressable memory, where altitude is the same dimension as in the three- dimensional dataset, and 5 is a new dimension measuring distance along the pass made by the UAV (step 602). For example, in a Gauss flightpath, the value of 5 monotonically increases along the pass, until the UAV reaches a determined point and then the value of 5 starts again from zero. With regards to a semi-Gauss flightpath, the value of 5 monotonically increases until the UAV reaches whatever obstacle the UAV must avoid, and then the UAV preforms a U-turn and backtracks along the same path (at a different altitude) while the value of 5 decreases. Both methods, including the identification of crossing a home point and detection of U-turn points are automatically processed by a processor having addressable memory.
[0054] Once the dataset is represented in altitude-^ space, a Delaunay Triangulation may be performed by the processor (step 604). The resulting triangulation may then be applied by the processor on the dataset’s original Cartesian X, Y, Z space to produce a fully closed or semi-closed surface (step 606), with all the original scalar values (e.g. gas concentrations) intact.
[0055] FIG. 7 depicts a high-level flowchart of a method embodiment 700 of determining a volumetric flowrate of inflowing gas species concentration, according to one embodiment. In addition to the measurements made using an IR sensor mounted to an airframe of the UAV, an anemometer may be set up on-site at a suitable location to detect unaffected wind measurements. As the UAV is sampling species concentrations, the anemometer may measure instantaneous wind speed in three dimensions (step 702). These measurements may be recorded at synchronized times as the species concentrations are made. An appropriate aerodynamic surface roughness length may be selected to represent the local ground conditions surrounding the measurement survey site. The wind speed measurements taken by the anemometer may then be extrapolated from the anemometer measurement altitude to the altitude of the UAV at that specific point in time (step 704). This may be done by a processor having addressable memory using a log-law boundary condition model for flows near rough boundaries. In one embodiment, the wind direction remains uncorrected, and only the magnitude of the wind speed is scaled using the log-law. The altitude corrected wind vector from the anemometer may then be assumed to be the wind vector at the location the gas concentration measurement is detected and a surface is created (step 706). In moderate wind speed conditions, the stability of the atmospheric boundary layer increases, resulting in less wind variance, therefore giving better correlation between the anemometer measurement and the wind at the sensor.
[0056] With the surface now created, a surface normal unit vector may be calculated by the processor at each point in the dataset; the dot product of the surface normal vector and the wind vector may then be calculated by the processor. A volumetric flux rate of airflow may then by calculated by the processor for every point on the surface (step 708). The volumetric flux rate of airflow represents how many cubic meters of air is entering or leaving the control volume per square meter ( dS ).
[0057] In one embodiment, a high pass filter may be applied over the input gas concentration measurements provide accurate relative gas measurements (step 710). The filter removes any low frequency sensor drift, while still resolving all details from emissions. The time constant chosen for the filter may be based on analysis of the sensor stability.
[0058] In one embodiment, multiplying the above wind flux values from step 708 by the volume fraction of gas (m3 CH4 or CCk/m3 air) with the processor provides the volumetric flux rate of gas at each point (m3 gas / s / m2 area).
[0059] Because the control surface does not completely encompass the control volume, and the measurements were not taken concurrently, the standard incompressible equation of continuity may not hold true.
V - u ¹ 0 [0060] Therefore, the net flux may not simply be calculated by integrating the point gas flux measurements across the entire surface; rather, to calculate the flux of gas entering the control volume, the surface may be kept at a threshold to where wind is only flowing into the control volume (step 712). With this surface, the volumetric flux rate of gas can be integrated by the processor to give the total inward volumetric flux of gas into the control volume (m3 species / s) (step 714).
Figure imgf000015_0001
[0061] Once the species flux rate has been established, a total air volumetric rate may be determined by the processor as well by integrating the wind velocity normal component to give a total air volumetric flux rate. By dividing the species volumetric flux rate by the air volumetric flux rate, the volumetric flowrate averaged inflowing species concentration may be determined by the processor (step 716).
[0062] Although a similar solution could be obtained by simply averaging all the values of volumetric flux rate of gas at each point, the triangulation of the real-world dataset yields varied triangles, and this difference in size should be considered when calculating the average incoming volumetric gas flux rate. This process to get the total background concentration value is “volumetric flow averaging” and is necessary for calculating convective quantities such as species concentrations. In this case, the integral operator is not commutative because the wind values vary on the surface, and therefore the two integrals must be performed separately, and their results divided together.
[0063] The volumetric-flow-rate averaged concentration of the inflowing gas may then be subtracted from the concentration values measured where anemometry indicates gas is flowing out of the control volume, providing the enhanced concentrations with the background concentration component removed (step 718).
[0064] With the enhanced concentration values, the final net flux rate can be calculated by the processor. Multiplying, with the processor, the component of wind velocity normal to the control surface by the concentration enhancement, the flux rate of the enhanced species concentration per unit area is determined (m3species/m2/second) (step 720).
[0065] This approach has the benefit that it satisfies the fundamental fluid dynamics equation for flow of a species through a control volume. Continuity of the species is used to ensure that all of the concentration upstream is subtracted from the concentration downstream, even if the oncoming flow has not got a uniform concentration of gas. Because the control volume is still open at the top, and the discrete sampling process means that there are gaps in the control surface, absolute integrals of continuity of mass and continuity of species cannot be simply applied. In addition, sampling all the points non-simultaneously, and in transient wind conditions, means that flux through any point in the control surface is constantly changing.
[0066] Despite fully considering the background concentration into calculations, there is still a significant degree of error incurred from wind speed measurement. In one embodiment, the disclosed process may utilize a ground weather station that is set up on-site in a location with the least amount of interference from other obstacles as possible. Even in the optimal location, the anemometer is not co-located with the UAV at any time, and so the reading at the anemometer will be different from the actual wind-speed at the UAV. This effect is particularly strong during light wind conditions, as a small change in the wind speed can incur a large change in the concentration values measured. In light wind conditions, the atmospheric boundary layer is also more unstable, resulting in more diffusion of the species plume, and therefore lower concentration values than with a more consistent wind.
[0067] FIG. 8 illustrates an example of a top-level functional block diagram of a computing device embodiment 800. The example operating environment is shown as a computing device 820 comprising a processor 824, such as a central processing unit (CPU), addressable memory 827, an external device interface 826, e.g., an optional universal serial bus port and related processing, and/or an Ethernet port and related processing, and an optional user interface 829, e.g., an array of status lights and one or more toggle switches, and/or a display, and/or a keyboard and/or a pointer-mouse system and/or a touch screen. Optionally, the addressable memory may, for example, be: flash memory, eprom, and/or a disk drive or other hard drive. These elements may be in communication with one another via a data bus 828. In some embodiments, via an operating system 825 such as one supporting a web browser 823 and applications 822, the processor 824 may be configured to execute steps of a process establishing a communication channel and processing according to the embodiments described above.
[0068] System embodiments include computing devices such as a server computing device, a buyer computing device, and a seller computing device, each comprising a processor and addressable memory and in electronic communication with each other. The embodiments provide a server computing device that may be configured to: register one or more buyer computing devices and associate each buyer computing device with a buyer profile; register one or more seller computing devices and associate each seller computing device with a seller profile; determine search results of one or more registered buyer computing devices matching one or more buyer criteria via a seller search component. The service computing device may then transmit a message from the registered seller computing device to a registered buyer computing device from the determined search results and provide access to the registered buyer computing device of a property from the one or more properties of the registered seller via a remote access component based on the transmitted message and the associated buyer computing device; and track movement of the registered buyer computing device in the accessed property via a viewer tracking component. Accordingly, the system may facilitate the tracking of buyers by the system and sellers once they are on the property and aid in the seller’s search for finding buyers for their property. The figures described below provide more details about the implementation of the devices and how they may interact with each other using the disclosed technology.
[0069] FIG. 9 is a high-level block diagram 900 showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein. Embodiments of the system may be implemented in different computing environments. The computer system includes one or more processors 902, and can further include an electronic display device 904 (e.g., for displaying graphics, text, and other data), a main memory 906 (e.g., random access memory (RAM)), storage device 908, a removable storage device 910 (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data), user interface device 911 (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface 912 (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card).
The communication interface 912 allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure 914 (e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices/modules are connected as shown.
[0070] Information transferred via communications interface 914 may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface 914, via a communication link 916 that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, an radio frequency (RF) link, and/or other communication channels. Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.
[0071] Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions. The computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram. Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc.
[0072] Computer programs (i.e., computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface 912. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.
[0073] FIG. 10 shows a block diagram of an example system 1000 in which an embodiment may be implemented. The system 1000 includes one or more client devices 1001 such as consumer electronics devices, connected to one or more server computing systems 1030. A server 1030 includes a bus 1002 or other communication mechanism for communicating information, and a processor (CPU) 1004 coupled with the bus 1002 for processing information. The server 1030 also includes a main memory 1006, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 1002 for storing information and instructions to be executed by the processor 1004. The main memory 1006 also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor 1004. The server computer system 1030 further includes a read only memory (ROM) 1008 or other static storage device coupled to the bus 1002 for storing static information and instructions for the processor 1004. A storage device 1010, such as a magnetic disk or optical disk, is provided and coupled to the bus 1002 for storing information and instructions. The bus 1002 may contain, for example, thirty -two address lines for addressing video memory or main memory 1006. The bus 1002 can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU 1004, the main memory 1006, video memory and the storage 1010. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.
[0074] The server 1030 may be coupled via the bus 1002 to a display 1012 for displaying information to a computer user. An input device 1014, including alphanumeric and other keys, is coupled to the bus 1002 for communicating information and command selections to the processor 1004. Another type or user input device comprises cursor control 1016, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 1004 and for controlling cursor movement on the display 1012.
[0075] According to one embodiment, the functions are performed by the processor 1004 executing one or more sequences of one or more instructions contained in the main memory 1006. Such instructions may be read into the main memory 1006 from another computer-readable medium, such as the storage device 1010. Execution of the sequences of instructions contained in the main memory 1006 causes the processor 1004 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory 1006. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.
[0076] The terms "computer program medium," "computer usable medium," "computer readable medium", and "computer program product," are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information. Computer programs (also called computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
[0077] Generally, the term "computer-readable medium" as used herein refers to any medium that participated in providing instructions to the processor 1004 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as the storage device 1010. Volatile media includes dynamic memory, such as the main memory 1006. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 1002. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
[0078] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
[0079] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor 1004 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the server 1030 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus 1002 can receive the data carried in the infrared signal and place the data on the bus 1002. The bus 1002 carries the data to the main memory 1006, from which the processor 1004 retrieves and executes the instructions. The instructions received from the main memory 1006 may optionally be stored on the storage device 1010 either before or after execution by the processor 1004. [0080] The server 1030 also includes a communication interface 1018 coupled to the bus 1002. The communication interface 1018 provides a two-way data communication coupling to a network link 1020 that is connected to the world wide packet data communication network now commonly referred to as the Internet 1028. The Internet 1028 uses electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 1020 and through the communication interface 1018, which carry the digital data to and from the server 1030, are exemplary forms or carrier waves transporting the information.
[0081] In another embodiment of the server 1030, interface 1018 is connected to a network 1022 via a communication link 1020. For example, the communication interface 1018 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link 1020. As another example, the communication interface 1018 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface 1018 sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.
[0082] The network link 1020 typically provides data communication through one or more networks to other data devices. For example, the network link 1020 may provide a connection through the local network 1022 to a host computer 1024 or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the Internet 1028. The local network 1022 and the Internet 1028 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 1020 and through the communication interface 1018, which carry the digital data to and from the server 1030, are exemplary forms or carrier waves transporting the information.
[0083] The server 1030 can send/receive messages and data, including e-mail, program code, through the network, the network link 1020 and the communication interface 1018. Further, the communication interface 1018 can comprise a USB/Tuner and the network link 1020 may be an antenna or cable for connecting the server 1030 to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.
[0084] The example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system 1000 including the servers 1030. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server 1030, and as interconnected machine modules within the system 1000. The implementation is a matter of choice and can depend on performance of the system 1000 implementing the embodiments. As such, the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.
[0085] Similar to a server 1030 described above, a client device 1001 can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 1028, the ISP, or LAN 1022, for communication with the servers 1030.
[0086] The system 1000 can further include computers (e.g., personal computers, computing nodes) 1005 operating in the same manner as client devices 1001, where a user can utilize one or more computers 1005 to manage data in the server 1030.
[0087] Referring now to FIG. 11, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA), smartphone, smart watch, set-top box, video game system, tablet, mobile computing device, or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or unmanned aerial vehicle (UAV) 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 11 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
[0088] FIG. 12 depicts a system 1200 for detecting trace gasses, according to one embodiment. The system may include one or more trace gas sensors located in one or more vehicles 1202, 1204, 1206, 1210. The one or more trace gas sensors may detect elevated trace gas concentrations from one or more potential gas sources 1220, 1222, such as a holding tank, pipeline, or the like. The potential gas sources 1220, 1222 may be part of a large facility, a small facility, or any location. The potential gas sources 1220, 1222 may be clustered and/or disposed distal from one another. The one or more trace gas sensors may be used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane, sulfur dioxide) in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, such as oil and gas, chemical production, and painting. Detection and quantification of leaks is also of value to environmental regulators for assessing compliance and for mitigating environmental and safety risks. In some embodiments, the at least one trace gas sensor may be configured to detect methane. In other embodiments, the at least one trace gas sensor may be configured to detect sulfur oxide, such as SO, S02, S03, S702, S602, S202, and the like. A trace gas leak 1224 may be present in a potential gas source 1220. The one or more trace gas sensors may be used to identify the trace gas leak 1224 and/or the source 1220 of the trace gas leak 1224 so that corrective action may be taken.
[0089] The one or more vehicles 1202, 1204, 1206, 1210 may include an unmanned aerial vehicle (UAV) 1202, an aerial vehicle 1204, a handheld device 1206, and a ground vehicle 1210. In some embodiments, the UAV 1202 may be a quadcopter or other device capable of hovering, making sharp turns, and the like. In other embodiments, the UAV 1202 may be a winged aerial vehicle capable of extended flight time between missions. The UAV 1202 may be autonomous or semi-autonomous in some embodiments. In other embodiments, the UAV 1202 may be manually controlled by a user. The aerial vehicle 1204 may be a manned vehicle in some embodiments. The handheld device 1206 may be any device having one or more trace gas sensors operated by a user 1208. In one embodiment, the handheld device 1206 may have an extension for keeping the one or more trace gas sensors at a distance from the user 1208. The ground vehicle 1210 may have wheels and/or treads in one embodiment. In other embodiments, the ground vehicle 1210 may be a legged robot. In some embodiments, the ground vehicle 1210 may be used as a base station for one or more UAVs 1202. In some embodiments, one or more aerial devices, such as the UAV 1202, a balloon, or the like, may be tethered to the ground vehicle 1210. In some embodiments, one or more trace gas sensors may be located in one or more stationary monitoring devices 1226. The one or more stationary monitoring devices may be located proximate one or more potential gas sources 1220, 1222. In some embodiments, the one or more stationary monitoring devices may be relocated.
[0090] The one or more vehicles 1202, 1204, 1206, 1210 and/or stationary monitoring devices 1226 may transmit data including trace gas data to a ground control station (GCS) 1212. The GCS may include a display 1214 for displaying the trace gas concentrations to a GCS user 1216. The GCS user 1216 may be able to take corrective action if a gas leak 1224 is detected, such as by ordering a repair of the source 1220 of the trace gas leak. The GCS user 1216 may be able to control movement of the one or more vehicles 1202, 1204, 1206, 1210 in order to confirm a presence of a trace gas leak in some embodiments.
[0091] In some embodiments, the GCS 1212 may transmit data to a cloud server 1218. In some embodiments, the cloud server 1218 may perform additional processing on the data. In some embodiments, the cloud server 1218 may provide third party data to the GCS 1212, such as wind speed, temperature, pressure, weather data, or the like.
[0092] It is contemplated that various combinations and/or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further, it is intended that the scope of the present invention herein disclosed by way of examples should not be limited by the particular disclosed embodiments described above.

Claims

WHAT IS CLAIMED IS:
1. A system comprising: a processor (402) having addressable memory (426), wherein the processor is configured to: determine one or more flight paths (204) for an aerial vehicle (202, 408), wherein the determined flight path (204) creates a continuous surface (206) about one or more potential gas sources of a survey site (210); receive a trace gas data from one or more trace gas sensors (412) of the aerial vehicle of the continuous surface (206) as the aerial vehicle (202, 408) flies the determined one or more flight paths (204); and determine based on the received trace gas data whether a gas leak is present in the received survey site (210, 404) and a rate of the gas leak if present in the survey site (210, 404).
2. The system of claim 1, wherein the continuous surface (206) is a flux plane.
3. The system of claim 2, wherein the flux plane is a closed flux plane.
4. The system of claim 1, wherein the continuous surface is formed by any series of turns in the flight path for the aerial vehicle that forms the continuous surface when interpolated.
5. The system of claim 1, wherein the processor is further configured to: receive one or more flight platform capabilities (406) of the aerial vehicle, wherein the determined flight path is based on the received one or more flight platform capabilities.
6. The system of claim 5, wherein the processor is further configured to: receive a wind data (410) for the survey site (210, 404), wherein the determined flight path is further based on the received wind data.
7. The system of claim 6, wherein the wind data comprises instantaneous wind speed measurements in three dimensions from an anemometer (702).
8. The system of claim 1, wherein the aerial vehicle is an unmanned aerial vehicle
(UAV).
9. The system of claim 8, wherein the UAV is configured to fly the determined one or more flight paths autonomously.
10. The system of claim 8, wherein the UAV is configured to fly the determined one or more flight paths semi-autonomously.
11. The system of claim 1, wherein the continuous surface comprises a beehive-shaped continuous surface (300).
12. The system of claim 1, wherein the continuous surface comprises a cone-shaped continuous surface (304, 312).
13. The system of claim 1, wherein the continuous surface comprises at least one of: a right angle (308), an arc, and a continuous curve in the flight path for the aerial vehicle.
14. The system of claim 1, wherein the aerial vehicle flies a determined flight path of the one or more flight path two or more times, and wherein the received trace gas data is averaged from each of the two or more flights.
15. The system of claim 1, wherein the processor is further configured to, for creating the continuous surface: convert a dataset into an altitude-s space (602); perform a triangulation on the dataset in the altitude s-space (604); and apply the performed triangulation on a Cartesian X, Y, Z space of the dataset to produce at least one of: a fully closed surface and a semi-closed surface (606).
16. A method comprising: determining, by the processor, one or more flight paths (204) for an aerial vehicle (202, 408), wherein the determined flight path (204) creates a continuous surface (206) about one or more potential gas sources of a survey site (210); receiving, by the processor, a trace gas data from one or more trace gas sensors (412) of the aerial vehicle of the continuous surface (206) as the aerial vehicle (202,
408) flies the determined one or more flight paths (204); and determining, by the processor, based on the received gas data whether a gas leak is present in the received survey site (210, 404) and a rate of the gas leak if present in the survey site (210, 404).
17. The method of claim 16, wherein the continuous surface (206) is a flux plane.
18. The method of claim 17, wherein the flux plane is a closed flux plane.
19. The method of claim 16, wherein the continuous surface is formed by any series of turns in the flight path for the aerial vehicle that forms the continuous surface when interpolated.
20. The method of claim 16, further comprising: receiving, by the processor (402), one or more flight platform capabilities (406), wherein the determined flight path is based the received one or more flight platform capabilities (406).
21. The method of claim 20, further comprising: receiving, by the processor, a wind data (410) for the survey site (210, 404), wherein the determined flight path is further based the received one or more flight platform capabilities (406), and the received wind data (410).
22. The method of claim 16, wherein the aerial vehicle is an unmanned aerial vehicle
(UAV).
23. The method of claim 22, wherein the UAV is configured to fly the determined one or more flight paths autonomously.
24. The method of claim 22, wherein the UAV is configured to fly the determined one or more flight paths semi-autonomously.
5. The method of claim 22, wherein creating the continuous surface further comprises: converting, by the processor, a dataset into an altitude-s space (602); performing, by the processor, a triangulation on the dataset in the altitude s-space (604); and applying, by the processor, the performed triangulation on a Cartesian X, Y, Z space of the dataset to produce at least one of: a fully closed surface and a semi-closed surface (606).
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US18/962,544 US20250164994A1 (en) 2019-10-04 2024-11-27 Closed surface flight pattern generation for unmanned aerial vehicle (uav) flux plane assessment of large facilities

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115931008A (en) * 2023-02-27 2023-04-07 昆明人为峰科技有限公司 A topographic surveying and mapping equipment operation status monitoring system and monitoring method
WO2023133345A1 (en) * 2022-01-10 2023-07-13 Cameron International Corporation Method and apparatus for greenhouse gas emission management
FR3142556A1 (en) * 2022-11-30 2024-05-31 Totalenergies Onetech Method for measuring the contents of at least one gas emitted by a source in a plume propagating in the atmosphere from the source, method, and associated drone
WO2024141721A1 (en) * 2022-12-30 2024-07-04 Aeromon Oy Method and system for measuring emission rate of airborne emission species
US12254622B2 (en) 2023-06-16 2025-03-18 Schlumberger Technology Corporation Computing emission rate from gas density images
US12292310B2 (en) 2022-12-15 2025-05-06 Schlumberger Technology Corporation Machine learning based methane emissions monitoring
CN120761590A (en) * 2025-09-08 2025-10-10 湖南大学 Aerial sliding contact inspection robot
US12480922B2 (en) 2022-12-09 2025-11-25 Schlumberger Technology Corporation Methods and systems for characterizing methane emission employing mobile methane emission detection
US12480924B2 (en) 2022-08-03 2025-11-25 Schlumberger Technology Corporation Automated record quality determination and processing for pollutant emission quantification
US12555125B2 (en) 2023-06-09 2026-02-17 Schlumberger Technology Corporation Emission detecting camera placement planning using 3D models
US12571708B2 (en) 2020-04-08 2026-03-10 Centre National De La Recherche Scientifique Method for calculating a stream of at least one gas emitted by a source into the atmosphere, measurement method, and associated system and kit
US12577871B2 (en) 2022-08-03 2026-03-17 Schlumberger Technology Corporation Linear cut generation method for sensor inversion constraint imposition

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12333921B2 (en) * 2023-06-08 2025-06-17 Saudi Arabian Oil Company Unmanned aerial system for autonomous gas leakage detection, quantification, and mitigation
WO2025260037A1 (en) * 2024-06-14 2025-12-18 Seekops Inc. Source localization and source attribution
CN119023903A (en) * 2024-08-21 2024-11-26 北京大学深圳研究生院 A mobile carbon flux high-precision monitoring system and monitoring method thereof

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170003684A1 (en) * 2014-01-28 2017-01-05 EXPLICIT ApS A method and an unmanned aerial vehicle for determining emissions of a vessel
US20180127093A1 (en) * 2016-11-02 2018-05-10 California Institute Of Technology Positioning of In-Situ Methane Sensor on a Vertical Take-Off and Landing (VTOL) Unmanned Aerial System (UAS)
WO2018121478A1 (en) * 2016-12-30 2018-07-05 华为技术有限公司 Air-to-ground communication system, method, and device
US20180209902A1 (en) 2014-08-25 2018-07-26 Isis Geomatics Inc. Apparatus and method for detecting a gas using an unmanned aerial vehicle
US20180292374A1 (en) 2017-04-05 2018-10-11 International Business Machines Corporation Detecting gas leaks using unmanned aerial vehicles
US20190204189A1 (en) 2018-01-02 2019-07-04 Sniffer Robotics, LLC Apparatus and method for collecting environmental samples

Family Cites Families (204)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3780566A (en) 1972-03-07 1973-12-25 Argus Chem Apparatus for continuously monitoring air-pollution
US4135092A (en) 1978-04-12 1979-01-16 Geomet Exploration, Inc. Method of quantifying fugitive emission rates from pollution sources
US4233564A (en) 1978-09-05 1980-11-11 Hazeltine Corporation Apparatus for changing the scale of a logarithmic signal
US4507558A (en) 1983-02-22 1985-03-26 Honeywell Inc. Selective leak-detector for natural gas
SE8802536D0 (en) 1988-07-07 1988-07-07 Altoptronic Ab METHOD AND APPARATUS FOR SPECTROSCOPIC MEASUREMENT OF THE CONCENTRATION OF A GAS IN A SAMPLE
US4988833A (en) 1989-08-29 1991-01-29 W. L. Gore & Associates, Inc. Retractable coiled electrical cable
US5047639A (en) 1989-12-22 1991-09-10 Wong Jacob Y Concentration detector
US5075619A (en) 1990-04-06 1991-12-24 Tektronix, Inc. Method and apparatus for measuring the frequency of a spectral line
US5317156A (en) 1992-01-29 1994-05-31 Sri International Diagnostic tests using near-infrared laser absorption spectroscopy
US5291265A (en) 1992-06-03 1994-03-01 Aerodyne Research, Inc. Off-axis cavity absorption cell
ATE241315T1 (en) 1993-08-12 2003-06-15 Kurashiki Boseki Kk NON-INVASIVE METHOD AND INSTRUMENT FOR MEASURING BLOOD SUGAR LEVELS
US5767780A (en) 1993-09-22 1998-06-16 Lockheed Martin Energy Research Corporation Detector for flow abnormalities in gaseous diffusion plant compressors
DE29500705U1 (en) 1995-01-18 1995-03-02 Future Technics Medizin und Technik GmbH, 96515 Sonneberg Device for determining a certain proportion of gas in the air
JPH08247939A (en) 1995-03-15 1996-09-27 Anritsu Corp Gas concentration measuring device
US5822058A (en) 1997-01-21 1998-10-13 Spectral Sciences, Inc. Systems and methods for optically measuring properties of hydrocarbon fuel gases
US6295859B1 (en) * 1997-04-18 2001-10-02 The B. F. Goodrich Co. Method and system for remotely determining column density of trace gases
US6064488A (en) 1997-06-06 2000-05-16 Monitor Labs, Inc. Method and apparatus for in situ gas concentration measurement
AU3401499A (en) 1998-04-20 1999-11-08 Horace Rekunyk Infrared remote monitoring system for leak
US6356350B1 (en) 1998-07-30 2002-03-12 Southwest Sciences Incorporated Wavelength modulation spectroscopy with multiple harmonic detection
US6549630B1 (en) 2000-02-04 2003-04-15 Plantronics, Inc. Signal expander with discrimination between close and distant acoustic source
US6597462B2 (en) 2000-03-01 2003-07-22 Lambda Physik Ag Laser wavelength and bandwidth monitor
US6509566B1 (en) 2000-06-22 2003-01-21 Ophir Corporation Oil and gas exploration system and method for detecting trace amounts of hydrocarbon gases in the atmosphere
SE516843C2 (en) 2000-07-12 2002-03-12 Bo Galle Method for measuring gaseous emissions and / or flux
WO2002004903A1 (en) 2000-07-12 2002-01-17 Macquarie Research Ltd Optical heterodyne detection in optical cavity ringdown spectroscopy
US6632402B2 (en) 2001-01-24 2003-10-14 Ntc Technology Inc. Oxygen monitoring apparatus
TW522226B (en) 2001-02-20 2003-03-01 Tlv Co Ltd Portable leak detector
US7022992B2 (en) 2002-01-17 2006-04-04 American Air Liquide, Inc. Method and apparatus for real-time monitoring of furnace flue gases
US20030230716A1 (en) 2002-04-12 2003-12-18 Infrared Industries, Inc. Multi-gas analyzer
US10521960B2 (en) 2017-05-03 2019-12-31 General Electric Company System and method for generating three-dimensional robotic inspection plan
US10739770B2 (en) 2018-01-16 2020-08-11 General Electric Company Autonomously-controlled inspection platform with model-based active adaptive data collection
US10633093B2 (en) 2017-05-05 2020-04-28 General Electric Company Three-dimensional robotic inspection system
JP3091618U (en) 2002-07-24 2003-02-07 船井電機株式会社 Optical pickup
ATE434757T1 (en) 2002-09-06 2009-07-15 Tdw Delaware Inc METHOD FOR DETECTING GASES USING ABSORPTION SPECTROSCOPY
US6949734B2 (en) 2003-04-22 2005-09-27 Itt Manufacturing Enterprises, Inc. Active remote sensing using a spectral lock-in technique
US7966155B2 (en) 2004-06-04 2011-06-21 William K. Warburton Method and apparatus for improving detection limits in x-ray and nuclear spectroscopy systems
WO2006005014A2 (en) 2004-06-30 2006-01-12 Valtronics, Inc. Gas sample conditioning system
US7477377B2 (en) 2004-07-21 2009-01-13 Southwest Sciences Incorporated Dense pattern optical multipass cell
US20060044562A1 (en) 2004-08-25 2006-03-02 Norsk Elektro Optikk As Gas monitor
US7469183B2 (en) 2005-01-24 2008-12-23 International Business Machines Corporation Navigating UAVs in formation
US20100004798A1 (en) 2005-01-25 2010-01-07 William Kress Bodin Navigating a UAV to a next waypoint
US8147302B2 (en) 2005-03-10 2012-04-03 Aircuity, Inc. Multipoint air sampling system having common sensors to provide blended air quality parameter information for monitoring and building control
US8001841B2 (en) 2005-10-14 2011-08-23 Olympus Ndt Ultrasonic fault detection system using a high dynamic range analog to digital conversion system
US7421911B2 (en) 2005-12-20 2008-09-09 Desrochers Eric M Duct probe assembly system for multipoint air sampling
US7800751B1 (en) 2006-02-27 2010-09-21 Southwest Sciences Incorporated Dense pattern multiple pass cells
US8018981B2 (en) 2006-04-12 2011-09-13 Li-Cor, Inc. Multi-pass optical cell with actuator for actuating a reflective surface
US20080169934A1 (en) 2006-08-11 2008-07-17 Steve Lang Portable gas detector
US7999232B2 (en) 2006-12-22 2011-08-16 Photonic Innovations Limited Gas detector
US20090326792A1 (en) 2007-05-06 2009-12-31 Mcgrath Alan Thomas Method and system for increasing the degree of autonomy of an unmanned aircraft by utilizing meteorological data received from GPS dropsondes released from an unmanned aircraft to determine course and altitude corrections and an automated data management and decision support navigational system to make these navigational calculations and to correct the unmanned aircraft's flight path
JP2009075823A (en) 2007-09-20 2009-04-09 Toho Gas Co Ltd Gas leak detection communication system and gas leak detection communication apparatus
EP2072979B1 (en) 2007-12-21 2012-02-29 Siemens Aktiengesellschaft Method for measuring the concentration of a gas component in a measuring gas
US8060270B2 (en) * 2008-02-29 2011-11-15 The Boeing Company System and method for inspection of structures and objects by swarm of remote unmanned vehicles
JP2009276339A (en) 2008-04-16 2009-11-26 Ngk Spark Plug Co Ltd Sensor
DE112008003880T5 (en) 2008-05-27 2011-04-14 Sadik Hafizovic Device for lock-in amplification of an input signal and method for generating a reference signal for a lock-in amplifier
WO2009156437A2 (en) * 2008-06-25 2009-12-30 Shell Internationale Research Maatschappij B.V. Method and system for screening an area of the atmosphere for sources of emissions
US9267881B2 (en) 2008-11-06 2016-02-23 Li-Cor, Inc. Hybrid gas analyzer with thermally insulated flow cell
US8121798B2 (en) * 2008-11-24 2012-02-21 Itt Manufacturing Enterprises, Inc. Gas flux determination using airborne DIAL LIDAR and airborne wind measurement
EP2199790A1 (en) 2008-12-19 2010-06-23 Duvas Technologies Limited System and apparatus for measurement and mapping of pollutants
CA2751209C (en) 2009-02-02 2018-08-21 Planetary Emissions Management System of systems for monitoring greenhouse gas fluxes
US8515609B2 (en) * 2009-07-06 2013-08-20 Honeywell International Inc. Flight technical control management for an unmanned aerial vehicle
US8451120B2 (en) 2009-08-14 2013-05-28 Accenture Global Services Limited System for relative positioning of access points in a real time locating system
CA2681681A1 (en) 2009-10-06 2010-06-08 Colin Irvin Wong Mapping concentrations of airborne matter
US8811439B2 (en) 2009-11-23 2014-08-19 Seminex Corporation Semiconductor laser assembly and packaging system
US20110150035A1 (en) 2009-12-17 2011-06-23 Hanson Ronald K Non-intrusive method for sensing gas temperature and species concentration in gaseous environments
US8508740B2 (en) 2010-01-04 2013-08-13 University Corporation For Atmospheric Research Optical multi-pass cell
US8731889B2 (en) 2010-03-05 2014-05-20 Schlumberger Technology Corporation Modeling hydraulic fracturing induced fracture networks as a dual porosity system
IT1401884B1 (en) * 2010-10-06 2013-08-28 Tea Sistemi S P A METHOD FOR QUANTIFYING A FLOW OF GAS FUGITIVE BY MEANS OF VERTICAL CONCENTRATION MEASUREMENTS
US8665442B2 (en) 2011-08-18 2014-03-04 Li-Cor, Inc. Cavity enhanced laser based isotopic gas analyzer
US9329365B2 (en) 2011-09-23 2016-05-03 Goodrich Corporation Wide field of view monocentric lens system for infrared aerial reconnaissance camera systems
EP2776673B1 (en) 2011-11-03 2022-01-19 Fastcap Systems Corporation A logging apparatus
WO2013096396A1 (en) 2011-12-20 2013-06-27 The Board Of Trustees Of The Leland Stanford Junior University A method for calibration-free scanned-wavelength modulation spectroscopy for gas sensing
ITTO20120894A1 (en) 2012-10-12 2014-04-13 Sea Marconi Technologies Di Vander Tumiatti S A S CO-PRODUCTION PROCEDURE OF BIOENERGY AND INTEGRATED CONVERSION OF BIOMASS AND URBAN WASTE
TWI665437B (en) 2012-10-16 2019-07-11 巴哈馬商愛克斯崔里斯科技有限公司 Method and apparatus for determining at least one point of entry of smoke into a smoke detection system, and smoke detector
US9599597B1 (en) 2012-12-22 2017-03-21 Picarro, Inc. Systems and methods for likelihood-based detection of gas leaks using mobile survey equipment
EP2948761B1 (en) * 2013-01-23 2023-06-28 California Institute of Technology Miniature tunable laser spectrometer for detection of a trace gas
US8880241B2 (en) 2013-02-20 2014-11-04 Farrokh Mohamadi Vertical takeoff and landing (VTOL) small unmanned aerial system for monitoring oil and gas pipelines
CA2937917A1 (en) 2013-03-14 2014-09-18 Total S.A. Systems and methods for monitoring and controlled capture of air samples for analysis
US9183371B2 (en) 2013-03-15 2015-11-10 Tyfone, Inc. Personal digital identity device with microphone
US9263787B2 (en) 2013-03-15 2016-02-16 Dockon Ag Power combiner and fixed/adjustable CPL antennas
US9266611B2 (en) 2013-06-20 2016-02-23 University Of Florida Research Foundation, Inc. Flight path development for remote sensing vehicles in a moving reference frame
US9947051B1 (en) 2013-08-16 2018-04-17 United Services Automobile Association Identifying and recommending insurance policy products/services using informatic sensor data
GB2518010A (en) 2013-09-09 2015-03-11 Crfs Ltd Frequency discriminator
WO2015073687A1 (en) 2013-11-13 2015-05-21 Schlumberger Canada Limited Unmanned aerial vehicles for well monitoring and control
US10250821B2 (en) * 2013-11-27 2019-04-02 Honeywell International Inc. Generating a three-dimensional model of an industrial plant using an unmanned aerial vehicle
US11290662B2 (en) * 2014-05-01 2022-03-29 Rebellion Photonics, Inc. Mobile gas and chemical imaging camera
US9756263B2 (en) 2014-05-01 2017-09-05 Rebellion Photonics, Inc. Mobile gas and chemical imaging camera
WO2015172056A1 (en) * 2014-05-09 2015-11-12 Kairos Aerospace Inc. Systems and methods for detecting gas leaks
US9183731B1 (en) 2014-05-15 2015-11-10 Umm Al-Qura University Emergency detection and alert device and system utilizing a mobile communication device
US9783293B2 (en) * 2014-05-20 2017-10-10 Verizon Patent And Licensing Inc. Unmanned aerial vehicle platform
EP3855276A1 (en) * 2014-09-05 2021-07-28 SZ DJI Technology Co., Ltd. Multi-sensor environmental mapping
DE102014013822B4 (en) 2014-09-23 2024-04-25 Schütz GmbH Meßtechnik Operating procedure for a gas detector
US20160214715A1 (en) * 2014-11-21 2016-07-28 Greg Meffert Systems, Methods and Devices for Collecting Data at Remote Oil and Natural Gas Sites
US10598562B2 (en) 2014-11-21 2020-03-24 Picarro Inc. Gas detection systems and methods using measurement position uncertainty representations
CA2872783A1 (en) 2014-12-01 2016-06-01 David Andrew Risk Gas emission detection device, system and method
US9250175B1 (en) 2014-12-16 2016-02-02 Aerodyne Research, Inc. Optical multi-pass cell for long path-length spectroscopy
CN104458588B (en) 2014-12-24 2017-02-22 威特龙消防安全集团股份公司 Bidirectional self-cleaning type optical fiber gas sensor probe
US20160202225A1 (en) * 2015-01-09 2016-07-14 Case Western Reserve University System for Detecting a Gas and Method Therefor
US10365646B1 (en) 2015-01-27 2019-07-30 United Services Automobile Association (Usaa) Systems and methods for unmanned vehicle management
US9983126B2 (en) 2015-02-06 2018-05-29 Block Engineering, Llc Quantum cascade laser (QCL) based gas sensing system and method
US11768508B2 (en) 2015-02-13 2023-09-26 Skydio, Inc. Unmanned aerial vehicle sensor activation and correlation system
WO2016130994A1 (en) 2015-02-13 2016-08-18 Unmanned Innovation, Inc. Unmanned aerial vehicle remote flight planning system
WO2016162673A1 (en) 2015-04-10 2016-10-13 Bae Systems Plc Long range sensor apparatus and method of providing a long range sensor apparatus
US10023323B1 (en) 2015-04-29 2018-07-17 X Development Llc Estimating wind from an airborne vehicle
US10240998B2 (en) 2015-05-12 2019-03-26 The United States Of America, As Represented By The Secretary Of Commerce Determining a location and size of a gas source with a spectrometer gas monitor
GB2538563B (en) 2015-05-22 2017-08-02 Optosci Ltd Gas sensing apparatus
US9715235B2 (en) 2015-06-05 2017-07-25 The Boeing Company Autonomous unmanned aerial vehicle decision-making
US20170158353A1 (en) 2015-08-07 2017-06-08 Mark Schmick Remote Aerodrome for UAVs
DE102015216272A1 (en) 2015-08-26 2017-03-02 Airbus Operations Gmbh Modular robot kit, swarm of modularized robots, and task accomplishment by a swarm of modularized robots
US10662765B2 (en) 2015-09-18 2020-05-26 Schlumberger Technology Corporation Wellsite emissions monitoring and control
WO2017058901A1 (en) 2015-09-28 2017-04-06 Ball Aerospace & Technologies Corp. Differential absorption lidar
US9970756B2 (en) * 2015-10-06 2018-05-15 Bridger Photonics, Inc. High-sensitivity gas-mapping 3D imager and method of operation
US11287407B2 (en) 2015-10-19 2022-03-29 University Of North Texas Dynamic reverse gas stack model for portable chemical detection devices to locate threat and point-of-source from effluent streams
US9810627B2 (en) 2015-10-27 2017-11-07 Nec Corporation Flexible three-dimensional long-path gas sensing by unmanned vehicles
NL2017595A (en) 2015-11-10 2017-05-26 Asml Netherlands Bv Proximity sensor, lithographic apparatus and device manufacturing method
US10069918B2 (en) 2015-11-11 2018-09-04 Ut-Battelle, Llc Global communication and control
AU2016359163A1 (en) * 2015-11-23 2018-07-05 Kespry Inc. Autonomous mission action alteration
KR101770254B1 (en) 2015-11-30 2017-08-22 엘케이테크넷(주) Gas leak detection system using smart-phone and hydrocarbon detection device
US20170168487A1 (en) 2015-12-11 2017-06-15 International Business Machines Corporation System and method for tracking pollution
US10083616B2 (en) 2015-12-31 2018-09-25 Unmanned Innovation, Inc. Unmanned aerial vehicle rooftop inspection system
CA3001023A1 (en) 2016-01-08 2017-07-13 Pictometry International Corp. Systems and methods for taking, processing, retrieving, and displaying images from unmanned aerial vehicles
WO2017127711A1 (en) 2016-01-20 2017-07-27 Ez3D, Llc System and method for structural inspection and construction estimation using an unmanned aerial vehicle
FR3047073B1 (en) 2016-01-21 2019-08-02 Pfeiffer Vacuum REMOTE CONTROL FOR LEAK DETECTOR AND LEAK DETECTION MODULE
CN109073544B (en) 2016-02-11 2021-06-15 汤姆·鲁宾 Long optical path absorption cell
JP6691721B2 (en) 2016-02-15 2020-05-13 株式会社トプコン Flight planning method and flight guidance system
US10429546B1 (en) 2016-02-25 2019-10-01 Intellisense Systems, Inc. Weather sensor including vertically stacked multi-power modules
CN113342038B (en) 2016-02-29 2024-08-20 星克跃尔株式会社 Method and system for generating maps for unmanned aerial vehicle flight
US10023311B2 (en) 2016-03-10 2018-07-17 International Business Machines Corporation Automatic painting system with drone, user interface and computer vision
US10533965B2 (en) 2016-04-19 2020-01-14 Industrial Scientific Corporation Combustible gas sensing element with cantilever support
US10180393B2 (en) 2016-04-20 2019-01-15 Cascade Technologies Holdings Limited Sample cell
WO2017201194A1 (en) 2016-05-18 2017-11-23 MultiSensor Scientific, Inc. Hydrocarbon leak imaging and quantification sensor
AU2017268056B2 (en) 2016-05-18 2021-08-05 Lineriders Inc. Apparatus and methodologies for leak detection using gas and infrared thermography
US10448555B2 (en) 2016-05-27 2019-10-22 Cnh Industrial America Llc System and method for scouting vehicle mapping
CN205749271U (en) 2016-06-07 2016-11-30 绍兴国正安全技术检测有限公司 A kind of intelligent portable infrared gas analyser
US11391669B2 (en) 2016-07-07 2022-07-19 Nec Corporation Gas detection system
US9915562B2 (en) 2016-08-12 2018-03-13 Abb, Inc. Method of increasing power within an optical cavity with long path lengths
US10703474B2 (en) 2016-08-20 2020-07-07 The Hi-Tech Robotic Systemz Ltd Tethered unmanned aerial vehicle
US10775297B2 (en) 2016-08-24 2020-09-15 Ecotec Solutions, Inc. Laser absorption spectroscopy system and method for discrimination of a first and a second gas
WO2018045377A1 (en) 2016-09-05 2018-03-08 Brewer Science Inc. Energetic pulse clearing of environmentally sensitive thin-film devices
CN106568516A (en) 2016-11-08 2017-04-19 广西大学 Thermometer
IL249780B (en) 2016-12-26 2020-08-31 Wolfson Noah System and method for predicting presence of hazardous airborne materials in a region to be protected
CN106769977A (en) 2016-12-30 2017-05-31 武汉市欧睿科技有限公司 A kind of hand-held high-precision gas quantitative leak detector
US10704981B2 (en) * 2017-01-04 2020-07-07 General Electric Company Remote leak detection system
GB201700905D0 (en) 2017-01-19 2017-03-08 Cascade Tech Holdings Ltd Close-Coupled Analyser
US20200065433A1 (en) 2017-02-22 2020-02-27 Middle Chart, LLC Method and apparatus for construction and operation of connected infrastructure
US10329017B2 (en) 2017-03-13 2019-06-25 General Electric Company System and method for integrating flight path and site operating data
US10031040B1 (en) 2017-03-28 2018-07-24 Palo Alto Research Center Incorporated Method and system for analyzing gas leak based on machine learning
WO2018193578A1 (en) * 2017-04-20 2018-10-25 エスゼット ディージェイアイ テクノロジー カンパニー リミテッド Flight path establishment method, information processing device, program and recording medium
WO2018227153A1 (en) 2017-06-09 2018-12-13 Resnick Blake Drone implemented border patrol
US10962437B1 (en) 2017-06-27 2021-03-30 Picarro, Inc. Aggregate leak indicator display systems and methods
US20190011920A1 (en) 2017-07-07 2019-01-10 Sharper Shape Oy Method and system for generating flight plan of unmanned aerial vehicle for aerial inspection
US10330592B2 (en) 2017-07-21 2019-06-25 Serguei Koulikov Laser absorption spectroscopy isotopic gas analyzer
US10365199B2 (en) 2017-07-26 2019-07-30 Met One Instruments, Inc. Twin-spot light absorbing particulate monitoring instrument
US10427786B2 (en) 2017-09-14 2019-10-01 At&T Intellectual Property I, L.P. Drone authentication system
US10489649B2 (en) 2017-09-28 2019-11-26 At&T Intellectual Property I, L.P. Drone data locker system
CN107703075A (en) 2017-10-10 2018-02-16 黑龙江聚晶科技有限公司 Distributed concentration of methane gas detection means based on Fibre Optical Sensor
US11275068B2 (en) * 2017-10-31 2022-03-15 Honeywell International Inc. Remote gas sensing using UAVs
WO2019099567A1 (en) * 2017-11-14 2019-05-23 Bridger Photonics, Inc. Apparatuses and methods for anomalous gas concentration detection
US10928549B2 (en) 2017-11-21 2021-02-23 United States Of America As Represented By The Administrator Of Nasa High altitude UAV for monitoring meteorological parameters
US11557212B2 (en) * 2017-12-21 2023-01-17 Beijing Xiaomi Mobile Software Co., Ltd. Method and device for determining flight path of unmanned aerial vehicle
US10466106B2 (en) 2017-12-22 2019-11-05 Nec Corporation Gas concentration measurement by 2F signal trough distance
WO2019135494A1 (en) 2018-01-08 2019-07-11 주식회사 에스오에스랩 Lidar device
US10607406B2 (en) 2018-01-25 2020-03-31 General Electric Company Automated and adaptive three-dimensional robotic site surveying
US12066353B2 (en) * 2018-02-01 2024-08-20 Bridger Photonics, Inc. Apparatuses and methods for gas flux measurements
US11226323B2 (en) 2018-04-27 2022-01-18 International Business Machines Corporation Air-pollution emission source monitoring
CN112327274B (en) 2018-06-08 2022-11-22 上海禾赛科技有限公司 Laser radar
US12399164B2 (en) * 2018-06-19 2025-08-26 Seekops Inc. Emissions estimate model algorithms and methods
EP3811172B1 (en) * 2018-06-19 2025-08-20 SeekOps Inc. Method and system for determining a gas source location
EP3811171B1 (en) 2018-06-19 2025-02-26 SeekOps Inc. Emissions estimate model algorithms and methods
EP3591379B1 (en) 2018-07-04 2022-01-26 Q.E.D. Environmental Systems Limited Portable optical spectroscopy device for analyzing gas samples
US11891192B2 (en) 2018-07-23 2024-02-06 Shanghai Autoflight Co., Ltd. Landing platform for unmanned aerial vehicle
WO2020028353A1 (en) 2018-07-30 2020-02-06 Seekops Inc. Ultra-lightweight, handheld gas leak detection device
CN109030374A (en) 2018-08-16 2018-12-18 上海禾赛光电科技有限公司 Data managing method and data management terminal for laser gas detector
US10325485B1 (en) 2018-09-11 2019-06-18 Rockwell Automation Technologies, Inc. System or process to detect, discriminate, aggregate, track, and rank safety related information in a collaborative workspace
US11062614B2 (en) * 2018-09-12 2021-07-13 Alliance Solutions Group, Inc. Systems and methods for collecting and analyzing hazardous materials information using an unmanned aerial vehicle
US12281983B2 (en) 2018-10-22 2025-04-22 Seekops Inc. UAV-borne, high-bandwidth, lightweight point sensor for quantifying greenhouse gases in atmospheric strata
US10816458B2 (en) * 2018-12-10 2020-10-27 General Electric Company Gas analysis system
US10753864B2 (en) * 2018-12-10 2020-08-25 General Electric Company Gas analysis system
EP3911571A4 (en) * 2019-01-15 2022-10-19 Bridger Photonics, Inc. DEVICES, SYSTEMS AND METHODS FOR MEASUREMENT OF GAS FLOW WITH MOBILE PLATFORMS
CN109780452B (en) 2019-01-24 2021-02-26 天津中科飞航技术有限公司 Gas leakage unmanned aerial vehicle inspection concentration inversion method based on laser remote measurement technology
US10955294B2 (en) * 2019-02-04 2021-03-23 Honeywell International Inc. Optical sensor for trace-gas measurement
US20220165162A1 (en) 2019-04-05 2022-05-26 Seekops Inc. Route optimization for energy industry infrastructure inspection
EP3948202A4 (en) 2019-04-05 2023-01-04 SeekOps Inc. TIME AND DATA EFFICIENT LEAK DETECTION ASSURANCE
WO2020206006A1 (en) 2019-04-05 2020-10-08 Seekops Inc. Analog signal processing for a lightweight and compact laser-based trace gas sensor
JP7334063B2 (en) 2019-05-24 2023-08-28 株式会社ディスコ Manufacturing method of mold chip
US11761938B2 (en) 2019-06-21 2023-09-19 General Electric Company Sensing system and method
US11649782B2 (en) 2019-07-16 2023-05-16 Baker Hughes Oilfield Operations Llc Gas emission monitoring and detection
GB2600323B (en) 2019-08-05 2024-04-03 Teledyne Flir Detection Inc Radiation source localization systems and methods
WO2021055902A1 (en) 2019-09-20 2021-03-25 Seekops Inc. Spectral fitting of compact laser-based trace gas sensor measurements for high dynamic range (hdr)
US11105784B2 (en) * 2019-10-04 2021-08-31 Sensors Unlimited, Inc. System and method of sensing for petroleum, oil, and gas leaks using optical detection
US20210109074A1 (en) 2019-10-14 2021-04-15 Seekops Inc. Gas measurement instrument on unmanned vehicle
US11614430B2 (en) 2019-12-19 2023-03-28 Seekops Inc. Concurrent in-situ measurement of wind speed and trace gases on mobile platforms for localization and qualification of emissions
US11988598B2 (en) 2019-12-31 2024-05-21 Seekops Inc. Optical cell cleaner
US12055485B2 (en) 2020-02-05 2024-08-06 Seekops Inc. Multispecies measurement platform using absorption spectroscopy for measurement of co-emitted trace gases
WO2021158916A1 (en) 2020-02-05 2021-08-12 Seekops Inc. Multiple path length optical cell for trace gas measurement
CN211508182U (en) 2020-04-15 2020-09-15 深圳市利拓光电有限公司 Power semiconductor laser device with constant temperature control function
US11710411B2 (en) * 2020-05-08 2023-07-25 The Travelers Indemnity Company Systems and methods for autonomous hazardous area data collection
CN112213443B (en) 2020-05-25 2021-05-14 南京大学环境规划设计研究院集团股份公司 Method for correcting deviation of atmospheric pollutant concentration monitoring value of rotor unmanned aerial vehicle
US11748866B2 (en) 2020-07-17 2023-09-05 Seekops Inc. Systems and methods of automated detection of gas plumes using optical imaging
US12475798B2 (en) * 2020-07-17 2025-11-18 Seekops Inc. UAS work practice
US11619562B2 (en) * 2020-10-06 2023-04-04 Abb Schweiz Ag Systems and methods for efficiently identifying gas leak locations
WO2022093864A1 (en) 2020-10-27 2022-05-05 Seekops Inc. Methods and apparatus for measuring methane emissions with an optical open-cavity methane sensor
WO2022211837A1 (en) 2021-04-02 2022-10-06 Seekops Inc. Multispecies measurement platform using absorption spectroscopy for measurement of co-emitted trace gases
US20220357231A1 (en) * 2021-05-06 2022-11-10 Abb Schweiz Ag Technologies for improved visualization of gas leak detection data
US20230213413A1 (en) * 2021-10-01 2023-07-06 Arthur W. Mohr, JR. Apparatus and method for collecting environmental samples
WO2023239351A1 (en) * 2022-06-06 2023-12-14 Landmark Graphics Corporation Emissions estimations at a hydrocarbon operation location using a data-driven approach

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170003684A1 (en) * 2014-01-28 2017-01-05 EXPLICIT ApS A method and an unmanned aerial vehicle for determining emissions of a vessel
US20180209902A1 (en) 2014-08-25 2018-07-26 Isis Geomatics Inc. Apparatus and method for detecting a gas using an unmanned aerial vehicle
US20180127093A1 (en) * 2016-11-02 2018-05-10 California Institute Of Technology Positioning of In-Situ Methane Sensor on a Vertical Take-Off and Landing (VTOL) Unmanned Aerial System (UAS)
WO2018121478A1 (en) * 2016-12-30 2018-07-05 华为技术有限公司 Air-to-ground communication system, method, and device
US20180292374A1 (en) 2017-04-05 2018-10-11 International Business Machines Corporation Detecting gas leaks using unmanned aerial vehicles
US20190204189A1 (en) 2018-01-02 2019-07-04 Sniffer Robotics, LLC Apparatus and method for collecting environmental samples

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
See also references of EP4038357A4
VILLA: "An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives", SENSORS, 12 July 2016 (2016-07-12), XP055759031, Retrieved from the Internet <URL:https://www.mdpi.com/1424-8220/16/7/1072/pdf> *

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12571708B2 (en) 2020-04-08 2026-03-10 Centre National De La Recherche Scientifique Method for calculating a stream of at least one gas emitted by a source into the atmosphere, measurement method, and associated system and kit
WO2023133345A1 (en) * 2022-01-10 2023-07-13 Cameron International Corporation Method and apparatus for greenhouse gas emission management
US12480924B2 (en) 2022-08-03 2025-11-25 Schlumberger Technology Corporation Automated record quality determination and processing for pollutant emission quantification
US12577871B2 (en) 2022-08-03 2026-03-17 Schlumberger Technology Corporation Linear cut generation method for sensor inversion constraint imposition
FR3142556A1 (en) * 2022-11-30 2024-05-31 Totalenergies Onetech Method for measuring the contents of at least one gas emitted by a source in a plume propagating in the atmosphere from the source, method, and associated drone
WO2024115599A1 (en) * 2022-11-30 2024-06-06 Totalenergies Onetech Method for measuring the contents of at least one gas emitted by a source in a plume propagating through the atmosphere from the source, and associated method and drone
US12480922B2 (en) 2022-12-09 2025-11-25 Schlumberger Technology Corporation Methods and systems for characterizing methane emission employing mobile methane emission detection
US12292310B2 (en) 2022-12-15 2025-05-06 Schlumberger Technology Corporation Machine learning based methane emissions monitoring
WO2024141721A1 (en) * 2022-12-30 2024-07-04 Aeromon Oy Method and system for measuring emission rate of airborne emission species
EP4643111A4 (en) * 2022-12-30 2026-04-15 Aeromon Oy METHOD AND SYSTEM FOR MEASURING THE EMISSION RATE OF AIRBORNE EMISSION SPECIES
CN115931008A (en) * 2023-02-27 2023-04-07 昆明人为峰科技有限公司 A topographic surveying and mapping equipment operation status monitoring system and monitoring method
CN115931008B (en) * 2023-02-27 2023-05-30 昆明人为峰科技有限公司 Operating state monitoring system and monitoring method for topographic surveying and mapping equipment
US12555125B2 (en) 2023-06-09 2026-02-17 Schlumberger Technology Corporation Emission detecting camera placement planning using 3D models
US12254622B2 (en) 2023-06-16 2025-03-18 Schlumberger Technology Corporation Computing emission rate from gas density images
CN120761590A (en) * 2025-09-08 2025-10-10 湖南大学 Aerial sliding contact inspection robot

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