FR3146830A1 - System and method for automatic cleaning of tire tread bottom - Google Patents

Abstract

The invention relates to a method for cleaning a tread pattern of an identified tire that is damaged during retreading, the method implemented by at least one processor comprising a processing module that applies, to at least one neural network, the data representative of the captured images of the tread pattern of the identified tire. The invention also relates to a retreading method comprising the disclosed method. The invention further relates to an automatic cleaning system for cleaning a tread pattern of an identified tire during retreading. Figure for the abstract: Fig. 11

Description

System and method for automatic cleaning of tire tread bottom

The invention relates to a system and method for cleaning a tread pattern of a tire being retreaded. More particularly, the invention relates to the use of a cleaning device carrying a treatment tool that is capable of cleaning the tread patterns of tires identified to be retreaded.

Context

In the field of tire manufacturing and maintenance, retreading is a process known to restore a worn tire to serviceable condition by renewing the tread rubber and ply(ies). During the retreading process, old tread materials are removed and replaced with new materials. Retreading is a method that is known and regulated in several industries (including, but not limited to, transportation, aviation, mining, and agriculture). The damage could correspond to one or a few families of damage (including, but not limited to, tread damage, tear damage, cut damage, and puncture damage).

Regardless of the type of retreaded tires, the steps of a retreading process are essentially the same from the entry of a tire to a retreading plant until its exit. During the retreading process, a repair process is carried out during which the tires intended for retreading are examined so that existing damage can be identified and located. During the repair process, a visual detection of the damage is made (either by manual means or by automatic means (for example, one or more cameras) or by a combination of both). If we focus on the repair of “tread” type damage, during the repair process, a step of cleaning the damage is carried out using a compressed air blower and a soft brush. Then, a step of removing the remaining rubber is carried out using a hard brush to expose the first crown ply. The repair process continues with a visual detection of the presence of corrosion on the wire ropes (either by manual means or by automatic means (e.g., one or more cameras) or by a combination of both). If this visual detection confirms the presence of corrosion, a removal step is performed using a grinding wheel. These corrosion detection/removal steps are repeated on the different successive plies as long as corrosion is detected. After the repair process is completed, the retreading process can continue in a typical order of steps towards the exit of a tire from the retreading plant (the steps including, without limitation, a tread application step, a curing step, an inspection step and a shipping step). Until now, one or more steps of the damage repair process have been performed manually.

There are ongoing efforts to automate at least part of the repair process. For example, European patent EP2456612B1 discloses a system for automatically spraying a dissolving adhesive during the retreading of a tire. The adhesive spraying occurs after the repair process and just before the tread is installed. The adhesive is sprayed into holes and/or craters automatically detected by acquiring a three-dimensional (or “3D”) profile obtained by a profilometer. The sprayed adhesive is finally applied to the holes and/or craters by an automatic applicator.

European patent EP3551443B1 discloses a system for automatically repairing damage when retreading a tire. The disclosed system comprises a remote and fixed laser profilometer that scans the entire tread by rotating the carcass. The system also comprises a color camera embedded in a robot that takes pictures of damage once the rubber covering it has been removed. These color images are used to perform corrosion detection by analyzing the colors of the image (for example, by looking for the color red on the cables). The processing algorithm uses the three-dimensional (3D) information of the profilometer to detect damage. The processing used to detect damage includes the construction of an average profile and the comparison to this profile. These processing would be sufficient if the carcass could be considered as a rigid body. However, a carcass is rather a deformable body that can present many more or less local deformations (for example, a more or less pronounced out-of-roundness). These deformations can be considered as damages deviated from an average profile. Therefore, the disclosed system cannot adapt to all possible damages.

European patent EP2414153B1 discloses a machine that performs an automatic process for identifying and working on defects on used tires. The disclosed machine comprises a vision system that relies on an infrared projector to obtain 3D information on the damage being analyzed. During the process, an automatic scan of the working surface of the tire allows to identify the morphology of the damage and to make a comparison between the identified morphologies and a reference of known damage. A selection of the work and a corresponding tool is made to perform the selected work on the identified damage.

There is nothing in the prior art that proposes establishing a link between the identification and localization of damage and the cleaning of the damage using a processing tool in a completely automated manner. Thus, the disclosed invention concerns the automatic repair of damage of the “sculpture” type detected by learning. An algorithmic processing is based on thousands of learning images so that the algorithm learns to recognize the points of the image (three-dimensional and color) belonging to a sculpture of a tire identified for retreading. This approach is scalable and will become increasingly robust because it is possible to integrate images of undetected sculpture into the learning in order to extend the scope of use of the algorithm. In addition, the cleaning of the sculptures does not depend on any predefined recipe because the processing trajectory is defined on the fly according to the shape of the sculpture.

The invention relates to a method for cleaning a tread pattern of an identified tire damaged during retreading, the method implemented by at least one processor comprising a processing module which applies, to at least one neural network, the data representative of the captured images of the tread pattern of the identified tire, characterized in that the method comprises the following steps:
- a step of providing an automatic cleaning system of which the processor is a part to automatically recognize an order of treatment of damage in the sculpture of the identified tire;
- a step of calibrating a cleaning device which is part of the automatic cleaning system, the cleaning device comprising a treatment device supported by a pivoting elongated arm which extends to a free end where a treatment tool is arranged along a common longitudinal axis;
- a step of positioning the identified tire in a treatment space where the cleaning device treats it;
- a step of positioning at least one camera which is part of the automatic cleaning system at a reference distance relative to an identified tire rolling surface;
- a sculpture detection step comprising sector-by-sector processing for the identified tire incorporating one or more scans followed by processing of the sculptures of the identified tire to recover a color and three-dimensional (3D) image of each scanned sector;
- a step of creating the three-dimensional (3D) trajectories that the cleaning device should follow to treat the sculptures;
- a step of simplification of the vertical and horizontal paths during which a 3D point associated with each point of each path is recovered;
- a step of adding approach and withdrawal points to complete the 3D trajectory obtained after the path simplification step, during which, at the start and end of each vertical and horizontal path, an approach point and a withdrawal point are added allowing the cleaning device to start and finish processing the path that corresponds to a tread pattern on the identified tire rolling surface; and
- a final step of calculating the orientations of the processing tool during which a 3D orientation of the processing tool is calculated at each point of each vertical and horizontal path;
so that the cleaning device is set in motion so that the treatment device can place the treatment tool to carry out the cleaning of the tread of the identified tire.

In some embodiments of the cleaning method of the invention, during the tread detection step, the sector-by-sector processing for the identified tire comprises:
- for each tire sector identified, a sector scanning step followed by processing of the scanned sector;
- a scan of all successive sectors of the identified tire followed by processing of all successive scanned sectors; or
- a scan of the tire being processed at a station other than the one where processing of the scanned sectors is carried out;
so that a treatment of one or more scanned sectors is carried out by the cleaning device.

In some embodiments of the cleaning method of the invention, the step of calibrating the cleaning device comprises a calibration phase for estimating the pose of the camera relative to a base of the cleaning device.

In certain embodiments of the cleaning method of the invention, the step of calibrating the cleaning device comprises a step of positioning a calibration target in the treatment space, during which the camera implements a process for estimating the pose of the camera in the reference frame of the brushing device.

In some embodiments of the cleaning method of the invention, during the step of positioning the identified tire, the identified tire is positioned in proximity to the cleaning device on a rotating drum disposed in the treatment space, such that, during a repair process of a retreading process, the drum rotates the identified tire so that it can be treated sector by sector.

In some embodiments of the cleaning method of the invention, the step of positioning the camera comprises a step of verifying a scan carried out by the camera comprising the following steps:
- a step of identifying a central horizontal zone of the scan which is used to verify that the distance between the identified tire and the camera is correct;
- a step of calculating an average distance (D _avg ) of the points inside the horizontal central zone;
- a step of preserving the 3D points in the form of a surface mask comprising a binary image representing the points belonging to the rolling surface of the identified tire;
- a step of calculating again the average distance (D _avg ) by only considering the points of the horizontal central zone which belong to the rolling surface of the identified tire to obtain a distance (D _{avg_surface} );
- a step of comparing the distance (D _{avg_surface} ) with the reference distance of the camera;
- a step of calculating the two vertical lines which delimit the start and the end of the surface mask to enable verification that the camera is centered in relation to the rolling surface of the identified tire;
- a step of calculating a central vertical line located in the middle of the vertical lines; and
- where the calculated central vertical line deviates from the central line of the image beyond a predetermined threshold, a step of applying a lateral correction to the position of the camera to recenter it relative to the rolling surface of the identified tire.

In certain embodiments of the cleaning method of the invention, the step of detecting the sculptures comprises a step of applying a neural network to the color and 3D image to determine which points represent sculptures of the identified tire to be treated with the treatment tool, during which, at each image, the surface mask is recovered at the output of the neural network where each white point indicates that the point represents a sculpture to be cleaned.

In some embodiments of the cleaning method of the invention, the step of creating the three-dimensional (3D) trajectories comprises the following steps:
- a data preparation step, during which the points belonging to the contours of all the white objects present in the surface mask are extracted to obtain a set of polygons with holes; and
- a step of extracting horizontal and vertical two-dimensional (2D) paths, during which the holes present inside the polygons extracted during the data preparation step are deleted, this step comprising the following steps:
- a step of calculating a morphological skeleton of each polygon comprising the extraction of all the central points of each polygon, in which each morphological skeleton is made up of nodes representing ends of the morphological skeleton and/or the crossing points, and of paths comprising points located between two nodes;
- a step of classifying each path into horizontal path category or vertical path category, during which paths of the same category are merged with close ends and, once the merger is carried out, the direction of travel of each path is fixed; and
- a step of developing 3D trajectories which is carried out once the vertical and horizontal paths have been extracted, during which the vertical paths and the horizontal paths are ordered;
so that the ordered set of vertical and horizontal paths gives the processing order of all the sculptures of the scanned sector of the identified tire.

In some embodiments of the cleaning method of the invention, during the step of adding the approach and withdrawal points:
- a first approach point is constructed by offsetting it by a predetermined distance (L) from the first point or the last point of the path, and, once offset, the first approach point is brought closer to the camera by the distance (L); and
- a second approach point is constructed from the first approach point by bringing this second approach point closer to the camera by a second distance (L2) from the first approach point.

In some embodiments of the cleaning method of the invention, the step of calculating the orientations of the treatment tool comprises a step of calculating the 3D normal at each point as well as the direction vector between each pair of successive points, in which the direction vector gives the direction to follow between two points of each path, where the normal gives the orientation of the Z axis of the treatment tool, the direction vector gives the orientation of the X axis of the treatment tool, and the Y axis is automatically fixed according to the fixing of the X and Z axes.

In some embodiments of the cleaning method of the invention, the processor refers to a table of various tire sizes to make a determination of one or more parameters of the identified tire.

In some embodiments of the cleaning method of the invention, the treatment tool comprises at least one brush.

The invention also relates to a retreading method comprising the disclosed cleaning method.

The invention further relates to an automatic cleaning system for cleaning a tread pattern of an identified tire being retreaded, the automatic cleaning system comprising:
- at least one cleaning device comprising a robot having a cleaning peripheral supported by a pivotable elongated arm and extending from the elongated arm to a free end where a treatment tool is arranged along a common longitudinal axis;
- a detection system for collecting information on the physical environment around the cleaning device; and
- a communication network which manages the incoming data to the automatic cleaning system from each cleaning device, the communication network comprising at least one communication server for executing programmed instructions stored in a memory of one or more processors of the automatic cleaning system to implement the cleaning method of the invention;
such that the cleaning device is configured on one or more parameters of the identified tire calculated by an image processing module incorporated in the memory of the processor to set the cleaning device in motion so that the brushing device can place the treatment tool to carry out the cleaning of the sculpture of the identified tire.

In some embodiments of the system of the invention, the detection system comprises at least one RGB-D type camera attached to at least one of the elongated arm and the processing peripheral of the cleaning device.

Other aspects of the invention will become apparent from the following detailed description.

The nature and various advantages of the invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which like reference numerals designate like parts throughout, and in which:
There represents a schematic cross-sectional view of an embodiment of a known tire.
There represents a schematic view of an embodiment of a cleaning device which is part of an automatic cleaning system of the invention.
There represents an example of a visualization of a portion of a tire obtained during a cleaning process of the invention.
There represents a schematic view of a positioning of a camera of the automatic cleaning system of the invention relative to a rolling surface of an identified tire being retreaded.
There represents a schematic view of input of color and 3D images to a neural network in the creation of surface masks performed during the cleaning process of the invention.
There represents a flow diagram of an algorithm that performs a process of generating 3D trajectories for processing sculptures.
There represents an example of an extraction of the contours associated with the mask of the sculptures created during the cleaning process of the invention.
There represents an example of a skeleton created during a process of generating 3D trajectories for processing sculptures.
There represents an example of a bounding box for classifying horizontal and vertical paths during a 3D trajectory generation process.
There represents an example of merging paths of the same category during a 3D trajectory generation process.
There represents an example of an order of vertical and horizontal paths retained at the end of a 3D trajectory generation process.
There represents an example of path simplification carried out during the cleaning process of the invention.
There represents an example of construction of the approach points and withdrawal points carried out during the cleaning process of the invention, and the represents an example of the approach and withdrawal points constructed.
There represents an example of a 3D orientation of a processing tool of the automatic cleaning system of the calculated during the cleaning process of the invention.

Detailed description

When considering the characteristics of a tire to be retreaded, its geometry must be taken into account. A tire is an object with a known geometry generally comprising several superimposed layers of rubber (or "layers"), as well as a metal or textile fiber structure constituting a reinforcing carcass of the tire structure. The nature of the rubber and the nature of the reinforcement are chosen according to the desired final characteristics. comprises a schematic representation of a tire 10 comprising, in a conventional manner, two circumferential beads intended to allow the tire to be attached to a rim. Each bead comprises an annular reinforcing bead. The constitution of a tire is typically described by a representation of its constituents in a meridian plane, that is to say a plane containing the axis of rotation of the tire. The radial, axial and circumferential directions respectively designate the directions perpendicular to the axis of rotation of the tire, parallel to the axis of rotation of the tire, and perpendicular to any meridian plane. The expressions "radially", "axially" and "circumferentially" respectively mean "in a radial direction", "in the axial direction" and "in a circumferential direction" of the tire. The expressions "radially inner" and "respectively radially outer" mean "closer, respectively further, from the axis of rotation of the tire, in a radial direction.

The tire 10 also includes a tread 12, added to the tire outer surface. The tread 12 is intended to come into contact with a ground via a rolling surface 12a. The tire 10 further includes a crown reinforcement comprising a working reinforcement 14 and a hoop reinforcement 16, the working reinforcement 14 having working layers represented by layers 14a and 14b. The tire 10 also includes two sidewalls (a sidewall 18 being shown in the figure) and a crown reinforcement 16. ) and two fillers 20 reinforced with a bead wire 22. A radial carcass layer 24 extends from one bead to the other, surrounding the bead wire in a known manner. The tread 12 comprises reinforcements consisting, for example, of superimposed layers comprising known reinforcing threads. In embodiments, the tire may include a rubber 26 which evacuates the static electricity produced during rolling.

The tread 12 is delimited, in the radial direction, by two circumferential surfaces, the most radially outer of which is the tread surface 12a and the most radially inner of which is called the tread base surface. The tread base surface (or “bottom surface”) is defined as the surface translated from the tread surface radially inward by a radial distance equal to the tread depth. It is common for this depth to be decreasing on the most axially outer circumferential portions (called “shoulders”) of the tread 12.

In addition, the tread of a tire is delimited, in the axial direction, by two lateral surfaces. The tread is further constituted by one or more rubber compounds. The expression "rubber compound" designates a rubber composition comprising at least one elastomer and a filler.

In order to obtain wet grip performance, cutouts are provided in the tread 12. A cutout designates either a well, a groove, an incision, or a circumferential furrow and forms a space opening onto the tread surface 12a. The performance of a tread pattern must be sufficiently constant despite the wear of said strip to ensure the durability of the tire. Consequently, it is necessary to maintain a certain thickness of rubber materials between the bottom face of the cutouts (grooves or furrows) and the reinforcing elements to guarantee the endurance of the tire. For example, grooves must have sufficient widths to allow evacuation of the liquid present on the surface of the ground regardless of the stage of wear of the tread (12).

Referring to the figures, in which like numbers identify like elements, the depicts an embodiment of a cleaning device 100 that is part of an automatic cleaning system (or "system") of the invention. The automatic cleaning system of the invention implements a method of the invention ("method") for cleaning by treatment tool a tread of an identified tire being retreaded. The disclosed method incorporates a machine learning method that is based on data corresponding to images obtained from the identified tire whose algorithm used analyzes the tread surface of the identified tire to install and insert the treatment tool into a cutout of the identified tire. It is understood that the method of the invention may be part of an existing retreading method to improve its repair process.

In one embodiment of the cleaning device 100 of the system of the invention, the cleaning device comprises a robot having a treatment peripheral 102 supported by a pivotable elongated arm 104. The treatment peripheral 102 extends from the elongated arm 104 to a free end 102a where a treatment tool is disposed along a common longitudinal axis. In the embodiment of the cleaning device 100 shown in FIG. , the treatment tool comprises a brush 106 which is selected from commercially available industrial brushes for cleaning tires during retreading processes (e.g., industrial rotary brushes made of synthetic or metal filaments). The following description refers to the brush 106, but it is understood that other equivalent treatment tools could be employed within the scope of the invention (including, without limitation, a knife, a rasp, a card, a milling cutter or a grinding wheel).

Attachment of the brush 106 (or attachment of an equivalent processing tool) to the processing device 102 may be accomplished by screwing an adapter to the free end 102a of the processing device. Attachment of the brush 106 is accomplished in a manner that allows its rotation relative to the longitudinal axis and also allows its spinning about a predefined axis of rotation during a repair process. It is understood that attachment of the brush 106 to the processing device 102 may be accomplished by one or more known attachment means (including, without limitation, welding, gluing, and the like). Thus, the robot facilitates cleaning a variety of cutouts without interrupting the rotation and/or spinning of the brush 106.

The cleaning device 100 is set in motion so that the processing device 102 can clean a tread pattern of an identified tire P (see ) by the cleaning device during a process performed by the system of the invention (as described below). It is understood that the tread pattern of the identified tire incorporates the cutouts found on its rolling surface. The term "identified tire" (in the singular or plural) is used herein to refer to a tire being retreaded and present in the physical environment of the system incorporating the cleaning device 100 (the physical environment being, for example, a retreading facility incorporating the cleaning device 100).

It is understood that the configuration of the cleaning device 100 is given by way of example. For example, the cleaning device 100 may comprise a fixed robot installed at a retreading facility, secured, for example, to a support relative to which the robot extends (e.g., a base 110 as shown in FIG. ). In this case, it is understood that the robot can be fixed to a ceiling, a wall, a floor or any support that allows the method of the invention to be carried out during a retreading process. It is understood that the cleaning device 100 can comprise at least one traveling robot. By "traveling", it is understood that the gripping device can be set in motion either by integrated movement means (for example, one or more integrated motors) or by non-integrated movement means (for example, one or more mobile means including autonomous mobile means). It is understood that the cleaning device 100 can be a conventional industrial robot or a collaborative robot or even a delta or cable robot.

The cutouts that can be cleaned by the cleaning device 100 during the method of the invention comprise grooves and furrows. The grooves are the cutouts allowing the evacuation of water. A groove has a width such that the facing material walls delimiting it do not come into contact with each other, when the tread passes through the contact surface, when the tire is subjected to recommended inflation and load conditions as defined in particular by the European standards of the “European Tyre and Rim Technical Organization” or “E.T.R.T.O” in its “Standards Manual 2020 – Commercial Vehicle Tyres”. The compression and shear deformations of the raised elements delimiting the groove condition the pressures in contact with the ground and therefore the wear.

A groove is a substantially circumferential groove, and the side faces are substantially circumferential in the sense that their orientation can vary locally around plus or minus 45° around the circumferential direction. All of the patterns belonging to the groove are found all around the tread, forming a substantially continuous whole (i.e., having discontinuities of less than 10% in length compared to the length of the patterns.) The circumferential grooves delimit ribs, each rib of which is composed of the patterns of the sculpture included between an axial edge of the tire and a neighboring axially outermost circumferential groove, i.e. included between two neighboring circumferential grooves.

The cutout depth is the maximum radial distance between the tire's rolling surface and the bottom of the cutout.

The cleaning device 100 of the system also includes a sensing system (not shown) for collecting information about the physical environment around the cleaning device. The sensing system includes one or more sensors (including one or more cameras) configured to perform two-dimensional (2D) and/or three-dimensional (3D) image sensing, 3D depth sensing, and/or other types of sensing of the physical environment around the pickup device (it is understood that the terms "sensor" and "camera" are used interchangeably). In embodiments of the cleaning device 100 of the type shown in FIG. , the sensor(s) of the detection system are attached to at least one of the elongated arm 104 and the processing peripheral 102 of the robot. In embodiments of the system of the invention, these sensors could be part of an overall detection system that employs these sensors together with one or more sensors positioned in the physical environment in which the cleaning device 100 operates.

In one embodiment of the cleaning device 100, the detection system comprises at least one camera 200 that provides 3D images represented as a set of 3D points with X, Y, Z coordinates, and red, green, blue color values (the "RGB" or "RGB-D" format) (referred to as an "RGB-D camera"). In this embodiment, an RGB-D camera is attached to at least one of the elongated arm 104 and the processing device 102 of the robot (the latter being illustrated as an example in FIG. ). Two or more RGB-D cameras may be oriented to achieve a predetermined overlap between the cameras' fields of view. As used herein, the term "camera" includes one or more cameras.

RGB-D cameras typically provide depth information using depth maps, being images where each pixel contains the distance between the camera and the corresponding point in space. Compared with traditional measurement methods such as manual measurement and other measurements based on electronic devices, 3D point cloud data from RGB-D type cameras have a much higher measurement rate. By using a sparser structure, a point cloud can be constructed from the RGB-D images by calculating the real world (e.g., X, Y, Z coordinates) with the intrinsic data of a scanning camera. Thus, information about the physical environment around the cleaning device 100 is obtained from the 3D point cloud data obtained from sensing technologies that are capable of capturing the 3D surface geometries of tires accurately and efficiently. These detection technologies could be selected from commercially available devices (selected, for example, from cameras sold under the brand name PHOTONEO™, cameras sold under the brand name ENSENSO® of the company IDS, cameras sold under the brand name ZIVID® of the company Zivid AS, artificial vision systems sold by the company Cognex Corp., and their equivalents).

The term "point cloud" (singular or plural) is used herein to refer to a collection or collections of data points in space. A camera or cameras (or equivalent device or devices) collect three-dimensional (3D) data and detect the surfaces of objects (e.g., tires identified for treatment during a retreading process) using a series of coordinates. Storing the information as a collection of spatial coordinates can save space, since many objects do not fill a large portion of the environment. Even if the information is not visual, interpreting the data as a point cloud helps to understand the relationship between multiple variables through classification and segmentation.

It is understood that one or more cameras may include one or more programming modes, including by learning, to feed, modify, and train at least one neural network. Although the embodiments are described herein with respect to the use of one or more neural networks (e.g., convolutional neural networks or “CNNs”) as a machine learning model, other types of machine learning models may be used. These include, but are not limited to, models using linear regression, logistic regression, decision trees, support vector machines, naive Bayes, nearest neighbor (knn), K stands for clustering, random forest, dimensionality reduction algorithms, gradient-based algorithms, neural networks (e.g., autoencoders, CNNs, RNNs, perceptrons, logarithmic short-term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, generative adversarial (GAN), etc.) and their complements and equivalents. The CNN(s) may be trained with ground truth data that is generated using sensor data representative of the motion of the cleaning device 100, including the positioning of the processing device 102 and the processing tool (e.g., the brush 106).

In some embodiments of the cleaning device 100, the camera 200 triggers when a segment of the identified tire enters the camera's field of view. In cases where a tire portion is not visible in the image obtained by the detection system of the cleaning device 100 (e.g., the camera 200), a grip point may be placed at a known position relative to the sensor (e.g., at a known horizontal distance and a known vertical distance relative to the camera position).

The detection system of the cleaning device 100 detects the presence of a tread damage pattern within the field of view of the detection system (e.g., the field of view of the camera 200), triggering it to capture an image of a tread surface of the tire identified for retreading. In all embodiments of the system of the invention, the system “searches” the image obtained by the detection system for the presence of the damage “seen” by the cleaning device 100. If no damage is detected, the detection system continues to obtain images until the tire search is exhausted.

The sensing system may determine information about the physical environment around the identified tire that may be used by a control system of the inventive system (the control system comprising, for example, software for planning movements of the cleaning device 100). The control system may be located on the robot or may be in remote communication with the robot. In embodiments of the inventive system, one or more 2D or 3D sensors mounted on the robot (including, without limitation, navigation sensors) may be integrated to provide a digital model of the physical environment (including, where applicable, the side(s), floor, and ceiling). Using the obtained data, the control system may cause the robot to move to navigate between positions for taking images of the tires during repair processes.

To properly manage the handling of the cleaning device 100 which ensures the clear capture of images of the identified tire (for example, the handling of the robot and the positioning of the brush 106 as shown in the ), the tire being retreaded must be identified and the positioning of the damage concerned detected. Thus, the detection data refers to a plurality of records representative of the positions of the damage of at least one identified tire or a portion of an identified tire tracked over time. For example, the detection data may include one or more of records of the positions of a reference point on a portion of the tire (e.g., the tread surface) over time or at defined time intervals; sensor data taken over time; a video stream that has been processed using a computer vision technique; and/or data indicative of the operating status of the cleaning device 100 over time. In some cases, the detection data may include data representative of one or more continuous movements of the cleaning device 100 before it stops to take one or more images of the tires being retreaded. The detection system is thus configured to generate the movement data of the cleaning device 100.

The detection system of the cleaning device 100 may receive a CAD file of the identified tire to match the location of a cut from the CAD file with the identified cut in real time to accurately locate and determine its coordinates. The detection system may receive the CAD file by data transmission methods known to those skilled in the art. The detection system may further include at least one camera and at least one sensor (not shown) to determine the location (i.e., coordinates) of the damage based on the data collected in real time and/or the contour profile of the identified tire.

In embodiments of the invention, the detection system of the cleaning device 100 may also comprise a motion capture device selected from infrared sensors, ultrasonic sensors, accelerometers, gyroscopes, pressure sensors, and/or equivalent devices. In these embodiments, the system (and particularly the cleaning device 100) learns the movements which perform the taking of images of the tires without intervention of an operator during the retreading processes.

To implement the method of the invention by computer means, the system of the invention comprises a communication network (or "network") which manages the data incoming to the system from various sources (for example, from at least one cleaning device 100 and its associated detection system). The communication network incorporates one or more communication servers (or "servers") each comprising one or more processors operatively connected to a memory. The memory is configured to store an application for analyzing data representative of the imaged tire cutouts. The one or more processors comprise an analysis application execution module which performs the processing of the images, the one or more processors of which are capable of executing programmed instructions stored in the memory to perform the steps of the method (as described below).

Input data to the automatic cleaning system of the invention may include general information regarding the identified tire. The general information includes stored data regarding the identification of the identified tire (including, without limitation, its production origin, distribution and/or storage, production date, retread history if applicable, and mounting position and history). The general information may also include the retread rank (if applicable) of the identified tire. Data corresponding to a retread rank of an identified tire is typically managed by the entity that manages the use of an identified tire(s) (e.g., a person(s) and/or company(ies)) and/or the manufacturer of such tire(s).

The term "processor" (or, alternatively, the term "programmable logic circuit") refers to one or more devices capable of processing and analyzing data and comprising one or more software programs for their processing (e.g., one or more integrated circuits known to those skilled in the art as being included in a computer, one or more controllers, one or more microcontrollers, one or more microcomputers, one or more programmable logic controllers (or "PLCs"), one or more application-specific integrated circuits, one or more neural networks, and/or one or more other known equivalent programmable circuits). The processor comprises one or more software programs for processing the data captured by the detection system (including the camera 200) of the cleaning device 100 (and the corresponding data obtained) as well as one or more software programs for identifying and localizing variances and identifying their sources in order to correct them.

In the system of the invention, the memory may include both volatile and non-volatile memory devices. The non-volatile memory may include solid state memories, such as NAND flash memory, "keep-alive memory" (or "KAM") for saving various operating variables while the processor is powered off, magnetic and optical storage media, or any other suitable data storage device that retains the data when the cleaning device 100 is disabled or loses power. The volatile memory may include static and dynamic RAM that stores program instructions and data, including a learning application.

In embodiments of the system of the invention, the processor can configure the cleaning device 100 (and in particular the processing peripheral 102) on one or more parameters of the identified tire calculated by an image processing module incorporated in the memory of the processor. The image processing module analyzes the images of the identified tire and, more precisely, the images of the damage seen by the detection system of the cleaning device 100.

The processor may also refer to a reference (e.g., a size chart of various tires) to make a final determination of a parameter or parameters of the identified tire. The reference may include known tire parameters corresponding to a plurality of known commercially available tires. For example, after the image processing module calculates one or more identified tire parameters, the processor may compare the calculated parameters with the known parameters stored in the reference. The processor may retrieve the known tire parameters corresponding to the commercially available tires that most closely match the calculated parameters to configure the processing device 102 (and thus place the brush 106 or equivalent processing tool in precise position to clean a corresponding cutout in the tread of the identified tire). The tire reference may include measurements corresponding to a plurality of commercially available tires. For example, for a tire size 225/50R17, the number "225" identifies the tire's cross-section in millimeters, the number "50" indicates the sidewall aspect ratio, and the measurement "R17" represents the rim diameter in inches (being approximately 43.18 centimeters).

Referring again to the , and further in Figures 3 to 15, a detailed description is given as an example of embodiments of a method of the invention (or "method") for cleaning a tread pattern of a tire being retreaded. The method of the invention is implemented by the automatic cleaning system of the invention. It is understood that the system can implement the method of the invention in any physical environment without prior knowledge of the pattern of damage to be repaired on the tires identified for retreading.

As used herein, the term “method” or “process” may include one or more steps performed by at least one computer system having one or more processors to execute instructions that perform the steps. Unless otherwise indicated, any sequence of steps is exemplary and does not limit the methods described to any particular sequence.

In the following description, embodiments of the method of the invention are described which differ in the accuracy of the information obtained by the detection system (e.g., the camera 200).

In carrying out the method of the invention, the automatic cleaning system of the invention incorporates a combination of vision and machine learning techniques to correctly and quickly reconstruct the observed scene from scattered three-dimensional (or “3D”) point clouds, derived from a view of the tire identified for retreading. The system of the invention therefore achieves a continuous improvement in the recognition of tread damage and its relative positioning along the tread surface of the identified tire.

By launching an embodiment of the method of the invention, the method comprises a step of calibrating the cleaning device 100. During this step, the robot is positioned to scan an identified tire. When a scan of the identified tire is performed with the camera 200, three-dimensional (3D) points are recovered which are expressed by their positioning relative to the camera. In order to be able to control the cleaning device 100 (for example, to indicate to it the position of each area to be brushed on the rolling surface of the identified tire), these 3D points must be expressed at the robot base (for example, see the base 110 of the cleaning device 100 of the ). The calibration step of the cleaning device 100 therefore comprises a calibration phase for estimating the pose of the camera 200 relative to its base 110.

In one embodiment of the method, the step of calibrating the cleaning device 100 comprises a step of positioning a calibration target in a treatment space where the robot will work. In one embodiment, the calibration target comprises a checkerboard that is positioned to allow a plurality of photos to be taken by varying the viewing angle each time. During this step, an optimization algorithm (incorporated in the camera 200) is used to estimate the pose of the camera 200 in the frame of reference of the treatment device 102. Once the calibration has been carried out, the robot is capable of being brought into a position seen by the camera 200. This calibration must be carried out as soon as the cleaning device 106 is installed in the treatment space.

The method further comprises a step of positioning an identified damaged tire in the treatment space where the cleaning device 100 will treat it. The identified tire is positioned in proximity to the cleaning device 100 on a rotating drum (as known in the state of the art). During a repair process of the retreading process, the drum rotates the identified tire so that it can be treated sector by sector. It is understood that the identified tire could be positioned on a work table or on an equivalent support so that the system of the invention can treat it. The support can be configured to move in a rotating manner, in an alternating vertical manner and/or in an alternating horizontal manner, thus allowing the treatment of a variety of tires.

The “sector by sector” processing refers to several embodiments of the method of the invention incorporating one or more scans followed by a processing of the tread patterns of the identified tire. In one embodiment of the method of the invention, the sector by sector processing comprises, for each sector of the tire being processed, a scan of the sector followed by a processing of the scanned sector (for example, a brushing of the tread patterns of the scanned sector carried out by the brush 106). In another embodiment of the method of the invention, the sector by sector processing comprises a scan of all successive sectors of the tire being processed followed by a processing (for example, a brushing) of all successive scanned sectors. In another embodiment of the method of the invention, the sector by sector processing comprises a scan of the tire being processed (either a scan by individual sectors or a scan of all successive sectors) on a station different from that where a processing (for example, a brushing) of the scanned sectors is carried out. In the latter case, at least one additional camera (not shown) would be used on the remote station to perform all the scans. Then, the camera 200 of the cleaning device 100 would be used to perform a single scan to locate the position of the sector in the field of the camera 200 among the set of scans performed on the remote station. In all these embodiments of the method of the invention, it is understood that a treatment would be performed on the sectors requiring treatment. Thus, if no damage requiring treatment is identified in a scan of a sector, the cleaning device 100 passes the sector concerned to a scanned sector where one or more damages are identified for treatment.

The method further comprises a step of positioning the camera 200 at a reference distance D _ref relative to the tread surface of the identified tire (i.e., the damaged surface). During this step, the camera 200 is centered relative to the tread surface of the identified tire (being a right/left positioning). For this, the robot begins by positioning the camera 200 in front of the identified tire in a position that allows the camera to see all possible tire dimensions (including diameters and widths). A scan is then performed by the camera 200, then it is analyzed to verify whether the camera 200 is correctly positioned.

Referring to the , the 3D points are expressed relative to a reference point placed at the center of the lens of the camera 200. If we represent this reference point in 3D by scanning part of a tire (see “3D visualization” in the ), the X axis is directed towards the lens of the camera 200, the Y axis is directed towards the ground and the Z axis is directed towards the identified tire. If we represent this reference in 2D (see "2D visualization" in the ), the origin of the reference point is at the center of the image: the X axis is oriented to the right, the Y axis is oriented downwards and the Z axis is oriented towards the tire.

Referring to the , the correct positioning of the camera 200 during this step is described. The areas 300 represent the points belonging to the tread surface of the identity tire. First, a horizontal central area of the scan is considered, which is used to verify that the distance between the identified tire and the camera 200 is correct. An average distance D _avg of the points is calculated within this horizontal central area, which is represented by the lines 300A. Then, the 3D points are kept, the distance in Z of which belongs to an interval [D _avg-margin , D _avg+margin ]. These points represent the points belonging to the tread surface of the identified tire (therefore the relative distances vary according to the parameters of the identified tire). All of these points can be represented in the form of a binary image (identified as the “surface mask” or the “binary mask”) where each point kept represents a point belonging to the tread surface of the identified tire (these points are represented by the areas 300 in the ). Then, to obtain a distance D _{avg_surface} , we calculate again the distance D _avg by considering only the points of the horizontal central zone that belong to the rolling surface of the identified tire. By comparing this distance with the reference distance D _ref of the camera 200 (because the camera 200 must always be positioned at the distance D _ref relative to the rolling surface of the identified tire), we can calculate the frontal correction to be applied to the camera. Then, we calculate the two vertical lines 300B which delimit the start and the end of the surface mask and therefore make it possible to verify that the camera 200 is well centered relative to the rolling surface of the identified tire (see the ). The central vertical line located in the middle of the vertical lines 300B is calculated. If this central vertical line deviates too much from the central line of the image, then a lateral correction will be applied to the position of the camera 200 to recenter it in relation to the rolling surface of the identified tire.

Considering the cleaning device 106, its initial positioning (and, in applicable cases, the initial orientation of the treatment peripheral 102, and thus, the brush 106 or an equivalent treatment tool) is determined from data obtained via the acquisition of images of the system and the physical environment in which the system operates (for example, as shown in FIG. ). The processor analysis application execution module may employ an automatic and adaptive repositioning algorithm to find an ideal starting position of the cleaning device 100, thus enabling the execution of programmed instructions stored in the memory to perform the image capture. The algorithm enables continuous improvement over all image captures, ensuring that the system (and particularly the cleaning device 100) improves from the experience it acquires, particularly in choosing the paths to follow when performing the cleaning processes during tire retreading processes.

The method further comprises a step of detecting the sculptures which is carried out sector by sector for the identified tire. During this step, for each sector, the cleaning device 100 positions the 3D camera to scan the sector and recover a color and three-dimensional (3D) image of the sector (it is recalled that sector-by-sector processing is involved during this step). At each point of the image, the color of the point is known but also its three-dimensional position relative to the camera 200.

Once the scan is done, this sculpture detection step includes a step of applying a neural network to the color and 3D image to determine which points represent sculptures to be brushed. For each image, a binary mask is retrieved at the output of the neural network where each white point indicates that the point represents a sculpture to be cleaned. Before being able to use the neural network, a learning phase is carried out to fix the parameters of the neural network. To do this, several input images are manually annotated to create hundreds of binary masks and indicate which points of the image represent sculpture areas to be cleaned (see ). This learning phase is performed once to fix the parameters of the neural network, after which the neural network can be used on new images.

During the neural network application step, if the input image does not contain any sculptures, the output will be a black-only mask. It is understood that false detections may appear in the neural network outputs (represented for example by the circled regions 5A shown in the ).

The method of the invention further comprises a step of creating the three-dimensional (3D) trajectories that the cleaning device 100 should follow to clean the sculptures. Once the binary mask of the sculptures is obtained during the step of detecting the sculptures, the processor of the system implements a process of generating the 3D trajectories for processing the sculptures (or "3D trajectory generation process") carried out by an algorithm as shown in the (the phrase “cleansing mask” as depicted in the refers to the binary mask of the sculptures).

The process of generating 3D trajectories includes a data preparation step. During this step, the cleaning of the binary mask is performed to remove possible noise and to round the contours. Then, during this step, the points belonging to the contours of all the white objects present in the binary mask are extracted (called "contour extraction"). Thus, a set of polygons with holes is obtained (see the which represents an example of an extraction of the contours associated with the mask of the sculptures).

The 3D trajectory generation step includes a step of extracting horizontal and vertical two-dimensional (2D) paths. During this step, small holes (possibly present inside the different polygons extracted during the data preparation step) are removed. During this step, the "morphological skeleton" of each polygon is then calculated. This consists of extracting all the central points of each polygon. The morphological skeleton (or "skeleton") is made up of nodes (which are the ends of the skeleton or the crossing points) and paths (which are the points located between two nodes). Referring to the , an example of a skeleton is shown with lines 8A representing the points of the polygons and lines 8B representing the points of the skeleton. Nodes 8C are indicated in the skeleton, and paths are located between two nodes 8C.

During the 3D trajectory generation step, each path is then classified as horizontal or vertical. To do this, its bounding box is calculated (see bounding box 9A of the ), and we compare the orientation of a vector composed of its width and height with the ordinate axis (see the ). If the calculated angle (represented by the angle Ꝋ in the ) exceeds a predetermined threshold, the path is then considered horizontal, and vertical otherwise.

For contours composed of multiple nodes and paths, the paths in vertical or horizontal are classified, and the paths of the same category (the vertical category and the horizontal category) are merged with close endpoints. Referring to the example shown in the , in skeleton 1, a polygon with six (6) nodes and (5) paths is represented. In skeleton 2, a classification of the paths is carried out to obtain three (3) horizontal paths and two (2) vertical paths. In skeleton 3, after merging the paths, two (2) horizontal paths and one (1) vertical path are obtained. Once the merging is carried out, it remains to freeze the direction of travel of each path. This point is very important to ensure that the cleaning device 100 will be able to travel all of the paths of the cutout without blocking. In embodiments of the method, all of the vertical paths are traveled from bottom to top and all of the horizontal paths are traveled from right to left. In other embodiments of the method, all of the vertical paths are traveled from top to bottom, and all of the horizontal paths are traveled from left to right.

The 3D trajectory generation step further comprises a 3D trajectory development step which is carried out once the vertical and horizontal paths have been extracted. During this development step (which aims to order the different paths to obtain the order in which the sculptures will be cleaned), the vertical paths are first ordered, then the horizontal paths. It is understood that the method of the invention also works by doing the reverse order (starting with the horizontal paths, then the vertical paths).

To order the vertical paths during this elaboration step, in one embodiment of the method, one begins with a vertical path having a starting point located at the furthest extent in the image. Referring to the example shown in , we start with the vertical path 11A having a starting point located furthest to the left in the image (it is understood that the process also works by starting with the vertical path 11B having a starting point located furthest to the right in the image). The next path will be the one whose starting point is closest to the end point of the previous path (for example, the central path 11C shown in the ). The order for the vertical paths is retained (as shown in the by the dotted vertical lines 11D).

To order the horizontal paths during this development step, in one embodiment of the method, one begins with a horizontal path having the starting point closest to the last ordered vertical path. The same reasoning as that used for the vertical paths is then applied to arrive at an order for the horizontal paths (represented by lines 11E in the ). The ordered set of vertical and horizontal paths gives the processing order of all the treads in the scanned sector of the identified tire (this order being represented by the dotted line 11F). This method makes it possible to obtain a processing order based on the parameters of the tire being processed and the identified damage. It is understood that the system could use other equivalent methods to order the vertical and horizontal paths, thus allowing the system processing flexibility to ensure effective cleaning for all treads.

The method of the invention further comprises a step of simplifying the paths during which the 3D point associated with each point of each vertical and horizontal path is recovered (it is recalled that the data from the camera 200 represents a color and 3D image). During this step, for each path, a simplification algorithm is applied to reduce the number of points. By limiting the number of points, the speed of travel of the cleaning device 100 during a cleaning process is increased). This simplification consists of approaching the path by a set of segments and ensuring that each segment does not stray too far from the initial path. For this, a threshold in millimeters is used which makes it possible to simplify each path. An example of simplification is shown in the in which diagram A (left) represents a less precise simplification with a few 12A points, and diagram B (right) represents a precise simplification with more 12A points. In both cases, we seek to simplify in 3D the path 12B and the set of segments (represented by the dotted lines 12C).

The method of the invention further comprises a step of adding the approach and withdrawal points to complete the 3D trajectory obtained after the simplification step. During this step, two approach and withdrawal points are added at the start and end of each vertical and horizontal path, which allow the cleaning device 100 to start and finish cleaning the path (which corresponds to a sculpture on the surface of the tire identified in treatment). These approach and withdrawal points are necessary to ensure that the brush 106 (or the equivalent treatment tool) does not damage the tire between the cleaning of two successive paths. Thus, these points must not be too close to the tire to ensure that the brush (or the equivalent treatment tool) will not touch the tire between two successive paths. At the same time, these points must also not be too far apart to avoid excessively long travel times between two successive paths.

Referring to the , the principle of construction of the different approach points is illustrated. The approach point 1 is constructed by offsetting it by a predetermined distance L from the first point of the path or from the last point of the path (in this case, the distance L from the last point 13A is shown). The offset is made in the same direction as the first segment of the path or the last segment of the path (in this case, the offset is made in the same direction as the last segment of the path). Once offset, the approach point 1 is brought closer to the camera 200 by the distance L. The approach point 2 is constructed from the approach point 1 by bringing this point 2 closer to the camera 200 by a predetermined distance L2 starting from the point 1. It is understood that the parameters L and L2 are adjustable. The approach and withdrawal points 13A, 13B are always located between the tread surface of the identified tire and the camera 200 (see ).

The method of the invention comprises a final step of calculating the orientations of the brush 106. During this step, the 3D orientation of the brush 106 is calculated at each point of each vertical and horizontal path. To do this, the 3D normal is calculated at each point as well as the direction vector between each pair of successive points (this vector gives the direction to follow between two points of each path). The normal gives the orientation of the Z axis of the brush 106, and the direction vector gives the orientation of the X axis of the brush (the Y axis is automatically fixed following the fixing of the X and Z axes). Referring to the example of the , points 15A represent some points of a path 15B , and line 15C represents the complete 3D trajectory. At each point 15A, the orientation calculated for the brush 106 is represented by the X axis, the Y axis and the Z axis (in red). It is understood that the calculation described applies in cases where the brush 106 is replaced by an equivalent processing tool.

The system of the invention can easily repeat the process steps in an order to thoroughly clean the grooves of the tire identified in the course of a retreading process.

Those skilled in the art will recognize that many image processing techniques can be used to select and determine the parameters of the target tires. Several commercially available image processing systems can be used.

In a tire retreading facility, a vision system may be used to detect the presence of a tire in the field of view of the camera 200, triggering the camera to capture the image of the identified tire for processing.

In embodiments of the method of the invention, one or more steps of the method may further comprise a step of scanning the physical environment containing the cleaning device 100. In embodiments of the method, this step further comprises a step of measuring the physical environment to arrive at an exact positioning of the brush 106 (or equivalent treatment tool). During this step, one or more sensors may be employed to capture data corresponding to the treatment tools and the tires in order to determine the shapes and/or positions of the individual tires. This information is relevant to enable precise modeling of the method of the invention in order to optimize the time of an associated retreading process.

A method of the invention may be carried out by the control of the PLC and may include preprogramming of the management information. For example, a setting of the method may be associated with the parameters of the tire being retreaded, and/or the properties of the tires treated in a retreading installation incorporating the cleaning device 100 (for example, aircraft tires, heavy-duty tires, etc.). The system of the invention (and/or an installation incorporating this system) may easily repeat one or more steps of the method of the invention in a determined order to ensure the substantially complete cleaning of the tread (and particularly the grooves) of the treated tire.

The system of the invention (and/or a retreading plant incorporating the system) may include preprogramming of management information. For example, a process setting may be associated with the parameters of the typical physical environments in which the system operates. In embodiments of the invention, the system (and/or a retreading plant incorporating the system) may receive voice commands or other audio data representing, for example, a start or stop of cleaning and/or a loading/unloading of a drum dedicated to retreading tires. A request may be made that includes a query for the current status of a current retreading process cycle. A generated response may be represented audibly, visually, tactilely (e.g., using a haptic interface), and/or virtually and/or augmentedly. This response, associated with the corresponding data, may be recorded in a neural network.

For all embodiments of the system, a monitoring system could be implemented. At least part of the monitoring system may be provided in a portable device such as a mobile network device (e.g., a mobile phone, a laptop, one or more network-connected wearable devices (including "augmented reality" and/or "virtual reality" devices), network-connected wearables, and/or any combinations and/or equivalents). It is envisaged that detection and comparison steps may be performed iteratively.

In one embodiment, the method of the invention may include a step of training the system to recognize values representative of the tires to be retreaded (e.g., values of the internal diameter and the external diameter) and to make a comparison with targeted values (e.g., to perform an order of tires incorporating the retreaded tires). Each step of the training may include a classification generated by self-learning means. This classification may include, without limitation, the parameters of the selected tires, the durations of the process cycles and the expected values at the end of a process in progress (e.g., the number of tires retreaded during a cycle to reach a desired level).

The terms "at least one" and "one or more" are used interchangeably. Ranges that are presented as being "between a and b" encompass the values "a" and "b".

Although particular embodiments of the disclosed apparatus have been illustrated and described, it will be understood that various changes, additions and modifications may be practiced without departing from the spirit and scope of the present disclosure. Accordingly, no limitations should be imposed on the scope of the disclosed invention except as set forth in the appended claims.

Claims (15)

A method of cleaning a tread pattern of an identified tire damaged during retreading, the method implemented by at least one processor comprising a processing module which applies, to at least one neural network, the data representative of the captured images of the tread pattern of the identified tire, characterized in that the method comprises the following steps:
- a step of providing an automatic cleaning system of which the processor is a part to automatically recognize an order of treatment of damage in the sculpture of the identified tire;
- a step of calibrating a cleaning device (100) which is part of the automatic cleaning system, the cleaning device (100) comprising a treatment device (102) supported by a pivoting elongated arm (104) which extends to a free end (102a) where a treatment tool is arranged along a common longitudinal axis;
- a step of positioning the identified tire in a treatment space where the cleaning device (100) treats it;
- a step of positioning at least one camera (200) which is part of the automatic cleaning system at a reference distance relative to an identified tire rolling surface;
- a sculpture detection step comprising sector-by-sector processing for the identified tire incorporating one or more scans followed by processing of the sculptures of the identified tire to recover a color and three-dimensional (3D) image of each scanned sector;
- a step of creating the three-dimensional (3D) trajectories that the cleaning device (100) should follow to treat the sculptures;
- a step of simplification of the vertical and horizontal paths during which a 3D point associated with each point of each path is recovered;
- a step of adding the approach and withdrawal points to complete the 3D trajectory obtained after the path simplification step, during which, at the beginning and end of each vertical and horizontal path, an approach point and a withdrawal point are added allowing the cleaning device (100) to start and finish the processing of the path which corresponds to a sculpture on the rolling surface of the identified tire; and
- a final step of calculating the orientations of the processing tool during which a 3D orientation of the processing tool is calculated at each point of each vertical and horizontal path;
such that the cleaning device (100) is set in motion so that the processing device (102) can place the processing tool to carry out the cleaning of the sculpture of the identified tire. The cleaning method of claim 1, wherein, during the tread pattern detection step, the sector-by-sector processing for the identified tire comprises:
- for each tire sector identified, a sector scanning step followed by processing of the scanned sector;
- a scan of all successive sectors of the identified tire followed by processing of all successive scanned sectors; or
- a scan of the tire being processed at a station other than the one where processing of the scanned sectors is carried out;
such that a processing of one or more scanned sectors is carried out by the cleaning device (100). The cleaning method of claim 1 or claim 2, wherein the step of calibrating the cleaning device (100) comprises a calibration phase for estimating the pose of the camera (200) relative to a base (110) of the cleaning device (100). The cleaning method of claim 3, wherein the step of calibrating the cleaning device (100) comprises a step of positioning a calibration target in the treatment space, during which the camera (200) implements a process for estimating the pose of the camera (200) in the reference frame of the brushing device (102). The cleaning method of claim 4, wherein, during the step of positioning the identified tire, the identified tire is positioned in proximity to the cleaning device (100) on a rotating drum arranged in the treatment space, such that, in the course of a repair process of a retreading process, the drum rotates the identified tire so that it can be treated sector by sector. The cleaning method of any one of claims 1 to 5, wherein the step of positioning the camera (200) comprises a step of verifying a scan performed by the camera (200) comprising the following steps:
- a step of identifying a horizontal central zone of the scan which is used to verify that the distance between the identified tire and the camera (200) is correct;
- a step of calculating an average distance (D _avg ) of the points inside the horizontal central zone;
- a step of preserving the 3D points in the form of a surface mask comprising a binary image representing the points belonging to the rolling surface of the identified tire;
- a step of calculating again the average distance (D _avg ) by only considering the points of the horizontal central zone which belong to the rolling surface of the identified tire to obtain a distance (D _{avg_surface} );
- a step of comparing the distance (D _{avg_surface} ) with the reference distance of the camera (200);
- a step of calculating the two vertical lines (300B) which delimit the start and the end of the surface mask to make it possible to verify that the camera (200) is centered relative to the rolling surface of the identified tire;
- a step of calculating a central vertical line located in the middle of the vertical lines (300B); and
- where the calculated central vertical line deviates from the central line of the image beyond a predetermined threshold, a step of applying a lateral correction to the position of the camera (200) to recenter it relative to the rolling surface of the identified tire. The cleaning method of claim 6, wherein the step of detecting the sculptures comprises a step of applying a neural network to the color and 3D image to determine which points represent sculptures of the identified tire to be treated with the treatment tool, during which, at each image, the surface mask is recovered at the output of the neural network where each white point indicates that the point represents a sculpture to be cleaned. The cleaning method of claim 7, wherein the step of creating the three-dimensional (3D) trajectories comprises the following steps:
- a data preparation step, during which the points belonging to the contours of all the white objects present in the surface mask are extracted to obtain a set of polygons with holes; and
- a step of extracting horizontal and vertical two-dimensional (2D) paths, during which the holes present inside the polygons extracted during the data preparation step are deleted, this step comprising the following steps:
- a step of calculating a morphological skeleton of each polygon comprising the extraction of all the central points of each polygon, in which each morphological skeleton is made up of nodes representing ends of the morphological skeleton and/or the crossing points, and of paths comprising points located between two nodes;
- a step of classifying each path into horizontal path category or vertical path category, during which paths of the same category are merged with close ends and, once the merger is carried out, the direction of travel of each path is fixed; and
- a step of developing 3D trajectories which is carried out once the vertical and horizontal paths have been extracted, during which the vertical paths and the horizontal paths are ordered;
so that the ordered set of vertical and horizontal paths gives the processing order of all the sculptures of the scanned sector of the identified tire. The cleaning method of any one of claims 1 to 8, wherein, during the step of adding the approach and withdrawal points:
- a first approach point is constructed by offsetting it by a predetermined distance (L) from the first point or the last point of the path, and, once offset, the first approach point is brought closer to the camera (200) by the distance (L); and
- a second approach point is constructed from the first approach point by bringing this second approach point closer to the camera (200) by a distance (L2) starting from the first approach point. The cleaning method of any one of claims 1 to 9, wherein the step of calculating the orientations of the treatment tool comprises a step of calculating the 3D normal at each point as well as the direction vector between each pair of successive points, wherein the direction vector gives the direction to follow between two points of each path, where the normal gives the orientation of the Z axis of the treatment tool, the direction vector gives the orientation of the X axis of the treatment tool, and the Y axis is automatically fixed according to the fixing of the X and Z axes. The cleaning method of any one of claims 1 to 10, wherein the processor refers to a table of various tire sizes to make a determination of one or more parameters of the identified tire. The cleaning method of any one of claims 1 to 11, wherein the treatment tool comprises at least one brush (106). A retreading process comprising the cleaning process of any one of claims 1 to 12. An automatic cleaning system for cleaning a tread pattern of an identified tire being retreaded, the automatic cleaning system comprising:
- at least one cleaning device (100) comprising a robot having a cleaning peripheral (102) supported by an elongated arm (104) pivotable and extending from the elongated arm (104) to a free end (102a) where a treatment tool is arranged along a common longitudinal axis;
- a detection system for collecting information on the physical environment around the cleaning device (100); and
- a communication network which manages the incoming data to the automatic cleaning system from each cleaning device (100), the communication network comprising at least one communication server for executing programmed instructions stored in a memory of one or more processors of the automatic cleaning system to implement the cleaning method of any one of claims 1 to 12;
such that the cleaning device (100) is configured on one or more parameters of the identified tire calculated by an image processing module incorporated in the memory of the processor to set the cleaning device (100) in motion so that the brushing device (102) can place the treatment tool to carry out the cleaning of the sculpture of the identified tire. The automatic cleaning system of claim 14, wherein the detection system comprises at least one RGB-D type camera (200) attached to at least one of the elongated arm (104) and the processing device (102) of the cleaning device (100).