WO2015101088A1 - 多关节机械臂智能控制方法、装置及系统 - Google Patents

多关节机械臂智能控制方法、装置及系统 Download PDF

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Publication number
WO2015101088A1
WO2015101088A1 PCT/CN2014/089443 CN2014089443W WO2015101088A1 WO 2015101088 A1 WO2015101088 A1 WO 2015101088A1 CN 2014089443 W CN2014089443 W CN 2014089443W WO 2015101088 A1 WO2015101088 A1 WO 2015101088A1
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Prior art keywords
joint
arm
intelligent control
boom
trajectory prediction
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PCT/CN2014/089443
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English (en)
French (fr)
Inventor
代晴华
谭凌群
蒲东亮
武利冲
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Sany Automobile Manufacturing Co Ltd
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Sany Automobile Manufacturing Co Ltd
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    • E—FIXED CONSTRUCTIONS
    • E04—BUILDING
    • E04G—SCAFFOLDING; FORMS; SHUTTERING; BUILDING IMPLEMENTS OR AIDS, OR THEIR USE; HANDLING BUILDING MATERIALS ON THE SITE; REPAIRING, BREAKING-UP OR OTHER WORK ON EXISTING BUILDINGS
    • E04G21/00—Preparing, conveying, or working-up building materials or building elements in situ; Other devices or measures for constructional work
    • E04G21/02—Conveying or working-up concrete or similar masses able to be heaped or cast
    • E04G21/04—Devices for both conveying and distributing
    • E04G21/0418—Devices for both conveying and distributing with distribution hose
    • E04G21/0445—Devices for both conveying and distributing with distribution hose with booms
    • E04G21/0463—Devices for both conveying and distributing with distribution hose with booms with boom control mechanisms, e.g. to automate concrete distribution
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1656—Program controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Program controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/40—Robotics, robotics mapping to robotics vision
    • G05B2219/40465—Criteria is lowest cost function, minimum work path
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/41—Servomotor, servo controller till figures
    • G05B2219/41273—Hydraulic

Definitions

  • the invention relates to the field of engineering machinery, in particular to a multi-joint mechanical arm intelligent control method, device and system.
  • the multi-joint ultra-long boom is a nonlinear coupling system. As the length of the boom increases and the number of joints increases, the difficulty of control is gradually increased, making it difficult to accurately control. How to reduce the labor intensity and handling complexity of the multi-joint ultra-long boom has become a difficult problem to be solved by those skilled in the art.
  • the present invention provides a multi-joint mechanical arm intelligent control method, device and system to further improve the control precision of the multi-joint mechanical arm and reduce the complexity of the manipulation.
  • the present invention discloses a multi-joint robot arm intelligent control method, which includes the following steps:
  • Step S11 receiving a velocity vector of the end of the robot arm input by the manipulator; step S12, performing trajectory prediction on each of the articulated arms of the multi-joint robot based on the velocity vector of the end of the arm and the state of the current arm; If the obtained motion trajectory of the next moment is abrupt with respect to the current time, the result of the trajectory prediction is discarded; and in step S13, the optimal planning solution is determined based on the motion constraint of the multi-joint robot arm and the result of the trajectory prediction; step S14, according to The optimal planning solution determines the cylinder driving amount of each of the pitch arms; and in step S15, determines a driving current according to the cylinder driving amount and the proportional characteristic of the actuator, and the driving current is used to drive the multi-joint boom and the turret The turn.
  • the motion constraint is a hydraulic system flow extreme value constraint
  • the optimal planning solution is a planning solution with a minimum system flow cost
  • step S15 further comprising step S16, detecting a state parameter of the driven multi-joint boom, and feeding back the state parameter of the multi-joint boom to the step S12.
  • the result of the trajectory prediction is jointly determined according to the velocity vector of the input end of the arm and the state parameter of the multi-joint boom.
  • the step S16 further includes the following sub-steps: performing signal filtering and deformation compensation on the acquired state parameters of the multi-joint boom, and feeding back the obtained result to the Step S12 is described.
  • the working principle of the multi-joint mechanical arm intelligent control method of the present invention is as follows: the trajectory prediction of the boom is performed based on the end speed of the mechanical arm outputted to the controller and the current posture of each articulated arm; and the optimal planning solution is determined based on the prediction result and the constraint, and further Determine the driving amount ⁇ L of each articulated arm; finally, according to the proportional relationship between the driving amount ⁇ L and the current i (current i is the actuator driving characteristic, that is, the magnitude of the current i determines the actuator driving capability, which determines the speed of the cylinder driving amount Size), accurately determine the drive current required to drive the boom, thereby achieving high-precision control of the multi-joint manipulator; and, as the trajectory prediction is made, if the predicted trajectory of the next moment occurs relative to the current moment If the mutation is made, the result of the trajectory prediction is discarded.
  • current i is the actuator driving characteristic, that is, the magnitude of the current i determines the actuator driving capability, which determines the speed of the
  • this kind of motion trend-based prediction can ensure that the movement state of the boom does not abruptly in the case of small amplitude motion, that is, the smooth motion of the boom can be well ensured.
  • the controller only needs to input the speed of the end of the arm through the speed input device, which is the controller's willingness to control the end of the boom, and can realize the action of the boom by completing the above steps. Control, therefore, is simple and easy, greatly reducing the complexity of the manipulation.
  • the present invention also discloses a multi-joint robot arm intelligent control device, comprising: a velocity vector receiving unit, a trajectory prediction unit, an optimal planning solution determining unit, a cylinder driving amount calculating unit, and a driving current calculating unit.
  • the speed vector receiving unit is configured to receive a velocity vector of the end of the robot arm input by the manipulator; the trajectory predicting unit is configured to perform each section of the multi-joint robot arm based on the velocity vector of the end of the arm and the state of the current arm The arm performs trajectory prediction, and if the predicted trajectory of the next moment is abrupt with respect to the current moment, the result of the trajectory prediction is abandoned; the optimal planning solution determining unit is used for the motion based on the multi-joint robot Determining an optimal planning solution by the result of the beam and the trajectory prediction; the cylinder driving amount calculating unit is configured to determine a cylinder driving amount of each of the pitch arms according to the optimal planning solution; and the driving current calculating unit is configured to use the driving amount of the cylinder and The proportional characteristic of the actuator determines the drive current, which is used to drive the rotation of the multi-joint boom and the turret.
  • the motion constraint is a hydraulic system flow extreme value constraint
  • the optimal planning solution is a planning solution with a minimum system flow cost
  • the multi-joint robot arm intelligent control device further includes a feedback unit configured to detect a state parameter of the driven multi-joint boom and feed back the state parameter of the multi-joint boom to the a trajectory prediction unit that further determines a prediction result according to the input velocity vector of the end of the robot arm and the state parameter of the multi-joint boom.
  • the feedback unit of the multi-joint robot arm intelligent control device further includes a signal filtering and deformation compensation sub-unit for performing signal filtering and deformation compensation on the acquired state parameters of the multi-joint boom.
  • the multi-joint robot arm intelligent control device of the present invention works as follows: the trajectory prediction of the boom is performed based on the end speed of the arm output to the controller and the current posture of each articulated arm. Based on the prediction results and constraints, the optimal planning solution is determined, and then the driving amount ⁇ L of each articulated arm is determined. Finally, according to the proportional relationship between the driving amount ⁇ L and the current i, the driving current required to drive the boom is accurately determined, thereby implementing multiple joints. High precision control of the robot arm. Further, since the trajectory prediction is performed, if the predicted motion trajectory at the next time is abrupt with respect to the current time, the result of the trajectory prediction is discarded.
  • this kind of motion trend-based prediction can ensure that the movement state of the boom does not abruptly in the case of small amplitude motion, that is, the smooth motion of the boom can be well ensured.
  • the controller only needs to input the speed of the end of the arm through the speed input device, which is the controller's willingness to control the end of the boom, and can realize the action of the boom by completing the above steps. Control, therefore, is simple and easy, greatly reducing the complexity of the manipulation.
  • the present invention also discloses a multi-joint mechanical arm intelligent control system, comprising the above-mentioned multi-joint mechanical arm intelligent control device, and a manipulator matched with the multi-joint mechanical arm intelligent control device, the manipulation The device is used to input the velocity vector at the end of the arm.
  • the manipulator is a universal handle.
  • the multi-joint mechanical arm intelligent control system includes the above-described multi-joint mechanical arm intelligent control device, the multi-joint mechanical arm intelligent control system also has the technical effect of the multi-joint mechanical arm intelligent control device. Since the structure and effect of the multi-joint robot arm intelligent control device have been described, the present invention will not be described herein.
  • FIG. 1 is a flow chart showing the steps of a first embodiment of a multi-joint robot arm intelligent control method according to the present invention
  • FIG. 2 is a flow chart showing the steps of a second embodiment of the intelligent control method for a multi-joint robot arm according to the present invention
  • FIG. 3 is a structural block diagram of an embodiment of a multi-joint mechanical arm intelligent control device according to the present invention.
  • FIG. 4 is a structural block diagram of a preferred embodiment of the multi-joint robot arm intelligent control device of the present invention.
  • FIG. 5 is a structural block diagram of an embodiment of a multi-joint robot arm intelligent control system according to the present invention.
  • FIG. 1 is a flow chart of steps of a first embodiment of a multi-joint robot arm intelligent control method according to the present invention. This embodiment includes the following five steps:
  • Step S11 receiving a velocity vector of the end of the robot arm input by the manipulator.
  • the end of the boom refers to the end of the last boom, the delivery hose end of the pump truck.
  • the manipulator can be a manipulator with a universal handle whose input directly reflects the controller's willingness to control the end of the boom, ie the speed and direction at which the end of the boom is desired.
  • Step S12 based on the velocity vector at the end of the arm and the state of the current boom, trajectory prediction is performed on each of the joint arms of the multi-joint robot; if the predicted motion trajectory at the next moment is abrupt with respect to the current moment, the discard is discarded.
  • the result of the trajectory prediction is the meaning of "mutation” is the incoherence of the boom movement, or the boom motion "repetition", suddenly crossing from one state to another, rather than a gradual change.
  • the state of the current boom mainly refers to the angle parameter of each section arm. Master of trajectory prediction If the end of the boom is to reach the speed V at the next moment and the state of the current moment, or the attitude ⁇ of the boom. Trajectory prediction based on velocity V and attitude ⁇ , and the results of trajectory prediction are known to those skilled in the art, and there are many methods, and the cost of implementation of different methods is different. The core of the invention is not here.
  • Step S13 determining an optimal planning solution based on the results of the motion constraint and the trajectory prediction of the multi-joint robot.
  • the cost of different methods is different.
  • the constraint is to find the conditions for obtaining the optimal planning result.
  • the optimal programming solution here may be the attitude ⁇ of each joint arm.
  • step S14 the cylinder driving amount of each pitch arm is determined according to the optimal planning solution ⁇ .
  • step S15 the driving current is determined according to the driving amount of the cylinder and the proportional characteristic of the actuator, and the driving current is used to drive the rotation of the multi-joint boom and the turret.
  • the working principle of the joint robot arm intelligent control method of the present embodiment is as follows: the trajectory prediction of the boom is performed based on the end speed of the arm output to the controller and the current posture of each arm. Based on the prediction result and the constraint, the optimal planning solution is determined, and then the driving amount ⁇ L of each arm is determined. Finally, according to the proportional relationship between the driving amount ⁇ L and the current i, the driving current required to drive the boom is accurately determined, thereby implementing multiple joints. High precision control of the robot arm.
  • the trajectory prediction is performed, if the predicted motion trajectory at the next time is abrupt with respect to the current time, the result of the trajectory prediction is discarded. Therefore, this is based on The prediction of the movement trend can ensure that the movement state of the boom does not abruptly change under the condition of small amplitude motion, that is, the smooth movement of the boom can be well ensured.
  • the controller only needs to input the speed of the end of the arm through the speed input device, and the speed is the controller's willingness to control the end of the arm frame, and the arm frame action can be realized by completing the above steps.
  • the control therefore, is simple and easy, greatly reducing the complexity of the manipulation.
  • FIG. 2 is a flow chart of steps of a second embodiment of a multi-joint robot arm intelligent control method according to the present invention, including the following steps:
  • Step S21 receiving the velocity vector v of the end of the robot arm input by the manipulator;
  • Step S22 based on the velocity vector at the end of the robot arm and the state of the current robot arm, performing trajectory prediction on each of the joint arms of the multi-joint robot; if the predicted motion trajectory at the next moment is abrupt with respect to the current moment, then discarding the The result of the trajectory prediction.
  • Step S23 based on the results of the hydraulic system flow Q extreme value constraint and the trajectory prediction of the multi-joint manipulator, determine the optimal planning solution, that is, the optimal planning solution is the planning solution with the lowest system flow Q cost.
  • the hydraulic system flow Q is the most important constraint parameter of the engineering machinery of the pump truck. This constraint is not established and the structure is difficult to implement according to the control idea. Therefore, system traffic constraints must be within the achievable range.
  • the end point is the fastest under the same conditions. Because the size of the cylinder corresponding to each section arm is different, the cylinder corresponding to the end point is the smallest. Therefore, moving the same distance, the amount of oil required at the end of the boom is the smallest. From another angle, the end point is the fastest under the same conditions.
  • Step S24 determining the cylinder driving amount of each pitch arm according to the optimal planning solution
  • Step S25 determining a driving current according to the driving amount of the cylinder and the proportional characteristic of the actuator, and the driving current is used for driving the rotation of the multi-joint boom and the turret;
  • Step S26 detecting the state parameter of the driven multi-joint boom, and feeding back the state parameter of the multi-joint boom to step S22, the result of the trajectory prediction is based on the input speed vector of the arm end and the state of the multi-joint boom The parameters are determined together.
  • step S26 further includes the following sub-steps, sub-step S261, performing signal filtering and deformation compensation on the acquired state parameters (attitudes) of the multi-joint boom, and feeding back the obtained results. Go to step S22.
  • the detection of the state parameter (attitude) of the multi-joint boom can be obtained by a cylinder displacement sensor or a rotary encoder.
  • step S26 and its sub-step S261 are added, that is, the closed-loop constant current driving is adopted, and the control precision is further higher, and the signal filtering and the deformation compensation further improve the control precision.
  • the present invention also discloses an embodiment of a multi-joint mechanical arm intelligent control device, with reference to FIG. 3.
  • the multi-joint robot arm intelligent control device embodiment includes a speed vector receiving unit 31, a trajectory predicting unit 32, an optimal plan solution determining unit 33, a cylinder driving amount calculating unit 34, and a driving current calculating unit 35.
  • the speed vector receiving unit 31 is for receiving a velocity vector of the end of the robot arm input by the manipulator.
  • the end of the boom refers to the end of the last boom, the delivery hose end of the pump truck.
  • the manipulator can be a manipulator with a universal handle whose input directly reflects the controller's willingness to control the end of the boom, ie the speed and direction at which the end of the boom is desired.
  • the trajectory prediction unit 32 is configured to perform trajectory prediction on each of the joint arms of the multi-joint robot based on the velocity vector at the end of the arm and the state of the current arm, and if the predicted trajectory of the next moment is abrupt with respect to the current moment, Then the result of the trajectory prediction is discarded.
  • the state of the current boom mainly refers to the angle parameter of each articulated arm.
  • the trajectory prediction is mainly based on the speed V to be reached at the next moment of the boom end and the state at the current time, or the attitude ⁇ of the boom. It is well known to those skilled in the art to perform trajectory prediction based on velocity V and attitude ⁇ , and it is known to those skilled in the art that there are many methods, and the cost of implementation of different methods is different.
  • the core of the invention is not here.
  • the optimal plan solution determining unit 33 is configured to determine an optimal plan solution based on the results of the motion constraint and the trajectory prediction of the multi-joint manipulator. There are many ways to predict the trajectory. The cost of different methods is different. The constraint is to find the conditions for obtaining the optimal planning result. In the specific implementation, the optimal programming solution here may be the attitude ⁇ of each joint arm.
  • the cylinder drive amount calculation unit 34 is configured to determine the cylinder drive amount of each of the pitch arms based on the optimal plan solution.
  • the drive current calculation unit 35 is configured to determine a drive current for driving the multi-joint boom and the turret according to the cylinder drive amount and the proportional characteristic of the actuator.
  • the working principle of the joint robot arm intelligent control method of the present embodiment is as follows: the trajectory prediction of the boom is performed based on the end speed of the arm output to the controller and the current posture of each arm. Based on the prediction result and the constraint, the optimal planning solution is determined, and then the driving amount ⁇ L of each arm is determined. Finally, according to the proportional relationship between the driving amount ⁇ L and the current i, the driving current required to drive the boom is accurately determined, thereby implementing multiple joints. High precision control of the robot arm.
  • this kind of motion trend-based prediction can ensure that the movement state of the boom does not abruptly in the case of small amplitude motion, that is, the smooth motion of the boom can be well ensured.
  • the controller only needs to input the speed of the end of the arm through the speed input device, and the speed is the controller's willingness to control the end of the arm frame, and the arm frame action can be realized by completing the above steps.
  • the control therefore, is simple and easy, greatly reducing the complexity of the manipulation.
  • the motion constraint is a hydraulic system flow Q extreme value constraint
  • the optimal planning solution is a planning solution with a minimum system flow cost.
  • the hydraulic system flow Q is the most important constraint parameter of the construction machinery of the pump truck.
  • the hydraulic pressure is the drive mechanism. This constraint condition If it is not established, the structure is difficult to implement in accordance with the control idea.
  • Each boom has a valve for oil supply.
  • the flow rate of the oil supply is extremely high, not unlimited. Therefore, the system flow restriction must be within the achievable range.
  • the source of the constraint depends only on the hydraulic system configuration of the equipment itself. , how large the valve is configured, the constraint will be fixed.
  • the end point is the fastest under the same conditions. Because the size of the cylinder corresponding to each section arm is different, the cylinder corresponding to the end point is the smallest. Therefore, moving the same distance, the amount of oil required at the end of the boom is the smallest. From another angle, the end point is the fastest under the same conditions.
  • FIG. 4 is a structural block diagram of a preferred embodiment of the multi-joint mechanical arm intelligent control device of the present invention.
  • the multi-joint robot arm intelligent control device further includes a feedback unit 36 for detecting the state parameter of the driven multi-joint boom and feeding back the state parameter of the multi-joint boom to the trajectory prediction unit 32.
  • the trajectory prediction unit further determines the prediction result according to the input velocity vector of the end of the arm and the state parameter of the multi-joint boom.
  • the feedback unit 36 further comprises a signal filtering and deformation compensation sub-unit for performing signal filtering and deformation compensation on the acquired state parameters of the multi-joint boom.
  • step S26 and its sub-step S261 are added, that is, closed-loop constant current driving is adopted, the control precision is further higher. Signal filtering and deformation compensation also improve the accuracy of the control.
  • the multi-joint mechanical arm intelligent control method and device of the present invention has the following advantages:
  • the present invention also discloses an embodiment of a multi-joint robot arm intelligent control system, comprising the above-mentioned multi-joint robot arm intelligent control device, and a manipulator matched with the multi-joint robot arm intelligent control device,
  • the remote controller is used to input the velocity vector of the end of the arm.
  • the manipulator is a universal handle.
  • the input device is controlled by a remote controller with a universal handle, and the operation direction of the handle of the remote controller is consistent with the moving direction of the end of the arm.
  • the present multi-joint robot arm intelligent control system embodiment includes a universal handle controller and a control device as an execution device of the controlled object.
  • the actuator includes a proportional valve block, a drive cylinder, and a multi-joint boom.
  • the multi-joint boom is further improved and a sensor is further connected to the control unit.
  • the multi-joint mechanical arm intelligent control system of the present embodiment is directed to the complicated problem of multi-joint ultra-long arm frame control, adopting a one-button automatic control idea (that is, using a universal handle to input the end of the multi-joint robot arm to be reached at the next moment) Speed), in this way, the controller only needs to open the intelligent control function button, shake the universal handle to complete any desired action. That is to say, the universal handle manipulator directly reflects the controller's willingness to control the end of the boom.
  • the system contains a high-speed constant current control device, which is responsible for intelligently controlled trajectory planning and optimal calculation, and precisely drives the actuator.
  • the logic module inside the control device is shown in Figure 3. It is a boom algorithm planning software for implementing control algorithm calculation, deformation compensation, trajectory prediction, optimal control calculation, and so on.
  • the controller transmits the control device to the control device according to the operation intention.
  • the control device provides a complete set of completed control methods, so that the precise drive actuator can be realized to perform the predetermined action of the actuator.
  • the multi-joint mechanical arm intelligent control system includes the above-described multi-joint mechanical arm intelligent control device, the multi-joint mechanical arm intelligent control system also has the technical effect of the multi-joint mechanical arm intelligent control device. Since the structure and effect of the multi-joint robot arm intelligent control device have been described, the present invention will not be described herein.

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Abstract

一种多关节机械臂智能控制方法、装置及系统。其中，多关节机械臂智能控制方法包括：接收由操控器输入的机械臂末端的速度矢量（S11）；基于所述机械臂末端的速度矢量，对多关节机械臂的各个节臂进行轨迹预测（S12）；基于多关节机械臂的运动约束和轨迹预测的结果，确定最优规划解（S13）；根据所述最优规划解，确定各个节臂的油缸驱动量（S14）；根据所述油缸驱动量及执行机构的比例特性，确定驱动电流，所述驱动电流用于驱动多关节臂架及转塔的回转（S15）。本控制方法、装置及系统可以提高多关节机械臂的控制精度，确保臂架的平稳运动，并且降低操控的复杂度。

Description

多关节机械臂智能控制方法、装置及系统
本申请要求于2013年12月31日提交中国专利局、申请号为201310749595.6、发明名称为“多关节机械臂智能控制方法、装置及系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及工程机械领域，特别涉及一种多关节机械臂智能控制方法、装置及系统。
背景技术
多关节超长臂架属于非线性耦合系统，随着臂架长度的增长及关节的增多，其操控难度逐步加大，很难进行精确控制。如何降低多关节超长臂架劳动强度及操控复杂度，成为本领域技术人员亟需解决的控制难题。
发明内容
有鉴于此，本发明提出一种多关节机械臂智能控制方法、装置及系统，以进一步提高多关节机械臂的控制精度，降低操控的复杂度。
第一方面，本发明公开了一种多关节机械臂智能控制方法，包括如下步骤：
步骤S11，接收由操控器输入的机械臂末端的速度矢量；步骤S12，基于所述机械臂末端的速度矢量和当前机械臂的状态，对多关节机械臂的各个节臂进行轨迹预测；若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果；步骤S13，基于多关节机械臂的运动约束和轨迹预测的结果，确定最优规划解；步骤S14，根据所述最优规划解，确定各个节臂的油缸驱动量；步骤S15，根据所述油缸驱动量及执行机构的比例特性，确定驱动电流，所述驱动电流用于驱动多关节臂架及转塔的回转。
进一步地，上述多关节机械臂智能控制方法中，所述步骤S13中，所述运动约束为液压系统流量极值约束，所述最优规划解为系统流量代价最小的规划解。
进一步地，上述多关节机械臂智能控制方法中，在步骤S15后，还包括步骤S16，检测被驱动后的多关节臂架的状态参数，并将所述多关节臂架的状态参数反馈至步骤S12，所述轨迹预测的结果根据输入的机械臂末端的速度矢量和多关节臂架的状态参数共同确定。
进一步地，上述多关节机械臂智能控制方法中，所述步骤S16还包括如下子步骤：对获取的所述多关节臂架的状态参数进行信号滤波以及形变补偿，并将获取的结果反馈至所述步骤S12。
本发明多关节机械臂智能控制方法工作原理如下：基于输出至控制器的机械臂末端速度和各关节臂的当前姿态进行臂架的轨迹预测；基于预测结果和约束，确定最优规划解，进而，确定各个关节臂的驱动量ΔL；最后，根据驱动量ΔL与电流i比例特性(电流i为执行机构驱动特性，即电流i大小决定执行机构驱动能力，该驱动能力决定了油缸驱动量的速度大小)，精确确定驱动臂架所需的驱动电流，进而实现多关节机械臂的高精度控制；并且，由于在进行轨迹预测的时候，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。因此，这种基于运动趋势的预测，在小振幅运动情况下可以确保臂架的运动状态不发生突变，即，可以很好的确保臂架的平稳运动。此外，对于本发明而言，操控者只需要通过速度输入装置输入机械臂末端的速度，该速度为操控者对臂架末端的控制意愿，即可通过完成上述各个步骤而实现对臂架动作的控制，因此，简单易行，极大地降低了操控的复杂度。
第二方面，本发明还公开了一种多关节机械臂智能控制装置，包括：速度矢量接收单元、轨迹预测单元、最优规划解确定单元、油缸驱动量计算单元以及驱动电流计算单元。其中，速度矢量接收单元用于接收由操控器输入的机械臂末端的速度矢量；轨迹预测单元用于基于所述机械臂末端的速度矢量和当前机械臂的状态，对多关节机械臂的各个节臂进行轨迹预测，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果；最优规划解确定单元用于基于多关节机械臂的运动约 束和轨迹预测的结果，确定最优规划解；油缸驱动量计算单元用于根据所述最优规划解，确定各个节臂的油缸驱动量；驱动电流计算单元用于根据所述油缸驱动量及执行机构的比例特性，确定驱动电流，所述驱动电流用于驱动多关节臂架及转塔的回转。
进一步地，上述多关节机械臂智能控制装置中，所述最优规划解确定单元中，所述运动约束为液压系统流量极值约束，所述最优规划解为系统流量代价最小的规划解。
进一步地，上述多关节机械臂智能控制装置中还包括反馈单元，该反馈单元用于检测被驱动后的多关节臂架的状态参数，并将所述多关节臂架的状态参数反馈至所述轨迹预测单元，所述轨迹预测单元进一步根据输入的机械臂末端的速度矢量和所述多关节臂架的状态参数共同确定预测结果。
进一步地，上述多关节机械臂智能控制装置的所述反馈单元还包括信号滤波及形变补偿子单元，用于对获取的所述多关节臂架的状态参数进行信号滤波以及形变补偿。
本发明多关节机械臂智能控制装置工作原理如下：基于输出至控制器的机械臂末端速度和各关节臂的当前姿态进行臂架的轨迹预测。基于预测结果和约束，确定最优规划解，进而，确定各个关节臂的驱动量ΔL；最后，根据驱动量ΔL与电流i比例特性，精确确定驱动臂架所需的驱动电流，进而实现多关节机械臂的高精度控制。并且，由于在进行轨迹预测的时候，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。因此，这种基于运动趋势的预测，在小振幅运动情况下可以确保臂架的运动状态不发生突变，即，可以很好的确保臂架的平稳运动。此外，对于本发明而言，操控者只需要通过速度输入装置输入机械臂末端的速度，该速度为操控者对臂架末端的控制意愿，即可通过完成上述各个步骤而实现对臂架动作的控制，因此，简单易行，极大地降低了操控的复杂度。
第三方面，本发明还公开了一种多关节机械臂智能控制系统，包括上述的多关节机械臂智能控制装置，以及与所述多关节机械臂智能控制装置相配合的操控器，所述操控器用于输入所述机械臂末端的速度矢量。
进一步地，上述多关节机械臂智能控制系统中，所述操控器为万向手柄。
由于多关节机械臂智能控制系统包括上述的多关节机械臂智能控制装置，因此，多关节机械臂智能控制系统也具有多关节机械臂智能控制装置的技术效果。由于多关节机械臂智能控制装置的结构和效果已经做了说明，因此，本发明在此不再赘述。
附图说明
图1为本发明多关节机械臂智能控制方法第一实施例的步骤流程图；
图2为本发明多关节机械臂智能控制方法第二实施例的步骤流程图；
图3为本发明多关节机械臂智能控制装置实施例的结构框图；
图4为本发明多关节机械臂智能控制装置优选实施例的结构框图；
图5为本发明多关节机械臂智能控制系统实施例的结构框图。
具体实施方式
需要说明的是，在不冲突的情况下，本发明中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本发明。
参照图1，图1为本发明多关节机械臂智能控制方法第一实施例的步骤流程图。该实施例包括如下五个步骤：
步骤S11，接收由操控器输入的机械臂末端的速度矢量。
对于泵车而言，臂架末端是指最后一节臂架的末端，即泵车的输送软管端。操控器可以采用带万向手柄的操控器，该操控器的输入直接反映操控者对臂架末端的控制意愿，即，希望臂架末端达到的速度大小和方向。
步骤S12，基于机械臂末端的速度矢量和当前臂架的状态，对多关节机械臂的各个节臂进行轨迹预测；若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。这里，“突变”的含义是臂架动作的不连贯，或者说，臂架动作“反复”，突然从一个状态跨越到另一个状态，而不是渐进的变化。
当前臂架的状态，主要是指各个节臂的角度参数。轨迹预测依据的主 要是臂架末端在下一时刻要达到的速度V以及当前时刻的状态，或者称臂架的姿态θ。根据速度V和姿态θ进行轨迹预测，获取轨迹预测的结果，对于本领域的技术人员来说是已知的，有很多方法，不同方法实现的代价不同。本发明的核心不在于此。
该步骤中，需要注意的是，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。这是基于下述考量：自然界的事物都是连续的，不连续的事情是不存在的，物体都是有质量的，有质量就有惯性，不连续即要改变的物体的运动状态，频繁的改变物体的运动状态，整个系统是不稳定的，不稳定的系统在控制上是禁止的。对应到泵车的节臂的运动而言，则是，节臂的运动趋势不允许“反复(即突变)”，以保证机械臂运动的平稳性。
步骤S13，基于多关节机械臂的运动约束和轨迹预测的结果，确定最优规划解。轨迹预测有很多种方法，不同方法实现的代价不同，约束就是要寻求获取最优的规划结果的条件。在具体实施时，这里的最优规划解，可以为各个节臂的姿态Δθ。
步骤S14，根据最优规划解Δθ，确定各个节臂的油缸驱动量。
各个节臂的长度与姿态(角度)具有特定的范式关系L＝S(θ)。现已经获取Δθ，则各个关节臂的驱动量ΔL是可以通过计算获取的。
此外，需要说明的是，连杆机构的约束关系是有推导关系的，在此只提供范式，在具体实施时，具体的形式与数值只需几何与推导与计算，对于本领域的技术人员来说，是已知的，在此不再进行过多的说明。
步骤S15，根据油缸驱动量及执行机构的比例特性，确定驱动电流，驱动电流用于驱动多关节臂架及转塔的回转。
本实施例关节机械臂智能控制方法工作原理如下：基于输出至控制器的机械臂末端速度和各节臂的当前姿态进行臂架的轨迹预测。基于预测结果和约束，确定最优规划解，进而，确定各个节臂的驱动量ΔL；最后，根据驱动量ΔL与电流i比例特性，精确确定驱动臂架所需的驱动电流，进而实现多关节机械臂的高精度控制。
并且，由于在进行轨迹预测的时候，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。因此，这种基于 运动趋势的预测，在小振幅运动情况下可以确保臂架的运动状态不发生突变，即，可以很好的确保臂架的平稳运动。
此外，对于本实施例而言，操控者只需要通过速度输入装置输入机械臂末端的速度，该速度为操控者对臂架末端的控制意愿，即可通过完成上述各个步骤而实现对臂架动作的控制，因此，简单易行，极大地降低了操控的复杂度。
参照图2，图2为本发明多关节机械臂智能控制方法第二实施例的步骤流程图，包括如下步骤：
步骤S21，接收由操控器输入的机械臂末端的速度矢量v；
步骤S22，基于机械臂末端的速度矢量和当前机械臂的状态，对多关节机械臂的各个节臂进行轨迹预测；若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。
步骤S23，基于多关节机械臂的液压系统流量Q极值约束和轨迹预测的结果，确定最优规划解，也就是说，最优规划解为系统流量Q代价最小的规划解。
液压系统流量Q是泵车这种工程机械最重要的约束参数，这个约束条件不成立，结构是很难按照控制的思路执行的。因此，系统流量约束必须在可实现的范围内。
采用基于代价最小约束条件下的运动规划，在同等条件下末端点最快。因为各个节臂所对应的油缸的大小不同，末端点所对应的油缸最小。因此，移动相同距离，臂架末端所需油量最小，换个角度说，在同等条件下末端点最快。
步骤S24，根据最优规划解，确定各个节臂的油缸驱动量；
步骤S25，根据油缸驱动量及执行机构的比例特性，确定驱动电流，驱动电流用于驱动多关节臂架及转塔的回转；
步骤S26，检测被驱动后的多关节臂架的状态参数，并将多关节臂架的状态参数反馈至步骤S22，轨迹预测的结果根据输入的机械臂末端的速度矢量和多关节臂架的状态参数共同确定。
并且，步骤S26还包括如下子步骤，子步骤S261，对获取的多关节臂架的状态参数(姿态)进行信号滤波以及形变补偿，并将获取的结果反馈 至步骤S22。
其中，多关节臂架的状态参数(姿态)的检测可以通过油缸位移传感器或旋转编码器来获取。
可以看出，本实施例由于增加了步骤S26及其子步骤S261，即，采用闭环恒流驱动，控制精度得到进一步的更高，而信号滤波和形变补偿也更加提高了控制的精度。
第二方面，本发明还公开了一种多关节机械臂智能控制装置的实施例，参照图3。
该多关节机械臂智能控制装置实施例包括：速度矢量接收单元31、轨迹预测单元32、最优规划解确定单元33、油缸驱动量计算单元34以及驱动电流计算单元35。
下面对各个单元的工作原理进行较为详细的说明。
速度矢量接收单元31用于接收由操控器输入的机械臂末端的速度矢量。对于泵车而言，臂架末端是指最后一节臂架的末端，即泵车的输送软管端。操控器可以采用带万向手柄的操控器，该操控器的输入直接反映操控者对臂架末端的控制意愿，即，希望臂架末端达到的速度大小和方向。
轨迹预测单元32用于基于机械臂末端的速度矢量和当前机械臂的状态，对多关节机械臂的各个节臂进行轨迹预测，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。
当前臂架的状态，主要是指各个关节臂的角度参数。轨迹预测依据的主要是臂架末端在下一时刻要达到的速度V以及当前时刻的状态，或者称臂架的姿态θ。根据速度V和姿态θ进行轨迹预测，获取轨迹预测的结果，对于本领域的技术人员来说是习知的，有很多方法，不同方法实现的代价不同。本发明的核心不在于此。
该步骤中，需要注意的是，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。这是基于下述考量：自然界的事物都是连续的，不连续的事情是不存在的，物体都是有质量的，有质量就有惯性，不连续即要改变的物体的运动状态，频繁的改变物体的运动状态，整个系统是不稳定的，不稳定的系统在控制上是禁止的。对应到泵车的节臂的运动而言，则是，节臂的运动趋势不允许“反复”(即“突变” 的一种形式)，以保证机械臂运动的平稳性。
最优规划解确定单元33用于基于多关节机械臂的运动约束和轨迹预测的结果，确定最优规划解。轨迹预测有很多种方法，不同方法实现的代价不同，约束就是要寻求获取最优的规划结果的条件。在具体实施时，这里的最优规划解，可以为各个节臂的姿态Δθ。
油缸驱动量计算单元34用于根据最优规划解，确定各个节臂的油缸驱动量。
各个节臂的长度与姿态(角度)具有特定的范式关系L＝S(θ)。现已经获取Δθ，则各个节臂的驱动量ΔL是可以通过计算获取的。
此外，需要说明的是，连杆机构的约束关系是有推导关系的，在此只提供范式，在具体实施时，具体的形式与数值只需几何与推导与计算，对于本领域的技术人员来说，是习知的，在此不再进行过多的说明。
驱动电流计算单元35用于根据油缸驱动量及执行机构的比例特性，确定驱动电流，驱动电流用于驱动多关节臂架及转塔的回转。
本实施例关节机械臂智能控制方法工作原理如下：基于输出至控制器的机械臂末端速度和各节臂的当前姿态进行臂架的轨迹预测。基于预测结果和约束，确定最优规划解，进而，确定各个节臂的驱动量ΔL；最后，根据驱动量ΔL与电流i比例特性，精确确定驱动臂架所需的驱动电流，进而实现多关节机械臂的高精度控制。
并且，由于在进行轨迹预测的时候，若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果。因此，这种基于运动趋势的预测，在小振幅运动情况下可以确保臂架的运动状态不发生突变，即，可以很好的确保臂架的平稳运动。
此外，对于本实施例而言，操控者只需要通过速度输入装置输入机械臂末端的速度，该速度为操控者对臂架末端的控制意愿，即可通过完成上述各个步骤而实现对臂架动作的控制，因此，简单易行，极大地降低了操控的复杂度。
进一步地，上述最优规划解确定单元33中，运动约束为液压系统流量Q极值约束，最优规划解为系统流量代价最小的规划解。液压系统流量Q是泵车这种工程机械最重要的约束参数，液压是驱动机构，这个约束条件 不成立，结构是很难按照控制的思路执行的。每一节臂架有一片阀供油，其供油的流量是有极的，不是无限的，因此，系统流量约束必须在可实现的范围内，约束的来源只取决于设备本身的液压系统配置，配置多大的阀，约束就定了。
采用基于代价最小约束条件下的运动规划，在同等条件下末端点最快。因为各个节臂所对应的油缸的大小不同，末端点所对应的油缸最小。因此，移动相同距离，臂架末端所需油量最小，换个角度说，在同等条件下末端点最快。
参照图4，图4为本发明多关节机械臂智能控制装置优选实施例的结构框图。
相对于上一实施例，多关节机械臂智能控制装置还包括反馈单元36，用于检测被驱动后的多关节臂架的状态参数，并将多关节臂架的状态参数反馈至轨迹预测单元32，轨迹预测单元进一步根据输入的机械臂末端的速度矢量和多关节臂架的状态参数共同确定预测结果。
进一步优选地，反馈单元36还包括信号滤波及形变补偿子单元，用于对获取的多关节臂架的状态参数进行信号滤波以及形变补偿。
可以看出，本实施例由于增加了步骤S26及其子步骤S261，即，采用闭环恒流驱动，控制精度得到进一步的更高。而信号滤波和形变补偿也更加提高了控制的精度。
综上，本发明多关节机械臂智能控制方法和装置具有如下优点：
第一、基于运动趋势预测，在小幅振动情况下确保不反复；
第二、基于系统流量代价最小约束条件下的运动规划，在同等条件下末端点最快；
第三、基于连续性约束条件下的运动规划，各自由度的运动量无突变；这里的连续性约束的含义是，可以表达为一种逻辑记忆的识别，前一次，本次，下一次的运动状态要尽可能一致；
第四、采用闭环恒流驱动，控制精度更高。
第三方面，本发明还公开了一种多关节机械臂智能控制系统实施例，包括上述的多关节机械臂智能控制装置，以及与所述多关节机械臂智能控制装置相配合的操控器，所述遥控器用于输入所述机械臂末端的速度矢量。 上述多关节机械臂智能控制系统中，优选操控器为万向手柄。操控输入装置，由一个带万向手柄的遥控器组成，遥控器手柄的操作方向与机械臂末端移动方向一致。
参照图5，可以看出，本多关节机械臂智能控制系统实施例包括万向手柄操控器、控制装置，作为被控对象的执行装置。该执行装置包括比例阀组、驱动油缸和多关节臂架。并且，为了实现闭环控制，进一步提高该多关节臂架还与控制单元之间还连接有传感器。
本实施例多关节机械臂智能控制系统针对多关节超长臂架操控复杂的问题，采用了一键式自动操控的思路(即采用万向手柄输入下一时刻要达到的多关节机械臂末端的速度)，这样，操控者只需要开启智能控制功能按钮，摇动万向手柄即可完成任意想实现的动作。也就是说，该万向手柄操控器直接反映操控者对臂架末端的控制意愿。
同时，该系统含有一个高速恒流的控制装置，负责智能操控的轨迹规划及最优计算，精确驱动执行机构。该控制装置内部的逻辑模块如图3所示，为臂架算法规划软件，用于实现控制算法的计算、形变补偿、轨迹预测、最优控制计算等。
实施时，由操控者根据操做意愿通过万向手柄操控器传递给控制装置，控制装置由于提供了一整套完成的控制方法，可以实现精确驱执行机构，进行使执行机构实现预定动作。
由于多关节机械臂智能控制系统包括上述的多关节机械臂智能控制装置，因此，多关节机械臂智能控制系统也具有多关节机械臂智能控制装置的技术效果。由于多关节机械臂智能控制装置的结构和效果已经做了说明，因此，本发明在此不再赘述。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种多关节机械臂智能控制方法，其特征在于，包括如下步骤：
    步骤S11，接收由操控器输入的机械臂末端的速度矢量；
    步骤S12，基于所述机械臂末端的速度矢量和当前机械臂的状态，对多关节机械臂的各个节臂进行轨迹预测；若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果；
    步骤S13，基于多关节机械臂的运动约束和轨迹预测的结果，确定最优规划解；
    步骤S14，根据所述最优规划解，确定各个节臂的油缸驱动量；
    步骤S15，根据所述油缸驱动量及执行机构的比例特性，确定驱动电流，所述驱动电流用于驱动多关节臂架及转塔的回转。
  2. 根据权利要求1所述的多关节机械臂智能控制方法，其特征在于，
    所述步骤S13中，所述运动约束为液压系统流量极值约束，所述最优规划解为系统流量代价最小的规划解。
  3. 根据权利要2所述的多关节机械臂智能控制方法，其特征在于，在步骤S15后，还包括：
    步骤S16，检测被驱动后的多关节臂架的状态参数，并将所述多关节臂架的状态参数反馈至步骤S12，所述轨迹预测的结果根据输入的机械臂末端的速度矢量和多关节臂架的状态参数共同确定。
  4. 根据权利要3所述的多关节机械臂智能控制方法，其特征在于，
    所述步骤S16还包括如下子步骤：
    对获取的所述多关节臂架的状态参数进行信号滤波以及形变补偿，并将获取的结果反馈至所述步骤S12。
  5. 一种多关节机械臂智能控制装置，其特征在于，包括：
    速度矢量接收单元，用于接收由操控器输入的机械臂末端的速度矢量；
    轨迹预测单元，用于基于所述机械臂末端的速度矢量和当前机械臂的状态，对多关节机械臂的各个节臂进行轨迹预测；若预测所得的下一时刻的运动轨迹相对于当前时刻发生突变，则放弃该轨迹预测的结果；
    最优规划解确定单元，用于基于多关节机械臂的运动约束和轨迹预测 的结果，确定最优规划解；
    油缸驱动量计算单元，用于根据所述最优规划解，确定各个节臂的油缸驱动量；以及
    驱动电流计算单元，用于根据所述油缸驱动量及执行机构的比例特性，确定驱动电流，所述驱动电流用于驱动多关节臂架及转塔的回转。
  6. 根据权利要求5所述的多关节机械臂智能控制装置，其特征在于，
    所述最优规划解确定单元中，所述运动约束为液压系统流量极值约束，所述最优规划解为系统流量代价最小的规划解。
  7. 根据权利要求6所述的多关节机械臂智能控制装置，其特征在于，还包括，
    反馈单元，用于检测被驱动后的多关节臂架的状态参数，并将所述多关节臂架的状态参数反馈至所述轨迹预测单元，所述轨迹预测单元进一步根据输入的机械臂末端的速度矢量和所述多关节臂架的状态参数共同确定预测结果。
  8. 根据权利要求7所述的多关节机械臂智能控制装置，其特征在于，
    所述反馈单元还包括信号滤波及形变补偿子单元，用于对获取的所述多关节臂架的状态参数进行信号滤波以及形变补偿。
  9. 一种多关节机械臂智能控制系统，其特征在于，包括：
    如权利要求5至8中任一项所述的多关节机械臂智能控制装置，以及，与所述多关节机械臂智能控制装置相配合的操控器，所述操控器用于输入所述机械臂末端的速度矢量。
  10. 根据权利要求9所述的多关节机械臂智能控制系统，其特征在于，
    所述操控器为万向手柄。
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