WO2021238049A1 - Procédé, appareil et dispositif de commande pour compensation de gravité auto-adaptative multi-charge d'un manipulateur - Google Patents

Procédé, appareil et dispositif de commande pour compensation de gravité auto-adaptative multi-charge d'un manipulateur Download PDF

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Publication number
WO2021238049A1
WO2021238049A1 PCT/CN2020/124400 CN2020124400W WO2021238049A1 WO 2021238049 A1 WO2021238049 A1 WO 2021238049A1 CN 2020124400 W CN2020124400 W CN 2020124400W WO 2021238049 A1 WO2021238049 A1 WO 2021238049A1
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Prior art keywords
tool
robotic arm
mass
gravity compensation
gravity
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PCT/CN2020/124400
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English (en)
Chinese (zh)
Inventor
甘博涵
许靖
乔天
文理为
杜思傲
董旭亮
荣健
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杭州键嘉机器人有限公司
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Priority to JP2022569139A priority Critical patent/JP7437081B2/ja
Priority to KR1020227042228A priority patent/KR20230003233A/ko
Publication of WO2021238049A1 publication Critical patent/WO2021238049A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/1633—Programme controls characterised by the control loop compliant, force, torque control, e.g. combined with position control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/1605—Simulation of manipulator lay-out, design, modelling of manipulator
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/1653—Programme controls characterised by the control loop parameters identification, estimation, stiffness, accuracy, error analysis

Definitions

  • This application relates to the technical field of robots, and specifically to a method, device and control equipment for gravity compensation of a mechanical arm.
  • robotic arms no longer only serve the production line, but also slowly enter various areas of life.
  • the traditional industrial robot arm needs to set a safety range, and personnel are strictly prohibited from entering its work area during operation to prevent injuries.
  • most of the application scenarios in life will have a lot of inconveniences when setting the safety range, and the efficiency is not high when man-machine cooperative operation is performed.
  • people have designed a collaborative robotic arm.
  • the collaborative robot arm has the ability to sense contact force and can react to the physical contact between the human body and the robot arm, thus allowing the operator and the robot arm to share the working space.
  • the emergence of collaborative robotic arms has greatly expanded the applications of robotic arms in home care, education and entertainment, health care, high-end manufacturing and other industries. The features of high efficiency, high precision, and high stability of robotic arms are used to improve all aspects of life.
  • Zero-force control technology means that during the process of dragging and teaching, the mechanical arm can move well in accordance with the external force, as if it is not affected by the gravity of the mechanical arm. This technology reduces the labor intensity of dragging and teaching, and increases the fluency of people in controlling the robotic arm. In order to enable the robotic arm to achieve zero-force control even when the end tool is clamped, it is necessary to calibrate the parameters of the robotic arm body and tools separately, and use reverse engineering methods to accurately calculate the masses and tools of each segment of the robotic arm. Centroid.
  • the gravity compensation scheme of a robotic arm generally requires the use of a weighing instrument to measure the mass of the end tool, and then use the suspension method or the support method to measure the center of mass of the tool. Then import the measured data into the control system of the robotic arm, and let the control system perform gravity compensation according to the parameters of the tool, so that the robotic arm can achieve zero-force control.
  • the tool is separated from the system. The measured parameters are easy to ignore the influence of the installation process on the quality and center of mass.
  • Gravity compensation can only be performed on the parameters of one tool at a time, and the tool must be stopped when switching tools. When the program requires multiple tools to switch frequently, the efficiency is low.
  • One of the objectives of the present application includes providing a multi-load adaptive gravity compensation method, device, control device, and readable storage medium of a mechanical arm, reducing operation steps, and improving efficiency and performance of gravity compensation.
  • an embodiment of the present application provides a multi-load adaptive gravity compensation method for a robotic arm, which includes the following steps:
  • step S1.1 the standard D-H method is used to construct the robotic arm joint coordinate system.
  • step S1.3 the robotic arm runs to any non-singular position in the workspace under no load, and the joint position and torque readings are sampled.
  • step S1.4 each tool is installed at the end of the robotic arm in stages, and step S1.3 is repeated to perform static position sampling.
  • step S1.4 for each tool, after the tool is installed at the end of the robotic arm, the current effective working space of the robotic arm corresponding to the tool is determined according to the size of the tool, and then Step S1.3 is repeated to perform static position sampling of the tool based on the effective working space.
  • step S1.5 the sampling data obtained in S1.3 and S1.4 are grouped according to tools, and then substituted into the gravity term in step S1.2 in turn.
  • step S1.6 for each tool, the parameter value calibrated when the tool is carried is compared with the parameter value calibrated without load, and based on the comparison result, the quality of the tool, the The relationship between the center of mass of the tool, the mass of the end arm in the robotic arm and the center of mass of the end arm is calculated, and the mass and center of mass of the tool are calculated.
  • an embodiment of the present application provides a multi-load adaptive gravity compensation device for a mechanical arm, the device including:
  • the model building module is configured to build a kinematic model of the robotic arm
  • the gravity reconstruction module is configured to reconstruct the gravity term of the dynamic model
  • Position sampling module configured for no-load static position sampling
  • the position sampling module is also configured to perform static position sampling after installing each tool
  • the parameter calibration module is configured to calculate the parameter values to be calibrated for each tool
  • the mass parameter calculation module is configured to calculate the mass and centroid of each tool separately;
  • the external force calculation module is configured to calculate the force exerted by the currently installed tool on the flange
  • the gravity compensation module is configured to compensate the gravity of the tool.
  • the model building module uses the standard D-H method to construct the robotic arm joint coordinate system during the process of building the kinematic model of the robotic arm.
  • the position sampling module controls the robot arm to run to any non-singular position in the working space when the robot arm is unloaded, and samples the joint position and torque readings.
  • the position sampling module installs each tool on the end of the robotic arm in stages, for each tool, according to the size of the tool, determine the current effective working space of the robotic arm corresponding to the tool, and then Based on the effective working space, the robot arm is repeatedly controlled to run to any non-singular position in the corresponding effective working space, and the joint position and torque readings are sampled.
  • the parameter calibration module groups the sampling data obtained by the position sampling module according to tools, and sequentially substitutes them into the gravity terms obtained by the gravity reconstruction module for calculation to obtain each The parameter value of the tool to be calibrated.
  • the quality parameter calculation module performs the calibration of the parameter value when the tool is carried with the parameter value of the no-load calibration for each tool. Compare and calculate the quality and center of mass of the tool based on the comparison result and the relationship between the quality of the tool, the center of mass of the tool, the mass of the end arm in the robotic arm, and the center of mass of the end arm.
  • an embodiment of the present application provides a control device for a robotic arm, including a memory and a processor, the memory stores a computer program that can run on the processor, and the processor executes the computer During the program, the aforementioned multi-load adaptive gravity compensation method of the robotic arm is realized.
  • an embodiment of the present application provides a readable storage medium on which a computer program is stored.
  • the computer program When the computer program is run by a processor, it executes the aforementioned multi-load adaptive gravity compensation method for a robotic arm.
  • One of the beneficial effects of the embodiments of the present application includes: constructing a robot arm joint coordinate system through the D-H method, and then constructing the center of mass position of each segment of the robot arm based on the joint coordinate system.
  • split the terms related to the joint position and the terms related to the mass center of mass.
  • the parameters to be calibrated need to be appropriately combined, and then the split terms are put into two matrices. Multiplying it still satisfies the original gravity term. Then sample the static position of the robotic arm in the no-load state, then install each tool on the end of the robotic arm, and then sample the static position separately.
  • the different tools of the sampled data are grouped and substituted into the gravity term, and the combined parameter values can be solved by SVD decomposition. Finally, the combined object parameter separation method can be used to extract the quality and centroid of the tool from the combined parameters. Based on the calibrated parameters and real-time joint position feedback under no-load conditions, the force applied by the currently installed end tool to the flange can be calculated. According to the measured external force on the flange, the system can know which tool is currently installed on the flange, so that the calibrated parameter value can be used to directly measure the external force after compensating the tool gravity, or apply the obtained mass and center of mass Into the configuration of the robotic arm.
  • This method simplifies the operation steps in actual application by pre-calculating and acquiring tool parameters, and greatly enhances the fluency of collaborative operation.
  • the calibrated tool parameters are more in line with the kinematics and dynamics of the robotic arm, thereby improving the performance of zero-force control.
  • Fig. 1 is a flowchart of a multi-load adaptive gravity compensation method for a mechanical arm provided by an embodiment of the present application.
  • FIG. 2 is a schematic diagram of the tool and the end of the robot arm of a multi-load adaptive gravity compensation method for a robot arm provided by an embodiment of the present application.
  • FIG. 3 is a schematic diagram of a multi-tool installation of a robotic arm in a multi-load adaptive gravity compensation method for a robotic arm provided by an embodiment of the present application;
  • FIG. 4 is a schematic diagram of the composition of a multi-load adaptive gravity compensation device of a mechanical arm provided by an embodiment of the present application.
  • the robotic arm is a seven-axis collaborative robotic arm, and each joint is equipped with high-precision position and torque sensors, which meets the configuration requirements for the robotic arm hardware in this application.
  • KUKA LBR Med 7 R800 seven-axis collaborative robot arm as an example to explain the actual operation.
  • the joint coordinate system of the robotic arm adopts the classic DH method (A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices, J. Denavit). , RS Hartenberg).
  • the D-H parameter table is shown below.
  • ⁇ i link angle
  • a i the length of the link
  • d i link offset
  • ⁇ i denotes an angle joint.
  • the gravitational term is equal to the joint torque of the manipulator, and the formula is expressed as:
  • the gravitational term is related to the joint angle ⁇ i , the mass mi and the center of mass c i .
  • the mass mi and the center of mass c i are directly related to the calibration of the tool and need to be extracted. Therefore, the gravity term G must be split as follows:
  • each tool is installed in sequence to the end of the robotic arm, and step S1.3 is repeated to perform a round of motion sampling for each tool, and the data set after the singular position is eliminated is saved.
  • step S1.3 is repeated to perform a round of motion sampling for each tool, and the data set after the singular position is eliminated is saved.
  • the current effective working space of the robotic arm corresponding to the tool is determined according to the size of the tool. Then repeat the step S1.3 based on the effective working space to sample the static position of the tool.
  • step S1.5 all collected data needs to be grouped according to tools, and each group of data is stacked into the equation set obtained in S1.2, as shown below:
  • ⁇ is a diagonal matrix, and all diagonal elements are matrices The singular value of ⁇ i and ⁇ 1 ⁇ 2 ... ⁇ n >0, so X can be found.
  • the process of performing step S1.5 requires the tool as a rigid body to be calibrated after being installed on the end of the robotic arm. Therefore, the mass and center of mass of the rigid body in the last segment of the calibrated parameters are actually the last segment of the robotic arm and the tool. Combine the following parameters. As a result, the quality and center of mass of the tool can be compared with the parameters calibrated with the tool and the parameters calibrated without load, and combined to characterize the relationship between the quality and center of mass of the tool and the mass and center of mass of the end arm of the robotic arm The centroid formula of the multi-body system of the relationship can be determined.
  • the parameter value calibrated when the tool is carried is compared with the parameter value calibrated without load, and based on the comparison result and the quality of the tool, The relationship between the center of mass of the tool, the mass of the end arm in the robotic arm and the center of mass of the end arm is calculated to obtain the mass and center of mass of the tool.
  • centroid formula of the multi-body system corresponding to the robotic arm system after clamping the tool has the following physical properties:
  • c c is the center of mass of the tool and the end arm
  • m t and c t are the mass and center of mass of the tool, respectively
  • m 7 and c 7 are the mass and center of mass of the end arm, respectively.
  • this application designs a system for automatically selecting tool parameters for gravity compensation.
  • the system uses the joint torque and the output of the position sensor as the input of the system, and calculates the force applied by the current tool at the end of the robotic arm inside the system to determine the type of tool clamped, and then applies the parameters calculated in S1 to complete gravity compensation.
  • the detailed implementation steps are explained below.
  • the parameters calibrated in the no-load state can be used to calculate the joint torque ⁇ robot at the current position caused by the manipulator body.
  • the real-time measured joint torque ⁇ measure is subtracted from ⁇ robot , and the joint torque ⁇ ext caused by the external force is obtained.
  • the Jacobian matrix again, the external force can be mapped from the joint space to the working space, and the external force on the end of the robotic arm (flange) in the working space can be calculated.
  • the difference between the tools will be reflected in the value of the external force.
  • the value in the XYZ direction of the external force can be used as the basis for distinguishing the tools; if the quality difference of the tools is small and the difference in the center of mass is large, the torque in the direction of the external force ABC can be considered as the distinction.
  • the torque in the direction of the external force ABC can be considered as the distinction.
  • the mass and center of mass of the tool calculated in step S1.6 can be directly written into the configuration of the robotic arm, and the built-in program of the robotic arm can calculate the external force applied on the tool ;
  • the parameters calibrated in step S1.5 can be directly used to calculate the external force received by the current tool. Therefore, the external force felt by the robotic arm is the external force after compensating for the gravity of the tool, and the control strategy that uses the external force as an input will also ignore the influence of the tool, that is, achieve zero-force control.
  • the present application also provides a multi-load adaptive gravity compensation device 100 of a mechanical arm, so as to realize the functions of the above-mentioned mechanical arm through various functional realization modules included in the multi-load adaptive gravity compensation device 100.
  • the multi-load adaptive gravity compensation method is executed.
  • the multi-load adaptive gravity compensation device 100 includes a model building module 110, a gravity reconstruction module 120, a position sampling module 130, a parameter calibration module 140, a mass parameter calculation module 150, an external force calculation module 160, and a gravity compensation module 170.
  • the model building module 110 is configured to build a kinematic model of the robotic arm.
  • the gravity reconstruction module 120 is configured to reconstruct the gravity term of the dynamic model.
  • the position sampling module 130 is configured to perform no-load static position sampling.
  • the position sampling module 130 is also configured to perform static position sampling after each tool is installed.
  • the parameter calibration module 140 is configured to respectively calculate the parameter values to be calibrated for each tool.
  • the mass parameter calculation module 150 is configured to calculate the mass and center of mass of each tool respectively.
  • the external force calculation module 160 is configured to calculate the force exerted on the flange by the currently installed tool.
  • the gravity compensation module 170 is configured to compensate the gravity of the tool.
  • the model building module 110 uses the standard D-H method to construct the robotic arm joint coordinate system during the process of building the kinematic model of the robotic arm.
  • the position sampling module 130 controls the robot arm to run to any non-singular position in the working space when the robot arm is unloaded, and samples joint positions and torque readings.
  • the position sampling module 130 installs each tool on the end of the robotic arm in stages, for each tool, it determines the current corresponding to the robotic arm according to the size of the tool. Effective working space, and then based on the effective working space, repeatedly control the manipulator to run to any non-singular position in the corresponding effective working space, and sample the joint position and torque readings.
  • the parameter calibration module 140 groups the sampling data obtained by the position sampling module according to tools, and sequentially substitutes them into the gravity term obtained by the gravity reconstruction module. Perform calculations to obtain the parameter values to be calibrated for each tool.
  • the present application also provides a control device of a mechanical arm, the control device including a memory and a processor.
  • the memory may include one or more computer program products, and the computer program product may include various forms of readable storage media, such as volatile memory and/or nonvolatile memory.
  • the volatile memory may include random access memory and/or cache memory, for example.
  • the non-volatile memory may include, for example, a read-only memory, a hard disk, a flash memory, and the like.
  • One or more computer programs can be stored on the readable storage medium, and the processor can run the computer programs to realize the functions represented by the above-mentioned multi-load adaptive gravity compensation method of the robotic arm and/or other desired Function.
  • Various application programs and various data such as various data used and/or generated by the application program, can also be stored in the readable storage medium.
  • the processor may be implemented in the form of at least one of a digital signal processor, a field programmable gate array, and a programmable logic array.
  • the processor may be a central processing unit or have data processing capabilities and/or instruction execution One or a combination of several processing units in other forms of capabilities, and can control other components in the control device to perform desired functions.
  • the processor can correspondingly execute the computer program stored in the memory to realize the function represented by the computer program.
  • the above-mentioned multi-load adaptive gravity compensation device 100 of the robotic arm can be stored in the memory of the control device in the form of software or firmware, and the processor of the control device The software function modules and computer programs included in the multi-load adaptive gravity compensation device 100 in the memory are executed to realize the functions corresponding to the above-mentioned multi-load adaptive gravity compensation method of the robotic arm.
  • the above scheme uses the D-H method to construct the robotic arm joint coordinate system, and then builds the center of mass position of each segment of the robotic arm based on the joint coordinate system.
  • split the terms related to the joint position and the terms related to the mass center of mass.
  • the parameters to be calibrated need to be appropriately combined, and then the split terms are put into two matrices. Multiplying it still satisfies the original gravity term.
  • sample the static position of the robotic arm in the no-load state then install each tool on the end of the robotic arm, and then sample the static position separately.
  • the different tools of the sampled data are grouped and substituted into the gravity term, and the combined parameter values can be solved by SVD decomposition.
  • the combined object parameter separation method can be used to extract the quality and centroid of the tool from the combined parameters.
  • the force applied by the currently installed end tool to the flange can be calculated.
  • the system can know which tool is currently installed on the flange, so that the calibrated parameter value can be used to directly measure the external force after compensating the tool gravity, or apply the obtained mass and center of mass Into the configuration of the robotic arm.
  • This method simplifies the operation steps in actual application by pre-calculating and acquiring tool parameters, and greatly enhances the fluency of collaborative operation.
  • the calibrated tool parameters are more in line with the kinematics and dynamics of the robotic arm, thereby improving the performance of zero-force control.
  • the embodiment of the application provides a multi-load adaptive gravity compensation method, device, control device, and readable storage medium for a robotic arm.
  • the robotic arm joint coordinate system is constructed by the DH method, and the coordinate system of each segment of the robotic arm is determined based on the joint coordinate system.
  • the position of the center of mass is used to establish the system.
  • split the terms related to the joint position and the terms related to the mass center of mass.
  • the parameters to be calibrated need to be appropriately combined, and then the split terms are put into two matrices. Multiplying it still satisfies the original gravity term. Then sample the static position of the robotic arm in the no-load state, then install each tool on the end of the robotic arm, and then sample the static position separately.
  • the different tools of the sampled data are grouped and substituted into the gravity term, and the combined parameter values can be solved by SVD decomposition. Finally, the combined object parameter separation method can be used to extract the quality and centroid of the tool from the combined parameters. Based on the calibrated parameters and real-time joint position feedback under no-load conditions, the force applied by the currently installed end tool to the flange can be calculated. According to the measured external force on the flange, the system can know which tool is currently installed on the flange, so that the calibrated parameter value can be used to directly measure the external force after compensating the tool gravity, or apply the obtained mass and center of mass Into the configuration of the robotic arm.
  • This method simplifies the operation steps in actual application by pre-calculating and acquiring tool parameters, and greatly enhances the fluency of collaborative operation.
  • the calibrated tool parameters are more in line with the kinematics and dynamics of the robotic arm, thereby improving the performance of zero-force control.

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  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Manipulator (AREA)

Abstract

L'invention concerne un procédé pour la compensation de gravité auto-adaptative multi-charge d'un manipulateur, le procédé comprenant les étapes suivantes : S1.1, construire un modèle cinématique pour un manipulateur ; S1.2, reconstruire un élément de gravité d'un modèle de dynamique ; S1.3, échantillonner dans une position statique sans charge ; S1.4, échantillonner dans une position statique après que des outils sont installés ; S1.5, calculer respectivement la valeur d'un paramètre à calibrer de chaque outil ; S1.6, calculer respectivement la masse et le centroïde de chaque outil ; S2.1, calculer une force qui est appliquée à une bride par l'outil qui est actuellement installé ; et S2.2, compenser la gravité de l'outil. Au moyen du procédé, des étapes de fonctionnement sont réduites, et l'efficacité et la performance de compensation de gravité sont améliorées.
PCT/CN2020/124400 2020-05-28 2020-10-28 Procédé, appareil et dispositif de commande pour compensation de gravité auto-adaptative multi-charge d'un manipulateur WO2021238049A1 (fr)

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JP2022569139A JP7437081B2 (ja) 2020-05-28 2020-10-28 ロボットアームの多負荷適応重力補償法、装置及び制御デバイス
KR1020227042228A KR20230003233A (ko) 2020-05-28 2020-10-28 매니퓰레이터의 다중 하중 적응 중력 보상 방법, 장치 및 제어 장치

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