WO2020118730A1 - Procédé et appareil de commande de conformité pour robot, dispositif et support d'informations - Google Patents

Procédé et appareil de commande de conformité pour robot, dispositif et support d'informations Download PDF

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Publication number
WO2020118730A1
WO2020118730A1 PCT/CN2018/121338 CN2018121338W WO2020118730A1 WO 2020118730 A1 WO2020118730 A1 WO 2020118730A1 CN 2018121338 W CN2018121338 W CN 2018121338W WO 2020118730 A1 WO2020118730 A1 WO 2020118730A1
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WIPO (PCT)
Prior art keywords
data
teaching
motion
variable impedance
movement
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PCT/CN2018/121338
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English (en)
Chinese (zh)
Inventor
欧勇盛
段江哗
徐升
王志扬
金少堃
田超然
王煜睿
熊荣
江国来
吴新宇
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中国科学院深圳先进技术研究院
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Priority to PCT/CN2018/121338 priority Critical patent/WO2020118730A1/fr
Publication of WO2020118730A1 publication Critical patent/WO2020118730A1/fr

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    • 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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/42—Recording and playback systems, i.e. in which the programme is recorded from a cycle of operations, e.g. the cycle of operations being manually controlled, after which this record is played back on the same machine
    • G05B19/423—Teaching successive positions by walk-through, i.e. the tool head or end effector being grasped and guided directly, with or without servo-assistance, to follow a 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
    • G05B2219/39—Robotics, robotics to robotics hand
    • G05B2219/39319—Force control, force as reference, active compliance

Definitions

  • the invention belongs to the field of computer technology, and particularly relates to a robot compliance control method, device, equipment and storage medium.
  • the trajectory of the robotic arm is generally pre-defined by the user, or a certain task environment is preset, and then the robot or the robotic arm can be repeatedly executed according to the plan.
  • the robotic arm operating in this mode cannot face environmental changes or sudden disturbances.
  • this mode also requires more arduous manual programming.
  • the use threshold is high (for example: to be able to program robots). More importantly, this robot control mode does not imply human operation habits, nor is it as flexible as human hands.
  • the robotic arm or robot should have learning capabilities and be more flexible and compliant.
  • the robot "Imitation Learning” (Imitation Learning) or “Teaching Learning” (Programming by Demonstration) is an important method to solve this problem.
  • the compliant behavior of a robot includes two aspects of action and force, so the learning of compliant behavior also includes two aspects of action learning and force learning.
  • the existing robot compliance control method independently models and learns the motion trajectory and force, and the learning effect is not good, which leads to inaccurate control results; based on Gaussian mixture model, Gaussian process and other offline regression methods to For imitation learning, the training time required is relatively long, and the training efficiency is low; the stability of the control cannot be guaranteed, and there may be situations where the robot interaction force is too large and hurts people.
  • the object of the present invention is to provide a robot compliance control method, device, equipment and storage medium, aiming to solve the problems of inaccurate control results and poor control effects caused by poor compliance of the existing robot compliance control methods.
  • the present invention provides a robot compliance control method, which includes the following steps:
  • the teaching data includes at least the movement data and interaction force data of the teaching movement
  • variable impedance Calculating the motion equation of the teaching motion based on the motion data in the teaching data, and simultaneously calculating the variable impedance parameter of the teaching motion based on the interaction force data in the teaching data, wherein the variable impedance
  • the parameters include at least variable stiffness parameters and variable damping parameters
  • the operation is controlled according to the equation of motion and the variable impedance parameter.
  • the present invention provides a robot compliance control device, the device including:
  • a data acquisition unit for acquiring teaching data of the teaching movement, wherein the teaching data includes at least the movement data and the interaction force data of the teaching movement;
  • a parameter calculation unit configured to calculate the motion equation of the teaching motion based on the motion data in the teaching data, and at the same time calculate the variable impedance parameter of the teaching motion based on the interaction force data in the teaching data,
  • the variable impedance parameter includes at least a variable stiffness parameter and a variable damping parameter;
  • the operation control unit is used for controlling operation according to the motion equation and the variable impedance parameter.
  • the present invention also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, which is implemented when the processor executes the computer program The steps of the robot compliance control method as described.
  • the present invention also provides a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, implements the steps of the robot compliance control method.
  • the invention obtains the teaching data of the teaching movement, calculates the motion equation of the teaching movement based on the movement data in the teaching data, and simultaneously calculates the teaching according to the interaction force data in the teaching data
  • the variable impedance parameter of the movement controls the operation according to the motion equation and the variable impedance parameter, thereby reducing manual programming during the robot compliance control process, lowering the threshold for robot use, and improving the flexibility and accuracy of robot control , And further improve the robot's generalization ability, intelligence and control effect.
  • Embodiment 1 is an implementation flowchart of a robot compliance control method provided in Embodiment 1 of the present invention
  • FIG. 2 is a schematic structural diagram of a robot compliance control device according to Embodiment 2 of the present invention.
  • FIG. 3 is a schematic structural diagram of a robot compliance control device according to Embodiment 3 of the present invention.
  • FIG. 4 is a schematic structural diagram of a computing device according to Embodiment 4 of the present invention.
  • FIG. 5 is a schematic structural diagram of a robot compliance control device according to Embodiment 2 of the present invention.
  • FIG. 6 is a schematic structural diagram of a computing device according to Embodiment 3 of the present invention.
  • FIG. 1 shows the implementation flow of the robot compliance control method provided in Embodiment 1 of the present invention. For convenience of description, only the parts related to the embodiment of the present invention are shown. The details are as follows:
  • step S101 the teaching data of the teaching movement is acquired.
  • the embodiments of the present invention are suitable for automatic control of robots.
  • Robots include a series of robot products that are not limited to robotic arms, humanoid robots, etc. with joints, links, and other structures, and can achieve telescopic and grasping actions.
  • the teaching data may include at least motion data and interaction force data of the teaching movement. Therefore, learning of the teaching movement may include action learning and force learning (ie, variable stiffness parameter and variable damping parameter learning).
  • the motion data may include position data and speed data of a preset point of the robot (eg, end effector), or the motion data may include angle and angle of a preset angle of the robot (eg, joint angle) Acceleration.
  • the motion data may also include one or more other parameters that can be used to completely describe the teaching motion, which is not limited by the present invention.
  • FIG. 2 shows a diagram of teaching a robot.
  • the teacher grasps the end effector of the robot with one hand and moves in a plane or space. Make a trajectory, and the other hand exerts a teaching force at the end.
  • the robot collects teaching data through its own motion capture system and a six-dimensional force sensor mounted on the wrist.
  • the motion data includes position data and velocity data of a preset sampling point of the robot (eg, end effector or end, etc.)
  • the position data and interaction force data of the end are sampled at time intervals to obtain a series of samples
  • the teacher controls the robot through the remote control or the teach pendant to perform the teaching operation, or teaches by hand.
  • the robot records the teaching data according to the teaching operation.
  • the instructor personally completes the teaching movement task.
  • the teaching data is collected by the robot's motion catcher, data glove, and force sensor according to the teaching movement.
  • the motion data includes position data and speed data of a preset point of the robot (for example, an end effector)
  • the position data related to the teaching motion may be obtained first, Interaction force data and time data, and then calculate the speed data related to the teaching movement based on the position data and the time data, thereby obtaining the movement data of the teaching movement.
  • the motion data includes the angle and angular acceleration of the preset angle of the robot (for example, the joint angle)
  • the angle data and interaction force related to the teaching motion may be obtained first Data and time data, and then calculate the angular acceleration data related to the teaching movement based on the angle data and the time data, thereby obtaining the movement data of the teaching movement.
  • step S102 the motion equation of the teaching motion is calculated based on the motion data in the teaching data, and at the same time, the variable impedance parameter of the teaching motion is calculated based on the interaction force data in the teaching data.
  • variable impedance parameter may include at least a variable stiffness parameter and a variable damping parameter.
  • trajectory and force are learned, so as to improve the learning effect and thus the accuracy of the control results.
  • the preset neural network model can be trained using the motion data to obtain the motion equation of the teaching motion, and the neural network can be trained according to the motion equation
  • the model is updated online, thereby improving the calculation efficiency of the motion equation, facilitating the subsequent use of the motion equation, and adapting to the needs of real-time online learning, thereby improving the learning effect.
  • the motion data when using the motion data to train the preset neural network model, can be incrementally learned one by one or block by block to obtain the motion equation of the teaching motion, thereby improving the accuracy of the motion equation Sex, thereby improving learning effectiveness.
  • the neural network model can be a support vector machine (Support Vector Machine, SVM), online sequence over-limit learning machine and other models that can be incrementally online learning, or other incremental online learning models, such as incremental support vector machine (ISVM), etc., the present invention does not limit this.
  • SVM Support Vector Machine
  • the online sequence over-limit learning machine has the characteristics of fast learning speed, strong generalization ability, and simple implementation. Therefore, preferably, the neural network model is an online sequence over-limit learning machine, that is, use The motion data trains the online sequence overrun learning machine, thereby improving the training efficiency.
  • the input and output are the position and speed (or the angle and angular acceleration of the joint angle) of the sampling point (for example, the robot end effector), so the online
  • FIG. 3 shows an exemplary structure of an online sequence overrun learning machine.
  • the activation function of the hidden layer of the online sequence overrun learning machine is g
  • the online sequence overrun learning we want to learn
  • the machine ie, the model to be learned
  • the number of hidden layer neurons is For the offset of the hidden layer, Is the weight of the hidden layer, the dimension is Is the weight of the output layer, the dimension is
  • W and b are randomly generated and fixed.
  • the training process only needs to determine the weight of the output layer. Optimization process to achieve.
  • the activation function g generally selects the sigmoid function (sigmoid function) or the hyperbolic tangent function (tanh function), the modified sigmoid function can also be used, for example, However, as long as it is satisfied And the monotonically increasing continuous and continuously differentiable functions all meet the requirements of the activation function, and are not limited here.
  • the training goal of the online sequence overrun learning machine is to find a set of optimal output layer weights
  • H + is the Moore-Penrose generalized inverse matrix of the matrix H.
  • the output layer weights can be obtained without iteration.
  • the constraints are added, the problem of solving the output layer weights becomes a constrained optimization problem.
  • the training process of the online sequence overrun learning machine includes an initial ELM batch learning process and a continuous sequential learning process, as follows:
  • N 1 is the newly arrived data, by The calculated initial output weight is among them, Whenever a new training sample is obtained When Recursively calculate output weights. among them,
  • variable stiffness parameter and the variable damping parameter may be calculated based on the interaction force data.
  • variable stiffness parameter when calculating the variable stiffness parameter, let Represents the collected interaction force (F) and corresponding time (q) information, where is the number of disturbance data samples obtained.
  • the variable stiffness parameter at time q is calculated from the force information in the time window [q-(w-1), q].
  • the length of the sliding time window is w, and the upper and lower bounds of the data points in the window are represented by L q and U q ,
  • the stiffness matrix K q is among them, And eigenvalues Proportional to the expression
  • the interactive force data will be continuously collected, and the new data will be sorted according to the time information and the values in the window will be taken to solve the stiffness. For example, when the data at time q+1 enters, the online update of the covariance is among them,
  • variable damping parameter B since the damping ratio is constant, the square root of the damping and the stiffness is linear, so it can be based on the formula To calculate the variable damping parameter B. Among them, ⁇ is a constant greater than 0.
  • the preset stability constraints and the interaction force data can be used to predict
  • the variable impedance model is trained to obtain the variable impedance parameters of the teaching movement, and the variable impedance model is updated according to the variable impedance parameters, so as to ensure the stability of the variable impedance control and avoid excessive robot interaction force that may cause injury. happening.
  • step S103 the operation is controlled according to the equation of motion and the variable impedance parameter.
  • the operation can be controlled according to the motion equation and the variable impedance parameter, thereby controlling the robot to reproduce the movement trajectory and interactive force of the teaching movement.
  • the trained neural network model for example, online sequence overrun learning machine
  • variable impedance models to control the trajectory and interaction force of the robot to reproduce the teaching movement.
  • FIG. 4 shows an exemplary diagram of teaching learning and reproduction of robot compliance control.
  • the instructor grasps the robot with one hand for teaching, and the robot collects Track information And force information F q , then according to the trajectory information Perform motion learning to obtain f( ⁇ ), and learn variable stiffness parameters and variable damping parameters according to the force information F q to get ⁇ B q ,K q ⁇ , and finally generate motion according to f( ⁇ ) and according to ⁇ B q ,K q ⁇ Variable impedance control to control the trajectory and interaction force of the robot to reproduce the teaching movement.
  • the motion equation of the teaching movement is calculated according to the movement data in the teaching data, and at the same time, the variation of the teaching movement is calculated according to the interaction force data in the teaching data Impedance parameters, according to the motion equation and variable impedance parameter control operation, thereby reducing the manual programming in the robot compliance control process, lowering the threshold for robot use, improving the flexibility and accuracy of robot control, and thus improving the robot's universal Ability, degree of intelligence and control effect.
  • FIG. 5 shows the structure of the robot compliance control device provided in Embodiment 2 of the present invention. For ease of explanation, only parts related to the embodiment of the present invention are shown, including: a data acquisition unit 51, a parameter calculation unit 52 and Operation control unit 53.
  • the data acquiring unit 51 is configured to acquire teaching data of the teaching movement, wherein the teaching data includes at least the movement data and the interaction force data of the teaching movement.
  • the learning of the teaching movement may include action learning and force learning (ie, variable stiffness parameter and variable damping parameter learning).
  • the motion data may include position data and speed data of a preset point of the robot (eg, end effector), or the motion data may include angle and angle of a preset angle of the robot (eg, joint angle) Acceleration.
  • the motion data may also include one or more other parameters that can be used to completely describe the teaching motion, which is not limited by the present invention.
  • the data acquisition unit 51 may include:
  • the first acquiring unit is used to acquire position data, interaction force data and time data related to the teaching movement
  • the first calculation unit is used to calculate the motion data according to the position data and the time data.
  • the speed data related to the teaching movement is calculated based on the position data and the time data, thereby obtaining the movement data of the teaching movement.
  • the data acquisition unit 51 may further include:
  • the second acquisition unit is used to acquire angle data, interaction force data and time data related to the teaching movement.
  • the second calculation unit is used to calculate the motion data according to the angle data and the time data.
  • the angular acceleration data related to the teaching movement may be calculated according to the angle data and the time data, thereby obtaining the movement data of the teaching movement.
  • the parameter calculation unit 52 is configured to calculate the motion equation of the teaching motion based on the motion data in the teaching data, and at the same time calculate the variable impedance parameter of the teaching motion based on the interaction force data in the teaching data, where the variable impedance parameter includes at least Variable stiffness parameters and variable damping parameters.
  • the trajectory and the force are simultaneously learned, thereby improving the learning effect, and thereby improving the accuracy of the control result.
  • the parameter calculation unit 52 may include:
  • the first training unit is used to train the preset neural network model using motion data to obtain the motion equation of the teaching movement, and update the neural network model online according to the motion equation, thereby improving the calculation efficiency of the motion equation and facilitating movement
  • the model training unit may include:
  • the incremental learning unit is used to incrementally learn the motion data in a one-by-one or block-by-block manner to obtain the motion equation of the teaching motion, thereby improving the accuracy of the motion equation and thereby improving the learning effect.
  • the neural network model is an online sequence overrun learning machine.
  • the parameter calculation unit 52 may include:
  • the second training unit is used to train the preset variable impedance model according to the preset stability constraints and interaction force data to obtain the variable impedance parameter of the teaching movement, and update the variable impedance model according to the variable impedance parameter, Therefore, the stability of the variable impedance control is ensured, and the situation that the interaction force of the robot is too large to cause injury is avoided.
  • the operation control unit 53 is used to control the operation according to the equation of motion and the variable impedance parameter.
  • the teaching data of the teaching movement is acquired by the data acquiring unit 51, the motion equation of the teaching movement is calculated according to the movement data in the teaching data by the parameter calculating unit 52, and at the same time according to the teaching data
  • the interactive force data calculates the variable impedance parameters of the teaching movement, and the operation is controlled by the operation control unit 53 according to the motion equation and the variable impedance parameters, thereby reducing the manual programming in the robot compliance control process, lowering the robot's use threshold, and improving the robot The flexibility and accuracy of the control, thereby improving the robot's generalization ability, intelligence and control effect.
  • each unit of the robot compliance control device may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into one software and hardware unit, which is not limited here. this invention.
  • FIG. 6 shows the structure of the computing device provided in Embodiment 4 of the present invention. For ease of description, only parts related to the embodiment of the present invention are shown.
  • the computing device 6 of the embodiment of the present invention includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60.
  • the processor 60 executes the computer program 62
  • the steps in the above embodiments of the robot compliance control method are implemented, for example, steps S101 to S103 shown in FIG. 1.
  • the processor 60 executes the computer program 62
  • the functions of the units in the above device embodiments are realized, for example, the functions of the units 51 to 53 shown in FIG.
  • the teaching data of the teaching motion is acquired, and the teaching data is calculated according to the motion data in the teaching data Teaching the motion equation of motion, and at the same time calculating the variable impedance parameters of the teaching motion based on the interactive force data in the teaching data, and controlling the operation according to the motion equations and variable impedance parameters, thereby reducing the manual programming and reducing the robot's compliance control process.
  • the use threshold of the robot is improved, and the flexibility and accuracy of the robot control are improved, thereby improving the robot's generalization ability, intelligence, and control effect.
  • a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the embodiments of the foregoing robot compliance control methods are implemented. For example, steps S101 to S103 shown in FIG. 1.
  • the functions of the units in the foregoing device embodiments are realized, for example, the functions of the units 51 to 53 shown in FIG. 5.
  • the teaching data of the teaching motion is acquired, the motion equation of the teaching motion is calculated according to the motion data in the teaching data, and the variable impedance of the teaching motion is calculated according to the interaction force data in the teaching data
  • the parameters control the operation according to the equations of motion and variable impedance parameters, thereby reducing manual programming during robot compliance control, lowering the threshold for robot use, improving the flexibility and accuracy of robot control, and thus improving the generalization of the robot Ability, degree of intelligence and control effect.
  • the robot compliance control method implemented when the computer program is executed by the processor may further refer to the description of the steps in the foregoing method embodiments, and details are not described herein again.
  • the computer-readable storage medium in the embodiments of the present invention may include any entity or device capable of carrying computer program code, and a recording medium, such as ROM/RAM, magnetic disk, optical disk, flash memory, and other memories.

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  • Robotics (AREA)
  • Physics & Mathematics (AREA)
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Abstract

La présente invention concerne un procédé de commande de conformité pour un robot. Le procédé consiste : à acquérir des données de démonstration d'un mouvement de démonstration ; à calculer une équation de mouvement du mouvement de démonstration en fonction de données de mouvement dans les données de démonstration et à calculer des paramètres d'impédance variable du mouvement de démonstration en fonction de données de force d'interaction dans les données de démonstration dans le même temps ; et à commander le fonctionnement en fonction de l'équation de mouvement et des paramètres d'impédance variable, de sorte que la programmation manuelle pendant la commande de conformité de robot est omise, la difficulté d'utilisation de robots est réduite et la conformité et la précision de la commande de robot sont améliorées, ce qui permet d'améliorer la capacité de généralisation, l'intelligence et l'effet de commande de robots. La présente invention concerne également un appareil de commande de conformité pour un robot, un dispositif et un support d'informations.
PCT/CN2018/121338 2018-12-14 2018-12-14 Procédé et appareil de commande de conformité pour robot, dispositif et support d'informations WO2020118730A1 (fr)

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CN114366552A (zh) * 2021-12-23 2022-04-19 威海经济技术开发区天智创新技术研究院 一种上肢康复训练外骨骼控制方法及系统
CN114571444A (zh) * 2022-03-01 2022-06-03 中南大学 一种基于Q-learning的扒渣机器人阻抗控制方法
CN114800489A (zh) * 2022-03-22 2022-07-29 华南理工大学 基于确定学习与复合学习联合的机械臂柔顺控制方法、存储介质及机器人
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CN114366552A (zh) * 2021-12-23 2022-04-19 威海经济技术开发区天智创新技术研究院 一种上肢康复训练外骨骼控制方法及系统
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WO2023124346A1 (fr) * 2021-12-28 2023-07-06 广东省科学院智能制造研究所 Procédé et système d'apprentissage de compétence motrice à rigidité variable et de régulation pour robot collaboratif
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CN114800489B (zh) * 2022-03-22 2023-06-20 华南理工大学 基于确定学习与复合学习联合的机械臂柔顺控制方法、存储介质及机器人
CN114848391A (zh) * 2022-04-28 2022-08-05 北京邮电大学 一种下肢康复机器人柔顺控制方法
CN114848391B (zh) * 2022-04-28 2023-08-29 北京邮电大学 一种下肢康复机器人柔顺控制方法
CN115421387A (zh) * 2022-09-22 2022-12-02 中国科学院自动化研究所 一种基于逆强化学习的可变阻抗控制系统及控制方法

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