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Robot admittance compliance control method and system

A compliant control and robot technology, applied in the field of control, can solve the problems of poor compliant operation of robots and achieve the effect of improving compliant

Active Publication Date: 2019-12-20
SHANGHAI ELECTRICGROUP CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The technical problem to be solved by the present invention is to overcome the defect of poor compliance of robot operation in human-computer interaction environment in clinical operation in the prior art, and to provide a method and system for robot admittance compliance control based on neural network strategy

Method used

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  • Robot admittance compliance control method and system
  • Robot admittance compliance control method and system

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Effect test

Embodiment 1

[0081] This embodiment provides a robot admittance compliance control method, such as figure 1 As shown, the robot admittance compliance control method includes:

[0082] Step 11, detecting the operating force applied to the robot, and detecting the moving speed and acceleration of the robot.

[0083] Step 12. Input the operating force, motion speed and acceleration into a trained neural network model, and the neural network model outputs admittance parameters, which include virtual damping and virtual mass.

[0084] The robot in this embodiment can be used for human-machine collaboration such as assisted medical treatment and artistic sculpture, and is especially suitable for assisted medical treatment. The embodiment of the present invention does not limit its specific application field. In addition, the robot should be understood in a broad sense, for example, the robot may be a mechanical arm capable of man-machine interaction and intelligent control.

[0085] The operat...

Embodiment 2

[0097] This embodiment provides a robot admittance compliance control method. Compared with Embodiment 1, this embodiment differs in that, as figure 2 As shown, before the step of inputting the operating force, motion speed and acceleration into a trained neural network model, it also includes:

[0098] Step 100, establishing a neural network model.

[0099] Step 101, train the neural network model by genetic algorithm.

[0100] In this embodiment, the neural network model structure adopts an adaptive system, and the adopted structure is a fully connected multi-layer feed-forward network with a single hidden layer. The fully connected multi-layer feedforward network includes an input layer, a hidden layer and an output layer. The hidden layer includes a plurality of neurons, and the output of the hidden layer is the input of the output layer. The number of layers and neurons per layer is a trade-off between simplicity, performance, and training time. Since the input layer ...

Embodiment 3

[0137] This embodiment provides a robot admittance compliance control system, such as Figure 6 As shown, the robot admittance compliance control system includes a detection module 31 and an admittance module 32;

[0138] The detection module 31 is used to detect the operating force applied to the robot, and detect the moving speed and acceleration of the robot;

[0139] The admittance module 32 is used to input the operating force, motion speed and acceleration into a trained neural network model, and the neural network model outputs admittance parameters, which include virtual damping and virtual mass.

[0140] The robot in this embodiment can be used in scenarios such as assisted medical treatment and art sculpture, and is especially suitable for assisted medical treatment. The embodiment of the present invention does not limit its specific application field. In addition, the robot should be understood in a broad sense, for example, the robot may be a mechanical arm capabl...

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Abstract

The invention discloses a robot admittance compliance control method and system. The robot admittance compliance control method comprises the following steps: detecting an operating force applied to arobot, and detecting movement velocity and acceleration of the robot; and inputting the operating force, the movement velocity and the acceleration into a trained neural network model, and outputtingadmittance parameters by the neural network model, wherein the admittance parameters comprise virtual damping and virtual mass. A compliance control algorithm realized by virtue of the neural networkmodel in the invention can change admittance parameters of a robot system in real time according to stiffness changes, namely different forces externally applied to the robot, of a human-computer interaction environment.

Description

technical field [0001] The invention relates to the field of control, in particular to a method and system for controlling the admittance compliance of a robot. Background technique [0002] Currently commercial human-computer interaction robots mainly adopt two methods of impedance control and admittance control. [0003] Impedance control refers to the control method of input displacement and output force. It has strong robustness to the uncertainty of model parameters, can limit the force of interaction, and can ensure very good performance and stability in a very rigid environment. However, impedance control cannot provide enough rigid behavior and compensate for friction, resulting in poor accuracy in flexible environments and free motion. Therefore, impedance control is not applicable in the occasion of human-computer interaction. [0004] Admittance control is to study the relationship between input force and output speed. It is more suitable for the scene of interac...

Claims

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Application Information

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IPC IPC(8): G05B13/04B25J9/16
CPCG05B13/048B25J9/161
Inventor 周朝政潘昕荻凌宇飞李丹
Owner SHANGHAI ELECTRICGROUP CORP
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