Redundant robot repetitive motion planning method with finite interval neural network adopted

Abstract

The invention relates to a redundant robot repetitive motion planning method based on a finite interval neural network. The expected trace rd(t) of a robot tail end actuator is provided in the Cartesian space, and the expected returning angle theta d(0) of each joint is provided as well. Asymptotic convergence property indexes are adopted for the repetitive motion of a robot, the quadratic optimization problem of trace panning of the redundant robot can be converted to solving a time-varying matrix equation, and the finite interval neural network can work as a solver. The method achieves finite-time convergent repetitive motion planning of the redundant robot under the condition of initial position deviation.

Description

technical field [0001] The invention relates to the motion planning technology of industrial robots, and in particular, proposes a redundant robot repetitive motion control method based on a finite interval neural network that can converge in a limited time and the initial position deviates from a desired trajectory. Background technique [0002] At present, industrial robots have been widely used in mechanical processing, medical treatment, food industry, industrial manufacturing, logistics, military and many other fields. They can perform heavy labor such as welding, spraying, and handling. Due to the complex working environment, it may collide with obstacles in the environment when moving. The redundant robot has good flexibility and fault tolerance. It can use the redundant degrees of freedom to enhance obstacle avoidance without affecting the operation of the end effector, and can complete variable tasks in a complex working environment. [0003] The number of joints t...

AI Technical Summary

Problems solved by technology

This non-repetitive problem may produce undesired joint configurations, resulting in unanticipated repeated operations of closed trajectories at the ends of redundant robots, and even unexpected and dangerous situations.

The most widely used pseudo-inverse control method cannot guarantee the repeatability of motion

In order to complete repetitive motion, self-motion is usually used to make up for it, and self-motion adjustment is often not efficient (see Klein C A and Huang C, Review of Pseudo Inverse Control for use with Kinematically Redundant Manipulators (based on pseudo-inverse control method Redundant Manipulators) Yu robot motion planning), IEEE Trans.Syst.Man.Cybern.1983,13(2):245-250; Tchon K, Janiak M.Repeatable approximation of the Jacobian pseudo-inverse (repeatable approximation of the Jacobian pseudo-inverse ), Systems and Control Letters, 2009, 58(12): 849-856)

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