Redundant robot repeated motion planning method adopting rapid double-power final state neural network

Abstract

The invention discloses a redundant robot repeated motion planning method adopting a rapid double-power final state neural network. The method comprises the following steps that a desired trajectory r-d(t) of a robot end effector is given in cartesian space, an expected return angle theta -d(0) of each joint is given; for the repeated motion of a robot, an asymptotic convergence performance indexis adopted, a problem is solved by converting an quadratic optimization problem of redundant robot trajectory planning into a time-varying matrix equation, and the rapid double-power final state neural network is used as a solver. Under the condition of initial position offset, a repeated motion planning task for rapid finite-time convergence of the redundant robot is achieved. According to the provided redundant robot motion planning method adopting the rapid double-power final state neural network, rapid finite-time convergence is achieved, calculation precision is high, and implementation is easy.

Description

technical field [0001] The present invention relates to a repetitive motion planning technology for industrial robots. Specifically, it proposes a repetitive motion planning method for redundant robots using a fast double-power final state neural network with rapid convergence in a limited time and when the initial position deviates from the expected trajectory. . Background technique [0002] A redundant robot refers to a class of robots that have more active joints than the minimum number of degrees of freedom required to perform the target task. Redundant robots can avoid defects such as low flexibility, singularity, and inability to avoid obstacles of non-redundant robots, and can complete variable tasks in complex working environments. [0003] The inverse kinematics solution of redundant robots is the basis of motion planning and trajectory control of redundant robots. There are infinitely many inverse kinematics solutions due to the extra degrees of freedom, so ther...

AI Technical Summary

Problems solved by technology

[0025] In order to overcome the shortcomings of existing redundant robot repetitive motion planning methods that cannot converge in a limited time and have low calculation accuracy, the present invention provides a fast double-power finalization method that has fast convergence in a limited time, high calculation accuracy, and is easy to implement. Redundant robot motion planning method based on dynamic neural network

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