Velocity‐layer Zhang equivalency for time‐varying joint limits avoidance of redundant robot manipulator

Abstract

In the engineering field, a complex system or problem can be transformed into a relatively simple system or problem through equivalency. In this paper, the repetitive motion planning scheme of redundant robot manipulators with time‐varying physical limits are to be optimized via Zhang equivalency (ZE) approach, which includes Zhang equality equivalency (ZEE) approach and Zhang inequality equivalency (ZIE) approach. The ZE approach is presented, theoretically proved, and applied to optimize the control scheme of redundant robot manipulators at the velocity layer. After that, an enhanced repetitive motion planning scheme via ZE (ERMPS‐ZE) on the basis of quadratic programming is finally built up, which is subject to a ZEE constraint and a time‐varying unified ZIE constraint for the solution of RMP in redundant robot manipulator systems. The simulation results on UR10 robot manipulator substantiate superiorities of the proposed scheme with regard to practicality, validity, and completeness over the traditional scheme. In addition, the results of simulation experiments under different limit situations show the adjusting manners and characteristics of the redundant robot manipulator adopting ERMPS‐ZE when the manipulator encounters physical limits, which validate the correctness and efficacy of the ERMPS‐ZE. Finally, the physical experiment also substantiates the practicability of the ERMPS‐ZE.

Advances on intelligent algorithms for scientific computing: an overview

“…Due to these redundant degrees of freedom, the robotic arm can be more flexibly adapted to different tasks and environments, as well as avoid obstacles or enhance motion performance by adjusting its posture. As a potent tool for real-time parallel processing, neural network models can be used for precise and flexible control of redundant robot manipulators (Xiao and Zhang, 2014;Zhang et al, 2014Zhang et al, , 2018cLiao and Liu, 2015;Jin et al, 2017b;Guo et al, 2018;Tan et al, 2019b;Xiao et al, 2019g;Li et al, 2020dTang et al, 2022;Zhou et al, 2022). More specifically, neural networks can be used in two ways.…”

“…This method has been validated on a 4-degree-of-freedom robot manipulator and is found to perform well in solving the redundancy problem of robotic manipulators. Tang et al used an enhanced planning scheme for redundant robot manipulator control (Tang et al, 2022), and a tuning strategy based on this scheme is found to achieve good results in the limit case. Zhang et al propose a differential scheme with varying parameters for the joint-angle drift (J-AD) problem of redundant robot manipulators (Zhang et al, 2018c).…”