A Novel Obstacle-Avoidance Solution With Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators
Weibing Li, Zilian Yi, Yanying Zou, Haimei Wu, Yang Yang, Yongping Pan
Abstract
Obstacle avoidance (OA) and joint-limit avoidance (JLA) are essential for redundant manipulators to ensure safe and reliable robotic operations. One solution to OA and JLA is to incorporate the involved constraints into a quadratic programming (QP), by solving which OA and JLA can be achieved. There exist a few non-iterative solvers such as zeroing neural networks (ZNNs), which can solve each sampled QP problem using only one iteration, yet no solution is suitable for OA and JLA due to the absence of some derivative information. To tackle these issues, this paper proposes a novel solution with a non-iterative neural controller termed NCP-ZNN for joint-constrained redundant manipulators. Unlike iterative methods, the neural controller involving derivative information proposed in this paper possesses some positive features including non-iterative computing and convergence with time. In this paper, the reestablished OA-JLA scheme is first introduced. Then, the design details of the neural controller are presented. After that, some comparative simulations based on a PA10 robot and an experiment based on a Franka Emika Panda robot are conducted, demonstrating that the proposed neural controller is more competent in OA and JLA.
BibTeX
@inproceedings{iros2023_anovelobstacleav,
title = {A Novel Obstacle-Avoidance Solution With Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators},
author = {Weibing Li and Zilian Yi and Yanying Zou and Haimei Wu and Yang Yang and Yongping Pan},
booktitle = {IROS 2023},
year = {2023}
}