Enhancing Safety and Manipulability of Redundant Manipulators: Accelerated Motion Generation in Dynamic Environments

Abstract

Motion generation in dynamic environments is crucial for human-machine interaction with redundant manipulators. In this context, we propose the Enhancing Safety and Manipulability (ESM) scheme, which integrates geometry-based dynamic obstacle avoidance, manipulability optimization,trajectory tracking, and joint limit avoidance into a unified scheme operating at the joint-angle level. The introduction of a flexible collision library enables the scheme to locate critical points based on geometry, while the incorporated obstacle speed allows the scheme to effectively avoid dynamic obstacles. In the ESM, manipulability is naturally set as the non-convex goal. To solve the ESM, the Accelerated Multi-agent recurrent Neural Network (AMNN) is proposed, which uses a meta-heuristic approach to construct activation functions, endowing the neural network with non-convex control capabilities. Subsequently, a GPU-based parallel computing method is implemented, significantly reducing computing time. Detailed simulations, experiments, and comparisons demonstrate the framework's effectiveness and superiority.