Efficient path planning for a dexterous arm–hand in complex environments

Abstract

Path planning represents a critical research direction for dexterous arm–hand (DAH) systems. However, path planning for high-degree-of-freedom manipulators presents the following challenges: (1) time-consuming collision detection, and (2) an expanded search space due to high-dimensional configurations, particularly in dynamic environments. In this paper, a new path planning strategy based on rapidly-exploring random tree (RRT) path is proposed for the DAH. Firstly, an adaptive step-size RRT (ADA-RRT*) algorithm is proposed to avoid the tunneling problem caused by discrete collision detection. Secondly, to improve the efficiency of the algorithm in high-dimensional spaces, a hierarchical planning framework is first introduced, consisting of coarse planning and fine planning. Coarse planning quickly finds a rough path with large steps without considering the tunneling problem, which then guides the fine planning. Then, the beetle antennae optimization algorithm and multi-objective optimization algorithm are used to optimize the global path, reducing path length and improving path safety. Finally, the execution of corresponding simulations and experiments demonstrates the effectiveness and efficiency of the proposed method.