Path Planning and Optimization of Humanoid Manipulator in Cartesian Space

doi:10.1007/s12204-022-2416-7

Abstract

Abstract: To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction, an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed. The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree, and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency. Then the generated path is reversely optimized to shorten the length of the planned path, and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion. Finally, the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2, a large humanoid service robot.

Key words: humanoid manipulator, path planning, rapidly-exploring random tree, greedy growth, reverse optimization, pose search

CLC Number:

TP242

LI Shiqi (李世其), LI Xiao∗ (李肖), HAN Ke (韩可), XIONG Youjun (熊友军), XIE Zheng (谢铮), CHEN Jinliang (陈金亮). Path Planning and Optimization of Humanoid Manipulator in Cartesian Space[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 614-620.

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