MULTI-OBJECTIVE TRAJECTORY PLANNING OF ROBOT MANIPULATOR IN A MOVING OBSTACLE ENVIRONMENT

Ying Huang, Minrui Fei, and Wenju Zhou

Keywords

Dynamic multi-objective optimization (DMO), DNSGA-II, clustering, robot manipulator, Pareto

Abstract

In a moving obstacle environment, the manipulator performs the multi-objective trajectory planning operation considering the consumed time, trajectory length, and joint jerk. Given the unsatisfactory results of the conventional dynamic non-dominated sorting algorithm (DNSGA-II), we propose a K-means clustering DNSGA-II (KMCDNSGA-II) in which the clustering mechanism is introduced for population improvement. In the optimization process, clustering analysis is conducted to investigate the diversity of each generation. In comparison with the traditional DNSGA-II, the improved KMCDNSGA-II and iKMCDNSGA-II enhance the global search capability and extend the Pareto front distribution in the optimization results. After obtaining the Pareto optimal solution set, we compare and analyze the optimization indexes. Finally, by comparing the conventional and improved algorithms in the simulation operation of the manipulator, the validity and feasibility of the improved algorithm are verified.

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