A Dual-Robot Cooperative Arc Welding Path Planning Algorithm based on Multi-Objective Optimization

Abstract

In this paper a novel improved multi-objective genetic algorithm (R-NSGA-Ⅱ) is proposed to solve the problem of dual-robot arc welding path planning. We strive to find a low-cost, fast and more efficient solution for large complex components. Firstly, a multi-objective optimization model of path planning is established by considering various variables and constraints in the actual welding process. Then, the R-NSGA-Ⅱ introduces a recombination mechanism to replace the mutation operation of NSGA-Ⅱ; this can increase the diversity of individuals and populations, and find the global optimal solution with greater probability. Finally, in order to verify feasibility and effectiveness of the proposed algorithm, it is used to plan some typical welding seams of a large complex component, the NSGA-Ⅱ and OMOPSO are used for comparison. The simulation demonstrates that R-NSGA-Ⅱ can obtain better Pareto front than the other two algorithms. Particularly, the R-NSGA-Ⅱ reduces the waiting time by 42.39% and the no-load distance by 10.50% compared with NSGA-Ⅱ.