Time-Optimal Path Planning for Dual-Welding Robots Based on Intelligent Optimization Strategy

Abstract

Dual-welding robots are widely used with the industry development, and dual-welding robots usually have to deal with a large number of weld joints. In this condition, traditional manual teaching method is time-consuming and inefficient. In this paper, an intelligent optimization strategy is proposed to realize time-optimal path planning for dual-welding robots. First, the welding robot path optimization problem is presented. Then, good diversity and convergence velocity of discrete group competition particle swarm optimization (GC-PSO) algorithm are tested. Compared with particle swarm optimization (PSO), genetic particle swarm optimization (GPSO) and chaos particle swarm optimization (CPSO) algorithms, GC-PSO algorithm shows its better optimization effectiveness. In addition, a method of collision detection and obstacle avoidance is given. At last, an intelligent optimization strategy is applied to time-optimal path planning for dual-welding robots, and the global optimal result can be obtained quickly. Simulation results show that the intelligent path planning strategy is effective and can be used for welding robot path optimization.