Real-Time Obstacle Avoidance Method based on Polar Coordination Particle Swarm Optimization in Dynamic Environment

Abstract

Based on the polar coordination particle swarm optimization (PPSO), this paper presents a novel method for the robot path planning in dynamic environment. It decomposes the task into a global planning stage and a local planning stage. PPSO algorithm can search for the global optimal path based on static obstacles information. When the robot moves along the optimal global path, an on-line real-time path planning strategy is adopted to avoid dynamic obstacles by means of predicting the future positions of moving obstacles. Simulation experiment shows that the method is more efficient than traditional particle swarm optimization (TPSO) and genetic algorithm (GA) for solving path planning problem. The feasibility and high stability of real-time obstacle avoidance strategy are demonstrated in dynamic environment.