Obstacle Avoidance Strategy of Intelligent Vehicle Path Planning Based on Particle Swarm Optimization

Abstract

As one of the most important parts of path planning, the merits of obstacle avoidance strategy directly affect the result of path planning. A kind of obstacle avoidance strategy of intelligent vehicle global path planning based on particle swarm optimization is proposed to optimize path planning algorithm and improve path planning result, which provides a solution for intelligent vehicle to avoid obstacles safely and timely. The proposed obstacle avoidance strategy include obstacle filling in non-goal area, collision detection based on slope contrast, marking of collision type, obstacle avoidance processing. Finally, intelligent vehicle obstacle avoidance path planning in complex environment is accomplished. Simulation results show that proposed path planning algorithm can seek a safe and collision-free path. By contrasting with the simulation result of traditional particle swarm optimization, a conclusion is drew that proposed strategy can effectively increase planning success rate, planned path is shorter, and planning time is lesser.