Robot Path Planning in Uncertain Environments Based on Particle Swarm Optimization

We propose a robot path planning method based on particle swarm optimization in an uncertain environment. We consider the case that a robot's cognition to its environment is not complete, i.e., the information of these obstacles in the environment is uncertain. We firstly construct a global environment model based on the uncertain information of these obstacles, and then give a globally optimal path by using particle swarm optimization. Finally, we present a local optimal strategy to handle the uncertain information detected by the robot in real-time. Our preliminary simulation results show that the proposed method is feasible and efficient.