Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm

This paper presents a path planning algorithm for car-like mobile robots operating on a known static rough terrain environment. The purpose of this approach is to find collision free and feasible paths with minimum length and terrain roughness. First, a new workspace modeling method is proposed to model the rough terrain environment. Then, considering the nonholonomic constraints of car-like robots, a MOPSO (multi-objective particle swarm optimization) based method is used to solve the problem. In the proposed algorithm, a new updating method for particle's global best position based on crowding radius is used to increase population diversity. And to improve the algorithm efficiency, a nonuniformity factor is adopted to update the particle's position when the path collides with obstacles. Finally, two simulation tests are designed using Microsoft Robotics Developer Studio 4 and Matlab. Results show the advantages of the proposed algorithm in finding Pareto optimal paths.