Multi-objective Particle Swarm Optimization for Robot Path Planning in Environment with Danger Sources

Aiming at robot path planning in an environment with danger sources, a global path planning approach based on multi-objective particle swarm optimization is presented in this paper. First, based on the environment map of a mobile robot described with a series of horizontal and vertical lines, an optimization model of the above problem including two indices, i.e. the length and the danger degree of a path, is established. Then, an improved multi-objective particle swarm optimization algorithm of solving the above model is developed. In this algorithm, a self-adaptive mutation operation based on the degree of a path blocked by obstacles is designed to improve the feasibility of a new path. To improve the performance of our algorithm in exploration, another archive is adopted to save infeasible solutions besides a feasible solutions archive, and the global leader of particles is selected from either the feasible solutions archive or the infeasible one. Moreover, a constrained Pareto domination based on the degree of a path blocked by obstacles is employed to update local leaders of a particle and the two archives. Finally, simulation results confirm the effectiveness of our algorithm.