Global path planning method for mobile robots based on the particle swarm algorithm

A global path planning method for mobile robots based on the guaranteed convergence particle swarm optimization algorithm is presented.A solution is provided for mobile robots to find the shortest path avoiding obstacles in a limited period of time.Firstly,an environmental map is set up and a path connecting the start point and the end point is coded as a particle.Then,a particular active region for particles is mapped out according to the location of obstacles.The initial particle population is generated within this region and particles fly in the active region to search for the optimum path.In the search process,both the acceleration coefficient and inertia weight of the particle swarm optimization algorithm are self-adaptively adjusted along with iteration processes.It is proved that the algorithm can plan out a simple and safe optimum path connecting the start point and the end point by simulation experiments.Comparative studies with a recently reported method show that the proposed algorithm has advantages such as faster search speed and higher search quality.