Global path planning for mobile robot based on improved particle swarm optimization

In conventional path planning methods, the length of the path is the principal factor, so the path we get is the shortest but is not flexible and is complex in realization. In order to overcome the above defects, a new path planning approach based on artificial potential field (APF) and particle swarm optimization (PSO) is presented in the paper. The first step is to make a danger degree map (DDM) based on the repulsive force of obstacles in the environment. Then the PSO whose fitness function is the weighted sum of the path length and the path danger degree is introduced to get a global optimized path. The proposed algorithm has the following three advantages. First, the particles don't need to avoid obstacles during the initial and update processes as the proposed method can avoid danger areas with obstacles automatically. The final path is not only short comparatively but also safe enough. Second, the proportion of length and danger degree in the fitness function can be changed according to the adjustment of weighted factors, so all kinds of paths whose length and danger degree are different can be got flexibly. Last, the method has a simple model and a rapid convergence which can meet the safe and real-time demands of robot navigation. The feasibility and effectiveness are proved by the simulation results.