Research Of Mobile Robot Path Planning Based On Fusion Algorithm Of PSO And ACO

In recent years. the development of mobile robot attracted more and more attentions of the researchers. With the promotion of the robot technology, the robots are required to adapt to the various environments, thus, path planning of the intelligence robot became more and more important. The task of path planning is to find an independent path, which is under some criterions and complex conditions. Ant Colony Algorithm (ACO) and Particle Swarm Optimization (PSO) belong to the evolutionary intelligence algorithms. They are introduced to the path planning research after Fuzzy Control, Neural Net. and Genetic Algorithm etc. PSO searches the best solution of a problem by remembering and following the excellent particle and updating own position and speed continuously. It has great advantage in solving complex optimization problems, especially the discrete issue.This paper discussed ACO and PSO in theory, analyzed their defects and advantages, and then proposed a new method by combining two methods. The new method was applied to the global path planning. Firstly, PSO was employed to get some suboptimal paths by its strong global search capability; Secondly, the suboptimal paths were converted into the initial pheromone distribution; Then, the best path was found by the positive feedback search mechanism of ACO; Finally, the fusion algorithm was verified by MATLAB. The simulation results demonstrated the effectiveness and superiority of the proposed algorithm compared with the ACO and PSO.