Research Of Path Planning For Mobile Robots Based On Social Group Search Algorithm

Robot path planning is an important topic in robotics. In recent years, some swarm intelligence algorithms have been applied to robot path planning problem. Genetic algorithm and particle swarm optimization are classic swarm intelligence algorithms and they both have their own advantages and disadvantages. On the basis of the two algorithms, we propose a new algorithm:Social group search algorithm which learns from the social behaviors of the animal groups in the natural world. We expect the new algorithm will retain the advantages of the genetic algorithm and particle swarm optimization and abandon their disadvantages. The new algorithm has been applied to the path planning problem.This thesis is organized as follows:In the first chapter, related works about robot path planning of domestic and foreign research are introduced. The second chapter describes the principle of genetic algorithm and particle swarm optimization and the social behavior of the animal groups; In the third chapter, we propose the social group search algorithm; In the fourth chapter, the social group search algorithm is applied to the path planning problem; In the fifth chapter, a simulation system is given and the results are analyzed. Finally, the conclusions are drawn and future works are given in the sixth chapter.The results of simulation demonstrate the effectiveness of the social group search algorithm.