The Research Of Mobile Robot Path Planning Based On Hybrid Particle Swarm Optimization

Robot path planning is a key link in robot navigation technology and the key to realize intelligent robots. The hybrid particle swarm optimization method for mobile robot path planning is proposed based on the analysis of the present research situation and development trend of mobile robot. The advantages and disadvantages of the different mobile robot path planning algorithm are studied. The path planning of mobile robot needs to solve two major problems:(1) Environmental Modeling.The environmental model need to be established for path planning of mobile robot. The grid method is used to establish environmental model and multiple obstacles are irregular in this paper.(2) Path search.Which search algorithm is used to planning a path so that the mobile robot can find a path without a collision with obstacles. How the mobile robots move from start position to goal position in the environmental model.Two kinds of hybrid algorithm for mobile robot path planning is proposed in this paper.One is a hybrid algorithm based on preference particle swarm optimization(PSO) algorithm and improved artificial potential field method, and the other is hybrid algorithm based on genetic algorithm and particle swarm algorithm.In this paper, the main works are given as follows.Firstly, according to the fast convergence speed of particle swarm optimization(PSO)and the strong ability of the obstacle avoidance of the artificial potential field, we proposed a hybrid path planning method which combines the particle swarm optimization and artificial potential field. The preference information of the obstacle avoidance is introduced into the basic particle swarm optimization algorithm. The proposed method can plan the better path and ensure the convergence. At the same time, the global path planning is realized by using the improved potential field function. To verify the validity of the proposed algorithm, the basic particle swarm algorithm and mixed algorithm based on preference particle swarm and artificial potential field method are compared in the simple simulation environment and complex environment respectively,. The results show that theproposed hybrid algorithm can plan a better global path.Secondly, a hybrid path planning based on particle swarm and genetic algorithm method is proposed in view of particle swarm optimization algorithm is easy to fall into local optimum in this paper. The crossover operation and mutation operation of genetic algorithm are introduced into the particle swarm algorithm to achieve the goal of increasing species diversity.The hybrid particle swarm algorithm was compared with genetic algorithm. The simulation results show that the hybrid path planning algorithm of particle swarm and genetic algorithm is superior in convergence speed and length of path planning by using genetic algorithms.Finally, the development trends of path planning problem for mobile robot are discussed based on summarized the previous research work. At the same time, the future research direction is pointed out.