Research Of Path Planning For Mobile Robot Based On Particle Swarm Optimization And Artificial Potential Field

With the innovation of science and technology and the rapid development of robot industry,mobile robot technology has become a hotspot in current scientific research.While the intelligent level of mobile robot has been improved constantly,its application environment is becoming more and more complex.So there is a higher requirements for path planning technology.Therefore,the research of path planning is of theoretical and practical significance to the development of mobile robot technology.In this dissertation,the mobile robot path planning technology based on particle swarm optimization algorithm and artificial potential field method has been studied.The main contents are as follows:(1)A self-adaptive particle swarm optimization algorithm(ASPSO)is proposed for global path planning.Aiming at the traditional PSO is easy to fall into local optimum and the convergence rate is slow,ASPSO is proposed.The algorithm dynamically adjusts the inertia weight to balance the local and global search ability through the fitness value of particle.The Gaussian mutation strategy is adopted to individual optimal position to increase the diversity of the population.The Caucasian mutation is adopted to global optimal position,and the Cauchy mutation probability is dynamically adjusted according to aggregation degree of the current population,which makes the particle swarm search for the optimal solution space and improve convergence rate.The improved algorithm is applied to the global path planning,and the simulation experiments verify the fast optimization ability of the improved algorithm in path planning.(2)An improved artificial potential field method is proposed for local path planning.Aiming at the objective unreachability and the local minimum of traditional APF,an improved artificial potential field method is proposed.In this method,the relative distance between the mobile robot and target is introduced into traditional repulsive potential field function to solve the target unreachability problem.By adjusting the repulsive angle,the mobile robot escapes the local minimum.In order to prevent the dynamic obstacle from colliding with the robot,the relative velocity between them is introduced in repulsive potential field function.The improved method is simulated in different environments,and the results show that it has good environmental adaptability and dynamic obstacle avoidance performance.(3)A path planning method of the mobile robot based on PSO and APF method is proposed.This method makes full use of the fast path optimization of self-adaptive particle swarm optimization algorithm and the dynamic real-time obstacle avoidance performance of improved artificial potential field method,to realize collision-less real-time path planning and improve the quality of the path planning.The feasibility and superiority of this method are verified by simulation experiments.