Path Planning Of Mobile Robots Based On Pso Algorithm

Path planning is an important field of mobile robot research, and as a basic part of mobile robot navigation, it aims to search for a non-collision path from the starting point to the target point according to an optimal or sub-optimal performance target. Intelligent computing has aroused extensive attention as a way of solving this problem. Particle swarm optimization (PSO) is a new swarm intelligent optimization algorithm, inspired by the swarm behavior of bird flock and fish school. Its peculiarities are simplicity in principle, less parameters, fast convergence and less professional knowledge needed. To apply the PSO algorithm to solve the path planning of mobile robot is the main task of this study. They are as follows:(1) It introduces the definitions and classifications about path planning of the mobile robot in detail, discusses the signification, development and, research areas of mobile robots. It also indicates basic principles of PSO algorithm, improved algorithms and its application.(2) The MALINK graph was used to describe the obstacle extreme points in the working space of the mobile robot. By means of Dijkstra algorithm a feasible non-collision suboptimal path can be obtained from a starting point to the target point.(3) Apply the original PSO algorithm to optimize the feasible non-collision suboptimal path so as to obtain the global optimal path. After reviewing the simulation results, we can find the original PSO algorithm is subjected to low success rate or fall in a local minimum on the condition of small number of particles, small number of iterations.(4) In view of the problems above-mentioned, the improved PSO algorithm based on combination of inertia weight and location limitation was proposed. Simulation results show that the improved algorithm can obtain high success rate and good optimized results on the condition of lesser particles and lesser iteration circles. This indicates that the improved algorithm is efficacious and valid.(5) Due to the random variables in PSO algorithm, it has to reduce the number of particles and iteration number if limiting the computation time in practice. Then it might reduce the success rate of the algorithm. For this problem, an algorithm to check the optimized result was proposed and this algorithm might conduct a run again if the optimal result does not meet the given conditions. The simulation results show that the algorithm is valid.