The Application And Research Of Particle Swarm Optimization In Mobile Robot Path Planning

Particle swarm optimization (PSO) is a kind of colony intelligent algorithm which emulates the behavior of bird colony. PSO transfers the process of seeking the best solution for specific problems to the process which particles scout the best position in a certain area. PSO is one of the intelligent algorithms which developed most quickly and has been used widely in the fields of circuits design, system recognition, pattern recognition, signal processing and control decision. Path planning is an important research topic of mobile robotics which aims to seek the best path from the starting position to expected destination in a certain physical space.This dissertation did some research of path planning under static known environment. After building the simulation environment of mobile robot by grids, this dissertation realize the path planning of mobile robot by using PSO and stochastic particle swarm optimization (SPSO) respectively and compared the result of two algorithms. Furthermore, this dissertation introduced a new target function which involved smoothness of paths for path planning, implemented simulation with this target function and compared its result with the one without path smoothness. Finally, this dissertation gave out the result of path planning experiment conducted by MT-R mobile robot which certificate that the path planning result of PSO and new target function achieved good performance in real task of mobile robot.