关键词: 自动牵引车, 全局路径规划, 改进粒子群优化, 人工神经网络, 作用函数

Abstract: Hyperbolic tangent function is used instead of sigmoid funtion as the action function for the artificial neural networks，which used to descript environment.This makes the ANN（Artificial Neural Networks） ideal for optimization arithmetic.PSO（Particle Swarm Optimization） has the advantage of fast convergence speed and few parameters to adjust，but premature convergence often occurs during optimization.SA（Simulated Annealing），intercross operator and aberrance operator are combined to improve PSO’s performance，a new IPSO（Improved Particle Swarm Optimization） is formed to solve AGV（Automated Guided Vehicles）’s global path planning problem.Simulation results demonstrate IPSO’s ability of seeking the global result is strong，convergence speed is fast than PSO.IPSO can plan a ideal path for AGV.

Key words: Automated Guided Vehicles（AGV）, global path planning, Improved Particle Swarm Optimization（PSO）, Artificial Neural Networks（ANN）, action function