Path planning for mobile robot using self-adaptive learning particle swarm optimization  

作　　者：Guangsheng LI Wusheng CHOU 

机构地区：[1]School of Mechanical Engineering and Automation, Beihang University [2]State Key Laboratory of Virtual Reality Technology and Systems, Beihang University

出　　处：《Science China(Information Sciences)》2018年第5期263-280,共18页中国科学（信息科学）（英文版）

基　　金：supported by National Basic Research Program of China (973 Program) (Grant No. 2013CB035503)

摘　　要：As a challenging optimization problem, path planning for mobile robot refers to searching an optimal or near-optimal path under different types of constrains in complex environments. In this paper, a self-adaptive learning particle swarm optimization （SLPSO） with different learning strategies is proposed to address this problem. First, we transform the path planning problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives： path length, col- lision risk degree and smoothness. Then, a novel self-adaptive learning mechanism is developed to adaptively select the most suitable search strategies at different stages of the optimization process, which can improve the search ability of particle swarm optimization （PSO）. Moreover, in order to enhance the feasibility of the generated paths, we further apply the new bound violation handling schemes to restrict the velocity and position of each particle. Finally, experiments respectively with a simulated robot and a real robot are conducted and the results demonstrate the feasibility and effectiveness of SLPSO in solving mobile robot path planning problem.As a challenging optimization problem, path planning for mobile robot refers to searching an optimal or near-optimal path under different types of constrains in complex environments. In this paper, a self-adaptive learning particle swarm optimization （SLPSO） with different learning strategies is proposed to address this problem. First, we transform the path planning problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives： path length, col- lision risk degree and smoothness. Then, a novel self-adaptive learning mechanism is developed to adaptively select the most suitable search strategies at different stages of the optimization process, which can improve the search ability of particle swarm optimization （PSO）. Moreover, in order to enhance the feasibility of the generated paths, we further apply the new bound violation handling schemes to restrict the velocity and position of each particle. Finally, experiments respectively with a simulated robot and a real robot are conducted and the results demonstrate the feasibility and effectiveness of SLPSO in solving mobile robot path planning problem.

关 键 词：path planning self-adaptive learning particle swarm optimization learning strategy learningmechanism boundary violations handling 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]