摘要
针对移动机器人避障路径规划问题,在基本群智能算法灰狼优化算法的基础上,提出改进灰狼优化算法,测试函数证明了算法的稳定性和收敛性,进而将其首次应用于移动机器人避障路径规划问题,通过对改进灰狼优化算法的移动机器人避障路径进行研究,并与基本灰狼优化算法、粒子群算法、遗传算法比较,仿真结果证明了算法的稳定性和收敛性,对路径规划领域有十分重要的研究意义。
Aiming at obstacle avoidance path planning of mobile robot,an improved gray wolf optimization algorithm is proposed in this paper based on the basic swarm intelligence algorithm. The test function proves the stability and convergence of the algorithm. Then,it is applied to obstacle avoidance path planning of mobile robot for the first time. By studying the obstacle avoidance path of mobile robot with the improved gray wolf optimization algorithm,it is compared with basic gray wolf optimization algorithm,particle swarm optimization algorithm and genetic algorithm. The simulation results show the stability and convergence of the proposed algorithm,which is of great significance in the field of path planning.
作者
刘宁宁
王宏伟
Liu Ningning;Wang Hongwei(School of Electrical Engineering,Xinjiang University,Urmqi 830047,China;School of Control Science and Control Engineering,Dalian University of Technology,Dalian 116024,Liaoning,China)
出处
《电测与仪表》
北大核心
2020年第1期76-83,98,共9页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(61863034)
关键词
移动机器人
路径规划
灰狼优化算法
改进灰狼优化算法
粒子群算法
遗传算法
mobile robot
path planning
gray wolf optimization algorithm
improved gray wolf optimization algorithm
particle swarm optimization
genetic algorithm
