基于改进灰狼优化算法的移动机器人路径规划

刘宁宁; 王宏伟

刘宁宁,王宏伟.基于改进灰狼优化算法的移动机器人路径规划[J].电测与仪表,2020,57(1):76-83,98.
Liu Ningning,Wang Hongwei.Path Planning of Mobile Robot Based on Improved Grey Wolf Optimization Algorithm[J].Electrical Measurement & Instrumentation,2020,57(1):76-83,98.

基于改进灰狼优化算法的移动机器人路径规划

Path Planning of Mobile Robot Based on Improved Grey Wolf Optimization Algorithm

DOI：10.19753/j.issn1001-1390.2020.001.010

中文关键词: 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

英文关键词:mobile robot path planning gray wolf optimization algorithm improved gray wolf optimization algorithm particle swarm optimization genetic algorithm

基金项目:国家自然科学基金(No.61863034)。

作者中文名	作者英文名	单位
刘宁宁	Liu Ningning	新疆大学电气工程学院乌鲁木齐
王宏伟	Wang Hongwei	大连理工大学控制科学与工程学院大连

摘要点击次数: 2188

中文摘要:

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、精确性和收敛性,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、准确性和收敛性,对路径规划领域有十分重要的研究意义。

英文摘要:

【】Aiming at obstacle avoidance path planning of mobile robot, an improved gray wolf optimization algorithm is proposed based on the basic swarm intelligence algorithm. The test function proves the stability, accuracy 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 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, accuracy and convergence of the algorithm, which is of great significance in the field of path planning.□□□□□□□

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