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粒子群优化算法融合行为动力学的路径规划方法研究

葛媛媛 张宏基 唐虹

葛媛媛, 张宏基, 唐虹. 粒子群优化算法融合行为动力学的路径规划方法研究[J]. 机械科学与技术, 2018, 37(2): 244-249. doi: 10.13433/j.cnki.1003-8728.2018.0213
引用本文: 葛媛媛, 张宏基, 唐虹. 粒子群优化算法融合行为动力学的路径规划方法研究[J]. 机械科学与技术, 2018, 37(2): 244-249. doi: 10.13433/j.cnki.1003-8728.2018.0213
Ge Yuanyuan, Zhang Hongji, Tang Hong. Research on Path Planning Method of Particle Swarm Optimization Algorithm and Fusion Behavior Dynamics[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 244-249. doi: 10.13433/j.cnki.1003-8728.2018.0213
Citation: Ge Yuanyuan, Zhang Hongji, Tang Hong. Research on Path Planning Method of Particle Swarm Optimization Algorithm and Fusion Behavior Dynamics[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 244-249. doi: 10.13433/j.cnki.1003-8728.2018.0213

粒子群优化算法融合行为动力学的路径规划方法研究

doi: 10.13433/j.cnki.1003-8728.2018.0213
基金项目: 

国家自然科学基金项目(10872160)与陕西省科技厅项目(2016GY-027)资助

详细信息
    作者简介:

    葛媛媛(1985-),讲师,硕士,研究方向为智能机器人导航及控制,Ge_yuanyuan022@163.com

    通讯作者:

    张宏基,讲师,博士,hongji_258@163.com

Research on Path Planning Method of Particle Swarm Optimization Algorithm and Fusion Behavior Dynamics

  • 摘要: 针对未知环境下采用竞争行为动力学协调方法进行移动机器人路径规划时存在机器人运动速度参数确定困难,并且在各基本行为竞争时容易产生行为参数振荡现象而导致规划出来的路径不优化等问题,提出了基于粒子群优化算法融合行为动力学进行路径规划的方法。该方法在行为动力学模型的基础上利用粒子群优化算法(Particle swarm optimization,PSO)对路径规划过程中的基本行为进行融合,代替了竞争行为动力学的行为参数协调,从而使移动机器人能够根据传感器采集的实时环境信息自动获取各个基本行为的权值。通过把该方法与基于竞争动力学行为协调方法的机器人路径规划进行了仿真对比实验,实验结果验证了该方法的可行性和优越性。
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出版历程
  • 收稿日期:  2016-09-07
  • 刊出日期:  2018-02-25

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