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移动机器人全局路径规划的模拟退火-教与学优化算法

吴宗胜 傅卫平

吴宗胜, 傅卫平. 移动机器人全局路径规划的模拟退火-教与学优化算法[J]. 机械科学与技术, 2016, 35(5): 678-685. doi: 10.13433/j.cnki.1003-8728.2016.0504
引用本文: 吴宗胜, 傅卫平. 移动机器人全局路径规划的模拟退火-教与学优化算法[J]. 机械科学与技术, 2016, 35(5): 678-685. doi: 10.13433/j.cnki.1003-8728.2016.0504
Wu Zongsheng, Fu Weiping. SA and Teaching-learning-based Optimization Algorithm for Mobile Robots Global Path Planning[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(5): 678-685. doi: 10.13433/j.cnki.1003-8728.2016.0504
Citation: Wu Zongsheng, Fu Weiping. SA and Teaching-learning-based Optimization Algorithm for Mobile Robots Global Path Planning[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(5): 678-685. doi: 10.13433/j.cnki.1003-8728.2016.0504

移动机器人全局路径规划的模拟退火-教与学优化算法

doi: 10.13433/j.cnki.1003-8728.2016.0504
基金项目: 

国家自然科学基金项目(10872160)资助

详细信息
    作者简介:

    吴宗胜(1974-),高级工程师,博士研究生,研究方向为智能机器人和机器视觉,wuzs2005@163.com

SA and Teaching-learning-based Optimization Algorithm for Mobile Robots Global Path Planning

  • 摘要: 提出一种新的基于模拟退火-教与学优化(SA-TLBO)算法的移动机器人全局路径规划方法。进行环境地图建模,通过坐标变换在路径的起点与目标点之间建立新的环境地图;引入模拟退火思想对基本的教与学优化算法进行改进;利用改进的算法对路径目标函数进行优化得到一条全局最优路径。仿真实验结果表明,该方法具有极快的收敛速度和较高的搜索精度,以及较好的全局寻优能力,能有效解决机器人全局路径规划的优化问题。
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出版历程
  • 收稿日期:  2015-01-06
  • 刊出日期:  2016-05-05

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