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

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

1.
西安理工大学机械与精密仪器工程学院, 西安 710048

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

计量
- 文章访问数: 157
- HTML全文浏览量: 22
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2015-01-06
- 刊出日期: 2016-05-05

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

1.
School of Mechanical and Precision Instrumental Engineering, Xi'an University of Technology, Xi'an 710048, China

摘要

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。
- 移动机器人 /
- 路径规划 /
- 模拟退火 /
- 教与学优化 /
- 最优路径
Abstract: In this paper, a novel simulated-annealing and teaching-learning based optimization (SA-TLBO) algorithm for mobile robots global path planning is presented. Firstly, a new map model for path planning between start-point and goal-point is built by coordinate system transformation. Then, the basic teaching-learning based optimization algorithm is modified in terms of the simulated annealing. Lastly, by utilizing the improved algorithm, the objective function of the path planning is optimized, then getting a global optimal path. The simulation experiment results show that the present method has a faster convergence rate and higher search accuracy with better global search ability, and can effectively solve the global optimization solution for robots global path planning.
- mobile robots /
- optimal path /
- path planning /
- simulated annealing (SA) /
- teaching-learning-based optimization(TLBO)

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参考文献(18)

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