基于改进人工势场法的移动机器人路径规划

doi:10.11772/j.issn.1001-9081.2020050640

计算机应用 ›› 2020, Vol. 40 ›› Issue (12): 3508-3512.DOI: 10.11772/j.issn.1001-9081.2020050640

基于改进人工势场法的移动机器人路径规划

徐小强, 王明勇, 冒燕

武汉理工大学自动化学院, 武汉 430070

收稿日期:2020-05-14 修回日期:2020-07-15 出版日期:2020-12-10 发布日期:2020-12-23
通讯作者: 冒燕(1978-),男,江苏如皋人,讲师,博士,主要研究方向:人工智能、模式识别、机器学习。maoyan@whut.edu.cn
作者简介:徐小强(1978-),男,河南南阳人,副教授,硕士,主要研究方向:人工智能、模式识别、机器学习;王明勇(1995-),男,湖北襄阳人,硕士研究生,主要研究方向:人工智能、机器学习
基金资助:
中央高校基本科研业务费专项资金资助项目（2019IVA045）。

Path planning of mobile robot based on improved artificial potential field method

XU Xiaoqiang, WANG Mingyong, MAO Yan

School of Automation, Wuhan University of Technology, Wuhan Hubei 430070, China

Received:2020-05-14 Revised:2020-07-15 Online:2020-12-10 Published:2020-12-23
Supported by:
This work is partially supported by the Fundamental Research Funds for the Central Universities （2019IVA045）.

摘要/Abstract

摘要： 针对传统人工势场法在路径规划过程中容易陷入陷阱区域和局部极小点的问题，提出了一种改进人工势场法。首先，提出安全距离概念，避免了不必要的路径，从而解决了路径过长和算法运行时间过长问题；然后，为避免机器人陷入局部极小点和陷阱区域，在算法中引入预测距离，使得算法可以在机器人陷入局部极小点或陷阱区域之前做出反应；最后，通过合理设置虚拟目标点，引导机器人避开局部极小点和陷阱区域。实验结果表明，改进算法可以有效解决传统算法容易陷入局部极小点和陷阱区域的问题；同时，相较于传统人工势场法，所提算法规划出的路径长度减少了5.2%，计算速度提高了405.56%。

关键词: 人工势场法, 路径规划, 虚拟目标点, 陷阱区域, 局部极小点

Abstract: Aiming at the problem that the traditional artificial potential field method is easy to fall into trap area and local minimum in the path planning process, an improved artificial potential field method was proposed. Firstly, the concept of safe distance was proposed to avoid unnecessary paths, so as to solve the problems of long path length and long algorithm running time. Then, in order to avoid the robot being trapped in the local minimum and trap area, the predictive distance was introduced into the algorithm, so that the algorithm was able to react before the robot being trapped in the local minimum or trap area. Finally, the robot was guided to avoid the local minimum and trap area by setting the virtual target points reasonably. The experimental results show that, the improved algorithm can effectively solve the problem that the traditional algorithm is easy to fall into the local minimum and trap area. At the same time, compared with those of the traditional artificial potential field method, the path length planned by this proposed algorithm is reduced by 5.2% and its speed is increased by 405.56%.

Key words: Artificial Potential Field method (APF), path planning, virtual target point, trap area, local minimum

中图分类号:

TP242.6

徐小强, 王明勇, 冒燕. 基于改进人工势场法的移动机器人路径规划[J]. 计算机应用, 2020, 40(12): 3508-3512.

XU Xiaoqiang, WANG Mingyong, MAO Yan. Path planning of mobile robot based on improved artificial potential field method[J]. Journal of Computer Applications, 2020, 40(12): 3508-3512.

参考文献

[1] SMOBOLESTAN S M,RASOOLI A,KHODAYGAN S. Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning[J]. Journal of Ambient Intelligence and Humanized Computing,2019,10(5):1841-1850.
[2] 陈余庆, 李桐训, 于双和, 等. 基于势场蚁群算法的机器人全局路径规划[J]. 大连理工大学学报, 2019, 59(3):316-322.(CHEN Y Q,LI T X,YU S H,et al. Global path planning of robots based on potential field ant colony algorithm[J]. Journal of Dalian University of Technology,2019,59(3):316-322.)
[3] 张强, 陈兵奎, 刘小雍, 等. 基于改进势场蚁群算法的移动机器人最优路径规划[J]. 农业机械学报, 2019, 50(5):23-32, 42. (ZHANG Q,CHEN B K,LIU X Y,et al. Ant colony optimization with improved potential field heuristic for robot path planning[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(5):23-32,42.)
[4] 童兴, 原帅琪, 方伟鹏, 等. 基于Dijkstra算法的矿井最佳避灾路线分类求取[J]. 工矿自动化, 2018, 44(4):94-99.(TONG X, YUAN S Q,FANG W P,et al. Classification of the best escape route of coal mine based on Dijkstra algorithm[J]. Industrial and Mine Automation,2018,44(4):94-99.)
[5] 吴红波, 王英杰, 杨肖肖. 基于Dijkstra算法优化的城市交通路径分析[J]. 北京交通大学学报, 2019, 43(4):116-121, 130.(WU H B,WANG Y J,YANG X X. Analysis of urban traffic vehicle routing based on Dijkstra algorithm optimization[J]. Journal of Beijing Jiaotong University,2019,43(4):116-121,130.)
[6] 武雅杰, 杨晶东. 基于A*算法的机器人路径规划[J]. 电子科技, 2017, 30(6):124-127.(WU Y J,YANG J D. Mobile robot path planning based on A* algorithm[J]. Electronic Science and Technology,2017,30(6):124-127.)
[7] 王维, 裴东, 冯璋. 改进A*算法的移动机器人最短路径规划[J]. 计算机应用, 2018, 38(5):1523-1526.(WANG W,PEI D,FENG Z. The shortest path planning for mobile robots using improved A* algorithm[J]. Journal of Computer Applications,2018,38(5):1523-1526.)
[8] MO H, XU L. Research of biogeography particle swarm optimization for robot path planning[J]. Neurocomputing,2015, 148:91-99.
[9] 王功亮, 王好臣, 李振雨, 等. 基于优化遗传算法的移动机器人路径规划[J]. 机床与液压, 2019, 47(3):37-40, 100.(WANG G L, WANG H C,LI Z Y,et al. Path planning for mobile robots based on optimized genetic algorithm[J]. Machine Tool and Hydraulics, 2019,47(3):37-40,100.)
[10] 刘二辉, 姚锡凡, 蓝宏宇, 等. 基于改进遗传算法的自动导引小车动态路径规划及其实现[J]. 计算机集成制造系统, 2018, 24(6):1455-1467.(LIU E H,YAO X F,LAN H Y,et al. AGV dynamic path planning based on improved genetic algorithm and its implementation[J]. Computer Integrated Manufacturing System, 2018,24(6):1455-1467.)
[11] 罗强, 王海宝, 崔小劲, 等. 改进人工势场法自主移动机器人路径规划[J]. 控制工程, 2019, 26(6):1091-1098.(LUO Q, WANG H B,CUI X J,et al. Autonomous mobile robot path planning based on improved artificial potential method[J]. Control Engineering,2019,26(6):1091-1098.)
[12] 韩伟, 孙凯彪. 基于模糊人工势场法的智能全向车路径规划[J]. 计算机工程与应用, 2018, 54(6):105-109, 167.(HAN W, SUN K B. Research on dynamic path planning of fuzzy artificial potential field method[J]. Computer Engineering and Applications,2018,54(6):105-109,167.)
[13] HU Y,KE W,CHANG L,et al. Research on multi-objective path planning of a robot based on artificial potential field method[J]. International Journal of Wireless and Mobile Computing,2018,15(4):335-341.
[14] KUMAR P B, RAWAT H, PARHI D R. Path planning of humanoids based on artificial potential field method in unknown environments[J]. Expert Systems,2018,36(2):Article No. e12360.
[15] YANG X,YANG W,ZHANG H,et al. A new method for robot path planning based artificial potential field[C]//Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications. Piscataway:IEEE,2016:1294-1299.
[16] HUANG Y,HU H,LIU X. Obstacles avoidance of artificial potential field method with memory function incomplex environment[C]//Proceedings of the 20108th World Congress on Intelligent Control and Automation. Piscataway:IEEE,2010:6414-6418.
[17] 陈倩, 张奇松, 侯丽. LBS中改进人工势场法的动态目标路径规划研究[J]. 电子设计工程, 2018, 26(5):76-80.(CHEN Q, ZHANG Q S,HOU L. Improved artificial potential field method for dynamic target path planning in LBS[J]. Electronic Design Engineering,2018,26(5):76-80.)
[18] 万方, 周风余, 尹磊, 等. 基于电势场法的移动机器人全局路径规划算法[J]. 机器人, 2019, 41(6):742-750.(WAN F,ZHOU F Y,YIN L,et al. Global path planning algorithm of mobile robot based on electric potential field[J]. Robot,2019,41(6):742-750.)
[19] 梁献霞, 刘朝英, 宋雪玲, 等. 改进人工势场法的移动机器人路径规划研究[J]. 计算机仿真, 2018, 35(4):291-294, 361. (LIANG X X,LIU C Y,SONG X L,et al. Research on improved artificial potential field approach in local path planning for mobile robot[J]. Computer Simulation,2018,35(4):291-294,361.)
[20] 程志, 张志安, 李金芝, 等. 改进人工势场法的移动机器人路径规划[J]. 计算机工程与应用, 2019, 55(23):29-34.(CHENG Z, ZHANG Z A,LI J Z,et al. Mobile robots path planning based on improved artificial potential field[J]. Computer Engineering and Applications,2019,55(23):29-34.)

基于改进人工势场法的移动机器人路径规划

Path planning of mobile robot based on improved artificial potential field method

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