计算机科学

计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 196-203.doi: 10.11896/jsjkx.210500020

• 人工智能 • 上一篇    下一篇

基于改进势场法的机器人路径规划

王兵1, 吴洪亮1, 牛新征2   

  1. 1 西南石油大学计算机科学学院 成都610500
    2 电子科技大学计算机科学与工程学院 成都611731
  • 收稿日期:2021-05-06 修回日期:2021-08-06 出版日期:2022-07-15 发布日期:2022-07-12
  • 通讯作者: 王兵(w9521423@sina.com)
  • 基金资助:
    四川省科技计划资助项目(2020YFG0054)

Robot Path Planning Based on Improved Potential Field Method

WANG Bing1, WU Hong-liang1, NIU Xin-zheng2   

  1. 1 School of Computer Science,Southwest Petroleum University,Chengdu 610500,China
    2 School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Received:2021-05-06 Revised:2021-08-06 Online:2022-07-15 Published:2022-07-12
  • About author:WANG Bing,born in 1977,associate professor,is a member of China Computer Federation.His main research interests include path planning and data mining.
  • Supported by:
    Science and Technology Planning Project of Sichuan Province(2020YFG0054).

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摘要： 针对传统人工势场法存在引力过大、容易陷入局部极小值、目标不可达以及容易陷入陷阱区域等问题,提出了基于路径优化策略和参数优化的改进势场法。通过引力补偿增益系数来避免引力过大的问题;根据环境信息采取不同的虚拟目标点设置策略以逃离局部极小值点;设置观察距离以识别障碍物的分布情况,选择不同的路径优化策略来避免目标不可达问题,机器人通过提前旋转角度来切向远离陷阱区域或采用安全路径通过该区域;采用改进差分进化算法求解有约束最优化问题,使得人工势场法的初始化参数不再根据经验来设置。仿真实验结果表明,改进势场法可以有效解决机器人陷入局部极小值、目标不可达等问题,并可优化机器人的行驶路径,提高机器人移动的安全性。相比传统人工势场法,改进势场法的路径长度减少了17.5%。

关键词: 参数优化, 路径规划, 路径优化策略, 人工势场法, 虚拟目标点

Abstract: Aiming at the problems of excessive gravity,local minimum point,unreachable target,trapped areas in traditional artificial potential field(APF) method,an improved potential field method based on path optimization strategy and parameter optimization is proposed.Firstly,a gravitational compensation gain coefficient is used to avoid the problem of excessive gravity.Secondly,the virtual target point setting strategy is used to solve the problem of local minimum point according to environmental information.The observation distance is set to identify the distribution of obstacles and different path strategies are selected to avoid unreachable target.Moreover,robot rotates in advance to move tangentially away from this area or uses a safe path to pass through the area.Finally,the differential evolution algorithm is used to solve the constrained optimization problem,so that the initialization parameters of artificial potential field method are no longer set based on experience.Simulation experiments show that the improved potential field method can effectively solve problems such as local minimums and unreachable targets.Compared with the traditional artificial potential field,the path length of the improved algorithm reduces by 17.5%.

Key words: Artificial potential field, Parameter optimization, Path optimization strategy, Path planning, Virtual target point

中图分类号: 

  • TP242
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