Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (7): 196-203.doi: 10.11896/jsjkx.210500020

• Artificial Intelligence • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP242
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