Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11): 220-225.doi: 10.11896/jsjkx.190900026

• Artificial Intelligence • Previous Articles     Next Articles

Multi-robot Collaborative Obstacle Avoidance Based on Artificial Potential Field Method

CHEN Jun-ling, QIN Xiao-lin, LI Xing-luo, ZHOU Yang-hao, BAO Bin-guo   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2019-09-03 Revised:2020-01-13 Online:2020-11-15 Published:2020-11-05
  • About author:CHEN Jun-ling,born in 1994,postgra-duate.His main research interests include multi-robot collaborative obstacle avoidance and so on.
    QIN Xiao-lin,born in 1953,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include spatial and spatio-temporal databases,data ma-nagement and security in distributed environment,etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61373015,61728204).

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Abstract: In recent years,with the increasing attention paid to robots,mobile robot technology has gradually become a research hotspot.Robot obstacle avoidance is an important research topic in mobile robotics,and it is one of the basic problems faced by mobile robots.Aiming at the application scenario of multi-robot,the artificial potential field method is optimized based on the full analysis of the existing robot obstacle avoidance algorithms,and the multi-robot obstacle avoidance algorithm MPF and formation obstacle avoidance algorithm AOA are proposed.MPF algorithm optimizes the problem of local minimum point in artificial potential field method,and increases the probability of robot reaching the target point.AOA algorithm combines with the existing formation obstacle avoidance algorithm to improve the efficiency of formation obstacle avoidance.Finally,different experimental environments are designed for MPF and AOA algorithms respectively.Experiment results show that,in different complex obstacle environments,MPF algorithm can guide the robot to the target point effectively and efficiently,while AOA algorithm can provide efficient and stable formation obstacle avoidance under different environmental complexity and number of robots.

Key words: Artificial potential field method, Mobile robot, Moving obstacles, Trajectory following, Virtual target point

CLC Number: 

  • TP311
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