An Improved Artificial Potential Field Method for Path Planning of Mobile Robot with Subgoal Adaptive Selection

  • Zenan Lin
  • Ming YueEmail author
  • Xiangmin Wu
  • Haoyu Tian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11740)


As a simple and effective method, artificial potential field method is often used in robot path planning. Based on this, an improved artificial potential field model is proposed to solve the local minimum problem by using a subgoal strategy. In order to show the subgoal adaptive selection feature of the robot, an obstacle potential field function is established and the effectiveness of the adaptive feature is verified by path planning simulation. A double closed-loop control strategy is adopted to track the trajectory planned by the improved artificial potential field method, and simulation results show that the improved artificial potential method is reliable and the robot can well track the trajectory under the action of the controller.


Path planning Improved artificial potential field method Subgoal adaptive selection Trajectory tracking 



This work was supported by National Natural Science Foundation of China under Grant (Nos. 61873047 and 61573078), Fundamental Research Funds for the Central Universities (DUT19ZD205), and State Key Laboratory of Robotics and System Grant (SKLRS-2019-KF-17).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Automotive EngineeringDalian University of TechnologyDalianChina
  2. 2.State Key Laboratory of Robotics and SystemHarbin Institute of TechnologyHarbinChina

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