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Obstacle Avoidance Strategy of Mobile Robot Based on Improved Artificial Potential Field Method
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  • Guojun Xu,
  • Wei Zhang,
  • Yan Song,
  • Yagang Wang
Guojun Xu
University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering

Corresponding Author:747672336@qq.com

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Wei Zhang
University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering
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Yan Song
University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering
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Yagang Wang
University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering
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Abstract

When there are obstacles around the target point, the mobile robot cannot reach the target using traditional Artificial Potential Field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three-point collinear or semi-closed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy-APF (BVO-APF) has been proposed in this paper. There are two main advantages of the proposed method. Firstly, by redefining the gravitational function as logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Secondly, the proposed method can avoid falling into local oscillation for both three-point collinear and semi-closed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.
26 Dec 2022Submitted to Journal of Field Robotics
26 Dec 2022Submission Checks Completed
26 Dec 2022Assigned to Editor
29 Dec 2022Review(s) Completed, Editorial Evaluation Pending
15 Jan 2023Reviewer(s) Assigned
15 Mar 2023Editorial Decision: Revise Major
24 Mar 20231st Revision Received
24 Mar 2023Submission Checks Completed
24 Mar 2023Assigned to Editor
24 Mar 2023Review(s) Completed, Editorial Evaluation Pending
25 Mar 2023Reviewer(s) Assigned
04 Apr 2023Editorial Decision: Accept