PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field

Chu, Yijie, Wu, Ziniu, Yue, Yong, Zhu, Xiaohui ORCID: 0000-0003-1024-5442, Lim, Eng Gee ORCID: 0000-0003-0199-7386 and Paoletti, Paolo ORCID: 0000-0001-6131-0377 (2022) PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field. APPLIED SCIENCES-BASEL, 12 (16). p. 8201.

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Official URL: http://dx.doi.org/10.3390/app12168201

Abstract

<jats:p>Path-keeping requires unmanned surface vehicles (USVs) to follow a planned path in autonomous navigation. It is essential for USVs to carry out autonomous tasks such as collecting various data of water quality and surrounding terrain for exploration and protection of water environments. However, due to obstacle avoidance and other factors such as wind, water waves, and dynamics of USVs, USVs usually deviate from the original planned path during autonomous navigation. This paper proposes a novel path-keeping algorithm based on the artificial potential field method (PK-APF) for USVs. To minimize the deviation between the actual path and the original planned path, the vertical distance and the virtual foot points of the current position of USVs to the original path (a line connecting the previous navigation point and the next navigation point) are calculated. When the vertical distance is larger than a threshold, we regard the vertical foot point as a virtual goal point to guide the USVs to navigate the original path in real-time to achieve high-precision path-keeping. Obstacle avoidance is simulated in the MATLAB and Virtual RobotX (VRX) simulators, and the influence of wind and water waves is considered in VRX. Experiments are conducted in typical scenarios and results show that PK-APF outperforms the traditional APF by at least 22%. The work provides an important basis for real-life environments. Substantial further work is planned for applying the method on a physical USV.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	path-keeping, artificial potential field, virtual foot points, USVs, VRX, MATLAB
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	09 Sep 2022 09:57
Last Modified:	14 Mar 2024 20:01
DOI:	10.3390/app12168201
Open Access URL:	https://doi.org/10.3390/app12168201
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3164000