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Authors: Xiaohui Zhu 1 ; Yong Yue 2 ; Hao Ding 3 ; Shunda Wu 4 ; MingSheng Li 4 and Yawei Hu 4

Affiliations: 1 Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, 215123, P. R. China, Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, U.K., School of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, 226019 and P. R. China ; 2 Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, 215123 and P. R. China ; 3 School of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, 226019, P. R. China, Nantong Research Institute for Advanced Communication Technologies, Nantong, Jiangsu Province, 226019 and P. R. China ; 4 School of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, 226019 and P. R. China

ISBN: 978-989-758-380-3

Keyword(s): Autonomous Navigation, Obstacle Avoidance, Improved APFM, USVs, Water Quality Monitoring.

Related Ontology Subjects/Areas/Topics: Industrial Automation and Robotics ; Industrial Engineering ; Informatics in Control, Automation and Robotics ; Robotics and Automation

Abstract: Unmanned surface vehicles (USVs) are getting more and more attention in recent years. Autonomous navigation and obstacle avoidance is one of the most important functions for USVs. In this paper, we proposed an improved angle potential field method (APFM) for USVs. A reversed obstacle avoidance algorithm was proposed to control the steering of USVs in tight spaces. In addition, a multi-position navigation route planning was also achieved. Simulation results in MATLAB show that the improved APFM can guide the USV to autonomously navigate and avoid obstacles around the USV during navigation. We applied the algorithm to a real USV, which is designed for water quality monitoring and tested in a real river system. Experimental results show that the improved APFM can successfully guide the USV to navigate based on the predefined navigation route while detecting both static and dynamic obstacles and avoiding collisions.

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Paper citation in several formats:
Zhu, X.; Yue, Y.; Ding, H.; Wu, S.; Li, M. and Hu, Y. (2019). An Improved APFM for Autonomous Navigation and Obstacle Avoidance of USVs.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-380-3, pages 401-408. DOI: 10.5220/0007922904010408

@conference{icinco19,
author={Xiaohui Zhu. and Yong Yue. and Hao Ding. and Shunda Wu. and MingSheng Li. and Yawei Hu.},
title={An Improved APFM for Autonomous Navigation and Obstacle Avoidance of USVs},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2019},
pages={401-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007922904010408},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - An Improved APFM for Autonomous Navigation and Obstacle Avoidance of USVs
SN - 978-989-758-380-3
AU - Zhu, X.
AU - Yue, Y.
AU - Ding, H.
AU - Wu, S.
AU - Li, M.
AU - Hu, Y.
PY - 2019
SP - 401
EP - 408
DO - 10.5220/0007922904010408

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