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
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.