An improved APFM for autonomous navigation and obstacle avoidance of USVs

Xiaohui Zhu, Yong Yue, Hao Ding, Shunda Wu, Ming Sheng Li, Yawei Hu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
EditorsOleg Gusikhin, Kurosh Madani, Janan Zaytoon
PublisherSciTePress
Pages401-408
Number of pages8
ISBN (Electronic)9789897583803
DOIs
Publication statusPublished - 2019
Event16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 - Prague, Czech Republic
Duration: 29 Jul 201931 Jul 2019

Publication series

NameICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
Volume2

Conference

Conference16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019
Country/TerritoryCzech Republic
CityPrague
Period29/07/1931/07/19

Keywords

  • Autonomous Navigation
  • Improved APFM
  • Obstacle Avoidance
  • USVs
  • Water Quality Monitoring

Cite this