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A practical environment potential field modelling method for complex geometric objects

Published online by Cambridge University Press:  15 September 2022

Zhongxian Zhu ,
Hongguang Lyu ,
Jundong Zhang ,
Yong Yin  and
Xiang Fan
Zhongxian Zhu
Affiliation:
Marine Engineering College, Dalian Maritime University, Dalian, China
Hongguang Lyu*
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Jundong Zhang
Affiliation:
Marine Engineering College, Dalian Maritime University, Dalian, China
Yong Yin
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Xiang Fan
Affiliation:
Shanghai Merchant Ship Design and Research Institute, Shanghai, China
*
*Corresponding author. E-mail: lhg@dlmu.edu.cn.
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Abstract

Several studies have been conducted on collision avoidance (CA) and path planning for maritime autonomous surface ships (MASS) based on artificial potential field (APF) and electronic navigation chart (ENC) data. However, to date, accurate, highly efficient, and automatic modelling of complicated geometry environment potential fields (EPFs) has not been realised. In this study, an accurate EPF model is established using ENC data to describe different types of obstacles, navigable areas, and non-navigable areas. The implicit equations of complex polygons are constructed based on the R-function theory, and the discrete-convex hull method is introduced to realise the automatic modelling of EPF. Moreover, collaborative CA and obstacle avoidance (OA) experiments are designed and conducted in a simulated environment and based on the ENC data. The results show that the proposed EPF modelling method is accurate, reliable, and time-efficient even with numerous ENC data and complex shapes owing to the R-function representation for geometric objects and discrete-convex hull method. The combination of improved APF and EPF models is proven to be effective for CA and OA. This paper presents a practical EPF modelling approach for APF-based ship path planning.

Keywords

unmanned surface vehicleautonomousship collisiontrajectory planningenvironment potential field modellingartificial potential fieldcollision avoidancemaritime autonomous surface ships

Information

Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 1 , January 2023 , pp. 38 - 61
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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