Autonomous ship navigation with an enhanced safety collision avoidance technique.

Autonomous marine vehicle Motion planning Obstacle avoidance

Journal

ISA transactions
ISSN: 1879-2022
Titre abrégé: ISA Trans
Pays: United States
ID NLM: 0374750

Informations de publication

Date de publication:
18 Oct 2023
Historique:
received: 01 12 2022
revised: 16 10 2023
accepted: 16 10 2023
medline: 5 11 2023
pubmed: 5 11 2023
entrez: 4 11 2023
Statut: aheadofprint

Résumé

The motion of an autonomous ship is different from that of ground and aerial robots due to its maneuvering and environmental constraints. As a result, many techniques have been introduced for autonomous ship path planning. This paper presents a novel technique for global and local navigation planning of autonomous ships under complex static and dynamic constraints. Our technique, termed safety-enhanced path planning (SPP), has been developed to avoid potential collisions with underwater obstacles near seaside areas. SPP pre-processes the map to preserve the shape of visible obstacles and mark a safety-outline around the shores. Subsequently, an offset safety line (OSL) is drawn about the original shore to protect the ship when passing close to threat-defined offshore areas. The global path is produced with an enhanced A* multi-directional algorithm, considering the kinematic constraint of the ship. To ensure optimal path quality, the global path is further refined with a smoothing filter to improve consistency and smoothness. Additionally, local navigation is introduced to help the autonomous ship avoid collisions with other obstacle ships. Local offset trajectories are produced with 4th and 5th degree polynomials along longitudinal and lateral coordinates in time t. Distance closest point approach (DCPA) is utilized for early obstacle prediction to help the ship maneuver in complex dynamic obstacle avoidance scenarios. The trajectory set is filtered with an efficient cost policy to obtain the best trajectory for dynamic collision avoidance. We conduct simulations in MATLAB and compared with other maritime path planning methods to verify the effectiveness of our approach.

Identifiants

pubmed: 37925231
pii: S0019-0578(23)00471-8
doi: 10.1016/j.isatra.2023.10.019
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Hub Ali (H)

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: hub@ia.ac.cn.

Gang Xiong (G)

State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing, Cloud Computing Center, Chinese Academy of Sciences, Donggguan 523808, China. Electronic address: gang.xiong@ia.ac.cn.

Qu Tianci (Q)

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: qutianci2021@ia.ac.cn.

Rajesh Kumar (R)

Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China. Electronic address: rajakumarlohano@gmail.com.

Xisong Dong (X)

State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Qingdao Academy of Intelligent Industries, Qingdao 266109, China. Electronic address: xisong.dong@ia.ac.cn.

Zhen Shen (Z)

State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Qingdao Academy of Intelligent Industries, Qingdao 266109, China. Electronic address: zhen.shen@ia.ac.cn.

Classifications MeSH