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Evolution of Unmanned Surface Vehicle Path Planning: A Comprehensive Review of Basic, Responsive, and Advanced Strategic Pathfinders

Authors :
Yijie Chu
Qizhong Gao
Yong Yue
Eng Gee Lim
Paolo Paoletti
Jieming Ma
Xiaohui Zhu
Source :
Drones, Vol 8, Iss 10, p 540 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Unmanned Surface Vehicles (USVs) are rapidly becoming mission-indispensable for a variety of naval operations, from search and rescue to environmental monitoring and surveillance. Path planning lies at the heart of the operational effectiveness of USVs, since it represents the key technology required to enable the vehicle to transit the unpredictable dynamics of the marine environment in an efficient and safe way. The paper develops a critical review of the most recent advances in USV path planning and a novel classification of algorithms according to operational complexity: Basic Pathfinders, Responsive Pathfinders, and Advanced Strategic Pathfinders. Each category can adapt to different requirements, from environmental predictability to the desired degree of human intervention, and from stable and controlled environments to highly dynamic and unpredictable conditions. The review includes current methodologies and points out the state-of-the-art algorithmic approaches in their experimental validations and real-time applications. Particular attention is paid to the description of experimental setups and navigational scenarios showing the realistic impact of these technologies. Moreover, this paper goes through the key, open challenges in the field and hints at the research direction to leverage in order to enhance the robustness and adaptability of path planning algorithms. This paper, by offering a critical analysis of the current state-of-the-art, lays down the foundation of future USV path planning algorithms.

Details

Language :
English
ISSN :
2504446X
Volume :
8
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Drones
Publication Type :
Academic Journal
Accession number :
edsdoj.9cb1f0ab609848539219a0865bf9b620
Document Type :
article
Full Text :
https://doi.org/10.3390/drones8100540