Energy-based USV maritime monitoring using multi-objective evolutionary algorithms
This study addresses the monitoring mission problem using an USV equipped with an on-board LiDAR allowing to monitor regions inside its coverage radius. The problem is formulated as a bi-objective coverage path planning with two conflicting objectives : minimization of the consumed energy and maximization of the coverage rate. To solve the problem, the authors use two popular multi-objective evolutionary algorithms : Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Archived Evolution Strategy (PAES). First, the authors compare the efficiency of these two algorithms and show that PAES allows to find solutions allowing to save more energy as compared to those provided by NSGA-II. Then, the authors propose a new method which improves the performance of evolutionary algorithms when solving covering path planning problems by reducing the chromosome size. The authors have applied this method on the used algorithms and simulation results shows a significant performance enhancement both PAES and NSGA-II.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Ouelmokhtar, Hand
- Benmoussa, Yahia
- Benazzouz, Djamel
- Ait-Chikh, Mohamed Abdessamed
- Lemarchand, Laurent
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0000-0003-0894-4076
- Publication Date: 2022-6-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 111182
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Serial:
- Ocean Engineering
- Volume: 253
- Issue Number: 0
- Publisher: Pergamon
- ISSN: 0029-8018
- EISSN: 1873-5258
- Serial URL: http://www.sciencedirect.com/science/journal/00298018
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Energy consumption; Genetic algorithms; Monitoring; Ships; Trajectory control
- Subject Areas: Energy; Marine Transportation; Vehicles and Equipment;
Filing Info
- Accession Number: 01846846
- Record Type: Publication
- Files: TRIS
- Created Date: May 25 2022 9:35AM