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 maximi...
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| Vydané v: | Ocean engineering Ročník 253; s. 111182 |
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| Jazyk: | English |
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Elsevier Ltd
01.06.2022
Elsevier |
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| ISSN: | 0029-8018, 1873-5258 |
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| Abstract | 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, we use two popular multi-objective evolutionary algorithms : Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Archived Evolution Strategy (PAES). First, we 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, we propose a new method which improves the performance of evolutionary algorithms when solving covering path planning problems by reducing the chromosome size. We have applied this method on the used algorithms and simulation results shows a significant performance enhancement both PAES and NSGA-II.
•Unmanned maritime surface drones for performing surveillance tasks.•Coverage area and energy consumption optimization.•Global covering path planning.•New methodology to enhance problem solving performance.•Performance and solution quality comparison with conventional methods. |
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| AbstractList | 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, we use two popular multi-objective evolutionary algorithms : Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Archived Evolution Strategy (PAES). First, we 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, we propose a new method which improves the performance of evolutionary algorithms when solving covering path planning problems by reducing the chromosome size. We have applied this method on the used algorithms and simulation results shows a significant performance enhancement both PAES and NSGA-II.
•Unmanned maritime surface drones for performing surveillance tasks.•Coverage area and energy consumption optimization.•Global covering path planning.•New methodology to enhance problem solving performance.•Performance and solution quality comparison with conventional methods. |
| ArticleNumber | 111182 |
| Author | Benazzouz, Djamel Ait-Chikh, Mohamed Abdessamed Lemarchand, Laurent Ouelmokhtar, Hand Benmoussa, Yahia |
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| Cites_doi | 10.1162/106365600568167 10.1016/j.oceaneng.2018.09.016 10.1016/j.oceaneng.2018.01.025 10.1016/j.isatra.2018.02.003 10.1016/j.oceaneng.2017.07.040 10.1016/j.eswa.2016.11.005 10.1155/2018/6392697 10.1109/4235.996017 10.4108/eai.23-4-2018.2277598 10.1016/j.oceaneng.2020.107043 10.1016/j.arcontrol.2016.04.018 10.1155/2019/2902170 10.1002/rob.20353 10.1016/j.asoc.2016.09.007 |
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| Title | Energy-based USV maritime monitoring using multi-objective evolutionary algorithms |
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