Near-Optimal Covering Solution for USV Coastal Monitoring using PAES

This paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programm...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2022-09, Vol.106 (1), Article 24
Main Authors: Ouelmokhtar, Hand, Benmoussa, Yahia, Diguet, Jean-Philippe, Benazzouz, Djamel, Lemarchand, Laurent
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Automatic
Computer Science
Control
Customization
Electrical Engineering
Energy costs
Engineering
Engineering Sciences
Evolutionary algorithms
Exact solutions
Heuristic methods
Integer programming
Linear programming
Mechanical Engineering
Mechatronics
Mixed integer
Monitoring
Multiple objective analysis
Operations Research
Pareto optimization
Robotics
Short Paper
Topical collection on Unmanned Systems
Traveling salesman problem
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Summary:This paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programming formulation is proposed to model the USV monitoring problem. It consists of a combination of the Covering Salesman Problem (CSP) and Travelling Salesman Problem with Profit (TSPP). Then, we use CPLEX software to provide exact solutions. On the other hand, a customized chromosome-size algorithm is used to find heuristic solution. The latter is a multi-objective evolutionary algorithm known as Pareto Archived Evolution Strategy (PAES). The obtained results showed that the exact solving of the USV monitoring mission problem with mixed-integer programming (MIP) methods needs extensive computational costs. However, the customized PAES was able to provide Near-optimal solutions for large-size graphs in much faster time as compared to the exact one.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-022-01717-x