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Article dans une revue

Multi-Objective Cooperated Path Planning of Multiple UAVs Based on Revisit Time

Abstract : This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the 3D mountainous environment. As a contribution to solving trade-offs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms including a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multiagent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.
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Soumis le : jeudi 24 juin 2021 - 08:46:17
Dernière modification le : mardi 24 mai 2022 - 21:42:50
Archivage à long terme le : : samedi 25 septembre 2021 - 18:07:30


Manuscript_ID 2020-05-I010866....
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Hassan Haghighi, Davood Asadi, Daniel Delahaye. Multi-Objective Cooperated Path Planning of Multiple UAVs Based on Revisit Time. Journal of Aerospace Information Systems, American Institute of Aeronautics and Astronautics, 2021, ⟨10.2514/1.I010866⟩. ⟨hal-03269427⟩



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