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Mission Planning for UAV Swarm with Aircraft Carrier Delivery: A Decoupled Framework

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Titel: Mission Planning for UAV Swarm with Aircraft Carrier Delivery: A Decoupled Framework
Autoren: Hongyun Zhang, Bin Li, Lei Wang, Yujie Cheng, Yu Ding, Chen Lu, Haijun Peng, Xinwei Wang
Quelle: Aerospace, Vol 12, Iss 8, p 691 (2025)
Verlagsinformationen: MDPI AG, 2025.
Publikationsjahr: 2025
Bestand: LCC:Motor vehicles. Aeronautics. Astronautics
Schlagwörter: UAV swarm, aircraft carrier delivery, path planning, mission planning, heuristic optimization algorithm, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Beschreibung: Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section.
Publikationsart: article
Dateibeschreibung: electronic resource
Sprache: English
ISSN: 2226-4310
Relation: https://www.mdpi.com/2226-4310/12/8/691; https://doaj.org/toc/2226-4310
DOI: 10.3390/aerospace12080691
Zugangs-URL: https://doaj.org/article/9d65a428a36d49d18e5c078c04e85f9a
Dokumentencode: edsdoj.9d65a428a36d49d18e5c078c04e85f9a
Datenbank: Directory of Open Access Journals
Beschreibung
Abstract:Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section.
ISSN:22264310
DOI:10.3390/aerospace12080691