Cross-platform mission planning for UAVs under carrier delivery mode

As battlefield scale enlarges, cross-platform collaborative combat provides an appealing paradigm for modern warfare. Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning, where path planning and target assignment are tightly coupled. In...

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Veröffentlicht in:Defence technology Jg. 53; S. 76 - 97
Hauptverfasser: Jin, Junhong, Zhang, Genlai, Li, Xin, Su, Xichao, Lu, Chen, Cheng, Yujie, Ding, Yu, Wang, Lei, Wang, Xinwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.11.2025
KeAi Communications Co., Ltd
Schlagworte:
Carrier delivery mode
Clustering algorithm
Cross-platform mission planning
Heuristic optimization algorithm
UAV
Heuristic optimization algorithm
UAV
Cross-platform mission planning
Clustering algorithm
Carrier delivery mode
ISSN:2214-9147, 2214-9147
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Zusammenfassung:As battlefield scale enlarges, cross-platform collaborative combat provides an appealing paradigm for modern warfare. Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning, where path planning and target assignment are tightly coupled. In this paper, we focus on UAV mission planning under carrier delivery mode (e.g., by aircraft carrier, ground vehicle, or transport aircraft) and design a three-layer hierarchical solution framework. In the first layer, we simultaneously determine delivery points and target set division by clustering. To address the safety concerns of radar risk and UAV endurance, an improved density peak clustering algorithm is developed by constraint fusion. In the second layer, mission planning within each cluster is viewed as a cooperative multiple-task assignment problem. A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy (VHSG) and a stochastic variable neighborhood search (SVNS), called VHSG-SVNS, is proposed for rapid solution. Based on the results of the first two layers, the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window. The cost between any two nodes is calculated by the A∗ algorithm, and the genetic algorithm is then implemented to determine the global route. Finally, a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework, where three layers cooperate well with each other to generate satisfactory combat scheduling. Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms. Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems, and can be potentially extended to other applications such as post-disaster search and rescue, forest surveillance and firefighting, logistics pick and delivery, etc.
ISSN:2214-9147
2214-9147
DOI:10.1016/j.dt.2025.06.025