Managing Aircraft Re-Spotting like Sliding-Block Puzzles in Aircraft Carrier

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ID: 333658
2026
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Abstract
Abstract Frequent carrier-based sorties and dense hangar layouts make tractor-assisted aircraft re-spotting a complex problem analogous to the sliding-block puzzle. Conventional multi-agent pathfinding methods often neglect the kinematic constraints of heterogeneous agents (i.e., tractors and traction systems) and fail to handle hangar-flight deck cross-layer transitions. These two critical limitations prevent the direct application of standard algorithms. In this paper, we propose a hierarchical intelligent aircraft re-spotting framework to tackle these challenges. First, based on the connectivity between spots, we construct a spot transition graph and generate the basic trajectory libraries together with the corresponding congestion relations offline. Second, we treat each intermediate layout as a node in the graph and thereby transform conflict-free transfer sequence planning into a layout transition path planning problem, which we solve with a dedicated A* algorithm. Finally, we model aircraft scheduling as a flexible job-shop scheduling problem, where tractors and elevators serve as processing machines. A delayed maneuvering strategy, combined with heuristic rules, is adopted to generate schedules from the extracted conflict-free transfer sequence. Simulations based on a Ford-class aircraft carrier validate the effectiveness of the proposed method, highlighting the benefits of multiple optional trajectories and the reliable cross-layer transfer capability. This work provides a practical and extensible solution for carrier aircraft support operations. Future work will focus on the varying elevator transfer capacities for different aircraft types and on tractor congestion caused by aircraft tie-downs. The ultimate goal is a universal re-spotting algorithm for shipborne aviation platforms.
Reference Key
openalex_W7223295019 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhilong Deng, Lei Wang, Zixu Fang, Bai Li, Xinwei Wang
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Journal journal of computational design and engineering
Year 2026
DOI
10.1093/jcde/qwag090
URL
https://doi.org/10.1093/jcde/qwag090
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