Numerous remarkable advancements have been made in accuracy, speed, and parallelism for solving the Unmanned Aerial Vehicle Route Planing (UAVRP). However, existing UAVRP solvers face challenges when attempting to scale effectively and efficiently for larger instances. In this paper, we present a generalization framework that enables current UAVRP solvers to robustly extend their capabilities to larger instances, accommodating up to 10,000 points, using widely recognized test sets. The UAVRP under a large number of patrol points is a typical large-scale TSP problem.Our proposed framework comprises three distinct steps. Firstly, we employ Delaunay triangulation to extract subgraphs from large instances while preserving global features. Secondly, we utilize an embedded TSP solver to obtain sub-results, followed by graph fusion. Finally, we implement a decoding strategy customizable to the user's requirements, resulting in high-quality solutions, complemented by a warming-up process for the heatmap. To demonstrate the flexibility of our approach, we integrate two representative TSP solvers into our framework and conduct a comprehensive comparative analysis against existing algorithms using large TSP benchmark datasets. The results unequivocally demonstrate that our framework efficiently scales existing TSP solvers to handle large instances and consistently outperforms state-of-the-art (SOTA) methods. Furthermore, since our proposed framework does not necessitate additional training or fine-tuning, we believe that its generality can significantly advance research on end-to-end UAVRP solvers, enabling the application of a broader range of methods to real-world scenarios.

翻译：在解决无人机路径规划（UAVRP）问题的精度、速度与并行性方面，已取得诸多显著进展。然而，现有UAVRP求解器在尝试高效扩展至更大规模算例时面临挑战。本文提出一种泛化框架，使当前UAVRP求解器能够鲁棒地将其能力扩展至更大规模算例（在广泛认可的测试集上可容纳高达10,000个点）。具有大量巡逻点的大规模UAVRP是典型的大规模旅行商问题。我们提出的框架包含三个独立步骤：首先，采用Delaunay三角剖分从大规模算例中提取子图，同时保留全局特征；其次，利用嵌入式TSP求解器获取子结果，随后进行图融合；最后，实施可根据用户需求定制的解码策略，辅以热图预热过程，从而获得高质量解。为验证本方法的灵活性，我们将两种代表性TSP求解器集成至框架中，并基于大规模TSP基准数据集与现有算法进行全面对比分析。结果明确表明，本框架能有效扩展现有TSP求解器处理大规模算例的能力，且持续优于当前最优方法。此外，由于所提框架无需额外训练或微调，我们相信其泛化性能显著推进端到端UAVRP求解器的研究，使更广泛的方法能够应用于实际场景。