Enhancing Large-scale UAV Route Planing with Global and Local Features via Reinforcement Graph Fusion

TL;DR

This paper introduces a scalable framework that enhances existing UAV route planning algorithms to efficiently handle large instances with up to 10,000 points, using graph fusion and a flexible decoding strategy.

Contribution

The proposed framework enables existing UAVRP solvers to scale to larger problem sizes without retraining, by combining subgraph extraction, graph fusion, and customizable decoding.

Findings

Framework scales solvers to large instances up to 10,000 points

Outperforms state-of-the-art methods on large TSP benchmarks

Does not require additional training or fine-tuning

Abstract

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.