用Transformer设计新启发式，提速无人机安全路径规划
Transformer-based Heuristic for Advanced Air Mobility Planning
- 基于Transformer构建学习型启发式，替代传统A*算法的静态评估
- 在多个风险地图上验证，计算效率提升显著且路径安全性达标
- 适合自动驾驶飞行器、智慧城市空管等需要实时安全导航的场景
自主无人飞行器（UAV）在城市空域飞行时，安全至关重要。风险感知路径规划是保障飞行安全的有效方法，可建模为约束最短路径（CSP）问题，即寻找满足预设安全约束的最短路径。求解CSP问题是NP难的，传统方法虽精确但计算耗时。此前我们提出ASD A*算法，在传统A*基础上引入额外安全维度，有效处理CSP问题。随后开发了基于Transformer的神经网络启发式，大幅降低计算开销并提升性能。本文进一步扩展数据集，包含更多风险地图与任务类型，优化模型结构，提升泛化能力；同时引入新的启发式策略和新型神经网络架构，显著增强整体方法的有效性。
原文摘要 · Abstract (English)
Safety is extremely important for urban flights of autonomous Unmanned Aerial Vehicles (UAVs). Risk-aware path planning is one of the most effective methods to guarantee the safety of UAVs. This type of planning can be represented as a Constrained Shortest Path (CSP) problem, which seeks to find the shortest route that meets a predefined safety constraint. Solving CSP problems is NP-hard, presenting significant computational challenges. Although traditional methods can accurately solve CSP problems, they tend to be very slow. Previously, we introduced an additional safety dimension to the traditional A* algorithm, known as ASD A*, to effectively handle Constrained Shortest Path (CSP) problems. Then, we developed a custom learning-based heuristic using transformer-based neural networks, which significantly reduced computational load and enhanced the performance of the ASD A* algorithm. In this paper, we expand our dataset to include more risk maps and tasks, improve the proposed model, and increase its performance. We also introduce a new heuristic strategy and a novel neural network, which enhance the overall effectiveness of our approach.
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