用强化学习快速规划灾后无人机巡检路线，又快又好。
Deep Reinforcement Learning for Drone Route Optimization in Post-Disaster Road Assessment
- 用注意力编码解码模型+强化学习自动规划路线，无需人工设计算法。
- 比商用求解器快100倍以上，方案质量提升20%~71%。
- 支持不同规模、无人机数量和时间限制，适合真实灾情应急场景。
灾后道路损毁快速评估对高效应急响应至关重要，但传统优化方法计算耗时长且需领域知识设计算法，难以适应紧急场景。本文提出一种基于注意力的编码解码模型（AEDM），用于灾后无人机巡检路径的快速决策。该方法采用深度强化学习，在无需算法设计知识的前提下生成高质量巡检路径。通过网络转换技术将基于边的路径问题转化为等效的节点形式，并结合合成道路网络生成技术解决大规模训练数据稀缺问题。模型采用具有多最优解能力的策略优化（POMO）进行多任务学习，以应对多种参数组合。实验表明，AEDM在方案质量上优于商用求解器20–71%、传统启发式方法23–35%，推理时间仅需1–2秒，相较传统方法的100–2000秒显著提升。模型在不同问题规模、无人机数量和时间约束下均表现出强泛化能力，在未见过的参数分布及真实道路网络上持续优于基线方法。该方法有效平衡了计算效率与解的质量，特别适用于需要快速决策的生命救援场景。代码已公开于https://github.com/PJ-HTU/AEDM-for-Post-disaster-road-assessment。
原文摘要 · Abstract (English)
Rapid post-disaster road damage assessment is critical for effective emergency response, yet traditional optimization methods suffer from excessive computational time and require domain knowledge for algorithm design, making them unsuitable for time-sensitive disaster scenarios. This study proposes an attention-based encoder-decoder model (AEDM) for rapid drone routing decision in post-disaster road damage assessment. The method employs deep reinforcement learning to determine high-quality drone assessment routes without requiring algorithmic design knowledge. A network transformation method is developed to convert link-based routing problems into equivalent node-based formulations, while a synthetic road network generation technique addresses the scarcity of large-scale training datasets. The model is trained using policy optimization with multiple optima (POMO) with multi-task learning capabilities to handle diverse parameter combinations. Experimental results demonstrate two key strengths of AEDM: it outperforms commercial solvers by 20--71\% and traditional heuristics by 23--35\% in solution quality, while achieving rapid inference (1--2 seconds) versus 100--2,000 seconds for traditional methods. The model exhibits strong generalization across varying problem scales, drone numbers, and time constraints, consistently outperforming baseline methods on unseen parameter distributions and real-world road networks. The proposed method effectively balances computational efficiency with solution quality, making it particularly suitable for time-critical disaster response applications where rapid decision-making is essential for saving lives. The source code for AEDM is publicly available at https://github.com/PJ-HTU/AEDM-for-Post-disaster-road-assessment.
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