Deep Reinforcement Learning for Real-Time Drone Routing in Post-Disaster Road Assessment Without Domain Knowledge

• URL: http://arxiv.org/abs/2509.01886v1
• Date: Tue, 02 Sep 2025 02:22:22 GMT
• Title: Deep Reinforcement Learning for Real-Time Drone Routing in Post-Disaster Road Assessment Without Domain Knowledge
• Authors: Huatian Gong, Jiuh-Biing Sheu, Zheng Wang, Xiaoguang Yang, Ran Yan,
• Abstract summary: Post-disaster road damage assessment is critical for effective emergency response.<n>Traditional optimization methods suffer from excessive computational time and require domain knowledge for algorithm design.<n>This study proposes an attention-based encoder-decoder model (AEDM) for real-time drone routing decision.
• Score: 14.07560120879767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 real-time 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 16--69\% in solution quality and achieves real-time 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.

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