面向灾害管理应用的深度强化学习驱动的能量感知无人机-无人地面车系统持续监视
Deep Reinforcement Learning Enabled Persistent Surveillance with Energy-Aware UAV-UGV Systems for Disaster Management Applications
AI总结：
本文提出基于编码器-解码器Transformer与多头注意力的深度强化学习框架，协同优化无人机与无人地面车的路径和充电会合点，以最小化任务点访问间隔，实现灾害场景下能量感知的持续监视，并在解质量和运行时间上优于启发式及现有学习方法。
摘要：
将无人机（UAV）与无人地面车（UGV）相结合，为灾害管理中的持续监视提供了一种有效解决方案。无人机擅长快速覆盖大面积区域，但其航程受电池容量限制。无人地面车虽然速度较慢，但可以携带更大容量的电池以支持长时间任务。通过将无人地面车用作移动充电站，无人机可以通过周期性补能来延长任务持续时间，从而发挥两个系统的互补优势。为了优化这一能量感知的无人机-无人地面车协同路径规划问题，我们提出了一种规划框架，用于确定无人机与无人地面车之间的最优路径和充电会合点。我们的解决方案采用了一个深度强化学习（DRL）框架，该框架构建于具有多头注意力机制的编码器-解码器Transformer架构之上。该架构使模型能够顺序选择访问任务点的动作，并协调无人机与无人地面车之间的充电会合。DRL模型经过训练，以最小化任务点的信息年龄周期（即连续两次访问之间的时间间隔），从而确保有效监视。我们在不同问题规模和分布下评估了该框架，并将其性能与启发式方法以及现有的基于学习的模型进行了比较。结果表明，我们的方法在解质量和运行时间上均持续优于这些基线方法。此外，我们通过案例研究展示了DRL策略在真实灾害场景中的适用性，并探索了其在在线任务规划中处理动态变化的潜力。将DRL策略适配于优先级驱动的监视，凸显了该模型在实时灾害响应中的泛化能力。
英文摘要：
Integrating Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles (UGVs) provides an effective solution for persistent surveillance in disaster management. UAVs excel at covering large areas rapidly, but their range is limited by battery capacity. UGVs, though slower, can carry larger batteries for extended missions. By using UGVs as mobile recharging stations, UAVs can extend mission duration through periodic refueling, leveraging the complementary strengths of both systems. To optimize this energy-aware UAV-UGV cooperative routing problem, we propose a planning framework that determines optimal routes and recharging points between a UAV and a UGV. Our solution employs a deep reinforcement learning (DRL) framework built on an encoder-decoder transformer architecture with multi-head attention mechanisms. This architecture enables the model to sequentially select actions for visiting mission points and coordinating recharging rendezvous between the UAV and UGV. The DRL model is trained to minimize the age periods (the time gap between consecutive visits) of mission points, ensuring effective surveillance. We evaluate the framework across various problem sizes and distributions, comparing its performance against heuristic methods and an existing learning-based model. Results show that our approach consistently outperforms these baselines in both solution quality and runtime. Additionally, we demonstrate the DRL policy's applicability in a real-world disaster scenario as a case study and explore its potential for online mission planning to handle dynamic changes. Adapting the DRL policy for priority-driven surveillance highlights the model's generalizability for real-time disaster response.
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