使用无人机集群扑灭野火：一种先预测后优化的方法
Using Drone Swarm to Stop Wildfire: A Predict-then-optimize Approach
AI总结：
本文针对无人机集群灭火面临的环境复杂、火势动态变化和计算复杂度高等挑战，提出先预测后优化方法，结合凸神经网络预测、混合整数规划与动态规划任务规划以及机会约束鲁棒优化，实验表明其显著优于基线方法。
摘要：
无人机集群与数据智能相结合可能成为未来扑灭野火的手段。然而，无人机集群灭火面临巨大挑战，例如野火现场高度复杂的环境条件、野火蔓延的高度动态特性以及无人机集群行动的巨大计算复杂度。我们开发了一种先预测后优化的方法来应对这些挑战，以实现有效的无人机集群灭火。首先，我们基于真实野火数据构建野火蔓延预测凸神经网络（Convex-NN）模型。然后，我们提出一个与动态规划（DP）相结合的混合整数规划（MIP）模型，以实现高效的无人机集群任务规划。我们进一步使用机会约束鲁棒优化（CCRO）来确保在不同情况下具有鲁棒的灭火性能。所构建的模型使用 Benders 分解和分支切割算法高效求解。在经过75个模拟野火环境训练后，MIP+CCRO 方法在多个测试集上表现出最佳性能，与普通 MIP 相比减少了37.3%的移动。它还显著优于 GA 基线，后者常常无法完全扑灭火灾。最终，我们将在下一阶段进行真实世界的火势蔓延和扑灭实验，以进一步验证。
英文摘要：
Drone swarms coupled with data intelligence can be the future of wildfire fighting. However, drone swarm firefighting faces enormous challenges, such as the highly complex environmental conditions in wildfire scenes, the highly dynamic nature of wildfire spread, and the significant computational complexity of drone swarm operations. We develop a predict-then-optimize approach to address these challenges to enable effective drone swarm firefighting. First, we construct wildfire spread prediction convex neural network (Convex-NN) models based on real wildfire data. Then, we propose a mixed-integer programming (MIP) model coupled with dynamic programming (DP) to enable efficient drone swarm task planning. We further use chance-constrained robust optimization (CCRO) to ensure robust firefighting performances under varying situations. The formulated model is solved efficiently using Benders Decomposition and Branch-and-Cut algorithms. After 75 simulated wildfire environments training, the MIP+CCRO approach shows the best performance among several testing sets, reducing movements by 37.3\% compared to the plain MIP. It also significantly outperformed the GA baseline, which often failed to fully extinguish the fire. Eventually, we will conduct real-world fire spread and quenching experiments in the next stage for further validation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
