用无人机群智能灭火，先预测火势再优化调度，显著减少行动次数。
Using Drone Swarm to Stop Wildfire: A Predict-then-optimize Approach
- 先用神经网络预测火势蔓延，再用混合整数规划优化任务分配。
- 相比基础模型减少37.3%的无人机移动，且成功率远超遗传算法。
- 适合研究智能应急响应、无人机协同控制或复杂环境决策的人参考。
无人机群结合数据智能有望成为未来灭火的关键手段。然而，真实火场环境复杂、火势动态变化快、无人机调度计算量大，带来巨大挑战。本文提出一种预测-优化联合框架：首先基于真实火灾数据构建火势蔓延预测的凸神经网络（Convex-NN）；随后设计耦合动态规划的混合整数规划（MIP）模型，实现高效无人机任务规划；进一步引入机会约束鲁棒优化（CCRO）以应对不确定性。采用贝德尔分解与分支定界算法高效求解。在75组模拟火情训练后，该方法在多个测试集上表现最优，相较纯MIP方案减少37.3%的无人机移动量，且显著优于遗传算法（GA）基准，后者常无法完全扑灭火焰。下一步将在真实场景中开展火势传播与灭火实验验证。
原文摘要 · Abstract (English)
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.
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