Wildfire Containment Using Multi-drone Systems: A Predict-Then-Optimize Approach
Aoran Cheng, Shijie Pan, Yiqi Sun, Kai Kang, Cristobal Pais, Yulun Zhou, Zuo-Jun Max Shen
AI summary
Problem
Multi-drone wildfire firefighting is hindered by complex, dynamic environments and the difficulty of integrating accurate fire spread predictions with efficient, robust task allocation algorithms.
Approach
The method first predicts future fire spread using a deep residual network (Firefighter-DResNet), then optimizes multi-drone task assignments and routing via a Mixed-Integer Programming model enhanced with Dynamic Programming and Chance-constrained Robust Optimization.
Key results
- Firefighter-DResNet surpasses state-of-the-art models by 78.1% and 86.4% in IoU for 20×20 and 40×40 environments
- Achieves over 54.7% and 27.3% higher precision than leading baselines
- Chance-constrained optimizer secures a 30% success rate in large-scale 40×40 scenarios where all baselines fail
- Seamlessly integrates prediction and optimization to enable robust, real-time multi-drone coordination
Why it matters
Offers emergency responders and disaster management agencies a scalable, data-driven solution for rapidly coordinating autonomous drone swarms in dynamic wildfire emergencies.
Abstract
A multi-drone system coupled with data in- telligence can be the future of wildfire fighting. However, multi-drone firefighting faces considerable challenges, such as complex environmental conditions, rapidly changing wildfire spread, and computational complexity in multi-drone opera- tions. We address these challenges by developing a predict-then- optimize approach to enable multi-drone firefighting. First, we create a wildfire spread prediction model Firefighter-DResNet based on previous wildfire data. Next, we propose a Mixed- Integer Programming (MIP) model coupled with Dynamic Programming (DP) to facilitate efficient multi-drone task plan- ning. We further use Chance-constrained Robust Optimization (CCRO) to ensure robust performance under varying condi- tions. After training with 75 varied wildfire environments, our approach demonstrates exceptional performance in controlling wildfires through multi-drone coordination. Our prediction model Firefighter-DResNet outperforms state-of-the-art models by 78.1% (20×20 environment) and 86.4% (40×40 environment) in terms of Intersection over Union (IoU), and achieves more than 54.7% and 27.3% higher precision. Subsequently, our chance-constrained optimizer succeeds in large-scale scenarios, achieving a 30% success rate in N=16 40×40 scenarios where all other baselines fail. This integrated solution significantly enhances the robustness and efficiency of autonomous multi- drone firefighting, marking a substantial leap towards practical deployment.