Safe and Energy-Aware Decentralized PDE-Constrained Optimization-Based Control of Multi-UAVs for Persistent Wildfire Suppression
It addresses safe and energy-aware coordination of multiple UAVs for persistent wildfire suppression, a practical problem for emergency response operations.
The paper develops a decentralized optimization-based control framework for multi-UAV wildfire suppression that ensures safety and energy sufficiency, validated through simulations and real quadcopter experiments over multiple charge cycles.
This paper presents a safe and energy-aware optimization-based control framework for multi-UAV wildfire suppression under localization and motion uncertainties. We first develop a centralized density-based controller that couples UAV motion and water deployment in a wildfire-specific control Lyapunov function. This framework is then extended to a decentralized setting suitable for large-scale operations using only local information. The controllers use control barrier function constraints to enforce both danger zone avoidance and the ability to reach a charging region. Simulations and real quadcopter experiments demonstrate the controller's effectiveness in fire suppression while preserving safety and energy sufficiency over multiple charge cycles.