A Motion Planning Scheme for Automated Wildfire Suppression

This paper presents a model predictive control (MPC) motion planning and control scheme for an automated firefighting system. The proposed automated firefighting system consists of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) cooperating to detect, localize, and handle the wildfire. In this automated cooperative framework, the UGVs have to reach to the fire (target) in an optimal way, optimal in the sense that the operation time and fuel consumption are minimized. Meanwhile, the UAVs gather and report the localization data to the system. Furthermore, the UAVs motions are intended to reduce the system uncertainty. The proposed motion planning scheme is designed to handle various sources of uncertainty as well as environment constraints through incorporating them to the conventional nonlinear model predictive control. Numerical simulations have been carried out to study the performance of the proposed MPC motion planning and control scheme. Simulation results demonstrate that the proposed algorithm is able to design an optimal trajectory while handling the system uncertainties and constraints.