A Reinforcement Learning Framework for UAV Path Planning Automation in Complex Fire and Rescue Environments

Fire and rescue UAVs employ AI methods to automate navigation over fire zones to detect and extinguish fire flames and/or rescue fire-trapped lives through low-cost and collision-free flight routes. This paper introduces a reinforcement learning framework that allows UAVs to detect and prioritise fire-trapped targets based on their risk levels. It establishes three simultaneous objectives, including maximised power conservation, rescue success rate, and flight safety, to plan fire and rescue operations in complex environments where field size, the number of trapped targets, and fire source count vary. An extensive empirical evaluation is conducted to test and evaluate the performance of the proposal against three well-known benchmarks, including Double Deep Q-network, Advantage Actor-Critic, and Genetic Algorithm. The results demonstrate that the proposed solution outperforms the benchmarks in most circumstances, especially when the fire and rescue environment is large and complex.