A Deep Reinforcement Learning Algorithm for Trajectory Planning of Swarm UAV Fulfilling Wildfire Reconnaissance
Kubilay Demır, Vedat Tümen, Selahattin Koşunalp, Teodor Iliev
Abstract
Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to detect missions in high-risk areas, have been gaining increasing interest, particularly in forest fire monitoring. Taking a large-scale area involved in a fire into consideration, a single UAV is often insufficient to accomplish the task of covering the whole disaster zone. This poses the challenge of multi-UAVs optimum path planning with a key focus on limitations such as energy constraints and connectivity. To narrow down this issue, this paper proposes a deep reinforcement learning-based trajectory planning approach for multi-UAVs that permits UAVs to extract the required information within the disaster area on time. A target area is partitioned into several identical subareas in terms of size to enable UAVs to perform their patrol duties over the subareas. This subarea-based arrangement converts the issue of trajectory planning into allowing UAVs to frequently visit each subarea. Each subarea is initiated with a risk level by creating a fire risk map optimizing the UAV patrol route more precisely. Through a set of simulations conducted with a real trace of the dataset, the performance outcomes confirmed the superiority of the proposed idea.