Drone swarm patrolling with uneven coverage requirements - Claudio Piciarelli; Gian Luca Foresti (IET Computer Vision, 2020)
Swarms of drones are being more and more used in many practical scenarios, such as surveillance, environmental monitoring, search and rescue in hardly‐accessible areas and so on. While a single drone can be guided by a human operator, the deployment of a swarm of multiple drones requires proper algorithms for automatic task‐oriented control. In this study, the authors focus on visual coverage optimisation with drone‐mounted camera sensors. In particular, they consider the specific case in which the coverage requirements are uneven, meaning that different parts of the environment have different coverage priorities. They model these coverage requirements with relevance maps and propose a deep reinforcement learning algorithm to guide the swarm. This study first defines a proper learning model for a single drone, and then extends it to the case of multiple drones both with greedy and cooperative strategies. Experimental results show the performance of the proposed method, also compared with a standard patrolling algorithm.