  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, etc.. 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 paper, we focus on
visual coverage optimization with drone-mounted camera sensors. In particular,
we consider the specific case in which the coverage requirements are uneven,
meaning that different parts of the environment have different coverage
priorities. We model these coverage requirements with relevance maps and
propose a deep reinforcement learning algorithm to guide the swarm. The paper
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.

Computer Vision and Pattern Recognition