Drone swarm patrolling with uneven coverage requirements

TL;DR

This paper introduces a deep reinforcement learning approach for controlling drone swarms to optimize visual coverage in environments with uneven coverage priorities, demonstrating improved performance over standard algorithms.

Contribution

It presents a novel deep reinforcement learning framework for multi-drone coverage with relevance maps, extending from single to multiple drones with cooperative strategies.

Findings

Effective coverage optimization with relevance maps

Improved performance over standard patrolling algorithms

Successful extension from single to multi-drone scenarios

Abstract

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.