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.

中文翻译：

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覆盖范围要求不均衡的无人机群巡逻

在许多实际场景中，越来越多的无人机被使用，例如监视，环境监测，难以接近的区域的搜救等。虽然可以由操作员来指导一架无人驾驶飞机，但是部署多架无人机需要针对自动任务控制的适当算法。在这项研究中，作者专注于使用无人机安装的摄像头传感器进行视觉覆盖率优化。特别是，他们考虑了覆盖范围要求不均匀的特定情况，这意味着环境的不同部分具有不同的覆盖范围优先级。他们使用相关图对这些覆盖范围需求进行建模，并提出了一种深度强化学习算法来指导群体。这项研究首先为单个无人机定义了合适的学习模型，然后将其扩展到具有贪婪和合作策略的多架无人机的情况。实验结果表明，与标准巡逻算法相比，该方法的性能更好。