The use of multi-rotor Unmanned Aerial Vehicles (UAVs) for search and rescue as well as remote sensing is rapidly increasing. Multi-rotor UAVs, however, have limited endurance. The range of UAV applications can be widened if teams of multiple UAVs are used. We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Examples of such environments include indoor scenarios, urban or natural canyons, caves, and tunnels, where the GPS signal is limited or blocked. The framework is based on a probabilistic decentralised Partially Observable Markov Decision Process which accounts for the uncertainties in sensing and the environment. The team can cooperate efficiently, with each UAV sharing only limited processed observations and their locations during the mission. The system is simulated using the Robotic Operating System and Gazebo. Performance of the system with an increasing number of UAVs in several indoor scenarios with obstacles is tested. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.

中文翻译：

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用于在杂乱和 GPS 拒绝环境中进行探索和目标寻找的多无人机系统

多旋翼无人机 (UAV) 用于搜救和遥感的使用正在迅速增加。然而，多旋翼无人机的续航能力有限。如果使用多架无人机组成的团队，则可以扩大无人机的应用范围。我们提出了一个框架，供无人机团队在具有障碍物的复杂 GPS 拒绝环境中合作探索和寻找目标。无人机团队在具有已知地图的杂乱环境中自主导航、探索、检测和找到目标。此类环境的示例包括室内场景、城市或自然峡谷、洞穴和隧道，其中 GPS 信号受到限制或阻塞。该框架基于概率分散的部分可观察马尔可夫决策过程，该过程考虑了传感和环境中的不确定性。团队可以高效合作，每架无人机在任务期间仅共享有限的处理过的观察结果及其位置。该系统使用机器人操作系统和 Gazebo 进行模拟。在几个有障碍物的室内场景中，随着无人机数量的增加，系统的性能得到了测试。结果表明，所提出的多无人机系统在时间成本、调查搜索区域的比例以及搜索和救援任务的成功率方面都有所改进。