Robotic fleets such as unmanned aerial and ground vehicles have been widely used for routine inspections of static environments, where the areas of interest are known and planned in advance. However, in many applications, such areas of interest are unknown and should be identified online during exploration. Thus, this paper considers the problem of simultaneous exploration, inspection of unknown environments and then real-time communication to a mobile ground control station to report the findings. The heterogeneous robots are equipped with different sensors, e.g., long-range lidars for fast exploration and close-range cameras for detailed inspection. Furthermore, global communication is often unavailable in such environments, where the robots can only communicate with each other via ad-hoc wireless networks when they are in close proximity and free of obstruction. This work proposes a novel planning and coordination framework (SLEI3D) that integrates the online strategies for collaborative 3D exploration, adaptive inspection and timely communication (via the intermit-tent or proactive protocols). To account for uncertainties w.r.t. the number and location of features, a multi-layer and multi-rate planning mechanism is developed for inter-and-intra robot subgroups, to actively meet and coordinate their local plans. The proposed framework is validated extensively via high-fidelity simulations of numerous large-scale missions with up to 48 robots and 384 thousand cubic meters. Hardware experiments of 7 robots are also conducted. Project website is available at https://junfengchen-robotics.github.io/SLEI3D/.

翻译：诸如无人飞行器和地面车辆等机器人集群已被广泛用于静态环境的例行巡检，其关注区域通常是已知且预先规划好的。然而，在许多应用中，这些关注区域是未知的，需要在探索过程中在线识别。因此，本文研究了在未知环境中同步进行探索、巡检，并将发现结果通过实时通信报告给移动地面控制站的问题。异构机器人配备了不同的传感器，例如用于快速探索的远程激光雷达和用于详细巡检的近距离相机。此外，此类环境中通常缺乏全局通信，机器人只能通过临时无线网络在彼此靠近且无障碍物时进行通信。本文提出了一种新颖的规划与协调框架（SLEI3D），该框架集成了协作式三维探索、自适应巡检和及时通信（通过间歇性或主动性协议）的在线策略。为应对特征数量与位置的不确定性，本研究开发了一种多层多速率规划机制，用于机器人子群之间及内部，以主动会合并协调其局部规划。所提框架通过大量大规模任务的高保真仿真（涉及多达48个机器人和38.4万立方米空间）进行了广泛验证，并进行了7台机器人的硬件实验。项目网站位于 https://junfengchen-robotics.github.io/SLEI3D/。