A flexible topological representation consisting of a two-layer graph structure built on-board an Unmanned Aerial Vehicle (UAV) by continuously filling the free space of an occupancy map with intersecting spheres is proposed in this \paper{}. Most state-of-the-art planning methods find the shortest paths while keeping the UAV at a pre-defined distance from obstacles. Planning over the proposed structure reaches this pre-defined distance only when necessary, maintaining a safer distance otherwise, while also being orders of magnitude faster than other state-of-the-art methods. Furthermore, we demonstrate how this graph representation can be converted into a lightweight shareable topological-volumetric map of the environment, which enables decentralized multi-robot cooperation. The proposed approach was successfully validated in several kilometers of real subterranean environments, such as caves, devastated industrial buildings, and in the harsh and complex setting of the final event of the DARPA SubT Challenge, which aims to mimic the conditions of real search and rescue missions as closely as possible, and where our approach achieved the \nth{2} place in the virtual track.

翻译：本文提出一种灵活的拓扑表达方法，通过在飞行过程中持续用相交球体填充占据地图的自由空间，构建一种机载双层图结构。现有主流规划方法大多在保持无人机与障碍物预设安全距离的前提下寻找最短路径。基于所提图结构进行规划时，仅在必要时才将距离缩减至预设值，其他情况下保持更安全的间距，同时计算速度较其它主流方法提升数个数量级。此外，我们展示了如何将该图结构转换为轻量级可共享的环境拓扑-体积地图，从而实现去中心化多机器人协作。该方法在数公里真实地下环境（包括洞穴、废墟工业建筑）及DARPA SubT挑战赛决赛的严苛复杂场景中成功验证——该项赛事旨在尽可能逼真模拟真实搜救任务条件，我们最终在虚拟赛道中取得第二名。