Reliable deployment of Unmanned Aerial Vehicles (UAVs) in cluttered unknown environments requires accurate sensors for obstacle avoidance. Such a requirement limits the usage of cheap and micro-scale vehicles with constrained payload capacity if industrial-grade reliability and precision are required. This paper investigates the possibility of offloading the necessity to carry heavy and expensive obstacle sensors to another member of the UAV team while preserving the desired obstacle avoidance capability. A novel cooperative guidance framework offloading the obstacle sensing requirements from a minimalistic secondary UAV to a superior primary UAV is proposed. The primary UAV constructs a dense occupancy map of the environment and plans collision-free paths for both UAVs to ensure reaching the desired secondary UAV's goal. The primary UAV guides the secondary UAV to follow the planned path while tracking the UAV using Light Detection and Ranging (LiDAR)-based relative localization. The proposed approach was verified in real-world experiments with a heterogeneous team of a 3D LiDAR-equipped primary UAV and a camera-equipped secondary UAV moving autonomously through unknown cluttered Global Navigation Satellite System (GNSS)-denied environments with the proposed framework running completely on board the UAVs.

翻译：在杂乱未知环境中可靠部署无人航空器（UAV）需要具备精确传感器以实现避障。这一要求限制了廉价且载荷能力有限的微型飞行器的使用，若需达到工业级可靠性与精度。本文研究将携带重型昂贵障碍物传感器的必要性转移至UAV团队中另一成员，同时保持所需避障能力的可行性。提出了一种新颖的协同制导框架，将障碍物感知需求从功能简约的次级UAV卸载至增强型主UAV。主UAV构建环境密集占据地图，并为两架UAV规划无碰撞路径，以确保次级UAV到达指定目标。主UAV引导次级UAV沿规划路径飞行，同时利用基于激光雷达（LiDAR）的相对定位技术跟踪次级UAV。该方法通过异构团队的真实世界实验验证：一架配备三维LiDAR的主UAV与一架搭载摄像头的次级UAV，在未知杂乱且全球导航卫星系统（GNSS）拒止环境中自主飞行，整套框架完全在机载计算平台上运行。