🤖 AI Summary
Micro-drones struggle with reliable localization and obstacle avoidance in GNSS-denied, unknown, and cluttered environments due to stringent payload and sensor constraints.
Method: This paper proposes a sensor-offloading heterogeneous cooperative navigation framework: a master drone equipped with a 3D LiDAR performs real-time dense occupancy mapping, LiDAR-SLAM, and distributed two-drone collision-free path planning; a lightweight follower drone, carrying only a monocular camera, achieves closed-loop visual tracking by leveraging relative pose estimates provided by the master’s LiDAR.
Contribution/Results: For the first time, perception, mapping, and decision-making are centralized on the master platform, drastically reducing computational and hardware requirements on the follower. Full onboard validation in GPS-denied, visually complex real-world environments demonstrates robust navigation: the micro-drone reliably traverses narrow passages and accurately reaches target locations, confirming the feasibility of highly reliable, resource-efficient cooperative navigation.
📝 Abstract
Reliable deployment of Unmanned Aerial Vehicles (UAVs) in cluttered unknown environments requires accurate sensors for Global Navigation Satellite System (GNSS)-denied localization and 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 sensors to another member of the UAV team while preserving the desired capability of the smaller robot intended for exploring narrow passages. A novel cooperative guidance framework offloading the 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 goals even in areas not accessible by the bigger robot. 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 micro-scale camera-equipped secondary UAV moving autonomously through unknown cluttered GNSS-denied environments with the proposed framework running fully on board the UAVs.