Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual-Inertial Approach

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 1; p. 404
• Main Authors: Chang, Ching-Wei, Lo, Li-Yu, Cheung, Hiu Ching, Feng, Yurong, Yang, An-Shik, Wen, Chih-Yung, Zhou, Weifeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Airborne/spaceborne computers; Automobiles; Cameras; Computation; Computer software industry; Control methods; deep learning; Design; Drone aircraft; Efficiency; Flight operations; kalman filter; Landing; object tracking; Sea vessels; Semiconductor industry; sensor fusion; Sensors; Trajectory control; Trajectory optimization; UAV; Unmanned aerial vehicles; Vehicles; VTOL; United States; United Kingdom; China; autonomous landing; deep learning; VTOL; kalman filter; object tracking; sensor fusion; optimal trajectory; UAV
• DOI: 10.3390/s22010404
• ISSN: 1424-8220

This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.