Automatic Drone Landing on a Boat: Theory and Preliminary Experimental Results

Recent rapid advances within autonomy for unmanned aerial vehicles (UAVs) make it possible to consider deploying them for critical operations in challenging environments, such as the ocean. Going even further, the combined operation of UAVs and autonomous surface vessels (ASVs) could be a strong enabler for exploration and search and rescue missions at sea, to name a few examples. The main contribution of this paper involves designing the perception, planning and control modules for the fundamental phase of such a collaborative operation, that is, the automatic drone landing of an UAV on an ASV. The perception system utilizes an Extended Kalman Filter that fuses the estimates from two computer vision systems, one based on deep learning and one on traditional edge detection approaches. The control system is a cascaded structure of position, velocity, and attitude control. The high-level planning is based on the GraphPlan algorithm and generates action sequences to solve three missions of different complexity based on a set of defined domain variables. The efficiency of the proposed solution is demonstrated via field trials involving a Parrot Anafi quadcopter landing on a helipad installed on DNV’s Revolt ASV. Our preliminary experimental results demonstrate that the system is capable of landing the UAV on the Revolt at sea, in conditions that were almost stationary, or involved forced rolling motion.