Session: 06-17-03 AI Technology for Ocean Engineering III

Paper Number: 123840

123840 - Automatic Drone Landing on a Boat: Theory and Preliminary Experimental Results 

Recent rapid advances within autonomy for unmanned aerial vehicles (UAV) make it possible to consider deploying them for critical operations at 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 a 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.

 

This paper presents the numerical simulations and experimental results of the work done in Falang (2023).
[1] M. Falang, “Autonomous uav landing on a boat - perception, control and mission planning,” Master’s thesis, Norwegian University of Science and Technology, 7034 Trondheim, Norway., 2022.

 

 

Presenting Author: Miguel Hinostroza Norwegian University of Science and Technology (NTNU)

Presenting Author Biography: Miguel Hinostroza received the Ph.D. and M.Sc. degrees in Naval Architecture and Ocean Engineering from University of Lisbon, Portugal in 2021 and 2015, respectively.

He is currently a Researcher with the Department of Engineering Cybernetics, Norwegian University of Science and Technology, Norway. His current research interests include AI planning, dynamics, and control of autonomous robots, motion planning, guidance and control, experimental and model test with academic and industrial aerial, ground and maritime robots.

Authors:

Martin Falang Norwegian University of Science and Technology
Miguel Hinostroza Norwegian University of Science and Technology (NTNU)
Peter Bull Hove Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology
Thomas Sundvoll Equinor ASA
Anastasios Lekkas Norwegian University of Science and Technology (NTNU)