ORCID
- Sharma, Sanjay: 0000-0002-5062-3199
Abstract
SummaryAutonomous landing on the deck of a boat or an unmanned surface vehicle (USV) is the minimum requirement for increasing the autonomy of water monitoring missions. This paper introduces an end-to-end control technique based on deep reinforcement learning for landing an unmanned aerial vehicle on a visual marker located on the deck of a USV. The solution proposed consists of a hierarchy of Deep Q-Networks (DQNs) used as high-level navigation policies that address the two phases of the flight: the marker detection and the descending manoeuvre. Few technical improvements have been proposed to stabilize the learning process, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Simulated studies proved the robustness of the proposed algorithm against different perturbations acting on the marine vessel. The performances obtained are comparable with a state-of-the-art method based on template matching.
DOI
10.1017/s0263574719000316
Publication Date
2019-04-08
Publication Title
Robotica
ISSN
0263-5747
Embargo Period
2019-10-08
Organisational Unit
School of Engineering, Computing and Mathematics
First Page
1
Last Page
16
Recommended Citation
Polvara, R., Sharma, S., Wan, J., Manning, A., & Sutton, R. (2019) 'Autonomous Vehicular Landings on the Deck of an Unmanned Surface Vehicle using Deep Reinforcement Learning', Robotica, , pp. 1-16. Available at: https://doi.org/10.1017/s0263574719000316
