Name
Title | ||
|---|---|---|
Autonomous Vehicular Landings on the Deck of an Unmanned Surface Vehicle using Deep Reinforcement Learning. |
Abstract | ||
|---|---|---|
Autonomous 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1017/S0263574719000316 | ROBOTICA |
Keywords | DocType | Volume |
Deep reinforcement learning,Unmanned aerial vehicle,Autonomous agents | Journal | 37 |
Issue | ISSN | Citations |
11 | 0263-5747 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Polvara | 1 | 2 | 1.31 |
| Sanjay Sharma | 2 | 6 | 4.03 |
| Jian Wan | 3 | 2 | 2.12 |
| Andrew Manning | 4 | 0 | 0.68 |
| R. Sutton | 5 | 8 | 3.63 |