| Autor: |
Andreas B. Martinsen, Anastasios M. Lekkas, Sébastien Gros, Jon Arne Glomsrud, Tom Arne Pedersen |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Frontiers in Robotics and AI, Vol 7 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2296-9144 |
| DOI: |
10.3389/frobt.2020.00032 |
| Popis: |
We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of environmental forces. The method's efficiency is evaluated via simulations and sea trials, with the unmanned surface vehicle (USV) ReVolt performing three different tracking tasks: The four corner DP test, straight-path tracking and curved-path tracking. The results demonstrate the method's ability to accomplish the control objectives and a good agreement between the performance achieved in the Revolt Digital Twin and the sea trials. Finally, we include an section with considerations about assurance for RL-based methods and where our approach stands in terms of the main challenges. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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