| Autor: |
Ramirez, Wilmer Ariza, Leong, Zhi Q., Nguyen, Hung D., Jayasinghe, S. G. |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Auton.Robot. 44 (2020) 1121-1134 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s10514-020-09922-z |
| Popis: |
Underwater vehicles are employed in the exploration of dynamic environments where tuning of a specific controller for each task would be time-consuming and unreliable as the controller depends on calculated mathematical coefficients in idealised conditions. For such a case, learning task from experience can be a useful alternative. This paper explores the capability of probabilistic inference learning to control autonomous underwater vehicles that can be used for different tasks without re-programming the controller. Probabilistic inference learning uses a Gaussian process model of the real vehicle to learn the correct policy with a small number of real field experiments. The use of probabilistic reinforced learning looks for a simple implementation of controllers without the burden of coefficients calculation, controller tuning or system identification. A series of computational simulations were employed to test the applicability of model-based reinforced learning in underwater vehicles. Three simulation scenarios were evaluated: waypoint tracking, depth control and 3D path tracking control. The 3D path tracking is done by coupling together a line-of-sight law with probabilistic inference for learning control. As a comparison study LOS-PILCO algorithm can perform better than a robust LOS-PID. The results shows that probabilistic model based reinforced learning is a possible solution to motion control of underactuated AUVs as can generate capable policies with minimum quantity of episodes. |
| Databáze: |
arXiv |
| Externí odkaz: |
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