eCite Digital Repository
Exploration of the applicability of probabilistic inference for learning control in underactuated autonomous underwater vehicles
Citation
Ariza Ramirez, W and Leong, ZQ and Nguyen, HD and Jayasinghe, SG, Exploration of the applicability of probabilistic inference for learning control in underactuated autonomous underwater vehicles, Autonomous Robots, 44 pp. 1121-1134. ISSN 0929-5593 (2020) [Refereed Article]
Copyright Statement
© Springer Science+Business Media, LLC, part of Springer Nature 2020
DOI: doi:10.1007/s10514-020-09922-z
Abstract
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 reinforcement 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 reinforcement 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 show that probabilistic model-based
reinforcement learning can be a deployable solution to motion control of underactuated AUVs as it can generate capable
policies with minimum quantity of episodes.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | machine learning, AUV, control, PILCO, LOS, underwater vehicle, path tracking, reinforcement learning |
| Research Division: | Engineering |
| Research Group: | Control engineering, mechatronics and robotics |
| Research Field: | Simulation, modelling, and programming of mechatronics systems |
| Objective Division: | Transport |
| Objective Group: | Water transport |
| Objective Field: | Autonomous water vehicles |
| UTAS Author: | Ariza Ramirez, W (Mr Wilmer Ariza Ramirez) |
| UTAS Author: | Leong, ZQ (Dr Zhi Leong) |
| UTAS Author: | Nguyen, HD (Dr Hung Nguyen) |
| UTAS Author: | Jayasinghe, SG (Dr Shantha Jayasinghe Arachchillage) |
| ID Code: | 145437 |
| Year Published: | 2020 |
| Web of Science® Times Cited: | 6 |
| Deposited By: | NC Maritime Engineering and Hydrodynamics |
| Deposited On: | 2021-07-21 |
| Last Modified: | 2021-09-29 |
| Downloads: | 0 |
Repository Staff Only: item control page