Online Model Adaptation of Autonomous Underwater Vehicles with LSTM Networks
Miguel Bande Firvida, Bilal Wehbe
Editors: Miguel Bande Firvida, Bilal Wehbe
In Online Model Adaptation of Autonomous Underwater Vehicles with LSTM Networks, (OCEANS-2021), 20.9.-12.9.2021, Porto, IEEE, pages 1-6, 2021. IEEE. ISBN: 978-0-692-93559-0.
This work addresses the online learning and adaptation of Autonomous Underwater vehicle (AUV) models. A framework is presented that employs long-short term memory (LSTM) networks which can be used to model the temporal dependencies in the AUV data. To reduce the effect of catastrophic forgetting, a memory efficient rehearsal method is developed with including and forgetting strategies to manage the butter of training samples. The proposed method is validated using experimental data proving its capability to adapt to real changes in the vehicles dynamics.
AUV dynamics, model learning, on-line adapta- tion