Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
Abstract
Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide high maneuverability for naval tasks such as surveillance and terrain exploration. Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics. We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. Our inverse model finds a set of fin kinematics with the multi-objective goal of reaching a target thrust and creating a smooth kinematic transition between flapping cycles. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.
Cite
Text
Lee et al. "Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26863Markdown
[Lee et al. "Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/lee2023aaai-data/) doi:10.1609/AAAI.V37I13.26863BibTeX
@inproceedings{lee2023aaai-data,
title = {{Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System}},
author = {Lee, Julian and Viswanath, Kamal and Sharma, Alisha and Geder, Jason and Pruessner, Marius and Zhou, Brian},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {15703-15709},
doi = {10.1609/AAAI.V37I13.26863},
url = {https://mlanthology.org/aaai/2023/lee2023aaai-data/}
}