Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System

@article{Lee_Viswanath_Sharma_Geder_Pruessner_Zhou_2024, title={Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26863}, DOI={10.1609/aaai.v37i13.26863}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lee, Julian and Viswanath, Kamal and Sharma, Alisha and Geder, Jason and Pruessner, Marius and Zhou, Brian}, year={2024}, month={Jul.}, pages={15703-15709} }