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

Autor:	Lee, Julian, Viswanath, Kamal, Geder, Jason, Sharma, Alisha, Pruessner, Marius, Zhou, Brian
Rok vydání:	2022
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 7 figures. Under review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.06369 Zobrazit plný text záznamu View this record from Arxiv