RIE News May 2022 issue featured research from Assistant Prof Pablo Valdivia y Alvarado regarding Underwater Stingray Robots, see page 5, RIE News: https://www.nrf.gov.sg/docs/default-source/default-document-library/nrf-magazine/nrf-magazine-(may-2022)a.pdf
Researchers from the Singapore University of Technology and Design (SUTD) developed a new approach to model the dynamics of underwater stingray-like robots using Machine Learning. This approach can enable more efficient swimming in complex underwater environments by accurately predicting required flapping motions for a set of given propulsive force targets. Bio-inspired soft robots are unique due to their elegant, natural movements. However, modelling and controlling soft robot bodies underwater are challenging due to their infinite degrees of freedom and complex dynamics. The research team focused on developing a suitable Deep Neural Network (DNN) model to predict desired flapping motions to achieve the required locomotion in a rapidly changing environment. Unlike traditional physics-based models, DNN models can provide minimal input-output relationships for the complex dynamics found in soft bodies.