Underwater Soft

Underwater soft robotics aims to develop robots with flexible bodies capable of navigating aquatic environments, often mimicking biological designs for enhanced maneuverability and efficiency. Current research emphasizes improving control systems through closed-loop feedback mechanisms using embedded sensors and machine learning algorithms like Bayesian Optimization and LSTM networks for accurate shape estimation and motion prediction. This field is significant for advancing bio-inspired robotics, enabling more efficient and adaptable underwater exploration, and potentially leading to new applications in areas such as environmental monitoring and search and rescue.