Robotic Fish

Robotic fish research aims to create autonomous underwater vehicles that mimic the agility and efficiency of biological counterparts, primarily for applications like underwater inspection, search and rescue, and environmental monitoring. Current research emphasizes developing advanced control algorithms, often employing deep reinforcement learning and neural networks (like LSTMs and CNNs), to optimize swimming performance and maneuverability in complex flow fields, including those with vortices. These advancements are improving the efficiency and robustness of robotic fish, bridging the gap between simulated and real-world performance and paving the way for more effective deployment in diverse aquatic environments.