Swimming Robot

Swimming robots are being developed to explore diverse underwater environments, from ocean depths to controlled laboratory settings, with primary objectives focused on efficient navigation and task completion. Current research emphasizes the use of reinforcement learning (RL) algorithms, particularly deep Q-networks (DQNs), to control robot locomotion and achieve tasks like object tracking and plume following, often complementing or replacing traditional PID controllers. These advancements are improving autonomous navigation capabilities and enabling applications such as environmental monitoring and underwater inspection, while also driving innovation in soft robotics and flow-based control strategies.