Real-Time Estimation for the Swimming Direction of Robotic Fish Based on IMU Sensors

An increasing number of underwater robots inspired by Carangidae are developed, which is characterized by high efficiency and flexibility. However, estimating the swimming direction of these robotic fish is challenging due to the constant swinging of the head during movement, which complicates precise control. In this study, we installed two low-cost inertial measurement unit (IMU) sensors separately on the head and tail parts of a double-joint robotic fish and presented a method for accurately and timely estimating the swimming direction. Firstly, we effectively compensated for the yaw angle drift of the IMU sensors through a fused Kalman Filter. Furthermore, we propose the Anti-Shake Estimation (ASE) algorithm to calculate the real-time swimming direction using filtered yaw angles at a high updating rate of 100Hz. Finally, we applied the method to swimming direction feedback control for evaluation and comparison. The results show that our ASE method performs better than other existing methods in straight-line swimming experiments. The experiment of S-curve swimming also demonstrates the effectiveness of our method in complex missions.