Dual-sensor fusion based attitude holding of a fin-actuated robotic fish

In nature, the lateral line system (LLS) is a critical sensor organ of fish for rheotaxis in complex environments. Inspired by the LLS, numbers of artificial lateral line systems (ALLSs) have been designed to the fish-like robots for flow field perception, assisting the robots to be stable in the face of flow disturbances. However, almost all pressure sensor based ALLSs face the challenge of the low signal to noise ratio (SNR), resulting in inaccurate perception information. To solve this problem, this paper describes a dual-sensor fusion method by integrating the ALLSs with the inertial measurement unit (IMU), and shows the excellent performance by a higher precision and lower latency attitude holding of robotic fish. First, low-pass filtering is performed on ALLS data with low-SNR. Second, the ALLS data is mapped to the angle of attack based on an artificial neural network. Finally, a fusion perception method is established based on the time correlation between ALLS and IMU. To demonstrate the efficacy of our proposed method, we compare the result of attitude holding by three methods (dual-sensor fusion method, IMU based method, and ALLS based method). Furthermore, dual-sensor fusion method is tested at varied flow velocities and varied desired angles of attack, indicating that the algorithm can enable the robotic fish to perform dynamic movements in the incoming flow. This work provides a method for the attitude control of autonomous underwater vehicles (AUVs) by fusing the sensory data of ALLS and IMU, which is also applicable to other flow sensors and IMU.