Motion Control of a multi-joint robotic fish based on biomimetic learning

Abstract

In this paper, a biomimetic learning approach is applied for motion control of a multi-joint robotic fish. In the learning approach, a general internal model (GIM) is employed to learn coordinated fish-like locomotion from observing live fish swimming. Owing to the scalabilities of the GIM, the learning approach is able to regenerate similar swim patterns with different temporal/spatial scales in the robot. Through experimental analysis, we find out that the motion states, namely speed and orientation, can be controlled by tuning the GIM parameters as well. Based on this control mechanism, feedback control strategies are designed to achieve desired motion. Finally, the effectiveness of the proposed motion control approach is verified by experiments.