Generation of robotic fish locomotion through biomimetic learning

This paper presents a novel biomimetic learning approach for a Carangiform robotic fish to learn swimming locomotion. A video recording system is first set up to capture real fish behaviors that are used as the training samples. Three basic Carangiform swimming motion patterns, “cruise”, “cruise in turning” and “C sharp turn”, are extracted from robotic perspective. A general internal model (GIM) is adopted as a universal central pattern generator (CPG). Based on the universal function approximation ability and the temporal/spatial scalabilities of GIM, biomimetic learning is performed such that the robotic fish is able to learn to generate the same or similar fish swimming motion patterns. The three swimming motion patterns are implemented on a multi-joint robotic fish. The effectiveness of the biomimetic learning approach is verified through experiment results.