Locomotion learning for an anguilliform robotic fish using central pattern generator approach

In this paper, we present locomotion learning for an Anguilliform robotic fish using a central pattern generator (CPG) approach. First, we give the overall structure of the CPG. Different from a traditional CPG that contains only coupled oscillators, our CPG consists of coupled Andronov-Hopf oscillators, an artificial neural network (ANN), and an outer amplitude modulator. Coupled oscillators, which possess a limit-cycle character, are used to generate inputs to excite the ANN. The ANN serves as a learning mechanism, from which we can obtain desired waveforms. By inputting different signals to the ANN, different desired locomotion patterns can be obtained. Outer amplitude modulator resizes the amplitudes of the ANN outputs according to task specifications. The CPG possess temporal scalability, spatial scalability, and phase-shift property; thus, we can obtain desired amplitudes, oscillation frequencies, and phase differences by tuning corresponding parameters. By extracting the swimming pattern from a real fish and using the CPG approach, we successfully generate a new swimming pattern and apply it to the robotic fish. The new pattern reserves the swimming characters of the real fish, and it is more suitable to be applied to the robotic fish. By using the new pattern, the robotic fish can perform both forward locomotion and backward locomotion, which are validated by experiments.