CFD-enabled Approach for Optimizing CPG Control Network for Underwater Soft Robotic Fish

Central Pattern Generators (CPG) nonlinear oscillation network is being increasingly used in the control of multi-joint collaborative robots. The motion attitude of robots can be effectively adjusted by tuning parameters of the CPG neural network. However, the mapping from CPG parameters to motion attitude is relatively complicated. To improve the motion performance, an optimization method combining computational fluid dynamics (CFD) and CPG network is proposed. In this work, we design a three-joint biomimetic soft robot fish following the body structure of trevally and an improved CPG network based on the Hopf model is incorporated into the control system. Directly optimizing the swimming performance through experiments is time consuming and complex, a mode of first adjusting parameters on the simulation platform and then refining on the robot is usually adopted. Therefore, a CFD simulation platform using hydrodynamic solutions has been established to assist in analyzing the swimming effect. Finally, the experimental results show that the swimming simulation by the CFD is highly similar to the real test, and the swimming performance after the improved CPG network optimization has been significantly increased.