Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.

We develop a multitask and multifidelity Gaussian process (MMGP) model to accurately predict and optimize the multiobjective performance of a flapping foil while minimizing the cost of high-fidelity data. Through a comparison of three kernels, we have selected and applied the spectral mixture kernel and validated the robustness and effectiveness of a multiacquisition function. To effectively incorporate data with varying levels of fidelity, we have adopted a linear prior formula-based multifidelity framework. Additionally, Bayesian optimization with a multiacquisition function is adopted by the MMGP model to enable multitask active learning. The results unequivocally demonstrate that the MMGP model serves as a highly capable and efficient framework for effectively addressing the multiobjective challenges associated with flapping foils.

To optimize flapping motion for pitching foil dynamics, we significantly expand the search space to include non-periodic feedback laws through the application of the cluster-based control method, which is integrated with interpolation and machine learning optimization techniques. This approach allows for dynamic adjustment between transient and steady-state conditions, leading to an 18% increase in thrust efficiency compared to traditional sinusoidal waveforms. By clustering flow field dynamics into two cycle categories with eight clusters and optimizing closed-loop control laws, we achieve accelerated convergence to the optimal thrust cycle. The proposed approach not only refines control strategies for pitching foil dynamics but also ensures robustness and efficiency in the system performance.