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.

Aquatic organisms are known for their ability to generate efficient propulsion with low energy expenditure. While existing research has sought to leverage bio-inspired structures to reduce energy costs in underwater robotics, the crucial role of control policies in enhancing efficiency has often been overlooked. In this study, we optimize the motion of a bio-mimetic robotic fish using deep reinforcement learning (DRL) to maximize propulsion efficiency and minimize energy consumption. Our novel DRL approach incorporates extended pressure perception, a transformer model processing sequences of observations, and a policy transfer scheme. Notably, significantly improved training stability and speed within our approach allow for end-to-end training of the robotic fish. This enables agiler responses to hydrodynamic environments and possesses greater optimization potential compared to pre-defined motion pattern controls. Our experiments are conducted on a serially connected rigid robotic fish in a free stream with a Reynolds number of 6000 using computational fluid dynamics simulations. The DRL-trained policies yield impressive results, demonstrating both high efficiency and propulsion. The policies also showcase the agent's embodiment, skillfully utilizing its body structure and engaging with surrounding fluid dynamics, as revealed through flow analysis. This study provides valuable insights into the bio-mimetic underwater robots optimization through DRL training, capitalizing on their structural advantages, and ultimately contributing to more efficient underwater propulsion systems.

The mutual interaction among multiple fish during schooling has significant implication on motion pattern control and hydrodynamic optimization. However, the collective motion of multiple objects in a flow field forms a vast parameter space, causing difficulty in comprehensively analyzing and considering each parameter. To address this issue, the problem is simplified to a foil pair oscillating in a side-by-side configuration in a two-dimensional flow. Moreover, the Gaussian process regression predictive algorithm is combined with the fast and robust boundary data immersion method CFD algorithm to form a iteration loop for value prediction of the large parameter space. Through a relatively small number of simulations (around 1000 data points), we obtained predictions for the entire four-dimensional parameter space that consists of more than 160 000 parameter sets, greatly improving the computational efficiency. After obtaining the predicted space, we analyzed the interactions between different parameters and specially described the mechanism that gives rise to the unique effect of phase difference on the efficiency of the overall system and individual foils.