Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research.

Biological fish exhibit remarkable adaptability and exceptional swimming performance through their powerful and flexible bodies. Therefore, designing a continuum flexible body is significantly important for the development of a robotic fish. However, it is still challenging to replicate these functions of a biological body due to the limitations of actuation and material. In this paper, based on a tensegrity structure, we propose a bionic design scheme for a continuum robotic fish body with a property of stiffness variation. Its detailed structures and actuation principles are also presented. A mathematical model was established to analyze the bending characteristics of the tensegrity structure, which demonstrates the feasibility of mimicking the fish-like oscillation propulsion. Additionally, the stiffness variation mechanism is also exhibited experimentally to validate the effectiveness of the designed tensegrity fish body. Finally, a novel bionic robotic fish design scheme is proposed, integrating an electronic module-equipped fish head, a tensegrity body, and a flexible tail with a caudal fin. Subsequently, a prototype was developed. Extensive experiments were conducted to explore how control parameters and stiffness variation influence swimming velocity and turning performance. The obtained results reveal that the oscillation amplitude, frequency, and stiffness variation of the tensegrity robotic fish play crucial roles in swimming motions. With the stiffness variation, the developed tensegrity robotic fish achieves a maximum swimming velocity of 295 mm/s (0.84 body length per second, BL/s). Moreover, the bionic tensegrity robotic fish also performs a steering motion with a minimum turning radius of 230 mm (0.68 BL) and an angular velocity of 46.6°/s. The conducted studies will shed light on the novel design of a continuum robotic fish equipped with stiffness variation mechanisms.

Aquatic animals such as fish and cetaceans can actively modulate their body stiffness with muscle to achieve excellent swimming performance under different situations. However, it is still challenging for a robotic swimmer with bionic propulsion mode to dynamically adjust its body stiffness to improve the swimming speed due to the difficulties in designing an effective stiffness adjustment structure. In this paper, based on the special torque mode of a motor, we propose an active variable stiffness control method for a robotic dolphin to pursue better swimming speed. Different from a variable stiffness structure design, a torque control strategy for the caudal motor is employed to imitate the physical property of a torsion spring to act as the variable stiffness component. In addition, we also establish a dynamic model with the Lagrangian method to explore the variable stiffness mechanism. Extensive experiments have validated the dynamic model, and then the relationships between frequency and stiffness on swimming performance are presented. More importantly, through integrating the dynamic model and torque actuation mode-based variable stiffness mechanism, the online performance optimization scheme can be easily realized, providing valuable guidance in coordinating system parameters. Finally, experiments have demonstrated the stiffness adjustment capability of the caudal joint, validating the effectiveness of the proposed control method. The results also reveal that stiffness plays an essential role in swimming motion, and the active stiffness adjustment can significantly contribute to performance improvement in both speed and efficiency. Namely, with the adjustment of stiffness, the maximum speed of our robotic dolphin achieves up to 1.12 body length per second (BL/s) at 2.88 Hz increasing by 0.44 BL/s. Additionally, the efficiency is also improved by 37%. The conducted works will offer some new insights into the stiffness adjustment of robotic swimmers for better swimming performance.

Biological fish exhibit a remarkably broad-spectrum visual perception capability. Inspired by the eye arrangement of biological fish, we design a fish-like binocular vision system, thereby endowing underwater bionic robots with an exceptionally broad visual perception capacity. Firstly, based on the design principles of binocular visual field overlap and tangency to streamlined shapes, a fish-like vision system is developed for underwater robots, enabling wide-field underwater perception without a waterproof cover. Secondly, addressing the significant distortion and parallax of the vision system, a visual field stitching algorithm is proposed to merge the binocular fields of view and obtain a complete perception image. Thirdly, an orientation alignment method is proposed that draws scales for yaw and pitch angles in the stitched images to provide a reference for the orientation of objects of interest within the field of view. Finally, underwater experiments evaluate the perception capabilities of the fish-like vision system, confirming the effectiveness of the visual field stitching algorithm and the orientation alignment method. The results show that the constructed vision system, when used underwater, achieves a horizontal field of view of 306.56°. The conducted work advances the visual perception capabilities of underwater robots and presents a novel approach to and insight for fish-inspired visual systems.

The flyingfish has long inspired the design of bioinspired robots capable of traversing both aquatic and aerial environments. This study introduces an untethered, miniaturized hybrid-driven robotic platform designed for multimodal motion across aquatic and aerial environments. Compared to previous designs, the proposed robot features a substantially reduced form factor while employing a synergistic propulsion strategy—combining high-frequency fish-like undulation with propeller-based thrust—to enable agile maneuvering on the water surface, underwater, and during aerial phases. Experimental results demonstrate that the swimming robot can attain a peak speed of 6.88 body lengths per second on the surface, which significantly outperforms single-mode counterparts while maintaining robust attitude control during rapid transitions. To enable high-speed pitch regulation and seamless water-to-air transitions, we derive a comprehensive dynamic model and develop a reverse-dynamics-based sliding-mode control scheme, integrated with $z$–$\theta$ plane line-of-sight guidance and a state observer. The robot's full cross-domain mobility is experimentally verified, including the complete transition sequence from submerged standby to attitude adjustment and aerial acceleration. In a safe indoor laboratory environment, the highest recorded aerial flying height exceeds approximately 5.5 times the body length. This work provides critical insights into the design and control paradigms for next-generation miniaturized aquatic robots, highlighting the promise of bioinspired hybrid propulsion and adaptive control strategies for cross-domain applications.

The development of bionic underwater robots has brought new vitality to ocean exploration. Motion control is crucial for the stability of underwater robots due to significant differences in flow field characteristics at various swimming speeds. This study focuses on vertical-plane motion and proposes a model predictive control method to achieve integrated control of depth position and pitch attitude for bionic robotic fish. First, based on a robotic tuna system, high-maneuverability vertical-plane motion configuration elements are analyzed and summarized, laying the foundation for motion stability and controllability. Second, through hydrodynamic sampling in aquatic environments, a system model covering the range of swimming speeds is established. Regarding the control method, the proposed motion planning approach converts the desired motion sequence into an equivalent “pitch-depth” trajectory curve. A nonlinear model predictive controller (NMPC) is then designed to track the trajectory curve, ultimately achieving the desired vertical-plane motion. Experimental results validate that the proposed method not only ensures control accuracy under both low and high-speed conditions, but also enables the execution of complex motion sequence control. This study provides a fresh perspective on the motion instability analysis of robotic fish at high swimming speed and a novel control framework for regulating continuous posture sequences in the vertical plane. Note to Practitioners—The motivation of this paper is to address the challenges associated with stable motion and control of robotic fish in the vertical plane, given the variability of flow field characteristics at different swimming speeds. Existing methods for controlling pitch attitude and depth in bionic underwater robots are typically designed for stable flow conditions encountered during low-speed swimming. However, the instability and agility of high-speed robotic fish movements have not been adequately considered. Additionally, the coupling between pitch attitude and depth poses challenges for joint control of their combined states. This paper proposes a configuration analysis and control methodology to achieve desired vertical-plane locomotion for robotic fish. Specifically, using a robotic tuna as the research subject, a configuration analysis method for high-maneuverability motion in the vertical plane is presented, providing a foundation for ensuring motion stability and controllability. To accurately evaluate the motion of robotic fish under varying flow speeds, a system model for vertical plane motion is constructed based on hydrodynamic data collected from aquatic environments. A motion planning approach is proposed to convert desired vertical plane motion sequences into controllable “pitch-depth” trajectory curves, and a nonlinear model predictive controller is designed to track these trajectories. Configuration simulations and control experiments validate the effectiveness of the proposed method. Hopefully, our proposed methods can provide valuable insights and support for high-maneuverability motion control and continuous posture sequences tracking of bionic underwater robots in the vertical plane.