Robotic fish with tunable stiffness soft tail based on bionic spinal muscle system

While recent advancements in hybrid propulsion systems for bionic robotic fish—combining biomimetic mechanisms with classical vector thrusters—demonstrate enhanced locomotion capabilities and application potential, challenges remain in modeling the coupled dynamics of heterogeneous propulsion mechanisms. This paper presents a hybrid-drive robotic fish architecture that synergistically integrates pectoral-fin-mounted propellers with a caudal-fin-based propulsion system. A three-dimensional dynamical model is developed to characterize the coupled interactions between the dual propulsion modes, incorporating a hydrodynamic computation framework that accounts for propeller wake effects on caudal fin performance. Systematic experimental validation confirms the model’s fidelity through quantitative analysis of swimming performance metrics, including cruising speed, turning radius, and trajectory tracking. The results show that the proposed hybrid propulsion strategy can effectively improve the swimming performance of the robotic fish, and the model can effectively predict the motions such as speed, turning diameter, and trajectory of the robotic fish, which provides a new idea for the development of bionic robotic fish.

A bionic robotic fish has great potential application prospect. High maneuverability swimming control of a bionic robotic fish has been one of the research hotspots in the robotic fish field. In this paper, an iterative learning method has been proposed to solve the trajectory tracking control problem of robotic fish swimming. First, a dynamic model of the multi-joint bionic robotic fish is established. By considering a three-joint robotic fish as an example, the unified expression of the dynamic equation of the three-joint bionic robotic fish is obtained by Lagrange method. Second, the iterative learning controller for controlling the bionic robotic fish is designed. Then the convergence of the iterative learning controller is proved. Finally, the trajectory tracking control simulation experiment based on iterative learning is conducted. The simulation results show that the trajectory tracking control method based on iterative learning for a bionic robotic fish is effective.

Bionic robotic fish has attracted increasing attention due to its potential applications. However, rapid positioning and precise control is a great challenge for the research of bionic robotic fish. In order to realize the bionic robot fish target detection, this paper proposes a bionic robot fish target detection method based on YOLOv3 with global vision. Firstly, the target detection data set of bionic robotic fish was generated and annotated. The data set included 4,074 sample images, including the motion states of robotic fish turning, swimming straight, stationary, and lighting changing. Secondly, The improved K-means clustering method was used to obtain a priori bounding box size suitable for robotic fish. Furthermore, by modifying and testing different internal parameters, a suitable robotic fish target detection model was obtained and tested. The experimental results showed that even in the case of deformation, light changing and water flow fluctuation, accurate detection can still be achieved. It verified the effectiveness of the proposed bionic robot fish target detection method based on YOLOv3.

Recent state-of-art researches on robot fish focus on revealing different swimming mechanisms and developing control methods to imitate the kinematics of the real fish formulated by the so-called Lighthill's theory. However, the reason why robot fish must follow this formula has not been fully studied. In this paper, we adopt a biomimetic untethered robot fish to study the kinematics of fish flapping. The robot fish consists of a wire-driven body and a soft compliant tail, which can perform undulatory motion using one motor. A dynamic model integrated with surrounding fluid is developed to predict the cruising speed, static thrust, dynamic thrust, and yaw stability of the robot fish. Three driving patterns of the motor are experimented to achieve three kinematic patterns of the robot fish, e.g., triangular pattern, sinusoidal pattern, and an over-cambered sinusoidal pattern. Based on the experiment results, it is found that the sinusoidal pattern generated the largest average static thrust and steady cruising speed, while the triangular pattern achieved the best yaw stability. The over-cambered sinusoidal pattern was compromised in both metrics. Moreover, the kinematics study has shown that the body curves of the robot fish were similar to the referenced body curves presented by the formula when using the sinusoidal pattern, especially the major thrust generation area. This research provides a guidance on the kinematic optimization and motor control of the undulatory robot fish.

Bionic robotic fish is one of the important equipment for marine resources exploration in recent years, as the key link of position control for bionic robotic fish, attitude detection is the basis for bionic robotic fish to complete the complex exploration tasks. In order to solve the problem of robotic fish attitude detection, it needs to implement the data fusion based on the obtained original data from accelerometer, magnetometer, gyroscope. However, it’s a nonlinear system for robotic fish attitude detection in practice, and it may occur abnormal data deviation caused by the system external interference or internal disturbance from the sensors, to solve this problem, in this paper, it proposes the limiting filtering-extended Kalman filtering (LF-EKF) algorithm, which combines the limiting filtering (LF) and extended Kalman filtering (EKF) algorithms to realize the data fusion and complete the attitude detection. The simulation results show that the proposed algorithm can obtain the results with good performance.

The present study focuses on summarizing the recent advancements in the field of fish swimming mode research and bionic robotic fish prototypes based on smart materials. It has been widely acknowledged that fish exhibit exceptional swimming efficiency and manoeuvrability compared to conventional underwater vehicles. In the pursuit of developing autonomous underwater vehicles (AUVs), conventional experimental methods often prove to be complex and expensive. Hence, the utilization of computer simulations for hydrodynamic modelling provides a cost-effective and efficient approach for analysing the swimming behaviour of bionic robotic fish. Additionally, computer simulations can provide data that are difficult to obtain through experimental methods. Smart materials, which integrate perception, drive, and control functions, are increasingly being applied to bionic robotic fish research. However, the utilization of smart materials in this field is still an area of ongoing research and several challenges remain unresolved. This study provides an overview of the current state of research on fish swimming modes and the development of hydrodynamic modelling. The application of four distinct types of smart materials in bionic robotic fish is then reviewed, with a focus on analysing the advantages and disadvantages of each material in driving swimming behaviour. In conclusion, the paper highlights the key technical challenges that must be addressed for the practical implementation of bionic robotic fish and provides insights into the potential future directions of this field.

Fishes have evolved different excellent swimming strategies. To study the influence of tail fin swing on the swimming performance of bionic robot fish, with one joint under the same tail swing frequency and amplitude, we designed a novel robot fish, driven by a double-cam mechanism. By designing the profile of the cam in the mechanism, the robot fish can achieve different undulatory motion trajectory of the caudal fin under the same tail swing frequency and amplitude. The mechanism simulated the undulatory motion of crucian carp. We studied the influence of undulatory motion on the swimming speed of robot fish, which was analyzed by dynamic analysis of the undulatory motion and experiments. According to the experimental results, we can find that the swimming speed of the robotic fish is different under various wave motions. When other conditions are the same, the speed that the robot fish can achieve by imitating the swing motion of the real fish is 1.5 times that of the robot fish doing the cycloid motion. The experimental results correspond to the kinetic analysis results. Furthermore, it is proven that the robot fish with a low caudal peduncle stiffness swims faster under a low swinging frequency, and the speed of a robot fish with a high caudal peduncle stiffness is higher under a high tail swinging frequency.

The butterfly stroke of man's swimming imitates a dolphin, which is excellent in swimming. Therefore, the authors created a swimming humanoid robot which swam like a dolphin. This swimming robot was able to swim in various ways and the operation sequence of servomotors for better swimming was acquired by the particle swarm optimization (PSO). Since fishes can swim skillfully in spite of their simple structure, the velocity distribution of water flow around a real swimming fish has been measured by PIV and analyzed by CFD in order to create new fish robots which can swim like a living fish. A flexible fish robot with 6 motor-string actuators was created and their operation sequence was semi-optimized by the genetic algorithm (GA). Moreover, a wireless-controlled small fish robot with two servomotors was created. It has been shown by PIV measurement that this fish robot with a suitable motor operation sequence generates the reverse Karman vortex street as a real swimming fish does.

SUMMARYWe have developed an underwater robotic fish using a unique three-layer electrostatic film motor. In the robotic fish, the unique motor actuates a flexible caudal fin to propel the robot via an elaborate power transmission system. In the present study, we describe the major disadvantages of the previous prototype of the robotic fish and improvements of the prototype. In addition, we present experimental evaluations related to the control parameters and locomotion performance of the robotic fish. These control parameters include the frequency and initial phase of AC voltage, and the amplitude and period of frequency sweeping. A simple theoretical model concerning the power transmission system of the robotic fish is also analyzed to provide a possible explanation for the unique swimming control. By appropriately adjusting these control parameters, we achieve cruising, emerging, submerging, and turning of the robotic fish even though only the caudal fin is active. Finally, we show smooth human-operated turn-around motion similar to that seen in real fish. Based on these experimental results, we further clarify the relationships between the open-loop motor pattern and motion parameters.

Bionic robotic fish has a significant impact on design and control of innovative underwater robots capable of both rapid swimming and high maneuverability. This paper explores the relationship between Central Pattern Generator (CPG) based locomotion control and energy consumption of a miniature self-propelled robotic fish. To this end, a real-time energy measurement system compatible with the CPG-based locomotion control is firstly built on an embedded system. Then, tests are conducted on the untethered actual robot. The results indicate that different CPG feature parameters involving amplitude, frequency, and phase lag play distinct roles in energy consumption under different swimming gaits. Specifically, energy consumption is positively correlated with the changes in the amplitude and frequency of CPGs, whereas the phase lag of CPGs has little influence on the energy consumption. It may offer important inspiration for improving energy efficiency and locomotion performance of versatile swimming gaits.

To address the complex dynamic control of multi‐source coupled drive systems in bionic robotic fish, this paper proposes a BP neural network–fuzzy fusion optimization algorithm. The method employs a parallel architecture that takes depth error, depth change rate, and pitch angle as inputs. Fuzzy control ensures rapid response to nonlinear disturbances, while an offline‐trained BP neural network enhances the modeling accuracy of complex dynamics. The core innovation involves dynamically adjusting the fusion weights of both controllers using sliding window error trends, enabling coordinated control of propeller speed, tail fin oscillation, and slider displacement. Experimental results indicate that under disturbance‐free conditions, the fused algorithm achieves an average integral absolute error (IAE) of 18.6 ms, representing reductions of 16.2% and 40.5% compared to individual fuzzy and BP neural network control, respectively. Response time is reduced to 23 s with overshoot limited to 45%. In anti‐interference and mass perturbation tests, the algorithm exhibits significantly better convergence speed and stability than conventional methods. The dynamic weight allocation mechanism successfully combines the rapidity of fuzzy logic with the precision of neural networks, offering an effective solution for multimodal motion control of underwater bionic robotic fish. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This paper takes the high-frequency vibration characteristics of a bionic robot fish as the research object. Through research on the vibration characteristics of a bionic fish, we quantified the role of voltage and beat frequency in high-speed and stable swimming. We proposed a new type of electromagnetic drive. The tail is made of 0° silica gel to simulate the elastic characteristics of fish muscles. We completed a series of experimental studies on the vibration characteristics of biomimetic robotic fish. Through the single-joint fishtail underwater experiment, the influence of vibration characteristics on parameters during swimming was discussed. In terms of control, the central mode generator control method (CPG) control model is adopted, and a replacement layer is designed in combination with particle swarm optimization (PSO). By changing the elastic modulus of the fishtail, the fishtail resonates with the vibrator, and the swimming efficiency of the bionic fish is improved. Finally, through the prototype experiment, it is found that the bionic robot fish can achieve high-speed swimming through high-frequency vibration.

PurposeFor precisely presenting the swimming behavior of fish robots underwater and the practical implementation purpose, this paper aims to investigate a well-formulated fish robot model which integrates the nonlinear rigid body dynamics, kinematics and models of actuators.Design/methodology/approachThis fish robot model is mainly built up by three basic parts: a balance mechanism, a four-links vibrator and a caudal fin. In the fish robot’s head, there is a balance mechanism used to control the rotations in pitch and roll directions of the fish robot by moving two movable masses. The four-links vibrator with three active joints actuated by DC motors is designed to vibrate the fish’s body. In the end of the fish robot body, a caudal fin which connects with the passive joint is developed to generate hydrodynamic thrust forces to propel the fish robot.FindingsFrom the real stability tests and control verification, it is obvious that this proposed model can precisely present the swimming behavior of fish robots and possesses the potential to develop a fish-like robotic prototype.Originality/valueA well-formulated model with dynamics of actuators is integrated for presenting the swimming behavior of carangiform locomotion type fish robots in this investigation. From the simulation results and the practical test of a real fish robot, the feasibility of this proposed model for building up real fish robots can be proven, and this proposed model is accurate enough to effectively present the swimming behavior of fish robots.

Robotic fish are ideal for surveying fish resources and performing underwater structural inspections. If a robot is sufficiently fishlike in appearance and does not use a screw propeller, real fish will not be easily surprised by it. However, it is comparatively difficult for such a robot to determine its own position in water. Radio signals, such as those used by GPS, cannot be easily received. Moreover, sound ranging is impractical because of the presence of rocks and waterweed in places where fish spend a lot of time. For practical applications such as photographing fish, a robotic fish needs to follow the target fish without losing awareness of its own position, in order to be able to swim autonomously. We have developed a robotic fish named FOCUS (FPGA Offline Control Underwater Searcher) which is equipped with two CMOS cameras and a field-programmable gate array (FPGA) circuit board for data processing. The forward-facing camera is used to track red objects, since this is the color of the fish of interest. In addition, using visual information obtained with the bottom-facing camera, the robot can estimate its present position. This is achieved by performing real-time digital image correlation using the FPGA. However, until now, the position estimation accuracy has been poor due to the influence of yaw and roll. In the present study, the position estimation method has been greatly improved by taking into account the yaw and roll values measured using gyro sensors.

This paper is concerned with the design, optimization, and motion control of a radiocontrolled, multi-link, free-swimming biomimetic robotic fish based on an optimized kinematic and dynamic model of fish swimming. The performance of the robotic fish is determined by both the fish’s morphological characteristics and kinematic parameters. By applying ichthyologic theories of propulsion, a design framework that takes into consideration both mechatronic constraints in physical realization and feasibility of control methods is presented, under which a multiple linked robotic fish that integrates both the carangiform and anguilliform swimming modes can be easily developed. Taking account of both theoretic hydrodynamic issues and practical problems in engineering realization, the optimal link-lengthratios are numerically calculated by an improved constrained cyclic variable method, which are successfully applied to a series of real robotic fishes. The rhythmic movements of swimming are driven by a central pattern generator (CPG) based on nonlinear oscillations, and up-and-down motion by regulation the rotating angle of pectoral fins. The experimental results verify that the presented scheme and method are effective in design and implementation.