Robotic Fish Research Articles

LUO-V2 and MSPerception: A multi-fin co-drive robotic fish and its multi-sensor-based experimental system

Experimental systems for analyzing robotic fish swimming play a crucial role in optimizing robotic fish, which prompted us to design an experimental system that combines information from multiple sensors to address the shortcomings of current experimental systems with a single measurement. Furthermore, we are interested in a multi-fin co-drive robotic fish. This paper presents the design of a robotic fish, LUO-V2, and an experimental system, MSPerception. The LUO-V2 represents a novel solution for developing multi-fin co-drive robotic fish. It also provides a controller for LUO-V2, which can synchronously control the multi-fin of the robotic fish. Furthermore, the MSPerception system has been designed based on the fusion of multi-sensor information, which can sense the average thrust, swimming speed, and flow field, which provides an effective tool for studying robotic fish swimming. MSPerception was employed to investigate the factors influencing the swimming performance of LUO-V2 and to compare it with the flow field around the caudal fin of a real fish. The experimental results demonstrated that the multi-fin co-drive significantly enhanced the robotic fish’s performance. This research can provide a theorectical foundation for developing multi-fin co-drive robotic fish and experimental systems.

Data-Assisted Dynamic Modeling of Bionic Robotic Fish and Its Precise Speed Control

Data-Assisted Dynamic Modeling of Bionic Robotic Fish and Its Precise Speed Control

Online Hydraulic Stiffness Modulation of a Soft Robotic Fish tail for Improved Thrust and Efficiency.

This paper explores online stiffness modulation within a single tail stroke for swimming soft robots. Despite advances in stiffening mechanisms, little attention has been given to dynamically adjusting stiffness in real-time, presenting a challenge in developing mechanisms with the requisite bandwidth to match tail actuation. Achieving an optimal balance between thrust and efficiency in swimming soft robots remains elusive, and the paper addresses this challenge by proposing a novel mechanism for independent stiffness control, leveraging fluid-driven stiffening within a patterned pouch. Inspired by fluidic-driven actuation, this approach exhibits high bandwidth and facilitates significant stiffness changes. We perform experiments to demonstrate how this mechanism enhances both thrust and swimming efficiency. The tail actuation and fluid-driven stiffening can be optimized for a specific combination of thrust and efficiency, tailored to the desired maneuver type. The paper further explores the complex interaction between the soft body and surrounding fluid and provides fluid dynamics insights gained from the vortices created during actuation. Through frequency modulation and online stiffening, the study extends the Pareto front of achievable thrust generation and swimming efficiency.

A Bio-Inspired Magnetic Soft Robotic Fish for Efficient Solar-Energy Driven Water Purification.

Solar-driven water evaporation is a promising solution for global water scarcity but is still facing challenges due to its substantial energy requirements. Here, a magnetic soft robotic bionic fish is developed by combining magnetic nanoparticles (Fe3O4), poly(N-isopropylacrylamide), and carboxymethyl chitosan. This bionic fish can release liquid water through hydrophilic/hydrophobic phase transition and dramatically reduce energy consumption. The introduced Fe3O4 nanoparticles endow the bionic fish with magnetic actuation capability, allowing for remote operation and recovery. Additionally, the magnetic actuation process accelerates the water absorption rate of the bionic fish as confirmed by the finite element simulations. The results demonstrate that bionic fish can effectively remove not only organic molecular dyes dissolved in water but also harmful microbes and insoluble microparticles from natural lakes. Moreover, the bionic fish maintains a good purification efficiency even after five recycling cycles. Furthermore, the bionic fish possesses other functions, such as salt purification and salt rejection. Finally, the mechanism of water purification is explained in conjunction with molecular dynamics calculations. This work provides a new approach for efficient solar-energy water purification by phase transition behavior in soft robotics.

Agile robotic fish based on direct drive of continuum body

Fish-like agile movements, such as fast swimming and rapid turning, are essential for biomimetic underwater robots but challenging to achieve simultaneously. We present a self-contained robotic fish capable of swimming at a speed of 6.3 body length per second and pivot turning at an angular speed of 1450° per second. These fast motions, comparable to real fish, are realized by directly oscillating a flexible body using an electromagnetic motor, eliminating the need for transmission parts and simplifying the structure. This direct-drive (DD) method improves mechanical robustness and generates fish-like deformations and subsequent rapid swimming. The Strouhal and swimming numbers of the robot match typical values observed in nature. Moreover, the observed frequency peaks in swimming are similar to computed values using a model, which guides the design of the robot. These results illustrate the DD method as a promising framework for the creation of highly versatile biomimetic underwater robots.

A dynamic model of tensegrity robotic fish considering soft fish skin and tail

A dynamic model of tensegrity robotic fish considering soft fish skin and tail

A robotic fish processing line enhanced by machine learning

This paper presents the design of a comprehensive automatic fish processing line utilizing machine learning algorithms. The processing line encompasses several essential steps, including fish identification by type, fish sorting by size, fish orientation based on shape, and fish cutting at the optimal chopping points. The primary objective of this design is not just automation but also maximizing economic benefits by preserving the maximum amount of fish meat during the cutting process, achieved through the application of machine learning algorithms. To accomplish these goals, we employ a combination of transfer learning and convolutional neural networks to establish criteria for actions across all stages of automatic fish processing. At the heart of the processing station is a conveyor belt equipped with numerous sensors and lenses. Positioned along this conveyor belt are two robotic arms, responsible for precise positioning and cutting operations, all guided by the machine learning algorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.

Locating Dipole Source Using Self-Propelled Robotic Fish With Artificial Lateral Line System

Locating Dipole Source Using Self-Propelled Robotic Fish With Artificial Lateral Line System

Design and multi parameter performance optimization of the bionic robotic fish driven by tail fin

In this paper, we investigate the optimization of body contour parameters and the parameters of the tail fin in bionic robotic fish. A new type of robotic fish with fully flexible shell was proposed, followed by the analysis and optimization of the resistance properties of the profile. Subsequently, a dynamic model considering tail fin deformation was established. We use a combination of Fluent simulation and particle swarm optimization (PSO) algorithm to seek the key parameters for an enchanced performance. Upon multi-parameter performance optimization, the obtained results indicate that the fully flexible shell robotic fish can achieve a high swimming speed of 0.69 m/s (equivalent to 1.2 BL/s) and exhibit excellent turning maneuverability with a turning radius of 0.8 body lengths. This paper provides insights for optimizing the performance of robotic fish and enhancing its underwater operational capabilities.

Finite-Time Line-of-Sight Guidance-Based Path-Following Control for a Wire-Driven Robot Fish.

This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish driven by LOS. First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time convergence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional-integral-derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation. Finally, the yaw-tracking-based path-tracking control strategy is applied to the robotic fish, whose motion is generated by an improved central pattern generator (CPG) and equipped with a six-axis inertial measurement unit for real-time swimming direction. Quantitative comparisons in tank experiments validate the effectiveness of the proposed method.

Numerical study of a self-propelled biomimetic robotic fish driven by pectoral fins in labriform mode

Numerical study of a self-propelled biomimetic robotic fish driven by pectoral fins in labriform mode

A Manta Ray-Inspired Fast-Swimming Soft Electrohydraulic Robotic Fish

A Manta Ray-Inspired Fast-Swimming Soft Electrohydraulic Robotic Fish

Hydrodynamic pressure sensing for a biomimetic robotic fish caudal fin integrated with a resistive pressure sensor

Micro-sensors, such as pressure and flow sensors, are usually adopted to attain actual fluid information around swimming biomimetic robotic fish for hydrodynamic analysis and control. However, most of the reported micro-sensors are mounted discretely on body surfaces of robotic fish and it is impossible to analyzed the hydrodynamics between the caudal fin and the fluid. In this work, a biomimetic caudal fin integrated with a resistive pressure sensor is designed and fabricated by laser machined conductive carbon fibre composites. To analyze the pressure exerted on the caudal fin during underwater oscillation, the pressure on the caudal fin is measured under different oscillating frequencies and angles. Then a model developed from Bernoulli equation indicates that the maximum pressure difference is linear to the quadratic power of the oscillating frequency and the maximum oscillating angle. The fluid disturbance generated by caudal fin oscillating increases with an increase of oscillating frequency, resulting in the decrease of the efficiency of converting the kinetic energy of the caudal fin oscillation into the pressure difference on both sides of the caudal fin. However, perhaps due to the longer stability time of the disturbed fluid, this conversion efficiency increases with the increase of the maximum oscillating angle. Additionally, the pressure variation of the caudal fin oscillating with continuous different oscillating angles is also demonstrated to be detected effectively. It is suggested that the caudal fin integrated with the pressure sensor could be used for sensing thein situflow field in real time and analyzing the hydrodynamics of biomimetic robotic fish.

Propulsion performance of a cownose ray inspired robotic fish flapping pectoral fins near the ground

Cownose rays usually swim near the ground in the ocean and their swimming is inevitably affected by the ground effect. In this work, the hydrodynamic performance of cownose rays during near-ground swimming is investigated by experimental tests. The flapping of the pectoral fin of the robotic and numerical rays presents a high similarity in our kinematics comparison. We find a positive correlation between the flapping projected area in the direction of inflow and the thrust coefficient. The ground effect is found to have a great effect on the cownose rays swimming through our experiments and numerical simulations. The thrust and pitching moment increases as the near-substrate height decreases. Moreover, this ground effect is more prominent for symmetrical flapping compared with asymmetrical flapping. The flapping frequency and amplitude can also affect hydrodynamic performance significantly. This work may provide insight into the kinematics control of the pectoral fin flapping-based bio-inspired robots during near-substrate swimming.

Optimization of swimming mode for elongated undulating fin using multi-agent deep deterministic policy gradient

Optimizing speed and propulsive efficiency are the most crucial survival skills for biomimetic robots. This paper investigates a swimming mode controller inspired by the black Knifefish to govern the fast-swimming gait with high propulsive efficiency for an elongated undulating fin robot. The proposed swimming mode controller is composed of a couple of Hopf oscillator-based central pattern generators (CPG) to generate the moving gait of robotic fish and a novel variant of Reinforcement Learning (RL) known as Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) for optimizing the propulsive efficiency. The proposed swimming controller facilitates the autonomous optimization of the oscillatory amplitude of the robotic fish to improve its propulsive efficiency. The proposed MA-DDPG demonstrates an aptitude for functioning within mixed cooperative-competitive environments. Furthermore, it effectively mitigates the drawback of zero amplitude in the updating process of conventional reinforcement learning (RL) methodologies. These findings highlight the potential utility of the MA-DDPG in optimizing the performance of multi-agent systems in dynamic, real-world scenarios. The simulation results show that the undulating fin robot reaches a maximum thrust of 0.9 N with a propulsive efficiency of 12.48 %, which is higher than that of traditional reinforcement learning methods.

Investigating the Influence of Counterflow Regions on the Hydrodynamic Performance of Biomimetic Robotic Fish.

Biomimetic robotic fish are a novel approach to studying quiet, highly agile, and efficient underwater propulsion systems, attracting significant interest from experts in robotics and engineering. These versatile robots showcase their ability to operate effectively in various water conditions. Nevertheless, the comprehension of the swimming mechanics and the evolution of the flow field of flexible robots in counterflow regions is still unknown. This paper presents a framework for the self-propulsion of robotic fish that imitates biological characteristics. The method utilizes computational fluid dynamics to analyze the hydrodynamic efficiency of the organisms at different frequencies of tail movement, under both still and opposing flow circumstances. Moreover, this study clarifies the mechanisms that explain how changes in the aquatic environment affect the speed and efficiency of propulsion. It also examines the most effective swimming tactics for places with counterflow. The results suggest that the propulsion effectiveness of robotic fish in counterflow locations does not consistently correspond to various tail-beat frequencies. By utilizing vorticity maps, a comparative analysis can identify situations when counterflow zones improve the efficiency of propulsion.

Vision‐Based Online Key Point Estimation of Deformable Robots

The precise control of soft and continuum robots requires knowledge of their shape, which has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built‐in sensors, resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on expensive tracking systems with reflective markers placed on all components, which are infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, a regression approach is presented for three‐dimensional key point estimation using a convolutional neural network. The proposed approach uses data‐driven supervised learning and is capable of online markerless estimation during inference. Two images of a robotic system are captured simultaneously at 25 Hz from different perspectives and fed to the network, which returns for each pair the parameterized key point or piecewise constant curvature shape representations. The proposed approach outperforms markerless state‐of‐the‐art methods by a maximum of 4.5% in estimation accuracy while being more robust and requiring no prior knowledge of the shape. Online evaluations on two types of soft robotic arms and a soft robotic fish demonstrate the method's accuracy and versatility on highly deformable systems.

Autonomous Vision-Based Navigation and Stability Augmentation Control of a Biomimetic Robotic Hammerhead Shark

The application potential of robotic fish embedded with intelligent visual navigation algorithms in underwater autonomous operation is on full display recently. However, the existing visual navigation methods are limited by the underwater visual conditions and the motion characteristics of robotic fish. To this end, this paper proposes a novel autonomous navigation framework integrated with visual stabilization control. In practice, a stereo vision-based navigation network is proposed to generate the guidance law. On this basis, a biomimetic robotic hammerhead shark with a controllable cephalofoil is developed, and a nonlinear model predictive controller for cephalofoil stabilization relying on the dynamic model is elaborately designed. Extensive simulations and underwater experiments are conducted to validate the effectiveness and superiority of the proposed methods, which significantly enhance exploration efficiency and reduce image jitter by 26.02% compared to the traditional methods. The obtained results provide a new idea for underwater robots to autonomously explore the ocean. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of vision-based underwater autonomous navigation for a biomimetic robotic fish that possesses underwater visual stability and good maneuverability. The existing visual navigation networks usually generate unexpected navigation instructions when dealing with complex or ambiguous underwater scenes. Additionally, image jitter caused by the rhythmic motion of robotic fish can lead to navigation failure. This paper suggests an integrated navigation framework that includes a biomimetic platform design, a visual stabilization controller, and an intelligent underwater navigation network. Specifically, a novel sphyrnidae-inspired robotic shark is designed as a new platform with superior motion performance. To enhance underwater visual stability, a nonlinear model predictive control-based visual stabilization controller is proposed. Furthermore, a deep stereo attention navigation network based on a parallax attention mechanism is proposed to improve the generalization of vision-based autonomous navigation. A series of underwater search experiments on the robotic shark demonstrate the effectiveness and superiority of the proposed navigation framework. Hopefully, our proposed methods can provide valuable guidance and support for universal underwater robot navigation to accomplish practical marine tasks, such as underwater rescue, resource exploitation, biological observation, and so on.

Trajectory tracking control of four-jointed robotic fish based on Udwadia-Kalaba theory

Trajectory tracking control of four-jointed robotic fish based on Udwadia-Kalaba theory

A Novel Variable‐Stiffness Tail Based on Layer‐Jamming for Robotic Fish

Fish have excellent swimming performance, and one key factor is their ability to autonomously adjust body stiffness, which can help them efficiently swim at different speeds and complex environments. At present, the variable‐stiffness design of robotic fish still suffers from structural complexity, severe deformation, and small variation range, which limits the application of variable‐stiffness theory in robotic fish. In this article, a variable‐stiffness tail is designed based on layer‐jamming for robotic fish, which can conveniently achieve online stiffness adjustment while maintaining the optimal stiffness distribution and the shape is unaffected. A modeling method for the tail is proposed by combining the mechanical characteristics of the layer‐jamming structure with the pseudo‐rigid body model. To validate the performance of the tail, a series of experiments are conducted, which show that the stiffness variation range of the tail is around 10 times, and the accuracy of the model in predicting the kinematics of the tail is also verified. Moreover, the thrust tests demonstrate that stiffness adjustment is beneficial for fish swimming at different frequencies. The proposed variable‐stiffness tail will promote the development of efficient underwater biomimetic robots.