Organizations: Department of Mechanical Engineering, The University of Tokyo, Tokyo, 113-8656, Japan · Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Robotic fish have attracted growing attention in recent years owing to their biomimetic design and potential applications in environmental monitoring and biological surveys. Among robotic fish employing the Body-Caudal Fin (BCF) locomotion pattern, motor-driven actuation is widely adopted. Some approaches utilize multiple servo motors to achieve precise body curvature control, while others employ a brushless motor to drive the tail via wire or rod, enabling higher oscillation and swimming speeds. However, the former approaches typically result in limited swimming speed, whereas the latter suffer from poor maneuverability, with few capable of smooth turning. To address this trade-off, we develop a wire-driven robotic fish equipped with a 2-degree-of-freedom (DoF) crank-slider mechanism that decouples propulsion from steering, enabling both high swimming speed and agile maneuvering. In this paper, we first present the design of the robotic fish, including the elastic skeleton, waterproof structure, and the actuation mechanism that realizes the decoupling. We then establish the actuation modeling and body dynamics to analyze the locomotion behavior. Furthermore, we propose a combined feedforward-feedback control strategy to achieve independent regulation of propulsion and steering. Finally, we validate the feasibility of the design, modeling, and control through a series of prototype experiments, demonstrating swimming, turning, and directional control.
Figures & tables
Fig. 1: The biomimetic fish robot based on a 2-DoF crank-slider mechanism
Fig. 2: Hardware design of the robotic fish: (a) rigid head shell; (b) segmented body sections; (c) passive caudal fin; (d) 2-DoF crank–slider actuation mechanism; (e) elastic spine; (f) driving wire; (g) pectoral fins.
Fig. 3: The 2-DoF crank-slider based actuation mechanism.
Fig. 4: Waterproof design of the fish: (a) O-ring based primary waterproof structure for the rigid head; (b) the skin composed of two sections that enclose the entire body of the robotic fish.
Fig. 5: Operating modes of the 2-DoF crank–slider actuation mechanism for directional control. (a) Principle of the symmetric mode; (b) reel output in symmetric mode; (c) principle of the asymmetric mode; (d) reel output in asymmetric mode.
Fig. 6: Dynamic model of the elastic tail.
Fig. 7: Required motor torque for achieving 1Hz oscillation, estimated using the dynamic model.
Fig. 8: Control framework of the robotic fish. (a) Controlled volume and reference frames; (b) block diagram of the control system; (c) swimming direction during body oscillation.
Fig. 9: Prototype of the robotic fish and its key components. (a) microcontroller unit (MCU) and onboard PC; (b) actuation mechanism; (c) waterproof silicone skin; (d) waterproof structure of the rigid head shell; (e) motors used in the prototype.
Fig. 10: Software architecture of the robotic fish.
Fig. 11: Flapping-in-air experiment for validating the theoretical flapping angle of the robotic fish. (a) Motion capture balls attached to each body section to measure position and orientation; (b) comparison between theoretical prediction and experimental data.
Fig. 12: Forward swimming experiment of the robotic fish in symmetric mode. (a) Swimming trajectory recorded in a water tank; (b) real-time position obtained from motion capture; (c) IMU measurements and motor loads during swimming.
Fig. 13: Turning experiment of the robotic fish in asymmetric mode. (a) Circular swimming trajectory in a water tank; (b) real-time position recorded by motion capture; (c) IMU measurements and motor loads during turning.
Fig. 14: Direction control experiment of the robotic fish in asymmetric mode. (a) Swimming trajectory in a water tank; (b) real-time position recorded by motion capture; (c) comparison of reference, actual, and motion-capture angles.
Magnetically actuated fish-like robots offer promising solutions for underwater exploration due to their miniaturization and agility; however, precise control remains a significant challenge because of nonlinear fluid dynamics, flexible fin hysteresis, and the variable-duration control steps inherent to the actuation mechanism. This paper proposes a comprehensive data-driven control framework to address these complexities without relying on analytical modeling. Our methodology comprises three core components: 1) developing a forward dynamics model (FDM) using a neural network trained on real-world experimental data to capture state transitions under varying time steps; 2) integrating this FDM into a gradient-based model predictive control (G-MPC) architecture to optimize control inputs for path following; and 3) applying imitation learning to approximate the G-MPC policy, thereby reducing the computational cost for real-time implementation. We validate the approach through simulations utilizing the identified dynamics model. The results demonstrate that the G-MPC framework achieves accurate path convergence with minimal root mean square error (RMSE), and the imitation learning controller (ILC) effectively replicates this performance. This study highlights the potential of data-driven control strategies for the precise navigation of miniature, fish-like soft robots.
Akiyuki Koyama, Hiroaki Kawashima
Graduate School of Information Science, University of Hyogo, Japan
Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.
In this paper, we introduce the Soft Lamprey-Inspired Dual Environment Robot (SLIDER) and a proper modeling and optimization procedure employed to design the robot. We represent the primary fluid environment actions - inertial effects, vortex forces, and viscous dissipation - using Lighthill's theory for large-amplitude elongated bodies. For structural design parameters such as internal pressure, tail size, and body stiffness, a fast, geometrically and materially nonlinear model is developed and validated. The fluid-structure interaction equations are solved implicitly with an efficient second-order box method. A pneumatic manifold robotic system is employed to actuate SLIDER in a quiescent water tank environment, allowing cross-comparison of computational and experimental results. We find that low-frequency swimming is dominated by resistant environmental forces, whereas higher-frequency swimming is primarily affected by inertial fluid forces. Using our efficient model alongside a genetic algorithm, we co-optimize a swimming control pattern and caudal fin design (subject to SLIDER's climbing morphology) to achieve a tethered swimming speed of 21.7 +/- 0.4 cm/s (0.59 Bl/s). Furthermore, we investigate the optimization procedure for a multimodal robot performing both swimming and climbing tasks.
Brian Van Stratum, James Gallentine, Caleb Rucker +3
FAMU/FSU College of Engineering, Tallahassee, FL 323109 · Mechanical Engineering, Vanderbilt University, Nashville Tennessee 37235 · Mechanical Engineering, The University of Tennessee, Knoxville, Tennessee 37996