This project focuses on the research and development of a novel bionic robot inspired by the morphology and locomotion of rays. The primary goal is to address the limitations of traditional underwater robots, such as high noise levels, excessive vortex generation, and heat production from high-speed rotating propellers. Our solution is an energy-efficient, environmentally friendly bionic robot suitable for diverse environments. We established mathematical and kinematic models for a propulsion mechanism driven by twelve coordinated servos controlling a biomimetic fin. Through systematic analysis of parameters including servo torque, arm length, spatial arrangement, fin material, and fin shape proportion, we proposed an optimal design for the propulsion system. This design grants our bionic robot significantly higher efficiency compared to conventional underwater vehicles, filling a technological gap in this type of propulsion mechanism domestically.
The designed robotic ray model utilizes a multi-servo linkage system to drive flexible rubber fins. This design leverages the advantages of coordinated multi-servo actuation, allowing the rubber fin to generate diverse undulatory patterns based on the swing form of the servo arms. To manage the complexity of wave propagation, twelve waterproof servos (25 kg torque rating) are symmetrically arranged, six on each side, at equal intervals. This configuration enables precise control over the fin’s motion. The servos are fixed to the hull via designed brackets, and their output shafts are connected to arms. The connection arms on one side of the body are arranged in a wavy pattern.
Balancing performance and payload capacity, we optimized the fin’s shape and aspect ratio. To ensure a combination of elasticity and strength, a 2mm-thick common rubber sheet was selected as the biomimetic material. Two rubber fin plates are hinged on either side of the main body. All connection arms on one side are fixed to their respective fin plate. For biomimetic fidelity and aesthetics, the rubber fins are cut into a semi-arc shape, wider in the middle and tapering at the ends. To accommodate the elongation of the fin plate during undulatory deformation, the interval between adjacent connection points on the fin (90 mm) is set larger than the interval between the corresponding servo arms (70 mm).
Technical Principles and Design Methodology
1. Fluid Dynamics Analysis and Hull Optimization
The hydrodynamic performance of the bionic robot hull is critical for efficient propulsion. We began by simplifying the computational model, focusing on the main hull structure and omitting non-essential details like servo ports and drainage holes for computational clarity. The simplified model was then placed within an external flow field for analysis. Adhering to the “four in front, eight behind” principle to ensure fully developed wake flow and computational accuracy, the hull was positioned in the front section of a sufficiently large flow domain.
The merged model was imported into ANSYS Fluent for computational fluid dynamics (CFD) simulation. The key parameter for optimization was the curvature radius of the leading edge of the hull. Multiple design schemes were generated by varying this curvature using SolidWorks surface modeling tools. The optimization strategy involved an initial coarse screening with large intervals, identifying a promising range, followed by a finer analysis with smaller intervals to determine the optimal value. Practical engineering constraints were also considered, avoiding an obsessive pursuit of theoretically perfect but impractical shapes. Five analysis models with leading-edge curvature radii (R) of 100 mm, 125 mm, 150 mm, 175 mm, and 200 mm were created.
For the numerical simulation, the hull was treated as a rigid body. The mesh was divided into three parts: the external flow field, the leading-edge section of the hull, and the remainder of the hull. Due to the complex curved geometry of the leading edge, an unstructured tetrahedral mesh was employed, with local refinement applied to this region. All meshes contained approximately 3.15 million elements and passed quality checks. The simulation was set as a steady-state analysis. The Reynolds number was calculated, and the turbulence model was set to the standard k-ε model. The pressure-velocity coupling scheme was SIMPLE, with default under-relaxation factors.
The primary evaluation metric was the maximum static pressure on the hull surface, as lower pressure typically indicates better hydrodynamic shape with reduced drag. The results for the five curvature values are summarized below.
| Curvature Radius, R (mm) | Maximum Static Pressure (Pa) | Relative Performance |
|---|---|---|
| 100 | P_100 | Poor |
| 125 | P_125 | Moderate |
| 150 | P_150 | Good |
| 175 | P_175 | Best (Initial) |
| 200 | P_200 | Moderate |
Analysis of the data trend indicated that a curvature radius of 175 mm provided the most favorable hydrodynamic performance (lowest maximum pressure). This value was then taken as the center for a final refinement step. Two additional models with R = 170 mm and R = 180 mm were analyzed.
| Curvature Radius, R (mm) | Maximum Static Pressure (Pa) | Conclusion |
|---|---|---|
| 170 | P_170 | Inferior to R=175mm |
| 175 | P_175 | Optimal |
| 180 | P_180 | Inferior to R=175mm |
In conclusion, the CFD simulations demonstrated that a leading-edge curvature radius of 175 mm offers the optimal balance of fluid dynamic efficiency and practical manufacturability for this bionic robot hull.
2. Kinematic Model of the Undulatory Fin Propulsion
The core innovation of this bionic robot is its bio-inspired undulatory fin. The motion of the fin is generated by N servos (N=12 in our design) arranged in a line along the fin’s root. Each servo arm produces an angular oscillation, which is transferred to the flexible fin. The vertical displacement y of a point on the fin at a distance x from the root and at time t can be modeled as a traveling wave equation:
$$y(x,t) = A(x) \cdot \sin\left(\frac{2\pi}{\lambda} x – \frac{2\pi}{T} t + \phi_0\right)$$
where:
- $A(x)$ is the amplitude envelope, often increasing from root to tip.
- $\lambda$ is the wavelength of the undulation along the fin.
- $T$ is the period of oscillation.
- $\phi_0$ is the initial phase.
The servo at position $x_i$ must create the appropriate displacement $y(x_i, t)$. Given the servo arm length $L_{arm}$ and its angular displacement $\theta_i(t)$, the vertical motion at the connection point is approximately $L_{arm} \sin(\theta_i(t))$. Therefore, the control law for each servo is derived by matching this motion to the desired wave form:
$$\theta_i(t) = \arcsin\left(\frac{1}{L_{arm}} \cdot A(x_i) \cdot \sin\left(\frac{2\pi}{\lambda} x_i – \omega t + \phi_0\right)\right)$$
where $\omega = 2\pi / T$ is the angular frequency. The thrust force generated by the fin can be estimated by analyzing the momentum change of the water. The average thrust $F_{thrust}$ over one period is proportional to the square of the lateral velocity of the fin tip and the fin area:
$$F_{thrust} \propto \rho S \left( \frac{\partial y(L,t)}{\partial t} \right)^2$$
where $\rho$ is water density, $S$ is the effective fin area, and $L$ is the fin length. This principle of generating thrust by creating a reverse von Kármán vortex street is far more efficient than rotary propellers, as it minimizes energy loss to turbulence.
3. Electronic System and Sensor Integration
This compact bionic robot is designed for reconnaissance and monitoring tasks, such as hydrographic data collection and military detection. It features a protective exoskeleton for collision resistance, the described undulatory fin propulsion for high thrust and strong anti-current capability, and a manipulator claw for object interaction.
The control system is hierarchical. Operator commands are sent via cable to an RS-485 communication module. An STM32 microcontroller receives and decodes these signals to control the servos on the manipulator claw for object grasping.
A suite of miniaturized sensors is integrated for environmental monitoring and real-time observation:
| Sensor Type | Model / Principle | Key Specifications | Output/Interface |
|---|---|---|---|
| Temperature | Waterproof Probe XH-T106 | Range: -50 to 105°C; Accuracy: ±1% | Analog/Resistance |
| Depth/Pressure | MS5837 I2C Pressure Sensor | Depth Resolution: 2 mm | I2C Digital |
| Dissolved Oxygen (DO) | Electrochemical Sensor 40XV ME2-02 with Signal Conditioning | – | UART (115200 baud), Calibrated % vol. |
| pH | Combination Electrode with Temperature Compensation (DS18B20) | Range: 0-14; Operating Temp: -55 to 125°C | Analog, corrected via microcontroller |
| Turbidity | Optical (Scattering/Transmission) | Response Time <500 ms; Operating Temp: -30 to 80°C | Analog, requires ADC |
| Total Dissolved Solids (TDS) | Conductivity-based, Frequency Method | Range: 0-2000 ppm; Accuracy: ±5% | UART, packaged data for TDS and temperature |
The overall underwater monitoring system comprises three major subsystems: Control, Power/Propulsion, and Structural/Auxiliary. The control system is based on a dual-controller architecture for robustness and task distribution.
| Component | Role | Key Functions |
|---|---|---|
| Raspberry Pi (Main Controller) | High-level processing | Image processing, data logging from inertial sensors. |
| Pixhawk (Co-Controller) | Low-level real-time control | Reads onboard IMU/compass; collects sensor data; controls servos, thrusters, lights; stabilizes attitude using sensor fusion. |
| STM32 | Dedicated I/O control | Interfaces with RS-485, manipulator servos, and some sensor modules. |
The power system includes batteries, servos, and the rubber fins. A 4200 mAh battery provides extended operation. A 24-channel servo control board manages the twelve fin servos. Remote control is enabled via a Bluetooth-connected gamepad. For obstacle avoidance, a diffuse-reflective photoelectric sensor (E18-D80NK) is employed, chosen for its long detection range and immunity to visible light interference.
4. Network Transmission Framework
Data transmission from the bionic robot is conceptualized using a layered network model. Each layer performs specific functions, receiving information from the layer above, processing it, and passing it to the layer below. At the application layer, protocols define how processes exchange messages. Protocol identification fields are added to the message header before transmission. Upon reception, these headers are examined to direct the message payload to the correct application socket (e.g., for video streaming or sensor telemetry). This encapsulation ensures reliable and organized data flow from the robot to the surface station.
Innovations and Advantages
The primary innovation lies in the application of reverse von Kármán vortex street principles. The pectoral fins generate translational waves that produce forward thrust. By controlling the wave pattern, frequency, wavelength, and propagation direction on the fin surface, we can precisely modulate the magnitude and direction of thrust. This biomimetic wave propulsion offers an effective method for momentum and energy transfer. Compared to traditional screw propellers, this bionic robot demonstrates superior acceleration during high-speed cruising and higher energy utilization efficiency, achieving an estimated 30% reduction in energy loss and a 40% improvement in propulsion efficiency. The system is inherently more efficient, flexible, and quiet.
Another significant innovation is the extensive use of 3D printing for rapid prototyping and part manufacturing. The hull and structural components are printed as single pieces using PLA material. This approach ensures high-strength, low-cost, and high-precision production without the need for traditional machining tools or molds. It simplifies assembly, facilitates design iteration, and aligns with lightweight and eco-friendly design principles, ultimately enhancing maneuverability and reducing production time and cost.
The selection of the fin material was the result of rigorous experimentation with various environmentally friendly options. We evaluated materials based on stiffness, ductility, and swimming performance. The final choice, a novel hybrid rubber compound, provides an optimal balance: sufficient flexibility to maintain surface continuity during undulation (minimizing turbulence and maximizing hydrodynamic efficiency) and adequate strength. Remarkably, by adjusting the fin’s angle of attack, the bionic robot can use its fins to support its body on the ground. This enables a periodic sinusoidal undulatory gait that allows for flexible movement not only underwater but also on land, snow, mudflats, and other complex terrains, making it a truly multi-environment bionic robot.
Market Prospects and Applications
As a fusion of fish propulsion biomechanics and robotics, this bionic robot presents a novel paradigm for next-generation underwater vehicles. It holds significant potential for operation in complex and hazardous underwater environments. Key application areas include:
- Military & Security: Underwater reconnaissance, mine detection, and surveillance.
- Search & Rescue: Assisting in underwater rescue operations.
- Scientific Research: Marine biology observation, archaeological exploration, and hydrological surveys.
- Aquaculture & Environmental Monitoring: Intelligent monitoring of fish health, water quality assessment (leveraging its integrated sensor suite).
- Infrastructure Inspection: The flexible, low-drag design allows it to navigate narrow spaces for ship hull inspection, pipeline surveying, dam structure assessment, and port maintenance.
- Multi-Terrain Operations: Its unique locomotion extends its utility to terrestrial applications like road condition monitoring, search and rescue in snowy or rubble-strewn environments, and planetary exploration concepts.
The commercial market for sophisticated bionic robot systems is still nascent, presenting substantial growth opportunities. While current consumer-grade underwater robots are relatively simple, future advancements will likely integrate more complex, human-assistive functionalities. Increased government focus on ocean exploration and blue economy initiatives is expected to provide further policy support and drive market expansion. Our product offers a novel perspective and a robust technological foundation, positioning it to be a significant contributor to the evolving field of bio-inspired robotics.