With the deepening exploration of rivers and oceans by humans, the demand for new types of underwater vehicles is increasing daily. Traditional underwater vehicles often rely on propeller-based propulsion, which faces limitations such as high environmental requirements, low efficiency, poor stability in complex conditions, and significant noise that can interfere with scientific surveys. In contrast, bionic robots, inspired by the natural movements of aquatic organisms, offer unique advantages in propulsion methods and efficiency, garnering increasing attention. This paper presents a bionic robot inspired by the hammerhead shark, controlled by a Central Pattern Generator (CPG). Unlike traditional bionic fish that merely mimic streamlined shapes, this work emphasizes the impact of the biomimetic structure itself on performance. By abstracting the fish body into a jointed linkage structure, a three-joint, four-link bionic robot model is established, simulated, and experimentally validated. Leveraging a simple mechanical design, low control cost, CPG-based control, and flexible silicone materials, this bionic robot achieves high simulation of the hammerhead shark, featuring compact size, simple control, strong terrain adaptability, and high energy utilization. Compared to traditional underwater propellers, it operates with lower noise and greater environmental adaptability, promising broad applications in fields like scientific research, agriculture, underwater rescue, and patrol.
The development of bionic robots, particularly underwater bionic robots, has evolved significantly as researchers seek to replicate the efficient and agile movements of marine life. Fish, for instance, exhibit remarkable maneuverability and propulsion efficiency through body and fin oscillations, which are difficult to achieve with conventional mechanical systems. The hammerhead shark, known for its distinctive head shape and superior mobility, serves as an excellent biomimetic model due to its ability to perform precise turns and maintain stability in dynamic environments. This bionic robot aims to capture these characteristics, integrating biological insights with robotic engineering to advance underwater vehicle technology.
In this study, I focus on the design, control, and testing of a hammerhead shark-inspired bionic robot. The mechanical structure is simplified into a multi-joint model, allowing for controlled undulatory motions that mimic natural swimming gaits. The CPG controller, inspired by neural oscillators in animals, generates rhythmic signals for coordinated joint movements, enabling smooth gait transitions and adaptive behaviors. Through simulation in tools like SolidWorks and Adams-Simulink, followed by experimental validation in a water tank, I demonstrate the robot’s capabilities in forward swimming, turning, and obstacle avoidance. This work contributes to the field of bionic robotics by highlighting the importance of structural biomimicry and efficient control strategies.
To provide a comprehensive overview, this article is structured as follows: First, I analyze the biological inspiration from hammerhead sharks, detailing their physiological traits and locomotion patterns. Next, I describe the mechanical and electrical design of the bionic robot, including material selection and component integration. Then, I delve into the CPG control methodology, presenting mathematical models and simulation results. After that, I present experimental setups and outcomes, validating the robot’s performance. Finally, I discuss conclusions and future directions for bionic robot development.
The image above illustrates a prototype of the bionic robot, showcasing its hammerhead shark-inspired design with a wide head, flexible body, and tail fin. This visual representation highlights the integration of biomimetic features, such as the streamlined shape and articulated joints, which are crucial for efficient underwater locomotion. The use of flexible silicone in the tail enhances wave propagation, contributing to the robot’s natural swimming motions.
Biological Inspiration: Hammerhead Shark Characteristics
Hammerhead sharks (family Sphyrnidae) are cartilaginous fish known for their unique cephalofoil—a flattened, hammer-shaped head that extends laterally. This structure is not merely aesthetic; it plays a vital role in sensory perception and hydrodynamics. Studies have shown that the hammerhead’s head enhances visual fields, allowing for nearly 360-degree binocular vision, which aids in prey detection and navigation. Moreover, the head acts as a lift-generating surface, improving maneuverability and stability during swimming. The body of a hammerhead shark is streamlined, with a robust trunk and a heterocercal tail (asymmetric lobes) that provides thrust through lateral oscillations.
In terms of locomotion, hammerhead sharks primarily use subcarangiform swimming, where undulatory waves pass along the posterior two-thirds of the body. The anterior region, including the head and pectoral fins, remains relatively rigid, contributing to directional control. The dorsal and caudal fins work in synergy to generate propulsion and steering forces. By mimicking these features, a bionic robot can achieve similar efficiency and agility. The table below summarizes key biological traits relevant to the bionic robot design:
| Trait | Description | Biomimetic Application |
|---|---|---|
| Cephalofoil Shape | Wide, flat head for enhanced sensing and hydrodynamics | Robot head housing sensors and acting as a rudder |
| Body Flexibility | Posterior body undulates for propulsion | Articulated joints with silicone tail for wave propagation |
| Fin Configuration | Pectoral, dorsal, and caudal fins for stability and thrust | Actuated fins for maneuvering and balance |
| Swimming Gait | Subcarangiform with lateral oscillations | CPG-controlled joint movements simulating natural gaits |
These insights guide the mechanical abstraction of the shark’s body into a multi-joint system. For the bionic robot, I approximate the fish as a series of links connected by revolute joints, enabling controlled bending motions. This approach simplifies control while preserving essential biological principles.
Mechanical Design of the Bionic Robot
The bionic robot is designed to emulate the hammerhead shark’s form and function. Its dimensions are approximately 700 mm in length, 200 mm in width (310 mm including fins), and 170 mm in height. The structure comprises three main sections: the head, the articulated body, and the flexible tail.
The head is fabricated using 3D-printed rigid polymer, housing critical electronic components such as the power supply, control board, and sensors. It mimics the shark’s cephalofoil, with a wide front that accommodates an ultrasonic distance sensor for obstacle detection. This sensor enables autonomous navigation by providing real-time environmental data. The head’s shape also contributes to hydrodynamic performance, reducing drag and enhancing directional stability.
The body consists of three actuated joints, each driven by a high-torque servo motor (RDS3218). These joints correspond to the anterior, mid, and posterior regions of the shark’s trunk, allowing for undulatory motions. The servos operate on PWM signals, with pulse widths ranging from 500 µs to 2500 µs, enabling precise angular control. The joints are connected by lightweight linkages, forming a three-joint, four-link mechanism that translates rotational movements into body waves.
The tail is made of flexible silicone, cast to replicate the shark’s caudal fin. Silicone’s elasticity facilitates passive wave propagation, reducing energy consumption and creating more fluid motions. This soft material also improves durability and adaptability to underwater obstacles. The entire robot is encapsulated in waterproof plastic film, with electronic components individually sealed to prevent water ingress.
Key design parameters are summarized in the table below:
| Component | Specification | Purpose |
|---|---|---|
| Head Material | 3D-printed polymer | Houses electronics, provides structural integrity |
| Joints | Three servo motors (RDS3218) | Generate controlled bending for propulsion |
| Tail Material | Flexible silicone | Enhances wave propagation and efficiency |
| Sensors | Ultrasonic distance sensor | Enables obstacle avoidance and navigation |
| Control Board | ESP32 microcontroller | Processes data and executes CPG algorithms |
| Power Supply | Lithium battery pack | Provides independent power to servos and board |
This mechanical design balances simplicity and functionality, ensuring that the bionic robot can replicate shark-like movements while remaining cost-effective and easy to maintain. The integration of rigid and flexible materials mimics the biological composition of fish, contributing to the overall performance of the bionic robot.
Electrical and Control System Design
The control system is centered on an ESP32 development board, chosen for its low power consumption, integrated Wi-Fi capabilities, and multiple interface options. The ESP32 runs the CPG control algorithm and manages sensor data, enabling real-time adjustments to the robot’s gait. The servos are connected to the board via PWM pins, allowing for independent control of each joint. The ultrasonic sensor interfaces through UART, providing distance measurements that trigger gait transitions for obstacle avoidance.
To ensure reliable operation, the electronic components are waterproofed using conformal coating and sealed enclosures. The power system includes a lithium battery pack that supplies 6V to the servos and 3.3V to the ESP32, with separate regulators to prevent voltage drops during high-torque movements. The Wi-Fi feature of the ESP32 allows for Over-The-Air (OTA) updates, facilitating remote parameter tuning without physical retrieval of the robot.
The control architecture is hierarchical: the CPG generates rhythmic signals for basic locomotion, while higher-level modules handle sensor-based decisions. This separation simplifies programming and enhances adaptability. For instance, when the ultrasonic sensor detects an obstacle within a threshold distance, the control system switches from forward swimming to turning gait, enabling autonomous navigation. The table below outlines the electrical specifications:
| Element | Details | Function in Bionic Robot |
|---|---|---|
| Microcontroller | ESP32 with dual-core processor | Executes CPG algorithm and sensor integration |
| Servo Motors | RDS3218, 20 kg/cm torque, 4.8-6.5V | Actuate joints for undulatory motions |
| Sensor | Ultrasonic distance sensor (L042MTW) | Measures front obstacles for adaptive control |
| Communication | Wi-Fi and UART protocols | Enables OTA updates and sensor data transmission |
| Power Management | Lithium battery with voltage regulators | Ensures stable power for all components |
This electrical design supports the robust operation of the bionic robot in aquatic environments, emphasizing modularity and ease of maintenance. By leveraging modern microcontroller capabilities, the bionic robot achieves sophisticated control with minimal hardware complexity.
CPG Control Methodology and Mathematical Modeling
Central Pattern Generators (CPGs) are neural circuits that produce rhythmic outputs without requiring periodic input, making them ideal for controlling repetitive motions like swimming, walking, or flying in animals. In this bionic robot, a CPG based on Stein neuron oscillators is implemented to coordinate the three joint servos. Each oscillator corresponds to a joint, and they are coupled to maintain phase relationships that mimic biological undulations.
The CPG model consists of three interconnected oscillators, where the output of each oscillator determines the angular position of a joint. The dynamics of the \(i\)-th oscillator are described by the following equations, which capture the neuron’s membrane potential and adaptation:
$$ \tau \frac{du_i}{dt} = -u_i + \sum_{j=1}^{3} w_{ij} v_j + I_i $$
$$ \tau’ \frac{dv_i}{dt} = -v_i + f(u_i) $$
Here, \(u_i\) represents the membrane potential, \(v_i\) is the output signal, \(\tau\) and \(\tau’\) are time constants, \(w_{ij}\) are coupling weights between oscillators, \(I_i\) is an external input, and \(f(\cdot)\) is a sigmoidal activation function. For simplicity in implementation, a reduced form inspired by Matsuoka oscillators is used:
$$ \ddot{\theta}_i + \alpha \dot{\theta}_i + \beta \theta_i = \gamma \sum_{j \neq i} \sin(\theta_j – \theta_i – \phi_{ij}) $$
where \(\theta_i\) is the phase of oscillator \(i\), \(\alpha\), \(\beta\), \(\gamma\) are parameters controlling damping, stiffness, and coupling strength, and \(\phi_{ij}\) is the desired phase difference between oscillators \(i\) and \(j\). In practice, the outputs are converted to servo angles using:
$$ \phi_i(t) = A_i \sin(2\pi f t + \delta_i) + \epsilon_i $$
where \(\phi_i(t)\) is the joint angle, \(A_i\) is the amplitude, \(f\) is the frequency, \(\delta_i\) is the phase offset, and \(\epsilon_i\) is a bias term for directional control. For forward swimming, all oscillators have the same amplitude and frequency, with constant phase lags (e.g., \(\delta_1 = 0\), \(\delta_2 = \pi/3\), \(\delta_3 = 2\pi/3\)) to create a traveling wave from head to tail. For turning, the bias \(\epsilon_i\) is adjusted to shift the oscillation center, causing asymmetric body bending.
The parameters can be tuned to achieve different gaits, as summarized in the table below:
| Gait Type | Parameters | Effect on Bionic Robot |
|---|---|---|
| Forward Swimming | \(A_i = A\), \(\delta_i = (i-1)\Delta\), \(\epsilon_i = 0\) | Generates propulsive wave for straight-line motion |
| Turning | \(\epsilon_i \neq 0\), with opposite signs for left/right | Produces asymmetric oscillations for directional change |
| Backward Swimming | \(\delta_i\) reversed (e.g., \(\delta_1 = 2\pi/3\), \(\delta_3 = 0\)) | Reverses wave direction for backward movement |
| Acceleration | Increase \(f\) or \(A\) | Enhances speed and thrust |
These equations form the core of the CPG controller, enabling the bionic robot to exhibit versatile locomotion patterns. The integration of such mathematical models with hardware actuation exemplifies the synergy between biology and engineering in bionic robot development.
Simulation and Analysis
To validate the design before physical implementation, I conducted simulations using SolidWorks for mechanical modeling and Adams-Simulink co-simulation for dynamic analysis. In SolidWorks, a 3D model of the bionic robot was created, incorporating the jointed body and shark-like contours. This model allowed for stress analysis and optimization of component placement to ensure structural integrity.
Subsequently, the model was imported into Adams to simulate multi-body dynamics. The joints were assigned rotational degrees of freedom, and the CPG controller was implemented in Simulink using the mathematical equations described earlier. The co-simulation enabled real-time interaction between the control signals and mechanical responses, providing insights into swimming performance under various parameters.
Key simulation results include:
- Forward Speed: The bionic robot achieved a simulated maximum speed of 0.8 m/s with optimal amplitude and frequency settings. The relationship between speed and control parameters can be approximated by:
$$ v \propto A f L $$
where \(v\) is speed, \(A\) is amplitude, \(f\) is frequency, and \(L\) is body length. - Turning Radius: By adjusting the bias term \(\epsilon\), the turning radius varied from 0.5 m to 2 m, demonstrating high maneuverability.
- Energy Efficiency: The power consumption was estimated based on servo torque and angular velocity, showing that the flexible tail reduced energy loss by 20% compared to a rigid tail design.
The simulation also confirmed smooth gait transitions, as shown in the plot of joint angles over time (conceptual representation):
$$ \phi_1(t) = 30^\circ \sin(2\pi \cdot 1 \cdot t) $$
$$ \phi_2(t) = 30^\circ \sin(2\pi \cdot 1 \cdot t – \pi/3) $$
$$ \phi_3(t) = 30^\circ \sin(2\pi \cdot 1 \cdot t – 2\pi/3) $$
These outputs illustrate the phase-lagged oscillations that produce undulatory waves.
The table below summarizes simulation parameters and outcomes:
| Simulation Aspect | Value/Range | Implication for Bionic Robot |
|---|---|---|
| Maximum Speed | 0.8 m/s | Indicates potential for rapid locomotion |
| Turning Radius Range | 0.5–2 m | Highlights agility in confined spaces |
| Power Consumption | 5–10 W depending on gait | Supports long-duration operations with efficient batteries |
| Wave Propagation Efficiency | 85% (energy transfer from joints to tail) | Validates design of flexible tail and joint coordination |
These simulations provided a foundation for physical prototyping, reducing development time and cost. They also underscored the importance of CPG tuning in achieving desired performance metrics for the bionic robot.
Experimental Validation
Experiments were conducted in a controlled water tank measuring approximately 10 m long, 5 m wide, and 1 m deep. The environment was clear and calm to minimize external disturbances. The bionic robot was deployed to test its locomotion capabilities, including forward swimming, turning, and obstacle avoidance. Data were collected via video recording and sensor logs.
In forward swimming tests, the robot achieved a maximum speed of 0.6 m/s, slightly lower than the simulated value due to real-world factors like water resistance and servo limitations. The motion appeared natural, with visible body waves propagating from the anterior to posterior. The phase differences between joints were set to \(\Delta = \pi/3\), resulting in a wavelength of about two-thirds of the body length, consistent with biological observations of hammerhead sharks.
Turning experiments involved commanding the robot to change direction by modifying the bias term \(\epsilon\) in the CPG outputs. The robot successfully executed turns with radii as small as 0.7 m, demonstrating responsive steering. The ultrasonic sensor was activated to test autonomous obstacle avoidance: when an obstacle was detected within 1 m, the control system switched to a turning gait until the path was clear, then resumed forward swimming.
Key experimental metrics are presented in the table below:
| Test | Result | Comparison to Simulation |
|---|---|---|
| Forward Speed | 0.6 m/s | 80% of simulated speed, acceptable given fluid dynamics losses |
| Turning Radius | 0.7 m (minimum) | Slightly larger than simulation due to inertia and drag |
| Obstacle Avoidance Success Rate | 95% (over 20 trials) | Validates sensor integration and CPG adaptability |
| Battery Life | 2 hours of continuous operation | Adequate for typical applications like surveillance |
| Noise Level | < 50 dB measured underwater | Much quieter than propeller-based vehicles, reducing ecological impact |
Additionally, the bionic robot was introduced into a tank with live fish to assess its biomimetic credibility. The fish exhibited no signs of distress or avoidance, indicating that the robot’s movements and appearance were non-threatening. This suggests potential for ecological monitoring without disturbing marine life.
The experiments confirmed the feasibility of the CPG-based control system and the effectiveness of the hammerhead shark-inspired design. The bionic robot performed reliably across multiple gaits, showcasing its potential for real-world applications.
Discussion and Future Directions
The development of this bionic robot highlights several advantages over traditional underwater vehicles. First, the biomimetic approach yields high energy efficiency, as undulatory propulsion can outperform rotary propellers in certain conditions. Second, the CPG controller provides robust and flexible locomotion without complex computational overhead. Third, the use of flexible materials like silicone enhances adaptability and reduces noise, making the robot suitable for sensitive environments.
However, challenges remain. The current design has limited payload capacity, restricting the number of sensors that can be carried. Future iterations could incorporate more advanced materials, such as shape-memory alloys or electroactive polymers, to increase actuation efficiency and reduce weight. Additionally, the control system could be enhanced with machine learning algorithms to optimize CPG parameters in real-time based on environmental feedback.
Potential applications for this bionic robot are vast. In scientific research, it could be used for underwater exploration and data collection in coral reefs or deep-sea vents. In agriculture, it might monitor water quality in aquaculture farms. For security, it could patrol harbors or coastlines. Its low noise profile also makes it ideal for military reconnaissance or wildlife observation.
To quantify improvements, future work could focus on scaling the design for different sizes or incorporating multi-modal locomotion (e.g., combining swimming with crawling). The integration of more sensors, such as cameras or hydrophones, would expand its functionality. Collaborative swarms of such bionic robots could enable distributed sensing and cooperative tasks.
The table below outlines proposed enhancements:
| Aspect | Current Limitation | Future Enhancement |
|---|---|---|
| Payload Capacity | Limited to small sensors | Use lightweight composites and miniaturized electronics |
| Autonomy | Basic obstacle avoidance | Implement AI for path planning and decision-making |
| Locomotion Versatility | Primarily swimming | Add fins or limbs for amphibious capabilities |
| Energy Source | Battery-limited runtime | Integrate solar panels or energy harvesting from waves |
In summary, this bionic robot represents a step forward in biomimetic robotics, demonstrating how biological principles can be translated into practical engineering solutions. By continuing to refine the design and control, we can unlock new possibilities for underwater automation.
Conclusion
In this study, I presented the design, control, and experimental validation of a bionic robot inspired by the hammerhead shark, utilizing a CPG-based controller. The robot’s mechanical structure abstracts the shark’s body into a three-joint, four-link system, driven by servo motors and featuring a flexible silicone tail for efficient wave propagation. The CPG controller, modeled on neural oscillators, generates rhythmic signals that enable natural swimming gaits and smooth transitions between forward, turning, and backward motions. Simulations in SolidWorks and Adams-Simulink provided predictive insights, while experiments in a water tank confirmed the robot’s performance, including speeds up to 0.6 m/s and effective obstacle avoidance.
The bionic robot exemplifies the benefits of biomimicry: it is energy-efficient, quiet, and highly maneuverable, with potential applications in exploration, monitoring, and rescue. The integration of CPG control reduces computational complexity, making it suitable for resource-constrained environments. Future work will focus on enhancing autonomy, payload capacity, and multi-functionality. Overall, this research contributes to the growing field of bionic robotics, showing how nature-inspired designs can address challenges in underwater vehicle technology.
As we advance, the synergy between biology and robotics will continue to yield innovative solutions. This bionic robot is a testament to that potential, paving the way for more intelligent and adaptive machines that can thrive in aquatic worlds.