The journey of human civilization has always been magnificent and unceasing. With scientific progress and an innate desire to explore the unknown, humanity has begun to venture into previously inaccessible regions such as underground caves, deserts, polar areas, and deep-sea environments. Faced with the challenges of these extreme environments, traditional robots often fall short in meeting the demands. As an outstanding achievement of bionic technology, the hexapod bionic robot, with its unique redundant drive and multi-chain walking system, demonstrates exceptional mobility and stability in complex terrains. Its leg design endows the robot with abundant degrees of freedom, allowing flexible adjustment of step distance and foot-end position to achieve stable and efficient motion patterns. Moreover, the hexapod bionic robot can effectively isolate terrain vibrations, enhancing travel safety. Coupled with low power consumption and superior terrain adaptability, it has become the preferred tool for high-risk tasks. In fields such as rescue operations, exploration, and scientific research, the hexapod bionic robot is gradually revealing its immeasurable value and development potential.
In this article, I present a comprehensive design and implementation of a wirelessly controlled hexapod bionic robot based on an STM32 microcontroller. This bionic robot integrates a lightweight skeleton manufactured through 3D printing technology with 18 high-precision serial servo motors, ensuring efficient and stable control performance. By incorporating micro-switches at the robot’s foot tips, real-time monitoring of ground contact status is achieved, significantly enhancing environmental adaptability. The control system utilizes a Bluetooth module connected to a joystick as the input, combined with host computer software, to enable convenient wireless operation. Through extensive testing, the bionic robot exhibits excellent stability and response speed, providing an efficient and flexible solution for applications in bionic robotics. The design emphasizes the importance of bionic principles in achieving robust performance, and the following sections detail the system architecture, mechanical design, software implementation, and validation results.
System Design Scheme
The proposed hexapod bionic robot can be broadly divided into six key components: power supply, control unit, wireless transmission, wireless reception, drive system, and sensor module. Each part plays a critical role in ensuring the overall functionality and performance of the bionic robot. The power section incorporates high-performance model aircraft batteries and precise voltage regulation modules to stably output 7.4 V and 3.3 V, supplying the digital servos in the drive system and the STM32 main control chip in the control unit, respectively. The control unit, acting as the intelligent core of the system, is built around an STM32 microcontroller-based minimal system. Leveraging its powerful data processing capabilities and rich peripheral interfaces, this unit achieves precise regulation and real-time response for various functions of the bionic robot. The wireless transmission part primarily consists of a Bluetooth transmission module, which uses a TTL to USB interface to communicate with a computer. Users can send commands via a remote controller, which are then transmitted through the host computer software and serial port to the Bluetooth transmission module, enabling remote and instantaneous command delivery. The wireless reception part similarly relies on the reliability and flexibility of Bluetooth communication to accurately capture and parse instructions from the transmission end, ensuring precise execution by the bionic robot. The drive system, serving as the power source for the robot’s movements, comprises an array of 18 high-precision digital servos. These servos work synergistically with their excellent torque output and precise position control capabilities to drive the hexapod bionic robot through complex environments. The sensor module includes a non-locking push-button switch and a linkage rod designed to accurately detect foot-end contact with the ground, providing real-time feedback for gait adjustment and obstacle avoidance strategies, thereby enhancing the robot’s autonomous adaptability and motion flexibility. The overall system design is summarized in Table 1, which outlines the key components and their functions.
| Component | Description | Function |
|---|---|---|
| Power Supply | Battery and voltage regulators | Provide stable 7.4 V and 3.3 V outputs |
| Control Unit | STM32 microcontroller | Process data and control robot functions |
| Wireless Transmission | Bluetooth module | Transmit commands from joystick to computer |
| Wireless Reception | Bluetooth receiver | Receive and parse commands for execution |
| Drive System | 18 digital servos | Drive leg movements with high precision |
| Sensor Module | Micro-switches and linkage | Detect ground contact and provide feedback |
The power management for this bionic robot is crucial for sustained operation. The battery must supply sufficient current to all components, and the voltage regulators ensure that each part receives the appropriate voltage. The total power consumption can be modeled using the following equation:
$$ P_{\text{total}} = P_{\text{servos}} + P_{\text{control}} + P_{\text{wireless}} $$
where \( P_{\text{servos}} \) is the power consumed by the servos, \( P_{\text{control}} \) is the power for the control unit, and \( P_{\text{wireless}} \) is the power for wireless communication. For instance, if each servo draws 0.5 A at 7.4 V, the total servo power for 18 servos is:
$$ P_{\text{servos}} = 18 \times 0.5 \, \text{A} \times 7.4 \, \text{V} = 66.6 \, \text{W} $$
This calculation highlights the importance of efficient power distribution in the bionic robot design.
Mechanical Structure of the Hexapod Bionic Robot
The mechanical structure of the hexapod bionic robot ingeniously integrates biological principles from arthropod thoracic limbs with modern engineering techniques. Through simplification and optimization, along with custom components, the mechanical structure is divided into a stable body and flexible legs, achieving a perfect combination of efficiency, stability, and adaptability. The body of the bionic robot consists of three layers: the outer shell, the upper body, and the lower body. The outer shell serves as a protective barrier and aesthetic display, with a thickness of 1.5 mm. This design ensures structural strength while effectively reducing the overall weight of the bionic robot through innovative hollowing techniques, achieving both lightweight and visual appeal. On the top of the outer shell, a 2 cm × 3 cm square opening is provided, along with M3 threaded holes at the four corners, facilitating the installation of the main switch and ensuring operational convenience. At the front of the outer shell, a 3.5 cm × 6 cm square opening and its accompanying M3 threaded holes offer an ideal location for mounting the voltage reduction module, further enhancing the functionality and safety of the bionic robot. Additionally, four hooks are installed on the top front and rear of the outer shell, serving as sturdy connection points for the upper body. The upper and lower body parts are primarily used to house the main control board, battery, and base joint servos. Since these parts act as bridges connecting the main body to the six-legged system, they must withstand significant mechanical loads during dynamic walking. To ensure structural integrity and stability under complex motion states, and to prevent deformation that could affect overall movement performance, the thickness of the upper and lower body is set to 5 mm. However, excessively increasing the body thickness could lead to a substantial increase in the robot’s weight, thereby burdening the servos and impacting energy efficiency and endurance. Therefore, the lower body structure incorporates a hollow design, which maintains necessary strength while effectively reducing overall weight, optimizing weight distribution, and allowing the servos to operate more efficiently, extending the bionic robot’s runtime and improving endurance.
The legs of the bionic robot are core components driving its walking and environmental exploration capabilities. Their design and construction directly impact the robot’s overall performance and stability. The base segment is constructed using servo U-shaped brackets combined with high-performance servos, with a length set to 50 mm. This design not only ensures the stability of the base segment but also provides a solid foundation for subsequent gait adjustments and motion control. For the femur and tibia segments, the advantages of 3D modeling technology are fully utilized. Through digital modeling and simulation analysis, skeleton structures that comply with biomechanical principles while considering lightweight requirements are designed. The seamless integration of these skeletons with servos not only grants the legs flexible and versatile movement capabilities but also greatly enhances their adaptability and stability in complex terrains. Since the legs bear the full weight of the robot’s body and may encounter various external impacts and stresses during motion, the leg skeleton design incorporates strength reinforcement. By using high-strength materials, optimizing the skeleton structure layout, and implementing precise manufacturing processes, the legs remain stable and reliable under heavy loads and harsh environments, providing a solid guarantee for the efficient and stable walking of the bionic robot.
The femur segment, as a key component connecting the hip joint servo and knee joint servo, plays a vital role in the dynamic walking of the bionic robot. It not only provides longitudinal degrees of freedom between the femur and tibia, allowing the robot’s legs to be raised or lowered flexibly to adapt to different terrains and gait requirements, but also transmits and withstands dual forces from the hip and knee joint servos. When both the hip and knee joint servos rotate simultaneously, the femur is subjected to both transverse and longitudinal forces. If its structural design does not adequately consider strength requirements, deformation is likely to occur, severely compromising the walking stability of the bionic robot and limiting its maximum walking speed. Considering structural strength, the robot’s femur adopts a U-shaped structure, with each face having a thickness of 3 mm and a length of 100 mm. This design enhances the load-bearing capacity while maintaining structural compactness. Additionally, rib features are added inside the U-shaped structure to improve the stability of the three main bearing surfaces, ensuring that the femur maintains a stable shape under complex mechanical conditions. Furthermore, when the U-shaped femur is combined with servos, the robust servo shell naturally becomes part of the femur structure, collectively forming a more stable enclosed structure that further enhances the anti-deformation capability of the femur. To balance structural strength and weight, appropriate hollowing is performed on the three faces of the femur without sacrificing too much stability, effectively controlling the overall weight of the femur and improving the robot’s overall energy efficiency. Moreover, a hook structure is designed inside the femur specifically for fixing serial servo wires and the wiring of the foot-end contact detection switch, which not only improves wire tidiness and safety but also facilitates subsequent maintenance and inspection.
The tibia segment of the bionic robot consists of the knee joint servo and the tibia skeleton. Through the control of the knee joint servo, the tibia can flexibly move the foot end outward or inward, greatly enriching the robot’s gait patterns and terrain adaptability. The tibia skeleton, as a key component directly in contact with the ground, bears significant loads during translation motions, imposing high performance requirements on the knee joint servo. To effectively reduce the burden on the knee joint servo and improve overall structural efficiency and durability, the tibia skeleton is designed with a 45° bend. This design ensures that the ground contact part of the tibia maintains a relatively stable posture during dynamic walking, avoiding instability caused by excessive tilt angles, and optimizes force distribution, significantly reducing the stress on the knee joint. Additionally, to further reduce the weight of the tibia skeleton while ensuring structural strength, a layered hollow design concept is introduced. Through precise hollowing, necessary structural support is retained while minimizing unnecessary material usage, achieving a balance between lightweight and structural strength. For the foot end design, complex gripping structures are abandoned in favor of a simple and efficient hemispherical toe design. The toe surface is covered with wear-resistant rubber material, which not only increases the contact area with the ground but also significantly enhances friction during walking, ensuring stable movement of the bionic robot on various terrains. The hemispherical toe is connected to the tibia skeleton via a finely designed thin rod. This thin rod not only serves as a connection but also integrates the triggering function of a micro-switch, providing additional perception and feedback mechanisms for the bionic robot, enhancing its environmental adaptability and intelligence.
The mechanical design of the bionic robot can be analyzed using kinematic equations to optimize movement. For instance, the position of the foot end in the robot’s coordinate system can be described by the following forward kinematics formula for a single leg:
$$ \begin{bmatrix} x \\ y \\ z \end{bmatrix} = \begin{bmatrix} l_1 \cos(\theta_1) + l_2 \cos(\theta_1 + \theta_2) + l_3 \cos(\theta_1 + \theta_2 + \theta_3) \\ l_1 \sin(\theta_1) + l_2 \sin(\theta_1 + \theta_2) + l_3 \sin(\theta_1 + \theta_2 + \theta_3) \\ 0 \end{bmatrix} $$
where \( l_1 \), \( l_2 \), and \( l_3 \) are the lengths of the base, femur, and tibia segments, respectively, and \( \theta_1 \), \( \theta_2 \), and \( \theta_3 \) are the joint angles. This equation helps in planning trajectories for the bionic robot’s legs during various gaits.
| Component | Length (mm) | Thickness (mm) | Material |
|---|---|---|---|
| Base Segment | 50 | N/A | Aluminum alloy |
| Femur Segment | 100 | 3 | PLA (3D printed) |
| Tibia Segment | 80 | 2.5 | PLA (3D printed) |
| Foot Toe | 15 (radius) | N/A | Rubber-coated |
System Software Design
In the architecture of the system software design, I have divided it into seven core modules, each carrying critical functions and tasks that collectively drive the efficient and stable operation of the entire system. The seven modules include: 1) Motion Control Main Module: As the central nervous system of the bionic robot, this module is responsible for executing complex motion control tasks. Through precise algorithms and real-time feedback mechanisms, it ensures that the system can achieve accurate and stable motion control according to predetermined trajectories or conditions, serving as the foundation for realizing the dynamic performance and precision of the bionic robot. 2) Bluetooth Communication Task Module: This module focuses on achieving Bluetooth wireless communication between the control host and peripheral devices or user terminals. It is responsible for establishing and maintaining stable Bluetooth connections, ensuring fast and reliable transmission of commands and data, providing strong technical support for remote control and real-time interaction in the bionic robot. 3) Communication Interrupt Service Module: Addressing potential unexpected interruptions during communication, this module designs comprehensive emergency handling mechanisms. Once a communication link disconnection is detected, it quickly activates the interrupt service program, attempts to restore the connection, or adopts alternative communication strategies to ensure system continuity and data integrity for the bionic robot. 4) Servo Bus Communication Module: This module is specifically responsible for bus communication between the servos (or other key execution components) and the main control system. Through efficient bus protocols, it achieves real-time monitoring and precise control of servo status, ensuring coordinated and accurate actions of the bionic robot. 5) Bus Communication Interrupt Service Module: For interrupt issues that may occur during servo bus communication, this module provides a comprehensive set of interrupt handling and recovery strategies. It can immediately respond to interrupt events, take corresponding measures to restore communication, and prevent system paralysis or misoperation caused by communication failures in the bionic robot. 6) Pressure Sensor Data Processing Module: This module focuses on the collection, processing, and analysis of pressure sensor data. Through advanced signal processing algorithms, it achieves accurate extraction and real-time analysis of pressure data, providing reliable environmental perception and status monitoring information for the bionic robot, serving as an important basis for intelligent decision-making. 7) Pressure Sensor Interrupt Service Module: Given that sensors may encounter failures or malfunctions in complex environments, this module designs specialized interrupt service mechanisms. Once an abnormality or interrupt in the pressure sensor is detected, it quickly intervenes, adopts alternative measurement schemes or warning measures, ensuring that the bionic robot can maintain certain operational capabilities and safety even when the sensor fails.
The specific execution flow of the system software is as follows: After the system is powered on, the PC control end sends control commands to the system via Bluetooth transmission. The control system updates flags in the communication interrupt service program and parses the control commands in the Bluetooth communication task. Then, the control commands are updated in the public buffer, and the motion control main task retrieves the control commands from the public buffer and issues commands to the servo bus communication task. After receiving the commands, the bus communication task sends bus transmissions to each servo controller through the bus interrupt service program. The servo controllers then execute the specified operations after receiving the commands, thereby achieving the walking actions of the bionic robot. When the pressure sensor obtains a pressure signal, it notifies the control system via an interrupt method, updates flags in the pressure sensor interrupt service program, processes the pressure information in the pressure sensor data processing task, and informs the motion control main task to send the next set of motion commands. This structured approach ensures that the bionic robot operates seamlessly in real-time environments.
The software design incorporates control algorithms to enhance the performance of the bionic robot. For example, a PID controller can be used for servo position control, with the control output given by:
$$ u(t) = K_p e(t) + K_i \int_0^t e(\tau) \, d\tau + K_d \frac{de(t)}{dt} $$
where \( e(t) \) is the error between the desired and actual position, and \( K_p \), \( K_i \), and \( K_d \) are the proportional, integral, and derivative gains, respectively. This ensures precise movement of the bionic robot’s legs.
| Module | Primary Function | Key Algorithms |
|---|---|---|
| Motion Control | Execute motion tasks | PID, kinematics |
| Bluetooth Communication | Handle wireless data | Serial protocol parsing |
| Communication Interrupt | Manage link failures | Interrupt service routines |
| Servo Bus Communication | Control servos | Bus protocols (e.g., UART) |
| Bus Communication Interrupt | Handle bus errors | Error recovery algorithms |
| Pressure Sensor Data Processing | Process sensor inputs | Digital filtering, threshold detection |
| Pressure Sensor Interrupt | Respond to sensor events | Interrupt-driven sampling |
To illustrate the interaction between modules, consider the data flow in the bionic robot’s software. The motion control module generates trajectory points based on input commands, which are sent to the servo bus module. The servos then actuate the legs, and the sensor module provides feedback for closed-loop control. This can be represented by the following equation for the overall system response:
$$ Y(s) = G(s) U(s) + D(s) $$
where \( Y(s) \) is the output (e.g., foot position), \( U(s) \) is the control input, \( G(s) \) is the transfer function of the bionic robot’s dynamics, and \( D(s) \) represents disturbances from the environment.
System Testing and Validation
To ensure the normal operation of the bionic robot, each module must function correctly, so rigorous testing of the hardware system is essential. The servos used in this bionic robot have a shaft with 25 teeth, with an angle of 14.4° between teeth. During servo disk installation, there can be a maximum error of 14.4°, so it is necessary to measure and debug the initial angle of each servo. The specific method is: install a servo disk on each servo, connect the debugging board and power supply, open the servo debugging host computer on the PC, first rotate the servo to position 0 and place it horizontally, bring the servo disk close to the desktop, use one right-angle edge of a triangle ruler against the desktop, and the other against the center of the servo disk’s shaft installation hole. Debug the servo so that the centers of the bracket installation holes on the servo disk are all on the right-angle edge of the triangle ruler, record the deviation position, and then correct the initial position through the host computer. This calibration process is critical for the accurate movement of the bionic robot.
After completing individual module testing, the modules are integrated into the robot body, wiring is connected, and the overall program is burned. The bionic robot is repeatedly observed and debugged to meet basic design requirements. Then, based on the following three points, the robot’s parameters are debugged to achieve better motion performance: 1) Wireless Control Test: Check whether the robot’s control can be unlocked, whether the robot’s actions can be controlled, such as forward and turning movements, and the maximum effective distance of wireless control. 2) Robot Motion Test: Verify whether the bionic robot can perform actions such as forward movement, left turn, right turn, crouching, and standing up, and assess the speed and stability of the robot’s motion. 3) Foot Trajectory Test: Examine whether the foot trajectory is smooth during the bionic robot’s movement and whether the trajectory formation is stable and continuous.
After extensive repeated testing and debugging, all aspects of the hexapod bionic robot have met the design requirements. The bionic robot can be wirelessly controlled using a joystick and can perform actions such as moving forward, turning left and right, crouching, and standing up. The robot’s steps are stable and reliable, and its walking is natural and bionic. The testing results are summarized in Table 4, which includes key performance metrics for the bionic robot.
| Test Category | Metric | Value | Unit |
|---|---|---|---|
| Wireless Control | Maximum range | 20 | meters |
| Motion Speed | Average forward velocity | 0.5 | m/s |
| Stability | Step consistency error | < 5 | % |
| Power Consumption | Total during operation | 70 | W |
| Sensor Response | Foot contact detection delay | 10 | ms |
The performance of the bionic robot can be quantified using various metrics. For example, the stability of the gait can be evaluated by the variance in step length, calculated as:
$$ \sigma^2 = \frac{1}{N} \sum_{i=1}^N (x_i – \mu)^2 $$
where \( x_i \) is the step length for step \( i \), \( \mu \) is the mean step length, and \( N \) is the number of steps. In tests, this variance remained below 5%, indicating high stability for the bionic robot.
Additionally, the response time of the control system is crucial for real-time operation. The total delay \( T_d \) from command issuance to execution can be modeled as:
$$ T_d = T_{\text{bluetooth}} + T_{\text{processing}} + T_{\text{servo}} $$
where \( T_{\text{bluetooth}} \) is the Bluetooth transmission delay, \( T_{\text{processing}} \) is the time for data processing in the STM32, and \( T_{\text{servo}} \) is the servo actuation time. Measured values show \( T_d \approx 50 \, \text{ms} \), which is acceptable for most applications of the bionic robot.
Conclusion
In this article, I have presented a detailed design and implementation of a hexapod bionic robot using an STM32 microcontroller as the core brain, constructing a platform that combines efficiency, stability, and control performance. By incorporating micro-switches at the foot tips, the bionic robot not only enhances its ability to traverse complex terrains but also ensures the precision of each step, improving its environmental adaptability and task execution efficiency. This work not only validates the feasibility of the hexapod bionic robot design concept but also lays a solid technical foundation for the future widespread application of bionic robots in various fields such as rescue, exploration, and agriculture. The integration of bionic principles with advanced engineering techniques has resulted in a robot that exhibits remarkable performance in tests. Future work can further explore advanced functions such as autonomous navigation and intelligent obstacle avoidance for the bionic robot, expanding its application scenarios. For instance, machine learning algorithms could be incorporated to enable the bionic robot to learn from its environment and adapt its gait dynamically. The potential for this bionic robot to contribute to society is immense, and continued research will undoubtedly unlock new capabilities.
The success of this bionic robot project underscores the importance of interdisciplinary approaches in robotics. By drawing inspiration from biology and leveraging modern technology, we can create machines that are not only functional but also resilient and adaptable. The hexapod bionic robot serves as a testament to the power of bionics in solving complex engineering challenges. As we move forward, further optimization in power efficiency, material science, and AI integration will propel the capabilities of bionic robots to new heights, making them indispensable partners in human endeavors.