Mechanical arm control system based on SPI communication mode

Abstract

The utility model discloses a mechanical arm control system based on an SPI (Serial Peripheral Interface) communication mode, which comprises a master control module, a slave control module and a control module, and is characterized in that the master control module comprises a master control microcontroller, collects data of a plurality of MPU6050 sensors and FLEX bending sensors through the SPI communication mode, and sends attitude control signals to the slave control module through a master control HC-08 Bluetooth module; the slave control plate comprises a slave control microcontroller and a PCA9685 steering engine driving plate, and is used for controlling a plurality of steering engines to move and receiving the attitude control signal of the master control module through a slave control HC-08 Bluetooth module; and the power supply management plate comprises a power supply input unit, a DC-DC power supply conversion unit and an MOSFET intelligent power supply control unit, and is used for converting an input voltage into a required working voltage, controlling power supply according to a system state, and reducing standby power consumption of the system. The layout of multiple sensors reduces error accumulation due to drift or environmental interference.

Description

Technical Field

[0001] The utility model relates to the field of a robotic arm control system, in particular to a robotic arm control system based on an SPI communication mode. Background Art

[0002] The sensor data acquisition process of existing robotic arm control systems based on SPI communication is often affected by environmental interference and sensor drift, resulting in reduced posture control accuracy. Furthermore, the calibration process often requires manual work, which not only increases complexity but also easily leads to error accumulation. Furthermore, the high energy consumption of traditional systems also limits their application in continuous operation scenarios.

[0003] In existing technologies, most robotic arm systems use I2C communication for sensor data transmission, but I2C communication has certain limitations in data transmission speed and anti-interference capabilities. As the requirements for robotic arm precision and real-time performance gradually increase, existing communication methods and sensor layouts can no longer meet the high-precision and fast-response requirements of certain industrial and domestic applications. In addition, the use of a single sensor is often affected by factors such as drift and ambient temperature fluctuations, resulting in unstable data, which poses a challenge to the long-term stable operation of the robotic arm. Utility Model Content

[0004] To address these issues, this utility model proposes a robotic arm control system based on SPI communication. By employing multiple MPU6050 sensors and multiple FLEX bending sensors, data redundancy and fusion are achieved, improving data stability and accuracy. This multi-sensor layout effectively reduces the accumulated errors caused by drift or environmental interference in individual sensors, thereby enhancing the system's anti-interference capabilities and long-term stability. Furthermore, the use of SPI communication instead of traditional I2C communication significantly increases the speed and reliability of data transmission, ensuring stable operation in complex industrial environments.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a robotic arm control system based on SPI communication, comprising:

[0006] The main control board, including the main control microcontroller, is used to collect data from multiple MPU6050 sensors and FLEX bending sensors, process the data to obtain attitude control signals, and send the attitude control signals through the main control HC-08 Bluetooth module. It collects data from multiple sensors through SPI communication and uses an automatic calibration mechanism to adjust the sensor baseline value to ensure data accuracy during long-term use. The anti-interference mechanism also reduces the impact of environmental factors on the sensors.

[0007] The slave board, consisting of a slave microcontroller and PCA9685 servo driver board, controls the movement of multiple digital servos and receives attitude control signals from the master board via the slave HC-08 Bluetooth module. This Bluetooth module receives attitude control signals from the master board to drive the various joints of the robotic arm, achieving high-precision attitude control. The slave board connects to the servo driver board via an I2C interface, ensuring stable transmission and execution of control signals.

[0008] The power management module, comprising a power input unit, a DC-DC power conversion unit, and a MOSFET intelligent power control unit, converts the input voltage to the required operating voltage and controls power supply based on system status, reducing standby power consumption. It provides the system with a 3.3V or 5V operating voltage and recycles the servo's braking energy through an energy recovery circuit, achieving low-energy operation for the entire system. The DC-DC converter boasts an efficiency exceeding 95%, minimizing energy waste during system conversion. MOSFET intelligent switching provides dynamic power management based on load demand.

[0009] Preferably, the main control panel also includes an automatic calibration module for calibrating the data of the FLEX bending sensor and the MPU6050 sensor to reduce the influence of environmental interference and drift error.

[0010] Preferably, the number of the FLEX bending sensors is 5, and they are used to obtain bending data of 5 fingers.

[0011] Preferably, the number of the MPU6050 sensors is 3. The 3 MPU6050 sensors are set on the arms and joints to obtain arm posture information.

[0012] Preferably, the number of the digital servos is 9.

[0013] Preferably, the power management module further includes an energy recovery circuit for recovering and reusing braking energy when the servo is in operation.

[0014] Preferably, both the master microcontroller and the slave microcontroller use STM32F103C8T6 microcontrollers.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] High-precision control: By using multiple MPU6050 sensors and FLEX bending sensors, the system can achieve precise control of each joint of the robotic arm, ensuring accuracy and stability in different postures and improving work efficiency.

[0017] Automatic calibration function: The automatic calibration module can effectively reduce sensor drift errors, ensure that the robot arm maintains high precision after long-term operation, reduce manual intervention, and improve the autonomy and reliability of the system.

[0018] Low-power design: The intelligent control of the power management module significantly reduces the system's standby power consumption, extends battery life, and adapts to long-term, low-frequency usage scenarios.

[0019] Energy recovery and utilization: The energy recovery module can recycle the energy generated during the braking process of the steering gear, reducing energy waste and improving energy utilization efficiency.

[0020] Environmental adaptability: The system has good environmental adaptability and can operate stably in harsh environments such as high temperature and high humidity. It is suitable for a variety of complex environments.

[0021] Multi-scenario application: The system can be widely used in industrial automation, home automation and laboratory environments to meet the needs of different scenarios.

[0022] Improved user experience: In home automation scenarios, users can operate the robotic arm through mobile applications, improving the user experience.

[0023] Through the above beneficial effects, the intelligent robotic arm control system of the utility model has significant advantages in improving work efficiency, reducing energy consumption, and enhancing user experience, and has high practical value and market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the installation position of the sensor of the utility model on the glove;

[0025] Figure 2 This is a schematic diagram of the installation position of the servo of the utility model on the mechanical arm;

[0026] Figure 3 This is a schematic diagram of the overall structure of the control system of the utility model;

[0027] Figure 4 This is a schematic diagram of the main control panel structure of the utility model;

[0028] Figure 5 This is a schematic diagram of the main control microcontroller circuit structure of the utility model;

[0029] Figure 6 This is a schematic diagram of the circuit structure of the FLEX bending sensor of the utility model;

[0030] Figure 7 This is a schematic diagram of the circuit structure of the MPU6050 sensor of the utility model;

[0031] Figure 8This is a schematic diagram of the circuit structure of the main control HC-08 Bluetooth module of this utility model;

[0032] Figure 9 This is a schematic diagram of the structure of the slave control plate of the utility model;

[0033] Figure 10 This is a schematic diagram of the circuit structure of the slave microcontroller of the utility model;

[0034] Figure 11 This is a schematic diagram of the circuit structure of the utility model slave control HC-08 Bluetooth module;

[0035] Figure 12 This is a schematic diagram of the PCA9685 servo driver board and servo circuit structure of the utility model;

[0036] Figure 13 This is a schematic diagram of the structure of the power management module of the utility model;

[0037] Figure 14 This is a schematic diagram of the power input and output and voltage step-down stabilization structure of the main control panel of the utility model;

[0038] Figure 15 This is a schematic diagram of the power input structure of the slave control panel of the utility model.

[0039] Figure numerals: 101, master microcontroller; 110, master HC-08 Bluetooth module; 111, automatic calibration module; 102, MPU6050 sensor 1; 103, MPU6050 sensor 2; 104, MPU6050 sensor 3; 105, FLEX bending sensor 1; 106, FLEX bending sensor 2; 107, FLEX bending sensor 3; 108, FLEX bending sensor 4; 109, FLEX bending sensor 5; 201, slave microcontroller; 202, slave HC-08 Bluetooth module; 203, PCA9685 servo driver board; 204, first servo; 205, second servo; 206, third servo; 207, fourth servo; 208, fifth servo; 209, sixth servo; 210, seventh servo; 211, eighth servo; 212, ninth servo. DETAILED DESCRIPTION

[0040] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0041] In the description of this utility model, it should be noted that the terms "upper," "lower," "inner," "outer," "front end," "rear end," "both ends," "one end," "the other end," and the like, indicating directions or positions, are based on the directions or positions shown in the accompanying drawings and are intended solely to facilitate the description of this utility model and simplify the description. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on this utility model. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0042] In the description of this utility model, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "provided with," "connected," etc. should be understood in a broad sense. For example, "connected" can mean a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be internal communication between two components. Those skilled in the art will be able to understand the specific meanings of the above terms in this utility model based on the specific circumstances.

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] The current intelligent robotic arms mainly use bionic motion, that is, the operator wears gloves, which convert the operator's glove movements into robotic arm movements. The gloves are equipped with a main control panel, such as Figure 1 As shown, it includes 5 FLEX bending sensors (FLEX bending sensors 1-5) 105-109 and 3 MPU6050 sensors (MPU6050 sensors 1-3) 102-104. The FLEX bending sensors are used to detect the bending conditions of the 5 fingers, and the MPU6050 sensors are used to detect the arm posture conditions.

[0045] like Figure 2 As shown, the distribution of servos on the robotic arm is shown. The robotic arm includes a shoulder joint, an upper arm, a forearm and a palm. A first servo 204 is installed between the shoulder joint and the upper arm, a second servo 205 is installed between the upper arm and the forearm, the forearm and the palm are connected through a wrist joint, a third servo 206 is installed between the forearm and the wrist joint, a fourth servo 207 is installed between the wrist joint and the palm, and finger servos (fifth to ninth servos) 208-212 for managing finger movements are installed on the palm.

[0046] like Figure 3 As shown, the robotic arm control system based on the SPI communication method of this embodiment includes: a master control panel, a slave control panel and a power management panel. The power management panel supplies power to the master control panel and the slave control panel. The master control panel sends a control signal to the slave control panel to make the slave control panel move, thereby realizing the control of the movement of the robotic arm.

[0047] like Figures 4 to 8 As shown, the main control board, mounted on the glove, includes a main control microcontroller 101. This board collects data from multiple MPU6050 sensors (MPU6050 sensors 1-3) 102-104 and FLEX bend sensors 1-5) 105-109 via SPI communication. This data is processed to generate posture control signals. The MPU6050 sensor provides triaxial acceleration and triaxial angular velocity data, which are used to monitor the posture and motion of the robotic arm. The FLEX bend sensor senses the degree of bend in the robotic arm's joints, particularly changes in joint angle during complex movements.

[0048] Data acquisition process: The main control microcontroller 101 communicates with the MPU6050 sensor via the SPI interface. Each MPU6050 sensor sends real-time angular velocity and acceleration information to the main control microcontroller 101 via the SPI bus.

[0049] The FLEX bend sensor forms a voltage divider circuit with a fixed resistor. The output voltage at the divider point is connected to an ADC pin of an STM32F103C8T6 microcontroller. This analog voltage signal is converted into a digital signal and then transmitted to the slave control board via the master HC-08 Bluetooth module 110. The master microcontroller 101 uses an STM32F103C8T6 microcontroller. It connects to multiple MPU6050 sensors via the SPI interface to enable real-time acquisition of the manipulator's attitude data. The SPI clock, MOSI, and MISO pins are connected to the corresponding pins of each MPU6050 sensor, ensuring high-speed and reliable data transmission. The master control board also connects to the FLEX bend sensor via its ADC pin to detect changes in the manipulator's angle. Data accuracy is ensured through an automatic calibration mechanism. The HC-08 Bluetooth module is used for wireless data transmission between the master and slave boards. Its TX and RX pins are connected to the master board's UART1_RX and UART1_TX pins, respectively, ensuring reliable attitude data transmission. Its workflow includes: system initialization; the main control microcontroller 101 acts as the SPI master device and selects the slave device (sensor); the master device sends a read command to the slave device through the SPI interface; the slave device receives the read command and prepares the sensor data; the slave device sends the data to the main control microcontroller 101 through the SPI interface; the main control microcontroller 101 receives the data, verifies the receipt to ensure data integrity, and then further processes the data. The SPI communication ends and returns to the normal working process.

[0050] Data Fusion: After the main control module acquires data from the MPU6050 and FLEX sensors, it first performs data fusion. The purpose of data fusion is to calculate and complement the data from multiple sensors to eliminate errors from individual sensors and improve the accuracy and stability of posture perception. By processing the output data from these sensors using algorithms such as Kalman filtering or complementary filtering, the main control module can obtain accurate posture information for the robotic arm.

[0051] Attitude Calculation: After data fusion is complete, the main control board uses sensor data to calculate the target attitudes for each joint of the robotic arm. Based on these target attitudes, the main control board generates corresponding attitude control signals, typically in the form of PWM signals, to control the rotation angles of the servos. The main control board processes the target attitudes of each joint of the robotic arm as needed, converting them into specific commands for each servo. For example, it specifies the target angles for each joint and then converts these target angles into corresponding PWM control signals.

[0052] The attitude control signal generated by the master control module is transmitted through the HC-08 Bluetooth module to achieve wireless communication between the master control module and the slave control module.

[0053] The Bluetooth module is connected to the master microcontroller 101 via a UART interface and is responsible for sending posture control signals to the slave module. The advantage of wireless transmission is that it reduces the complexity of the cable connection of the robot arm and improves the flexibility of the system.

[0054] The main control board also includes an automatic calibration module 111, which is used to calibrate the data of the FLEX bend sensors (FLEX bend sensors 1-5) 105-109 and (MPU6050 sensors 1-3) 102-104, reducing the impact of environmental interference and drift errors. Its workflow includes: system startup; initialization of each module; the main control board enters automatic calibration mode; current posture data is collected from the MPU6050 sensors and FLEX bend sensors, and the current posture data is compared with the preset standard posture data. If it is within the threshold, the calibration is completed. If it is outside the threshold, the data does not match and the baseline data needs to be adjusted. The sensor baseline value is dynamically adjusted to reduce deviation. If it is within the threshold, the calibration is completed. If it is outside the threshold, the current posture data is re-collected for comparison, and the action is repeated to complete the calibration. To ensure the long-term accuracy of the system, an automatic calibration mechanism is introduced. This mechanism automatically corrects the sensor baseline value based on the static state of the robot arm to eliminate sensor drift caused by temperature changes, mechanical wear, etc. When the system detects that the robotic arm is stationary or the data deviation is large, the calibration mechanism will be activated to perform offset correction on the sensor output data so that the collected data remains highly accurate.

[0055] like Figures 9 to 12 As shown, the slave control board, mounted on the robotic arm, includes a slave microcontroller 201 and a PCA9685 servo driver board 203. It is used to control multiple servos (the first through ninth servos 204-212) and receives attitude control signals from the master control board via a slave HC-08 Bluetooth module 202. The slave microcontroller 201 utilizes an STM32F103C8T6 microcontroller. It communicates with the PCA9685 servo driver board via an I2C interface. The I2C SCL and SDA pins are connected to the PCA9685's clock and data lines, enabling precise control of the robotic arm's multiple servos. The slave control module receives and processes control signals. The slave control board receives attitude control signals from the master control board via the HC-08 Bluetooth module. Upon receiving the control signals, the slave microcontroller 201 forwards these signals to the PCA9685 servo driver board 203, generating specific PWM signals to drive the servos.

[0056] The PCA9685 servo driver board's function: The PCA9685 is a 16-channel PWM driver that can control up to 16 servos. After receiving attitude control signals, the slave microcontroller 201 maps them to the various channels of the PCA9685, thereby controlling the servos of each joint of the robotic arm.

[0057] Servo action execution: After receiving the PWM signal from the PCA9685 driver, the servo drives the various joints of the robotic arm to complete the corresponding action.

[0058] Each servo adjusts its own rotation angle according to the received PWM signal, so that each part of the robotic arm reaches the desired posture position.

[0059] Real-time adjustment and feedback: By collecting the status of the servos and the feedback data of the joints in real time, the main control module can adjust the corresponding control signals according to the actual posture to ensure that the robot arm moves accurately and smoothly.

[0060] If the posture error is detected to be outside the allowable range during the control process, the main control module will recalculate and adjust the control signal until the robotic arm reaches the target position.

[0061] like Figures 13 to 15 As shown, the power management module includes a power input unit 301, a DC-DC power conversion unit 302, and a MOSFET intelligent power control unit 303, which is used to convert the input voltage into the required operating voltage and control the power supply according to the system status to reduce the system standby power consumption. The DC-DC power conversion unit 302 uses an LM2596 switching voltage regulator to convert the input 12V or 24V voltage into the 3.3V or 5V voltage required by the system. The MOSFET intelligent power control unit 303 controls the switching of the power supply through a PWM signal to intelligently manage the power supply. The power management module also includes an energy recovery circuit 304, which is used to recover and reuse braking energy when the servo is in operation. The braking energy generated during the servo operation is recovered through a diode and a supercapacitor and reused to power the system, minimizing energy waste.

[0062] The process also includes a low-power judgment module, and the workflow includes: system startup; sensor data collection; automatic calibration; data fusion and noise filtering processing; Bluetooth transmission of attitude data; receiving data from the control board; driving the servo to control the movement of the robotic arm; judging whether to enter low-power mode, if not, continue normal operation, if so, enter low-power mode.

[0063] The energy recovery module is combined with the power management module to store the braking energy of the servo during movement through supercapacitors, and release it back to the system power supply when needed, thereby achieving efficient energy utilization.

[0064] Application in industrial automation: In industrial automation scenarios, the robotic arm of the present invention can be used for product assembly and handling work on the assembly line. Since the robotic arm in the industrial environment needs to cope with complex workflows and high-load tasks, the robotic arm control system of the present invention uses multiple MPU6050 sensors and FLEX bending sensors to achieve precise control of each joint of the robotic arm, thereby ensuring the accuracy and stability of the robotic arm in different postures. During the automatic calibration process, the main control board regularly calibrates the sensor through a timer to ensure the reliability and accuracy of the sensor in harsh environments such as high temperature and high humidity. At the same time, the power management board disconnects unnecessary loads through MOSFET switches when the robotic arm is on standby, thereby reducing overall energy consumption and ensuring the stability of the system under long-term working conditions.

[0065] Application in home automation: In home automation scenarios, the robotic arm of this utility model can be used to assist with household chores, such as helping the elderly or people with limited mobility to complete simple tasks such as picking up objects and pouring water. In order to achieve a good user experience in a home environment, the system will be connected to the mobile phone application through the HC-08 Bluetooth module, and the user can operate the robotic arm through the mobile phone. In this application scenario, the automatic calibration mechanism is also crucial. Through static calibration, it ensures that the robotic arm maintains an accurate posture under different factors such as ground inclination, temperature and humidity changes. At the same time, the power management module extends the battery life through low-power mode to adapt to long-term and low-frequency use in a home environment.

[0066] Laboratory Applications: In laboratory research environments, this robotic arm is used to simulate various mechanical control strategies and posture planning. Researchers can modify the control program in the main control module to study the impact of multi-sensor fusion algorithms on the robotic arm's accuracy and response speed. In this application, the automatic calibration mechanism can be manually triggered at any time based on experimental needs to ensure system accuracy before each experiment begins. Furthermore, the system's power management module intelligently adjusts the power supply status of different experimental devices, providing a flexible power management solution for experiments.

[0067] The entire system operates through a master control module, slave control modules, and a power management module. The master control module sends data acquisition commands to multiple sensors via the SPI interface and performs verification and analysis based on the data received from the sensors to ensure the stability and accuracy of data transmission. It also communicates with the slave control modules via a Bluetooth module. The slave control modules receive attitude control signals from the master control module and control multiple servos, achieving precise operation of the robotic arm. The power management module is responsible for power conversion and energy management, further reducing system energy consumption.

[0068] The main control board enters auto-calibration mode, collecting data on the current sensor status, comparing it to stored baseline data, and dynamically adjusting the baseline to reduce error accumulation over long-term use. This step ensures that the entire system maintains high accuracy and stability over long-term operation, further enhancing system reliability.

[0069] The automatic calibration module 111 in the main control section can detect sensor drift by comparing real-time sensor data with known standard postures. If drift is detected, the reference value is automatically adjusted to ensure that the posture accuracy of the robotic arm remains stable after long-term operation. This calibration process is triggered periodically by a set timer, or automatically executed when an abnormal posture data is detected. The main advantage of the automatic calibration mechanism is that it can effectively reduce the frequency of manual intervention while ensuring that the system has high control accuracy under different environmental conditions. The mechanism automatically calculates and adjusts the deviation by multiple sampling of sensor data in a static state to ensure that the system always maintains the best posture capture capability. The specific implementation of automatic calibration includes the following steps:

[0070] Data acquisition: When the robotic arm is stationary, the system collects multiple sets of data from each MPU6050 sensor and FLEX bending sensor through the SPI interface.

[0071] Deviation calculation: Compare the collected data with the standard posture data stored in the system and calculate the deviation value of each sensor.

[0072] Baseline adjustment: Use the PID control algorithm to calculate the adjustment amount based on the deviation value and dynamically update the sensor's baseline value to eliminate drift caused by factors such as temperature changes and device aging.

[0073] Calibration verification: After calibration, the system verifies the output of each sensor to ensure that the adjusted data conforms to the posture characteristics of the robot arm.

[0074] Automatic triggering: When the sensor's output posture data deviates significantly from the expected value, the system automatically triggers a calibration process to maintain posture control accuracy. This calibration mechanism enables the system to maintain high posture accuracy in various environments, ensuring the long-term stable operation of the robotic arm.

[0075] When idle, the system determines whether to enter low-power mode. If conditions are met, the system enters low-power mode to reduce energy consumption. If a new operation request is detected, it returns to normal operation. This low-power mode is controlled by the power management module, which maximizes system battery life through intelligent power management mechanisms.

[0076] The use of multiple MPU6050 sensors can achieve comprehensive posture perception by distributing them at various key nodes of the robotic arm. The layout of multiple FLEX bending sensors can accurately capture the bending state of each joint. The data fusion technology of multiple sensors can reduce the error accumulation caused by the drift of a single sensor to the system, and further improve the stability of posture capture through redundant acquisition. In addition, by combining the data information of these sensors and processing them using a data fusion algorithm, the system can effectively filter the noise in the sensor data, thereby further improving the accuracy and robustness of the posture data. In the specific implementation of sensor fusion, the system uses the Kalman filter algorithm to fuse the sensor data. First, the main control board obtains acceleration and angular velocity data from each MPU6050 sensor through the SPI interface, and obtains bending angle data from the FLEX sensor. The data fusion process includes the following steps:

[0077] Data synchronization: Time synchronization of data from different sensors to ensure the accuracy of data fusion.

[0078] Kalman filter: Use the Kalman filter to predict and update the acceleration and angular velocity data to remove noise from the sensor signals.

[0079] Posture calculation: The filtered sensor data is combined with the weighted average method to calculate the posture of the robot arm to obtain the optimal posture estimate.

[0080] Fusion result verification: The fusion results are verified to determine whether they conform to the physical motion characteristics of the robot arm. If not, a secondary fusion process is performed. Through this fusion strategy, the system can achieve high-precision posture capture in a multi-sensor environment and provide effective redundant data in the event of sensor failure or performance degradation, thereby improving system reliability.

[0081] To validate the performance of this new system, multiple experiments were conducted on the robotic arm control system. The results showed that, after implementing the automatic calibration mechanism, sensor drift error was reduced by approximately 30%, significantly improving the system's posture control accuracy after long-term operation. The intelligent control of the power management module reduced the system's standby power consumption by approximately 40%, extending the overall system's battery life.

[0082] The above is only an embodiment of the present invention, and common knowledge such as the specific structure and characteristics of the scheme are not described in detail here. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claim involved.

Claims

1. A robotic arm control system based on SPI communication, characterized in that: include: A main control module, including a main control microcontroller (101), is used to collect data from multiple MPU6050 sensors and FLEX bending sensors, process the data to obtain a posture control signal, and send the posture control signal through a main control HC-08 Bluetooth module (110); A slave control panel, comprising a slave control microcontroller (201) and a PCA9685 servo drive panel (203), is used to control the movement of multiple servos and receive attitude control signals sent by the master control panel via a slave control HC-08 Bluetooth module (202); The power management module comprises a power input unit (301), a DC-DC power conversion unit (302) and a MOSFET intelligent power control unit (303), and is used to convert the input voltage into a required operating voltage and control the power supply according to the system state to reduce the system standby power consumption.

2. The robotic arm control system based on the SPI communication mode according to claim 1, characterized in that: The main control panel also includes an automatic calibration module (111) for calibrating data of the FLEX bending sensor and the MPU6050 sensor to reduce the influence of environmental interference and drift error.

3. The robotic arm control system based on the SPI communication mode according to claim 1, characterized in that: The number of the FLEX bending sensors is 5.

4. The robotic arm control system based on the SPI communication mode according to claim 3, characterized in that: The number of the MPU6050 sensors is 3.

5. The robotic arm control system based on the SPI communication method according to claim 4, characterized in that: The number of the steering gears is 9.

6. The robotic arm control system based on the SPI communication method according to claim 1, characterized in that: The power management module further includes an energy recovery circuit (304) for recovering and reusing braking energy when the steering gear is in motion.

7. The robotic arm control system based on SPI communication mode according to claim 1, characterized in that: The master microcontroller (101) and the slave microcontroller (201) both use STM32F103C8T6 microcontrollers.

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