Bionic hand system and control method

Abstract

According to the technical scheme, the bionic hand system comprises a hand tissue obtaining module used for obtaining internal structure characteristics of the hand to obtain internal data; data acquisition is carried out on external and internal structural characteristics and forms of a human hand, a hand bone model is printed and manufactured according to the acquired data, then the hand bone works in a control driving mode, and different required grabbing postures are completed by utilizing different preset posture data, so that the hand bone model is printed and manufactured. The structure and the function of a human hand are highly simulated, so that the advantage of flexibility of the bionic hand is improved, the bionic dexterous hand which is highly anthropomorphic is constructed, the complex structure and the function of the human hand are simulated, the bionic dexterous hand adapts to a complex and changeable operation environment, and the bionic dexterous hand is realized through deep theoretical analysis, precise mechanical design and a precise control algorithm. A bionic hand prototype with high flexibility and adaptability is successfully constructed.

Description

Technical Field

[0001] The present invention relates to the field of robotics and biomedical engineering technology, and in particular to a bionic hand system and a control method. Background Art

[0002] The anthropomorphic bionic hand is a key actuator in robots and a crucial prosthetic aid for patients with upper limb disabilities. The research and development of this technology is not only a major breakthrough in robotics but also has far-reaching implications for improving the quality of human life. From a robotics perspective, research on the anthropomorphic bionic hand has driven the development of robotics. Traditional robotic hands typically have limited functionality and flexibility and adaptability. However, anthropomorphic bionic hands, mimicking the structure and function of human hands, are capable of performing more complex and precise operations, such as picking up objects of various shapes and sizes and performing precision assembly tasks. Such technological advancements will not only expand the application of robots in industry, healthcare, and service sectors, but also improve the efficiency and accuracy of their tasks.

[0003] For patients with upper limb disabilities, the research on humanoid bionic hands has important social significance. By applying this technology to the manufacture of prosthetic limbs, the functionality and practicality of prosthetic limbs can be greatly improved. This not only helps people with disabilities restore basic abilities in daily life, such as self-care, work and social interaction, but also helps improve their quality of life and social participation. In addition, highly simulated bionic hands can also help people with disabilities rebuild their self-confidence and social image on a psychological level. The research on humanoid bionic hands also has important scientific value for understanding the functions of human hands. By simulating the structure and function of human hands, researchers can explore the movement mechanism and perception ability of human hands in more depth, which has an important driving effect on the research of multiple disciplines such as neuroscience and biomechanics. With the continuous advancement of artificial intelligence and machine learning technology, the intelligence level of humanoid bionic hands is also constantly improving. This means that future bionic hands will not only be able to imitate the movements of human hands, but also continuously optimize their performance through learning and adaptation, which will further expand their application possibilities and fields.

[0004] The research on bionic hands in existing technologies still faces many challenges, such as structural limitations, large size, inconvenient installation, single transmission mechanism, insufficient anthropomorphic design, complex modeling, and difficult control. Mechanical parts are used instead of bionic hand joints to simplify the movement of the bionic hand, but this method cannot improve the dexterity and freedom of the human hand, and thus cannot achieve complex operational tasks. Summary of the Invention

[0005] The purpose of the present invention is to provide a bionic hand system and control method, which can improve the dexterity of the bionic hand by highly simulating the structure and function of the human hand, while solving the limitations of the existing technology, promoting the development of robotics and prosthetic assistive devices, and having a profound impact on the daily life and social participation of patients with upper limb disabilities, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solution: a bionic hand system, comprising:

[0007] The hand tissue acquisition module is used to acquire the internal structural features of the hand and obtain internal data, including the hand structure composed of bones, joints, ligaments, tendons, muscles, nerves, blood vessels and layered skin;

[0008] The hand model acquisition module is used to acquire the external shape data of the human hand bones by scanning to obtain external data, and then import the internal and external data into the 3D printer to complete the hand bone model production;

[0009] The hand drive module is used to connect the bones and joints of the hand bone model, as well as the bones themselves, and also to connect the metacarpal bones and phalanges. This allows the hand bones to be adjusted and changed in angle under the drive force, thereby achieving gripping operations.

[0010] The hand control module is used to control the hand drive module and complete the control operation by inputting and receiving instructions;

[0011] The hand grasping module achieves the required grasping state and posture based on the pre-imported different posture data. When the grasping and other forms are performed, the hand control module sends instructions to the hand driving module, causing the hand driving module to operate by controlling the hand model acquisition module, thereby realizing the grasping operation. The increase in the strength of the bionic hand realizes fine grasping and power grasping according to the increased force.

[0012] Exemplarily, the hand tissue acquisition module includes:

[0013] A data storage unit for storing internal hand data recorded during the existing dissection process;

[0014] The data import unit imports the pre-prepared data into the database and then extracts and uses the data;

[0015] The data analysis and processing unit is used to classify and process the acquired hand tissue data, and analyze it for use in subsequent control operations.

[0016] Exemplarily, the hand model acquisition module includes:

[0017] The scanning unit uses laser scanning to acquire the shape of the human hand bones, and stores and transmits the acquired data after acquiring the required hand bone shape data;

[0018] The printing unit realizes 3D printing through a 3D printer, edits the pre-scanned data using the 3D printer, and completes the 3D printing of the human hand bones based on the edited data to replicate the required model of the human hand bones.

[0019] Exemplarily, the scanning unit includes:

[0020] Laser scanner, which can scan the internal and external structure of human hands;

[0021] The printing unit comprises:

[0022] 3D printer, using Form3+ 3D printer with a 14.5×14.5 cm build platform to print the hand bone model.

[0023] Exemplarily, the hand driving module includes:

[0024] The execution unit is used to drive the hand model to grasp, hold, take and put, etc., to complete the bionic movements and postures similar to those of the human body, so as to achieve high precision and large force during bionic operation;

[0025] A storage unit is used to store the execution unit and also to install the execution unit;

[0026] The connecting unit is used to connect the tendons in the hand to realize the transmission work, so that the hand model can complete a passive degree of freedom of adduction and abduction;

[0027] The supporting unit is used to install the executing unit and the connecting unit. The connecting unit is installed on the executing unit to fix the connecting unit.

[0028] Exemplarily, the execution unit includes:

[0029] Servo motors are used to achieve driving work. Ten sets of Dynamixel servo motors are used to drive the bionic hand, including 9 MX-12W and 1 AX-18A. Each finger is controlled by a pair of servos to achieve flexion movement. When the thumb is flexed, the palm will adduct. This is achieved by using a servo with higher torque. One AX-18A is used to control the bending of the thumb.

[0030] The storage unit includes:

[0031] The storage base can be used to install and store ten sets of Dynamixel servo motors, ensuring the stable operation of the ten sets of Dynamixel servo motors.

[0032] Exemplarily, the connecting unit includes:

[0033] The PE wire can transmit force to achieve stretching when the hand is bent. It is supported by a high-strength polyethylene wire. The PE wire needs to be stretched by a servo motor to complete the stretching operation.

[0034] The support unit comprises:

[0035] The auxiliary steering plate is used to connect the PE line to the servo motor and fix the PE line connection.

[0036] Exemplarily, the hand control module includes:

[0037] The control unit is equipped with a core circuit board of an ATmega2560 microcontroller and an ATmega2560 microcontroller for controlling a hand drive module. The core circuit board is used to carry the ATmega2560 microcontroller, and the ATmega2560 microcontroller sends instructions to the hand drive module and enables the hand drive module to work.

[0038] Exemplarily, the hand grasping module includes:

[0039] The gripping posture pre-storage sharing unit can import pre-edited different gripping posture data into the database, and then the controller completes the scheduling and use of the database to realize the operation control of the required gripping posture.

[0040] The control method comprises the following steps:

[0041] Acquire the internal structural features of the hand to obtain internal data, including the hand structure composed of bones, joints, ligaments, tendons, muscles, nerves, blood vessels and layered skin;

[0042] The external shape data of the human hand bones are acquired by scanning to obtain external data, and then the internal data and external data are imported into a 3D printer to complete the modeling of the hand bones;

[0043] The hand bone model's bones and joints, as well as the bones themselves, are connected, as well as the metacarpal bones and phalanges, so that the hand bones can be adjusted and changed in angle under the driving force, thereby achieving a gripping operation.

[0044] Control the hand drive module and complete the control operation by inputting and receiving instructions;

[0045] Different posture data are imported in advance to achieve the required grasping state and posture. When performing grasping and other morphological operations, the hand control module sends instructions to the hand drive module, causing the hand drive module to operate by controlling the hand model acquisition module, thereby realizing the grasping operation. The increase in the strength of the bionic hand realizes fine grasping and power grasping according to the increased force.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention acquires data on the external and internal structural features and morphology of the human hand, prints and manufactures a hand bone model based on the acquired data, and then controls and drives the hand bones to work, using preset different posture data to complete the required different grasping postures, so as to achieve a high degree of simulation of the structure and function of the human hand, thereby enhancing the dexterity of the bionic hand. By constructing a highly anthropomorphic bionic dexterous hand, simulating the complex structure and function of the human hand, and making it adaptable to complex and changeable operating environments, the present invention successfully constructs a bionic hand prototype with high flexibility and adaptability through in-depth theoretical analysis, precise mechanical design and accurate control algorithm.

[0048] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a block diagram of the system of the present invention;

[0050] Figure 2 This is a flow chart of the control method of the present invention;

[0051] Figure 3 The key circuit and program flow chart of the present invention;

[0052] Figure 4 It is a flowchart of the action group algorithm of the present invention;

[0053] Figure 5 This is a schematic diagram of the bending sensor of the present invention;

[0054] Figure 6 This is an improved circuit diagram of the present invention with adjustable sensitivity;

[0055] Figure 7 Flowchart of the procedure for calibrating the bending sensor of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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.

[0057] The present invention provides a bionic hand system, comprising:

[0058] The hand tissue acquisition module is used to acquire the internal structural features of the hand and obtain internal data, including the hand structure composed of bones, joints, ligaments, tendons, muscles, nerves, blood vessels and layered skin;

[0059] The hand model acquisition module is used to acquire the external shape data of the human hand bones by scanning to obtain external data, and then import the internal and external data into the 3D printer to complete the hand bone model production;

[0060] The hand drive module is used to connect the bones and joints of the hand bone model, as well as the bones themselves, and also to connect the metacarpal bones and phalanges. This allows the hand bones to be adjusted and changed in angle under the drive force, thereby achieving gripping operations.

[0061] The hand control module is used to control the hand drive module and complete the control operation by inputting and receiving instructions;

[0062] The hand grasping module achieves the required grasping state and posture based on the pre-imported different posture data. When the grasping and other forms are performed, the hand control module sends instructions to the hand driving module, causing the hand driving module to operate by controlling the hand model acquisition module, thereby realizing the grasping operation. The increase in the strength of the bionic hand realizes fine grasping and power grasping according to the increased force.

[0063] When simulating the tendons and forearm muscle groups of the human hand, highly elastic silicone rubber sheaths and high-strength polyethylene wire (PE wire) are used as the tendons of the transmission system, and high-precision, high-torque Dynamixel servo motors are used as force actuators and forearm muscle groups.

[0064] When simulating the lateral and central bundles of the extensor hood of the human hand, a 1:1 model was carved on the cut-resistant plate, manually cut out of a 0.5 mm transparent silicone rubber holster, and attached to the dorsal side of each finger.

[0065] Experimental tests have shown that the hand-cut extensor tendon cap has a sliding mechanism when the finger is bent, and passively transmits torque at each finger joint to promote finger flexion movement. At the same time, the hand-cut tendon sheath has a bulging effect when the finger is bent, and can actively transmit the flexion force of the flexor muscle to the joint, dominating the power of finger flexion. As long as the servo motor continues to pull the wire, the torque of the finger joint will gradually increase.

[0066] Hand tissue acquisition module, including:

[0067] A data storage unit for storing internal hand data recorded during the existing dissection process;

[0068] The data import unit imports the pre-prepared data into the database and then extracts and uses the data;

[0069] Data analysis and processing unit, used to classify and analyze the acquired hand tissue data for use in subsequent control operations

[0070] Hand model acquisition module, including:

[0071] The scanning unit uses laser scanning to acquire the shape of the human hand bones, and stores and transmits the acquired data after acquiring the required hand bone shape data;

[0072] The printing unit realizes 3D printing through a 3D printer. The pre-scanned data is edited by the 3D printer, and the 3D printing of the human hand bones is completed according to the edited data to replicate the model required for the human hand bones.

[0073] Scanning unit, including:

[0074] Laser scanner, which can scan the internal and external structure of human hands;

[0075] The printing unit comprises:

[0076] 3D printer, using Form3+ 3D printer with a 14.5×14.5 cm build platform to print the hand bone model.

[0077] The accuracy during printing was 0.05mm, and the total printing time was 21 hours and 30 minutes.

[0078] Hand drive module, including:

[0079] The execution unit is used to drive the hand model to grasp, hold, take and put, etc., to complete the bionic movements and postures similar to those of the human body, so as to achieve high precision and large force during bionic operation;

[0080] A storage unit is used to store the execution unit and also to install the execution unit;

[0081] The connecting unit is used to connect the tendons in the hand to realize the transmission work, so that the hand model can complete a passive degree of freedom of adduction and abduction;

[0082] The supporting unit is used to install the execution unit and the connecting unit. The connecting unit is installed on the execution unit to fix the connecting unit.

[0083] Execution unit, including:

[0084] Servo motors are used to achieve driving work. Ten sets of Dynamixel servo motors are used to drive the bionic hand, including 9 MX-12W and 1 AX-18A. Each finger is controlled by a pair of servos to achieve flexion movement. When the thumb is flexed, the palm will adduct. This is achieved by using a servo with higher torque. One AX-18A is used to control the bending of the thumb.

[0085] The storage unit includes:

[0086] The storage base can be used to install and store ten sets of Dynamixel servo motors, ensuring their stable operation.

[0087] The servo model diagram is as follows:

[0088] Dynamixel Servo Model AX-18A MX-12W Operating voltage (V) 12 12 No-load speed (RPM) 97 470 Stall torque (N·m) 1.8 0.2 gear ratio 254 / 1 32 / 1 Resolution (°) 0.29 0.088 Range of motion (°) 300 360 Communication rate 7343bps – 1Mbps 8000bps - 4.5Mbps Weight (g) 56 55 Dimensions (mm) 32 ×40 ×50 32 ×40 ×50

[0089] The servo deflection data for each finger flexion movement is as follows:

[0090]

[0091] Connecting unit, comprising:

[0092] The PE wire can transmit force to achieve stretching when the hand is bent. It is supported by a high-strength polyethylene wire. The PE wire needs to be stretched by a servo motor to complete the stretching operation.

[0093] The tight layout structure of the PE line simulates the carpal tunnel of the human hand;

[0094] The support unit comprises:

[0095] Auxiliary steering plate, used to connect the PE line to the servo motor, plays the role of fixing the PE line connection

[0096] Hand control module, including:

[0097] The control unit is equipped with a core circuit board of an ATmega2560 microcontroller and an ATmega2560 microcontroller for controlling a hand drive module. The core circuit board is used to carry the ATmega2560 microcontroller, and the ATmega2560 microcontroller sends instructions to the hand drive module and enables the hand drive module to work.

[0098] Hand gripping module, including:

[0099] The gripping posture pre-storage sharing unit can import pre-edited different gripping posture data into the database, and then the controller completes the scheduling and use of the database to realize the operation control of the required gripping posture.

[0100] Grasping postures can be broadly divided into two main categories: fine grasp and power grasp. The fine grasping gesture category uses the fingertips to manipulate small objects, emphasizing precise control; while the power grasping gesture category uses the entire palm and all fingers to firmly grasp larger or heavy objects. The hand posture needs to be changed according to the shape of the object to wrap the object, emphasizing stability and strength. When the object becomes lighter and smaller in size, the grasping position changes from the palm to the fingertips.

[0101] The Arduino Mega 2560 is a powerful core circuit board centered around a USB interface and equipped with an ATmega2560 microcontroller. It has 54 digital input / output pins (15 of which support PWM output) and 15 analog input pins. In addition, the board is equipped with four UART interfaces, a 16MHz crystal oscillator, a USB interface, a power jack, an ICSP header, and a reset button, providing extremely high scalability and flexibility. The Arduino Mega 2560 is not only feature-rich, but also compatible with expansion boards designed for the Arduino UNO. The expansion board uses the Dynamixel Shield board, which is used to control Dynamixel servos when using Arduino. It can be installed above the Arduino board and can be used with the OpenCR controller. It supports TTL and RS-485 communication and is powered by a 12V 5A power supply. Finally, U2D2 and DYNAMIXEL Wizard 2.0 host computer software were used to modify and debug the servo parameters (such as ID, working mode, etc.). U2D2 is a compact USB communication converter that can control and operate the Dynamixel servo through a computer.

[0102] During the flexion movement of the finger, three joint angles are formed, namely the MCP joint angle, the PIP joint angle and the DIP joint angle. We use a joint angle array To represent the three joint angles formed when the fingers are flexed to the posture. As the actuator of the force, the rotation deviation of a pair of servos when the fingers are flexed to different postures is different. Taking the index finger as an example, when the index finger is flexed to posture 1, its joint angle is At this time, the rotation deviations of the servo No. 4 and servo No. 3 controlling the index finger are and ; When the index finger is flexed to position 2, its joint angle is At this time, the rotation deviations of the servo No. 4 and servo No. 3 controlling the index finger are and If we can know the data of the joint angle of each finger and the rotation deviation of each pair of servos when it is flexed to different postures, then we can use the control algorithm to make the bionic hand perform different postures. As shown in the table, we collected the data of the servo deviation corresponding to the joint angle of each finger when it is flexed to different postures through experiments, and calculated the finger joint angle through mathematical calculation. Deviation from the servo rotation The mapping relationship =

[0103] The experimental process is as follows: When the finger is in a natural state, read the starting position IniPos of the servo, then use the button to control the servo to rotate, so that the finger is flexed to position i, record the joint angle, and read the current position PresPos of the servo at this time, and finally calculate the deviation = current position – starting position , then draw a table of the joint angles at posture i about the Bias

[0104] The relationship between the joint angle of each finger and the servo deviation in different postures is shown in the following table:

[0105]

[0106] According to the data in the table above, we can see that each finger (thumb, index finger, middle finger, ring finger and pinky finger) has four different posture data. Each posture contains two biases (Bias1 and Bias2) and three joint angles ( We can use this data to establish a mapping relationship between the deviation and the joint angle. We have found the function = .

[0107] Due to the joint angle is a three-dimensional vector (representing the three joint angles of a finger), and the bias is two independent quantities (Bias1 and Bias2). We need to construct a mapping function for each joint separately, that is, each finger will have three mapping functions:

[0108]

[0109] Taking the thumb as an example, a linear model is constructed based on the provided data. If this method is suitable, the same method can be applied to other fingers. Now, calculations will be performed to find the relationship between the deviation amount and joint angle of the thumb;

[0110] We built three linear models, one for each joint angle , the model is in the form of ,in and is the coefficient, is the intercept, and for each joint of the thumb, the model parameters (coefficients and intercepts) are as follows:

[0111]

[0112] Now use the same method to build models for other fingers. The following table shows the linear model parameters calculated for each joint angle of each finger:

[0113] The linear model parameters of the joint angles of each finger are as follows:

[0114]

[0115] Each row represents a joint angle for a finger, and the table lists the linear model parameters for the corresponding joint angle. The model form is , where and are coefficients, and is the intercept. These models provide a linear mapping between the deviation and joint angle.

[0116] Servo rotation deviation data for each finger under different gestures

[0117]

[0118] The models of bending sensors are as follows:

[0119]

[0120] Since the lengths of the five fingers of a human hand are different, the index finger, middle finger, and ring finger are roughly the same length, so we chose three 112.24 mm long flex 4.5 sensors. The thumb and little finger are also roughly the same length, so we chose two 77 mm long flex 2.2 sensors.

[0121] The bending sensor circuit consists of a voltage divider and a single-ended operational amplifier to read the sensor signal. is the supply voltage, the voltage divider contains fixed resistors and flexible bending sensor resistors , and the operational amplifier is used as an impedance buffer. The main function of the operational amplifier is to convert the change of the flexible bending sensor into a voltage signal and provide a high input impedance to reduce the load effect, so that the resistance change of the sensor can be read more accurately. Its output voltage =Equal to the voltage at the non-inverting input (+). Since the input impedance of the op amp is very high, we can assume that almost no current flows between the + and - terminals, which means that all current flowing from Flow direction The current will also flow through .therefore, It can be calculated by the following formula:

[0122]

[0123] in:

[0124] is the supply voltage

[0125] Is a fixed resistor

[0126] is the resistance of the flexible bend sensor, whose value changes as the sensor bends

[0127] In flexible bend sensor applications, when the sensor bends, the value of R2 changes, resulting in The purpose of the operational amplifier in the circuit is to ensure that this voltage change can be read accurately without being affected by subsequent circuits (such as analog-to-digital converters or other signal processing circuits).

[0128] To increase the sensitivity of the sensor, we can add a potentiometer R3 and a fixed resistor R4 to the feedback circuit. They are used to set the gain of the operational amplifier. This gain is determined by the following formula:

[0129]

[0130] This gain setting provides more flexibility. By adjusting the potentiometer, we can find the optimal output signal range. Next, we connect the AO analog voltage signal output of the bending sensor to the ADC acquisition port of the Arduino microcontroller, and use the Arduino's 5V pin to power the sensor and share the ground. Due to individual differences between sensors, each sensor needs to be calibrated before use, that is, the resistance value of the sensor in the straight state and when bent 90 degrees is measured and recorded.

[0131] Use the bending sensor to control the servo. When we wear the motion sensing gloves, the servo deflection when the fingers are stretched from full to full bend is just enough to allow the robot to bend from full extension. If this cannot be achieved, debugging is required; set a threshold to prevent the robot from over-bending and causing mechanical damage.

[0132] Wearing a sensing glove allows a human hand to directly control a robotic hand. Currently, many fine movements of robotic hands are achieved through teleoperation. In this process, the human hand's movements are captured by the sensing glove and converted into control signals to drive the bionic hand's movements. Specifically, the glove captures the angles of each joint of the human hand and converts this data into commands to control the robotic hand's actuators. These commands include information about gear ratios and the connection system configuration, as well as the m×n Jacobian matrix that associates m joint angles with n actuators. However, this approach does not achieve a perfect one-to-one mapping between the human hand and the robotic hand for two main reasons. First, robotic hands rarely have motors directly mounted at the joints because small motors are neither small enough to fit within the space of the finger joints nor capable of generating sufficient torque at the fingertips. Therefore, robotic hands must be driven by some kind of transmission system. This means that joint angle data directly obtained from the sensing glove cannot be used to directly control the robotic hand; the influence of the transmission system must be considered. Second, the kinematic models of many robotic hands are based on simplified mechanical joints, whose configuration and rotation axes may be very different from those of the human hand used for teleoperation, leading to mismatches during calibration.

[0133] The cables simulate the extensor and flexor tendons of the human hand. Unlike existing robotic arms, the design of this application document is highly consistent with the human hand in kinematics. This means that information about the distance the tendons move can be directly obtained, which greatly simplifies the control of our robotic arm because it avoids complex mapping transformations involving nonlinear relationships in the m×n Jacobian matrix.

[0134] In the grasping experiment, the right hand is used to hold the forearm of the bionic hand steadily, while the left hand remotely operates the bionic hand through the data glove. The movements of the bionic hand are not only controlled by the hand movements, but also guided by visual feedback. Since the total weight of the bionic hand system is only 962 grams and it has 10 built-in Dynamixel servo motors, the position and direction of the bionic hand can be easily adjusted to ensure accuracy before and after each grasping test.

[0135] The control method comprises the following steps:

[0136] Acquire the internal structural features of the hand to obtain internal data, including the hand structure composed of bones, joints, ligaments, tendons, muscles, nerves, blood vessels and layered skin;

[0137] The external shape data of the human hand bones are acquired by scanning to obtain external data, and then the internal data and external data are imported into a 3D printer to complete the modeling of the hand bones;

[0138] The hand bone model's bones and joints, as well as the bones themselves, are connected, as well as the metacarpal bones and phalanges, so that the hand bones can be adjusted and changed in angle under the driving force, thereby achieving a gripping operation.

[0139] Control the hand drive module and complete the control operation by inputting and receiving instructions;

[0140] Different posture data are imported in advance to achieve the required grasping state and posture. When performing grasping and other morphological operations, the hand control module sends instructions to the hand drive module, causing the hand drive module to operate by controlling the hand model acquisition module, thereby realizing the grasping operation. The increase in the strength of the bionic hand realizes fine grasping and power grasping according to the increased force.

[0141] Based on the above, in the human-computer interaction scenario, we can wear somatosensory gloves to control the work of the bionic hand.

[0142] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Bionic hand system, characterized in that, include: The hand tissue acquisition module is used to acquire the internal structural features of the hand and obtain internal data, including the hand structure composed of bones, joints, ligaments, tendons, muscles, nerves, blood vessels and layered skin; The hand model acquisition module is used to acquire the external shape data of the human hand bones by scanning to obtain external data, and then import the internal and external data into the 3D printer to complete the hand bone model production; The hand drive module is used to connect the bones and joints of the hand bone model, as well as the bones themselves, and also to connect the metacarpal bones and phalanges. This allows the hand bones to be adjusted and changed in angle under the drive force, thereby achieving gripping operations. The hand control module is used to control the hand drive module and complete the control operation by inputting and receiving instructions; The hand grasping module achieves the required grasping state and posture based on the pre-imported different posture data. When the grasping and other forms are performed, the hand control module sends instructions to the hand driving module, causing the hand driving module to operate by controlling the hand model acquisition module, thereby realizing the grasping operation. The increase in the strength of the bionic hand realizes fine grasping and power grasping according to the increased force.

2. The bionic hand system according to claim 1, characterized in that: The hand tissue acquisition module includes: A data storage unit for storing internal hand data recorded during the existing dissection process; The data import unit imports the pre-prepared data into the database and then extracts and uses the data; The data analysis and processing unit is used to classify and process the acquired hand tissue data, and analyze it for use in subsequent control operations.

3. The bionic hand system according to claim 1, characterized in that: The hand model acquisition module includes: The scanning unit uses laser scanning to acquire the shape of the human hand bones, and stores and transmits the acquired data after acquiring the required hand bone shape data; The printing unit realizes 3D printing through a 3D printer, edits the pre-scanned data using the 3D printer, and completes the 3D printing of the human hand bones based on the edited data to replicate the required model of the human hand bones.

4. The bionic hand system according to claim 3, characterized in that: The scanning unit comprises: Laser scanner, which can scan the internal and external structure of human hands; The printing unit comprises: 3D printer, using Form3+ 3D printer with a 14.5×14.5 cm build platform to print the hand bone model.

5. The bionic hand system according to claim 1, characterized in that: The hand driving module includes: The execution unit is used to drive the hand model to grasp, hold, take and put, etc., to complete the bionic movements and postures similar to those of the human body, so as to achieve high precision and large force during bionic operation; A storage unit is used to store the execution unit and also to install the execution unit; The connecting unit is used to connect the tendons in the hand to realize the transmission work, so that the hand model can complete a passive degree of freedom of adduction and abduction; The supporting unit is used to install the executing unit and the connecting unit. The connecting unit is installed on the executing unit to fix the connecting unit.

6. The bionic hand system according to claim 5, characterized in that: The execution unit includes: Servo motors are used to achieve driving work. Ten sets of Dynamixel servo motors are used to drive the bionic hand, including 9 MX-12W and 1 AX-18A. Each finger is controlled by a pair of servos to achieve flexion movement. When the thumb is flexed, the palm will adduct. This is achieved by using a servo with higher torque. One AX-18A is used to control the bending of the thumb. The storage unit includes: The storage base can be used to install and store ten sets of Dynamixel servo motors, ensuring the stable operation of the ten sets of Dynamixel servo motors.

7. The bionic hand system according to claim 6, characterized in that: The connecting unit includes: The PE wire can transmit force to achieve stretching when the hand is bent. It is supported by a high-strength polyethylene wire. The PE wire needs to be stretched by a servo motor to complete the stretching operation. The support unit comprises: The auxiliary steering plate is used to connect the PE line to the servo motor and fix the PE line connection.

8. The bionic hand system according to claim 1, characterized in that: The hand control module includes: The control unit is equipped with a core circuit board of an ATmega2560 microcontroller and an ATmega2560 microcontroller for controlling a hand drive module. The core circuit board is used to carry the ATmega2560 microcontroller, and the ATmega2560 microcontroller sends instructions to the hand drive module and enables the hand drive module to work.

9. The bionic hand system according to claim 1, characterized in that: The hand grasping module comprises: The gripping posture pre-storage sharing unit can import pre-edited different gripping posture data into the database, and then the controller completes the scheduling and use of the database to realize the operation control of the required gripping posture.

10. A control method, characterized in that The bionic hand system according to any one of claims 1 to 9 comprises the following steps: Acquire the internal structural features of the hand to obtain internal data, including the hand structure composed of bones, joints, ligaments, tendons, muscles, nerves, blood vessels and layered skin; The external shape data of the human hand bones are acquired by scanning to obtain external data, and then the internal data and external data are imported into a 3D printer to complete the modeling of the hand bones; The hand bone model's bones and joints, as well as the bones themselves, are connected, as well as the metacarpal bones and phalanges, so that the hand bones can be adjusted and changed in angle under the driving force, thereby achieving a gripping operation. Control the hand drive module and complete the control operation by inputting and receiving instructions; Different posture data are imported in advance to achieve the required grasping state and posture. When performing grasping and other morphological operations, the hand control module sends instructions to the hand drive module, causing the hand drive module to operate by controlling the hand model acquisition module, thereby realizing the grasping operation. The increase in the strength of the bionic hand realizes fine grasping and power grasping according to the increased force.

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