Mechanical arm system

Abstract

The invention discloses a mechanical arm system in the technical field of mechanical arms. The mechanical arm system comprises a mechanical arm body; the sensor assembly is used for acquiring state information, action information and environment information of the mechanical arm body; the host computer is used for transmitting the state information to the host computer and receiving a control instruction issued by the host computer; the upper computer is used for receiving and processing the action information, the environment information and the state information of the mechanical arm body and transmitting information frames to the information monitoring center, the lower computer and the voice module; the voice module is controlled by the upper computer to send out early warning information; and the information monitoring center is used for receiving the environment information and the state information from the upper computer, judging whether abnormity occurs or not, performing risk assessment, and uploading the environment information, the state information and a risk assessment result to a display screen for display. According to the mechanical arm system, through real-time state monitoring and real-time environment monitoring during use of the mechanical arm, safety risk early warning is achieved, and the use safety of the mechanical arm is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical arms, and in particular to a mechanical arm system. Background Art

[0002] The robot arm is a common automation equipment in the field of robotics. It can achieve functions such as grasping, handling, assembly, welding, and spraying by imitating human movements. The robot arm mainly replaces manual labor to perform some repetitive, high-intensity, and high-precision related work. Because the robot arm mainly imitates the human arm movements to work, the model is established by simplifying the human hand into a rotating joint and rods.

[0003] The types of robotic arms can be divided according to the number of joints, which can be divided into 2-DOF robotic arms, 3-DOF robotic arms, 5-DOF robotic arms, and even 7-DOF robotic arms. Among the multi-freedom robotic arms, the 6-DOF robotic arm is taken as an example. The first three joints determine the range of the robotic arm's operating space, and the last three joints determine the robotic arm's posture transformation in space. Therefore, the more degrees of freedom the robotic arm has, the more range of motion and spatial postures it has, but the kinematic model and motion control are also more difficult. Based on the design of six joints, it is necessary to provide driving force for each joint. Generally, a motor can be used to drive the rotation of the robotic arm joints, and the automated movement of the robotic arm can be achieved by controlling the rotation of the motor. However, simple automation can only meet some simple repetitive tasks.

[0004] With the development of artificial intelligence technology and the use of smart sensors, nowadays, various types of robotic arms have been widely used in industries such as industrial production, automated assembly, welding, medical and service fields. Therefore, the requirements for the safe use of robotic arms are getting higher and higher. However, there is currently a lack of risk assessment and risk warning for the use status of robotic arms; at the same time, there is a lack of safety monitoring and safety warning measures for the working environment of robotic arms. Summary of the invention

[0005] The present application provides a robotic arm system, which implements safety risk warning and improves the safety of the robotic arm by real-time status monitoring and real-time environment monitoring when the robotic arm is in use.

[0006] The embodiment of the present application provides a robotic arm system, including a robotic arm body, a sensor assembly, a lower computer, a host computer, a voice module, an information monitoring center and a display screen; The robot body is provided with a motor drive module, and the motor drive module is used to control the operation of the robot body; A sensor component is used to obtain status information, motion information and environmental information of the robot body; The lower computer is used to transmit the status information of the robot body to the upper computer, receive the control instructions issued by the upper computer, and control the movement of the robot body through the motor drive module; The upper computer is used to receive and process the action information and environmental information of the robot body transmitted by the sensor component, the status information of the robot body uploaded by the lower computer, and transmit information frames to the information monitoring center, the lower computer, and the voice module through the CAN protocol; A voice module, which is controlled by the host computer to send out warning information; The information monitoring center is used to receive environmental information and status information from the host computer, determine whether an abnormality occurs, conduct risk assessment, and upload the environmental information, status information, and risk assessment results to the display screen; The display screen is used to display the environmental information, status information, and risk assessment result information of the robot body.

[0007] The beneficial effects of the above embodiment are: the robotic arm system monitors the safety of the working environment and the working status of the robotic arm through the information monitoring center; performs safety analysis and visual display through the collected information, and assists with voice reminders to provide safety warnings from both visual and auditory aspects.

[0008] Based on the above embodiments, the present application can be further improved as follows: In one embodiment of the present application, the robot arm body includes: a base, a waist joint portion, an upper arm portion, an elbow joint portion, a lower arm portion, a wrist joint portion and an end effector; The waist joint part is rotatably mounted on the base to support the rotation; The upper arm part has two ends respectively hinged to the waist joint part and the elbow joint part; The elbow joint is connected to the upper arm and the lower arm and is used for pitching and lowering movements; The forearm part is connected with the elbow joint part and the wrist joint part and is used for rotational movement; The wrist joint is connected to the forearm and is used for pitching and elevating movements; The end effector is connected to the wrist joint and is used to grasp the target object.

[0009] In one of the embodiments of the present application, the sensor assembly includes a position sensor, a force sensor, a temperature sensor, a first depth camera, a second depth camera and a laser radar; Position sensors are installed at various joints in the robot body to collect the joint position and angle information of the robot body and upload it to the lower computer; The force sensor is installed at each joint and the end effector of the robot body to obtain the force and torque information of the robot body, assist in controlling the grasping force and accuracy, and upload it to the lower computer; Temperature sensors are installed in various parts of the robot body to collect temperature information of the robot body and upload it to the lower computer; The first depth camera is installed at the wrist joint to observe the characteristics and spatial state of the target object and upload them to the host computer; The second depth camera and laser radar are installed around the robot body to obtain the motion information and environmental information of the robot body and upload them to the host computer; Among them, the joint position and angle information, force and torque information, and temperature information are all status information of the robot body; the upper computer is also used to receive and process the relevant information of the target object transmitted by the first depth camera, and send control instructions to the lower computer accordingly.

[0010] In one of the embodiments of the present application, a driving device and a synchronous belt reduction system are provided in the upper arm part and the lower arm part. The driving device drives the upper arm part and the lower arm part to rotate through the synchronous belt reduction system. The synchronous belt reduction system includes a belt drive, a reducer, a bracket and a bearing to improve the rotation accuracy and load capacity.

[0011] In one embodiment of the present application, a drive device, a reducer and a bearing are provided in the elbow joint and the wrist joint, so as to increase the output torque of the robot arm and reduce the burden of the joint rotation.

[0012] In one of the embodiments of the present application, the lower computer is an STM32 controller and the upper computer is a Raspberry Pi.

[0013] In one embodiment of the present application, the risk assessment method is as follows: According to the robot arm state detection model, the state information and motion information of the robot arm body are collected. The state information includes force and torque information, temperature information, motion information, joint position and angle information; The host computer extracts features from the received status information and motion information, and uploads the extracted feature information to the trained neural network model; the trained neural network model is used to obtain the risk probabilities of the robotic arm, including the probability of normal use of the robotic arm, the probability of temperature influence, the probability of joint torque influence, and the probability of motion distortion; The risk probabilities are processed according to the fusion data algorithm, and the results are compared with the preset values, so that the robotic arm can be divided into three risk levels: dangerous, normal, and good.

[0014] In one embodiment of the present application, the neural network model training method is as follows: Acquire historical data during the use of the robot body, the historical data including the state information and action information of the robot body, and corresponding risk probability information, the risk probability information is summarized from the historical work process and / or manual experience; The historical data is divided into a training group and a validation group at a ratio of 8:2. The state information and action information of the training group are used for feature extraction, and the extracted feature information and the corresponding risk probability information are uploaded to the neural network model for training. The model parameters are adjusted through the back propagation algorithm to minimize the error between the predicted risk probability and the actual risk probability. The trained model is then verified using the validation group data to evaluate the generalization ability and accuracy of the model, and finally a trained neural network model is obtained.

[0015] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The robotic arm system monitors the safety of the robotic arm's working environment and the working status of the robotic arm through the information monitoring center; performs safety analysis and visual display through the collected information, and assists with voice reminders to provide safety warnings from both visual and auditory aspects.

[0016] 2. The robotic arm system is equipped with multiple sensors to enrich the robotic arm's perception capabilities and improve its cognitive capabilities. The processed data can be visualized through the display, including robotic arm status data, working environment safety data, and robotic arm risk assessment level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0018] Figure 1 This is a structural block diagram of a robotic arm system in Example 1 of the present application; Figure 2 This is a schematic diagram of the structure of the robot arm body in Example 1 of the present application; Figure 3 This is a schematic diagram of the robotic arm grasping process in Example 1 of the present application; Figure 4 This is a schematic diagram of the internal structure of the robotic arm base in Example 1 of the present application; Figure 5 This is a schematic diagram of the internal structure of the main arm of the robot in Example 2 of the present application; Figure 6 This is a schematic diagram of the finite element analysis process of the robotic arm in Example 1 of the present application; Figure 7 This is a schematic diagram of the risk assessment process of the robotic arm in Example 2 of the present application.

[0019] In the figure, 1. base, 2. waist joint, 3. upper arm, 4. elbow joint, 5. forearm, 6. wrist joint, 7. first depth camera, 8. end effector, 9. gear transmission group, 10. reducer, 11. belt drive. DETAILED DESCRIPTION

[0020] The present invention is further explained below in conjunction with specific implementation methods. It should be understood that these implementation methods are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0021] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0022] In the description of the present invention, it should be noted that the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be 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 it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] In the description of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in the present invention and the features of different embodiments or examples without contradiction.

[0024] Embodiment 1: like Figure 1 As shown, a robotic arm system includes a robotic arm body, a sensor assembly, a lower computer (STM32 controller), a host computer (Raspberry Pi), a voice module, an information monitoring center and a display screen.

[0025] The robot body is provided with a motor drive module, and the motor drive module is used to control the operation of various components of the robot body; Specifically, Figure 2As shown, the robot body includes: a base 1, a waist joint part 2, an upper arm part 3, an elbow joint part 4, a lower arm part 5, a wrist joint part 6, a first depth camera 7 and an end effector 8; The bottom 1 is provided with threaded holes for mounting the robot arm at a specific location such as a workbench, assembly line, mobile chassis or other carrier; The waist joint part 2 is used to support the mechanical arm to rotate and is connected to the mechanical arm base 1 and the upper arm 3; The upper arm part 3 has two ends respectively hinged to the waist joint part 2 and the elbow joint part 4; The elbow joint 4 is connected with the upper arm 3 and the lower arm 5 and is used for pitching and lowering movements; The forearm part 5 is connected with the elbow joint part 4 and the wrist joint part 3 and is used for rotational movement; A wrist joint 6 connected to the forearm 5 for pitching and lowering movements, and a first depth camera 7 is mounted on the wrist joint 6; The end effector 8 is connected to the wrist joint 6 and is used to grasp the target object.

[0026] A sensor component is used to obtain status information, motion information and environmental information of the robot body; The sensor assembly includes a position sensor, a force sensor, a temperature sensor, a first depth camera, a second depth camera and a laser radar; Position sensors are installed at various joints in the robot arm to collect the joint position and angle information of the robot arm body and upload it to the lower computer (STM32 controller); Force sensors are installed at various joints and end effectors in the robot body to obtain force and torque information of the robot body, assist in controlling the grasping force and accuracy, and upload it to the lower computer (STM32 controller); Temperature sensors are installed in various parts of the robot body to collect temperature information of the robot body and upload it to the lower computer (STM32 controller); The first depth camera is installed at the wrist joint to observe the characteristics and spatial state of the target object and upload it to the host computer (Raspberry Pi); The second depth camera and laser radar are installed around the robotic arm to obtain the motion information and environmental information of the robotic arm and upload them to the host computer (Raspberry Pi); Among them, joint position and angle information, force and torque information, and temperature information are all status information of the robot body.

[0027] The lower computer (STM32 controller) is used to transmit the status information of the robot body to the upper computer, receive the control instructions issued by the upper computer, and control the movement of the robot body through the motor drive module.

[0028] The upper computer (Raspberry Pi) is used to receive and process the action information and environmental information of the robotic arm body transmitted by the sensor component, the status information of the robotic arm body uploaded by the lower computer, and transmit information frames to the information monitoring center, the lower computer, and the voice module through the CAN protocol; it is also used to receive and process the relevant information of the target object transmitted by the first depth camera, and issue control instructions to the lower computer accordingly; it is also used to receive and process the environmental information transmitted back by the second depth camera and the lidar, and transmit it to the information monitoring center.

[0029] The voice module is controlled by the host computer (Raspberry Pi) to send out warning information; An example is as follows: When a person enters the working area while the robotic arm is working, the second depth camera will identify the person who enters the working area and transmit the collected data information to the host computer and information monitoring center for data processing, reminding the staff that they have entered the operating range of the robotic arm, pay attention to personal safety and keep a safe distance or stay away from the working area.

[0030] The information monitoring center is used to receive environmental information and status information from the host computer (Raspberry Pi), determine whether there is an abnormality, conduct risk assessment, and upload the environmental information, status information, and risk assessment results to the display screen; The display screen is used to display the environmental information, status information, and risk assessment result information of the robot body.

[0031] The information monitoring center receives environmental information and status information from the host computer, performs data fusion, analysis, and risk assessment on the received data information, and uploads the results of the analysis to the display screen. Through the display screen, it can always check whether the working status of the robot arm is good, whether the working area is safe, and whether there is an abnormal state; when the working status of the robot arm has an adverse reaction, the information monitoring center will analyze the received data and feedback the abnormal results of the robot arm to the display screen. The display screen displays the working environment data of the robot arm, the robot arm's own status data, and the risk assessment level of the robot arm (such as: below 60 is dangerous; 60-80 is normal; above 80 is good), which can be observed in real time, which is conducive to the staff to quickly understand the relevant conditions.

[0032] Among them, the robot control system mainly includes the robot, the host computer, the lower computer, and the sensor. The sensor collects relevant data information and encodes it through the lower computer to form different data frames and uploads it to the host computer or directly receives the data information from the host computer. The host computer decodes the received data information and distinguishes the data category according to different frame headers. It comprehensively judges whether the robot arm is working normally by processing the position sensor information, visual sensor (first depth camera, second depth camera) information, laser radar information and force sensor information, so as to control the robot arm to continue working, avoid obstacles, or stop working.

[0033] The following is an example of the robot grasping process: Figure 3 As shown, when the first depth camera recognizes the target object, the target information is transmitted to the upper computer for data processing to determine whether it meets the target feature points. If so, the target coordinate points are obtained, and finally the processed data information is sent to the lower computer through the CAN protocol. The lower computer decodes the received data, distributes the rotation data of each motor of the robotic arm accordingly, controls the robotic arm to move to the specified position, and performs actions such as grasping the target object. After the grasping is completed, the task completion information is fed back, and the current position information of the robotic arm is fed back.

[0034] The following further explanations are required about the above-mentioned robotic arm: like Figure 4 As shown, the above-mentioned mechanical arm base 1 is composed of a driving device, a gear reduction system including a gear transmission group 9 and a reducer 10, a transmission rod, and a bearing, and is fixed by external screw holes. The driving device and the synchronous belt reduction system are both provided in the large arm part 3 and the small arm part 5. Figure 5 As shown, the driving device drives the upper arm part 3 to rotate relative to the waist joint part 2 through a synchronous belt deceleration system. The synchronous belt deceleration system includes a belt drive 11, a reducer 10, a bracket and a bearing to improve the rotation accuracy and load capacity. The elbow joint part 4 and the wrist joint part 6 are both provided with a driving device and a bearing. The elbow joint part 4 is also provided with a reducer, which can increase the output torque of the robot arm while reducing the burden of the joint rotation.

[0035] The robot arm model uses a six-degree-of-freedom structural design, which can increase the working range of the robot arm and enrich the spatial posture of the robot arm, and can complete some complex movements. After completing the basic structural model, it is necessary to perform finite element analysis on the robot arm base, robot arm waist joint, robot arm upper arm, robot arm elbow joint, robot arm forearm, and robot arm wrist joint. The finite element analysis uses the following method: Figure 6 The conventional method shown is sufficient to verify whether the material strength is feasible and to modify the model in areas with severe stress and strain. Structural simulation can reduce consumables, accurately select materials, and avoid the risk of robot arm collapse due to material problems.

[0036] The use of gear reduction systems, synchronous belt reduction systems, bearings and other transmission methods can increase torque while reducing the output speed, which can improve the load capacity and operation accuracy of the robot arm, allowing the robot arm to complete some fine operations; Choose electric drive, hydraulic drive or even solar drive according to the operation requirements to provide power for the movement of the robot arm; According to the designed robot model, the DH model of the robot is established, and the DH parameter model of the robot (joint angle θ, connecting rod torsion angle α, connecting rod offset d, connecting rod length) is created. According to the DH convention, the two adjacent connecting rods are represented by matrices through homogeneous transformation. Finally, the posture equation of the end effector of the robot is solved to complete the kinematic solution of the robot. Based on the posture equation of the end effector (8) obtained by the kinematic solution of the robot, the angles of each joint of the robot are inversely calculated. According to the different robot models, the analytical method or algebraic method can be selected to solve each joint.

[0037] Robotic arm trajectory planning: The trajectory refers to determining the path between the starting position and the target position, and planning the motion posture of the robot on the path. Using trajectory planning based on Cartesian space, combined with the designed robot model and the constraints within the working range of the robot, the motion path and posture of the robot are planned.

[0038] The following further explanations are required about the above sensor components: The sensor selection of the above sensor assembly is based on the monitoring of the robot's own state, using position sensors, force sensors, and temperature sensors to jointly collect the robot's own information, and using visual sensors (first depth camera, second depth camera) to respectively collect the robot's working environment information, robot's motion information, and target image information; Perception system design, the visual system consists of the first depth camera, the second depth camera and the laser radar. The second depth camera and the laser radar are used to obtain the robot arm motion information and environmental information, and the first depth camera obtains the target image information. The acquired data information is transmitted to the host computer for data processing. The host computer extracts the received data and uploads the extracted features to the information monitoring center. The feature fusion technology is used to fuse the extracted feature information. Force sensors are installed at the joints and end effectors of the robot arm to obtain force and torque, and assist in controlling the grasping force and accuracy; position sensors are installed at each joint to collect the position and angle information of the robot arm joints; temperature sensors collect the temperature data of the robot arm. The data information collected by these three types of sensors is sent to the lower computer, and then the lower computer encodes the data and uploads it to the upper computer for decoding and data processing, and finally uploads it to the information monitoring center for data fusion.

[0039] The following further explanations are required about the above-mentioned information monitoring center: Receive data processed by the host computer, and fuse the feature information extracted by the host computer; feature fusion technology can reduce data processing pressure and improve data analysis accuracy, thereby improving data accuracy and reliability; Diagnose and evaluate the received data to determine the robot arm's usage status and risk assessment level, as well as whether there are safety issues around the robot arm; Based on the above results, it is determined whether the safety warning threshold has been reached, and a safety warning is issued through the voice module.

[0040] Embodiment 2: like Figure 7 As shown, the risk assessment method in the above embodiment 1 is as follows: According to the robot arm state detection model, the state information and motion information of the robot arm body are collected. The state information includes force and torque information, temperature information, motion information, joint position and angle information; The host computer extracts features from the received status information and motion information, and uploads the extracted feature information to the trained neural network model; the trained neural network model is used to obtain the risk probabilities of the robotic arm, including the probability of normal use of the robotic arm, the probability of temperature influence, the probability of joint torque influence, and the probability of motion distortion; The risk probabilities are processed according to the fusion data algorithm, and the results are compared with the preset values, so that the robotic arm can be divided into three risk levels: dangerous, normal, and good.

[0041] Among them, since the data mainly collected by this system are divided into image data and sensor data, the neural network model uses the CNN model to process these two types of data; the fusion data algorithm is based on the DS evidence theory, which can merge data from different sensors and handle conflicts between data. At the same time, the DS evidence theory can integrate evidence from different fault diagnosis modules to more accurately analyze fault problems, which is suitable for risk assessment of robotic arms.

[0042] The neural network model training method is as follows: Acquire historical data during the use of the robot body, the historical data including the state information and action information of the robot body, and corresponding risk probability information, the risk probability information is summarized from the historical work process and / or manual experience; The historical data is divided into a training group and a validation group at a ratio of 8:2. The state information and action information of the training group are used for feature extraction, and the extracted feature information and the corresponding risk probability information are uploaded to the neural network model for training. The model parameters are adjusted through the back propagation algorithm to minimize the error between the predicted risk probability and the actual risk probability. The trained model is then verified using the validation group data to evaluate the generalization ability and accuracy of the model, and finally a trained neural network model is obtained.

[0043] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. The robotic arm system monitors the safety of the robotic arm's working environment and the working status of the robotic arm through the information monitoring center; performs safety analysis and visual display through the collected information, and assists with voice reminders to provide safety warnings from both visual and auditory aspects.

[0044] 2. The robotic arm system is equipped with multiple sensors to enrich the robotic arm's perception capabilities and improve its cognitive capabilities. The processed data can be visualized through the display, including robotic arm status data, working environment safety data, and robotic arm risk assessment level.

[0045] 3. The structure of the robot arm uses a combination of a gear reduction system, a synchronous belt reduction system and bearings to improve the load capacity and accuracy of the robot arm and improve its performance.

[0046] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A robotic arm system, characterized in that: include: Robotic arm body, sensor components, lower computer, upper computer, voice module, information monitoring center and display screen; The mechanical arm body is provided with a motor drive module, and the motor drive module is used to control the operation of the mechanical arm body; The sensor assembly is used to obtain status information, motion information and environmental information of the robot body; The lower computer is used to transmit the state information of the robot body to the upper computer, and receive the control instructions issued by the upper computer, and control the movement of the robot body through the motor drive module; The upper computer is used to receive and process the action information and environmental information of the robot body transmitted by the sensor assembly, the status information of the robot body uploaded by the lower computer, and transmit information frames to the information monitoring center, the lower computer, and the voice module through the CAN protocol; The voice module is controlled by the host computer to send out warning information; The information monitoring center is used to receive the environment information and status information of the host computer, determine whether an abnormality occurs, and perform risk assessment, and upload the environment information, status information, and risk assessment results to the display screen; The display screen is used to display the environmental information, status information, and risk assessment result information of the robotic arm body.

2. The robotic arm system according to claim 1, characterized in that: The robot arm body comprises: a base, a waist joint, an upper arm, an elbow joint, a lower arm, a wrist joint and an end effector; The waist joint part is rotatably mounted on the base to support rotation; The upper arm portion has two ends hinged to the waist joint portion and the elbow joint portion respectively; The elbow joint is connected to the upper arm and the lower arm and is used for pitching and lowering movements; The forearm portion is connected to the elbow joint portion and the wrist joint portion and is used for rotational movement; The wrist joint is connected to the forearm and is used for pitching and elevating movements; The end effector is connected to the wrist joint and is used to grasp the target object.

3. The robotic arm system according to claim 2, characterized in that: The sensor assembly includes a position sensor, a force sensor, a temperature sensor, a first depth camera, a second depth camera and a laser radar; The position sensor is installed at each connection point in the robot body to collect the joint position and angle information of the robot body and upload it to the lower computer; The force sensor is installed at each connection point in the robot body and at the end effector to obtain the force and torque information of the robot body and upload it to the lower computer; The temperature sensor is installed in various parts of the robot body to collect temperature information of the robot body and upload it to the lower computer; The first depth camera is installed at the wrist joint to observe the characteristics and spatial state of the target object and upload them to the host computer; The second depth camera and the laser radar are installed around the robot body to obtain the motion information and environmental information of the robot body and upload them to the host computer; Among them, the joint position and angle information, force and torque information, and temperature information are all status information of the robotic arm body; the host computer is also used to receive and process the relevant information of the target object transmitted by the first depth camera, and send the control instructions to the slave computer accordingly.

4. The robotic arm system according to claim 2, characterized in that: The upper arm and the lower arm are both provided with a driving device and a synchronous belt deceleration system. The driving device drives the upper arm and the lower arm to rotate through the synchronous belt deceleration system. The synchronous belt deceleration system includes a belt drive, a reducer, a bracket and a bearing.

5. The robotic arm system according to claim 2, characterized in that: The drive device, the reducer and the bearing are arranged in the elbow joint and the wrist joint.

6. The robotic arm system according to claim 1, characterized in that: The lower computer is an STM32 controller, and the upper computer is a Raspberry Pi.

7. The robotic arm system according to claim 1, characterized in that: The risk assessment method is as follows: Collect state information and motion information of the robot body according to the robot state detection model, wherein the state information includes force and torque information, temperature information, motion information, joint position and angle information; The host computer extracts features from the received state information and motion information, and uploads the extracted feature information to the trained neural network model; the risk probabilities of the robotic arm are obtained through the trained neural network model, including the probability of normal use of the robotic arm, the probability of temperature influence, the probability of joint torque influence, and the probability of motion distortion; The risk probabilities of each item are processed according to the fusion data algorithm, and the results are compared with the preset values, so as to classify the robotic arm into three risk levels: dangerous, normal, and good.

8. The robotic arm system according to claim 7, characterized in that: The neural network model training method is as follows: Acquire historical data during the use of the robotic arm body, wherein the historical data includes the state information and action information of the robotic arm body, and corresponding risk probability information of each item, wherein the risk probability information is derived from historical work progress and / or manual experience; The historical data is divided into a training group and a verification group, and the state information and action information of the training group are subjected to feature extraction. The extracted feature information and the corresponding risk probability information are uploaded to the neural network model for training, and then the trained neural network model is verified through the verification group data.

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