Collaborative robot system and collaborative robot monitoring method

Abstract

The application relates to a collaborative robot system and a collaborative robot monitoring method. The collaborative robot system comprises a collaborative robot and a monitoring platform. The collaborative robot sends state data to the monitoring platform, and the state data comprises running state data and wear state data. The monitoring platform controls the working condition of the collaborative robot according to the running state data, and obtains the service life of the collaborative robot according to the wear state data. Real-time monitoring of the collaborative robot can be realized, and the collaborative robot can be maintained in time. Moreover, the working condition of the collaborative robot can be controlled through the monitoring platform, so that the collaborative robot can be controlled to be in a suitable running condition, and the service life of the collaborative robot is improved.

Description

Technical Field

[0001] This application relates to the field of collaborative robot monitoring technology, and in particular to a collaborative robot system and a collaborative robot monitoring method. Background Technology

[0002] Collaborative robots typically include multiple functional components such as harmonic reducers, motors, brakes, and encoders. A malfunction in any of these components can render the collaborative robot inoperable.

[0003] However, existing collaborative robots only require maintenance by operators when they have completely stopped operating. Furthermore, even if a collaborative robot is operational, prolonged exposure to unsuitable operating conditions will significantly reduce its lifespan. Summary of the Invention

[0004] Therefore, it is necessary to provide a collaborative robot system and a collaborative robot monitoring method to address the above problems.

[0005] In a first aspect, this application provides a collaborative robot system. The collaborative robot system includes a collaborative robot and a monitoring platform, wherein the collaborative robot and the monitoring platform are communicatively connected;

[0006] Collaborative robots send status data to a monitoring platform; the status data includes operational status data and wear status data.

[0007] The monitoring platform is used to control the working status of collaborative robots based on operational status data and to determine the lifespan of collaborative robots based on wear and tear data.

[0008] In one embodiment, the joint module of the collaborative robot is equipped with a lubricating grease detection device, which is communicatively connected to the monitoring platform, and the wear data includes lubricating grease detection data.

[0009] The lubricating grease detection device is used to detect the lubricating grease in the joint module, generate lubricating grease detection data, and send it to the monitoring platform;

[0010] The monitoring platform is used to determine the lifespan of collaborative robots based on lubricant testing data.

[0011] In one embodiment, the collaborative robot is connected to a vibration detection device, which is communicatively connected to a monitoring platform, and the operating status data includes vibration parameters.

[0012] The vibration detection device is used to detect the vibration state of the collaborative robot body, generate vibration parameters based on the vibration state, and send the vibration parameters to the monitoring platform.

[0013] The monitoring platform controls the working status of the collaborative robot based on vibration parameters.

[0014] In one embodiment, the collaborative robot includes a collaborative robot body and a controller connected to the collaborative robot body. The controller is communicatively connected to a monitoring platform, and the operating status data includes the operating data of the collaborative robot body.

[0015] The controller sends operational data to the monitoring platform; the monitoring platform controls the working status of the collaborative robot based on the operational data.

[0016] In one embodiment, the collaborative robot is connected to an environmental monitoring device, which is communicatively connected to a monitoring platform, and the status data also includes environmental status data.

[0017] The environmental monitoring device is used to detect the environmental status of the area where the collaborative robot is located, generate environmental status data based on the environmental status, and send the environmental status data to the monitoring platform.

[0018] In one embodiment, the monitoring platform is also used to generate maintenance information based on operating status data and wear status data.

[0019] In one embodiment, the monitoring platform is also used to classify and summarize the status data before displaying it.

[0020] In one embodiment, the monitoring platform is also used to receive data viewing requests and display the status data of the collaborative robot corresponding to the data viewing request.

[0021] Secondly, this application also provides a method for monitoring collaborative robots. The method includes:

[0022] Acquire status data sent by the collaborative robot, including operational status data and wear status data;

[0023] The working status of the collaborative robot is controlled based on the operating status data, and the service life of the collaborative robot is obtained based on the wear status data.

[0024] In one embodiment, after acquiring the status data sent by the collaborative robot, the method further includes:

[0025] Maintenance information is generated based on operating status data and wear status data.

[0026] The aforementioned collaborative robot system and monitoring method, wherein the collaborative robot system includes a collaborative robot and a monitoring platform, the collaborative robot sends status data to the monitoring platform, the status data including operational status data and wear status data. The monitoring platform controls the working condition of the collaborative robot based on the operational status data, and calculates the service life of the collaborative robot based on the wear status data. This enables real-time monitoring of the collaborative robot, facilitating timely maintenance. Furthermore, by controlling the working condition of the collaborative robot through the monitoring platform, the collaborative robot can be kept under suitable operating conditions, thereby extending its service life. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the structure of a collaborative robot system in one embodiment;

[0028] Figure 2 This is a schematic diagram of the joint module of a collaborative robot in one embodiment;

[0029] Figure 3 This is a flowchart illustrating a collaborative robot monitoring method in one embodiment;

[0030] Figure 4 This is a schematic diagram of the collaborative robot system in another embodiment;

[0031] Figure 5 This is a schematic diagram of the backend structure of the monitoring platform in one embodiment. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0033] In one embodiment, such as Figure 1 As shown, the collaborative robot system includes a collaborative robot 100 and a monitoring platform 200, with the collaborative robot 100 and monitoring platform 200 communicating with each other. The collaborative robot 100 sends status data to the monitoring platform 200. Specifically, the collaborative robot 100 can communicate with the monitoring platform 200 through a communication device. The communication device can be an external communication device independent of the collaborative robot 100, connected to the collaborative robot 100 via wired or wireless means. Alternatively, the communication device can be directly installed within the collaborative robot 100. After establishing a communication connection with the monitoring platform 200, the collaborative robot 100 can send status data to the monitoring platform 200 through the communication device.

[0034] The status data includes operational status data and wear status data. Operational status data characterizes the operational status of the collaborative robot 100. For example, operational status data may include the collaborative robot 100's operating speed, acceleration, trajectory, and operating time. Wear status data characterizes the hardware wear status of the collaborative robot 100. Specifically, it may include the wear status of various components within the collaborative robot 100. For example, wear status data may include the wear status data of the reducer, the motor, and the rotating actuators of the collaborative robot 100.

[0035] The monitoring platform 200 is used to control the working status of the collaborative robot 100 based on operational status data and to determine the service life of the collaborative robot 100 based on wear status data. Specifically, after acquiring operational status data from the collaborative robot 100, the monitoring platform 200 first determines the current working status of the collaborative robot 100 based on the operational status data. The working status of the collaborative robot 100 can include low-speed operation, medium-speed operation, high-speed operation, and fault shutdown status. Then, it determines whether the current working status of the collaborative robot 100 is reasonable. For example, it can determine whether the current operating speed of the collaborative robot 100 is too high or too low, or whether the current movement trajectory of the collaborative robot 100 will pass through singularities. In particular, when the working status of the collaborative robot 100 is a fault shutdown state, the monitoring platform 200 can generate fault alarm information.

[0036] If the current working condition of the collaborative robot 100 is unreasonable, the working condition of the collaborative robot 100 shall be controlled and adjusted to make it reasonable. Specifically, controlling and adjusting the working condition of the collaborative robot 100 may include adjusting the operating speed threshold, load limit, and movement trajectory of the collaborative robot 100.

[0037] Furthermore, the monitoring platform 200 can generate reference control information based on the current working status of the collaborative robot 100 and send the reference control information to the user. The user can then make further decisions on how to adjust the working status of the collaborative robot 100 based on the reference control information generated by the monitoring platform 200. Specifically, this can be achieved by issuing an adjustment command to the monitoring platform 200, which then controls and adjusts the working status of the collaborative robot 100 according to the adjustment command.

[0038] After acquiring wear status data from the collaborative robot 100, the monitoring platform 200 can determine the wear status of each component in the collaborative robot 100 based on the wear status data. The wear status can include heavy wear, moderate wear, and slight wear. Specifically, wear detection devices can be installed in the areas where each component is located within the collaborative robot 100. These devices are used to detect the wear status of each component. The specific detection methods of the wear detection devices may include, but are not limited to, collecting metal powder generated by friction during the operation of each component, and determining the wear status of the component based on the content of the collected metal powder.

[0039] Subsequently, the monitoring platform 200 can determine the service life of the collaborative robot 100 based on the wear and tear of its various components. The service life of the collaborative robot 100 can serve as one of the bases for determining whether it requires maintenance.

[0040] Furthermore, the monitoring platform 200 can send the status data, working conditions and lifespan of the collaborative robot 100 to the user, making it convenient for the user to monitor the collaborative robot 100.

[0041] It is understood that the monitoring platform 200 can acquire status data from multiple collaborative robots 100. These multiple collaborative robots 100 may include multiple collaborative robots of different models or brands.

[0042] In this embodiment, the collaborative robot system includes a collaborative robot 100 and a monitoring platform 200. The collaborative robot 100 sends status data to the monitoring platform 200, including operational status data and wear status data. The monitoring platform 200 controls the working condition of the collaborative robot 100 based on the operational status data and determines the service life of the collaborative robot 100 based on the wear status data. Real-time monitoring of the collaborative robot 100 is possible, facilitating timely maintenance. Furthermore, by controlling the working condition of the collaborative robot 100 through the monitoring platform 200, the collaborative robot 100 can be kept under suitable operating conditions, thereby extending its service life.

[0043] In one embodiment, such as Figure 2 As shown, the collaborative robot 100 has a lubricating grease detection device in its joint module. The lubricating grease detection device is communicatively connected to the monitoring platform 200, and the wear data includes lubricating grease detection data.

[0044] A collaborative robot 100 typically comprises multiple joint modules, each including a reducer assembly and a front end cover. The reducer assembly can be a harmonic reducer. A lubricating grease detection device can be positioned between the front end cover and the reducer assembly. This device detects the lubricating grease in the reducer assembly and generates lubricating grease detection data. Specifically, the device contacts and collects the lubricating grease from the reducer assembly. By analyzing the composition of the collected lubricating grease, the device generates lubricating grease detection data. This data may include information on deteriorated components and metal powder content in the lubricating grease.

[0045] After the lubricating grease detection device sends the lubricating grease detection data to the monitoring platform 200, the monitoring platform 200 can determine the service life of the collaborative robot 100 based on the lubricating grease detection data. For example, when the lubricating grease detection data includes data on deteriorated components and metal powder content, the monitoring platform 200 can determine the service life of the lubricating grease and the service life of the reducer assembly based on these data. When the monitoring platform 200 detects a high amount of deteriorated components or metal powder in the lubricating grease, it infers a reduced lubricating grease service life or a reduced reducer assembly service life, and can further calculate the service life of the lubricating grease and the reducer assembly based on the deteriorated component data and the metal powder content data.

[0046] In addition, a lubricating grease detection device can be installed between the front end cover and the reducer assembly in each joint module of the collaborative robot 100. The monitoring platform 200 obtains the service life of the collaborative robot 100 based on the lubricating grease detection data sent by each lubricating grease detection device.

[0047] In this embodiment, the joint module of the collaborative robot 100 is equipped with a lubricating grease detection device. This device detects the lubricating grease in the joint module, generates lubricating grease detection data, and sends it to the monitoring platform 200. The monitoring platform 200 uses the lubricating grease detection data to determine the service life of the collaborative robot 100. This allows for the detection of the collaborative robot 100's service life, facilitating the determination of when to perform maintenance.

[0048] In one embodiment, the collaborative robot 100 is connected to a vibration detection device, which is communicatively connected to a monitoring platform 200, and the operating status data includes vibration parameters.

[0049] The vibration detection device can be installed inside the collaborative robot 100. For example, it can be installed in the area where the motor, reducer assembly, or other key components of the collaborative robot 100 are located. The vibration detection device is used to detect the vibration state of the collaborative robot 100, specifically the vibration state of each rotating actuator, i.e., each joint. It generates vibration parameters based on the vibration state and sends these parameters to the monitoring platform 200. These vibration parameters may include vibration velocity, vibration acceleration, vibration frequency, etc. Furthermore, each vibration parameter corresponds to a preset vibration parameter threshold. Different models of collaborative robots have different preset vibration parameter thresholds, and different rotating joints within the same collaborative robot also have different preset vibration parameter thresholds.

[0050] The monitoring platform 200 controls the working status of the collaborative robot 100 based on vibration parameters. The specific steps include: after receiving vibration parameters from the vibration detection device, the monitoring platform 200 analyzes the difference between the vibration parameters and a preset vibration parameter threshold to determine whether the current working status of the collaborative robot 100 is reasonable. When the determination result is unreasonable, the monitoring platform 200 can control the collaborative robot 100 to operate under appropriate conditions. For example, when the vibration parameters include vibration acceleration, if the monitoring platform 200 detects that the current vibration acceleration of the collaborative robot 100 is greater than a preset vibration acceleration threshold, and determines that the current working status of the collaborative robot 100 is unreasonable, it can control the collaborative robot 100 to operate at a low or medium speed.

[0051] In this embodiment, the collaborative robot 100 is connected to a vibration detection device. The vibration detection device detects the vibration state of the collaborative robot 100, generates vibration parameters based on the vibration state, and sends the vibration parameters to the monitoring platform 200. The monitoring platform 200 controls the working condition of the collaborative robot 100 based on the vibration parameters. This allows for monitoring of the working condition of the collaborative robot 100 and control of its operation to ensure it operates under reasonable conditions, thereby improving its service life.

[0052] In one embodiment, the collaborative robot 100 includes a collaborative robot body 110 and a controller 120 connected to the collaborative robot body 110. The controller 120 is communicatively connected to a monitoring platform 200, and its operational status data includes the operational data of the collaborative robot body 110. The controller 120 can control the movement of the collaborative robot body 110 by sending control commands to it. Simultaneously, the controller 120 can send the operational data of the collaborative robot body 110 to the monitoring platform 200. This operational data may include joint velocities, joint accelerations, trajectory information, etc., during the operation of the collaborative robot body 110.

[0053] The specific steps by which the monitoring platform 200 controls the working status of the collaborative robot 100 based on operational data include: the monitoring platform 200 analyzes the received operational data of the collaborative robot body 110 based on the collaborative robot's kinematics to determine whether the current working status of the collaborative robot body 110 is reasonable. The reasonableness of the collaborative robot body 110's working status can be understood as whether its current working status might cause damage to the collaborative robot body 110. For example, if the end effector of the collaborative robot body 110 needs to reach a short straight path in a short time according to its current trajectory, the motor will run at extremely high speed. The rotational motion of the motor is converted into linear motion of the end effector through the reducer assembly and the rotational displacement of each joint, and then the motor decelerates rapidly to continue along the subsequent trajectory path. During this process, the rapid acceleration and high-speed operation of the motor will cause severe motor overheating and accelerate the wear of related components. In this case, the working status of the collaborative robot body 110 can be defined as unreasonable. Alternatively, if the end effector speed of the collaborative robot body 110 exceeds a preset threshold, it will also cause the motor to overheat and cause wear of related components. In this case, the working status of the collaborative robot body 110 can also be defined as unreasonable.

[0054] When the collaborative robot body 110 is not operating properly, the monitoring platform 200 can control the collaborative robot body 110 to operate under reasonable conditions. For example, when the monitoring platform 200 detects that the movement trajectory of the collaborative robot body 110 may pass through a singularity, the monitoring platform 200 can optimize the current movement trajectory of the collaborative robot body 110 and send the optimized trajectory to the controller 120 connected to the collaborative robot body 110. The controller 120 then controls the collaborative robot body 110 to operate along the optimized trajectory, avoiding the singularity. This improves the operating condition of the collaborative robot body 110 and extends its service life.

[0055] Furthermore, when the collaborative robot body 110 malfunctions, the controller 120 connected to the collaborative robot body 110 can send fault information to the monitoring platform 200. Upon receiving the fault information, the monitoring platform 200 can generate a fault notification message based on the fault information. This fault notification message may include information such as the fault type and the model of the malfunctioning robot. Further, the monitoring platform 200 can send the fault notification message to the user, allowing the user to be promptly informed of any malfunctions in the collaborative robot body 110 and to take appropriate action.

[0056] Similarly, the monitoring platform 200 can establish communication connections with multiple different controllers, each of which is connected to a corresponding collaborative robot body. The monitoring platform 200 can simultaneously receive operational data from different collaborative robot bodies through each controller and control the working status of its corresponding collaborative robot body based on the operational data.

[0057] In this embodiment, the controller 120 is used to send operating data to the monitoring platform 200. The monitoring platform 200 controls the working status of the collaborative robot body 110 according to the operating data, which can realize more comprehensive monitoring of the collaborative robot 100.

[0058] In one embodiment, the collaborative robot 100 is connected to an environmental monitoring device, which is communicatively connected to a monitoring platform 200, and the status data also includes environmental status data.

[0059] An environmental monitoring device can be installed within the area where the collaborative robot 100 is located to detect the environmental conditions within that area. Specifically, it can detect environmental conditions such as temperature, humidity, and noise within the area where the collaborative robot 100 is located. Based on the detected environmental conditions, the environmental monitoring device can generate environmental status data and send the data to the monitoring platform 200.

[0060] The monitoring platform 200 can determine whether the environmental condition of the area where the collaborative robot 100 is located is abnormal based on environmental status data. When the environmental condition of the area where the collaborative robot 100 is located is abnormal, the monitoring platform 200 can generate environmental abnormality alarm information. For example, the environmental abnormality alarm information may include abnormal temperature alarm information, abnormal noise alarm information, and other abnormal alarm information. In addition, the monitoring platform 200 can also send environmental status data to the user, enabling the user to monitor the environmental condition of the area where the collaborative robot 100 is located in real time. Furthermore, when the environmental condition is abnormal, the monitoring platform 200 can send environmental abnormality information to the user, allowing the user to respond promptly after receiving the environmental abnormality alarm information, check the environmental condition of the area where the collaborative robot 100 is located, and troubleshoot the abnormality. In particular, the environmental monitoring device may include temperature and humidity sensors, noise sensors, etc.

[0061] In this embodiment, the collaborative robot 100 is connected to an environmental detection device. The environmental detection device is used to detect the environmental status of the area where the collaborative robot 100 is located, generate environmental status data based on the environmental status, and send the environmental status data to the monitoring platform 200, thereby realizing environmental monitoring of the area where the collaborative robot 100 is located.

[0062] In one embodiment, the monitoring platform 200 is also used to generate maintenance information based on operating status data and wear status data.

[0063] The maintenance information includes operating condition improvement information and parts replacement information. Specifically, the monitoring platform 200 can determine the current working status of the collaborative robot 100 based on operational status data. When the current working status of the collaborative robot 100 is unreasonable, it can generate operating condition improvement information based on the operational status data. This information is used to improve the working status of the collaborative robot 100. For example, it may include motion trajectory optimization information, vibration parameter threshold adjustment information, and load adjustment information.

[0064] The monitoring platform 200 can also generate component replacement information based on wear status data. Specifically, the monitoring platform 200 can calculate the lifespan of each component based on its wear status data, and then recommend components for replacement based on their lifespan. It then summarizes the recommended replacement components to generate corresponding component replacement information. In particular, when the monitoring platform 200 receives lubricating grease detection data, it can calculate the lifespan of the lubricating grease based on this data and determine whether the lubricating grease needs to be replaced.

[0065] Similarly, the monitoring platform 200 can also send parts replacement information to the user, who can refer to the parts replacement information generated by the monitoring platform 200 to replace parts of the collaborative robot 100, or sub-parts of parts.

[0066] In this embodiment, the monitoring platform 200 is also used to generate maintenance information based on the operating status data and wear status data, which can remind users to maintain the collaborative robot 100 in a timely manner, and users can refer to the maintenance information generated by the monitoring platform 200 to decide how to maintain the collaborative robot 100.

[0067] In one embodiment, the monitoring platform 200 is also used to classify, summarize, and display the status data.

[0068] Specifically, the monitoring platform 200 can group collaborative robots 100 with similar or nearly identical status data into one category and display the categorized status data. Furthermore, when the status data includes operational status data and wear status data, the monitoring platform 200, after obtaining the working status of each collaborative robot based on the operational status data from different collaborative robots, can group collaborative robots with the same working status into one category. For example, collaborative robots operating under slight vibration conditions can be grouped into one category. Additionally, after obtaining the service life of the collaborative robots based on the wear status data, the monitoring platform 200 can group collaborative robots with similar service lives into one category. Alternatively, after determining whether a collaborative robot needs maintenance based on its service life, it can group collaborative robots requiring maintenance into one category.

[0069] After the monitoring platform has categorized and summarized 200 types of collaborative robots, it can also calculate the percentage of each category. For example, it can calculate the percentage of collaborative robots operating under mild vibration, the percentage of collaborative robots requiring maintenance, and the percentage of collaborative robots operating continuously for extended periods.

[0070] In this embodiment, the monitoring platform 200 is also used to classify and summarize the status data and display it, which can more intuitively and clearly show the working status and service life of the collaborative robot 100 to the user, making it convenient for the user to monitor the collaborative robot 100 in real time.

[0071] In one embodiment, the monitoring platform 200 is also used to receive data viewing requests and display the status data of the collaborative robot 100 corresponding to the data viewing request.

[0072] The monitoring platform 200 stores accounts and their corresponding permissions. Users can log in to the monitoring platform 200 to view the status data of the collaborative robot 100 within their authorized scope, as well as the current working status and lifespan of the collaborative robot 100.

[0073] Specifically, users can send data viewing requests to the monitoring platform 200. After obtaining the account of the currently accessing user based on the data viewing request, the monitoring platform 200 will display the status data of the collaborative robot 100 within the permission scope corresponding to that account, as well as the current working status and lifespan of the collaborative robot 100.

[0074] In this embodiment, the monitoring platform 200 is also used to receive data viewing requests and display the status data of the collaborative robot 100 corresponding to the data viewing request, which can ensure the data security of the collaborative robot 100.

[0075] In one embodiment, this application also provides a collaborative robot monitoring method, which can be applied to the monitoring platform 200 in any of the above embodiments. Figure 3 As shown, the method includes steps 202 and 204.

[0076] Step 202: Obtain the status data sent by the collaborative robot.

[0077] The status data includes operational status data and wear status data. Operational status data characterizes the operational status of the collaborative robot 100. For example, operational status data may include the collaborative robot 100's operating speed, acceleration, trajectory, and operating time. Wear status data characterizes the hardware wear status of the collaborative robot 100. Specifically, it may include the wear status of various components within the collaborative robot 100. For example, wear status data may include the wear status data of the reducer, the motor, and the rotating actuators of the collaborative robot 100.

[0078] Step 204: Control the working status of the collaborative robot based on the operating status data, and obtain the service life of the collaborative robot based on the wear status data.

[0079] Specifically, the current working status of the collaborative robot 100 is first determined based on operational status data. This status can include low-speed operation, medium-speed operation, high-speed operation, and a fault-locked state. Next, the rationality of the current working status is assessed. For example, it can be determined whether the robot's current operating speed is too high or too low, or whether its current trajectory passes through singularities. In particular, when the collaborative robot 100 is in a fault-locked state, the monitoring platform 200 can generate a fault alarm.

[0080] If the current working condition of the collaborative robot 100 is unreasonable, the working condition of the collaborative robot 100 shall be controlled and adjusted to make it reasonable. Specifically, controlling and adjusting the working condition of the collaborative robot 100 may include adjusting the operating speed threshold, load limit, and movement trajectory of the collaborative robot 100.

[0081] Wear data can be used to determine the wear status of each component in the collaborative robot 100. Wear status can be categorized as heavy wear, moderate wear, and slight wear. Based on the wear status of each component, the service life of the collaborative robot 100 can be calculated. The service life of the collaborative robot 100 can be used as one of the criteria for determining whether maintenance is required.

[0082] Furthermore, it displays the working status and lifespan of the collaborative robot 100, making it convenient for users to monitor the collaborative robot 100.

[0083] In one embodiment, after step 202, the collaborative robot monitoring method further includes step 302.

[0084] Step 302: Generate maintenance information based on operating status data and wear status data.

[0085] The maintenance information may include operational condition improvement information and parts replacement information. The operational condition improvement information is used to improve the working condition of the collaborative robot 100, and the parts replacement information is used to suggest which parts the user can replace.

[0086] Specifically, the current working status of the collaborative robot 100 can be obtained based on the operational status data. When the current working status of the collaborative robot 100 is unreasonable, working condition improvement information is generated based on the operational status data. The lifespan of each component is calculated based on the wear status data of each component, and then the components recommended for replacement are obtained based on the lifespan of each component. The components recommended for replacement are summarized to generate corresponding component replacement information.

[0087] Furthermore, maintenance information can be sent to users, who can then determine the specific maintenance plan for the collaborative robot 100 based on this information.

[0088] In one embodiment, the wear data includes lubricating grease detection data, and step 204 further includes step 402.

[0089] Step 402: Obtain the service life of the collaborative robot based on the lubricating grease detection data sent by the lubricating grease detection device.

[0090] In one embodiment, the operating status data includes vibration parameters, and step 204 further includes step 502.

[0091] Step 502: Control the working status of the collaborative robot according to the vibration parameters sent by the vibration detection device.

[0092] In one embodiment, the operating status data includes the operating data of the collaborative robot body 110, and step 204 further includes step 602.

[0093] Step 602: Control the working status of the collaborative robot body according to the operating data sent by the controller.

[0094] In one embodiment, the collaborative robot monitoring method further includes step 702.

[0095] Step 702: The status data is categorized, summarized, and then displayed.

[0096] In one embodiment, the collaborative robot monitoring method further includes step 802.

[0097] Step 802: Accept the data viewing request and display the status data of the collaborative robot corresponding to the data viewing request.

[0098] The aforementioned collaborative robot monitoring method acquires status data sent by the collaborative robot 100, including operational status data and wear status data. Based on the operational status data, the working condition of the collaborative robot 100 is controlled, and based on the wear status data, the service life of the collaborative robot 100 is obtained. This method enables monitoring of the working condition and service life of the collaborative robot 100, facilitating timely maintenance and repair. Furthermore, by controlling the working condition of the collaborative robot 100 to ensure it operates under appropriate conditions, the service life of the collaborative robot 100 can be extended.

[0099] To facilitate understanding of the above-described collaborative robot system and collaborative robot monitoring method, a more detailed specific embodiment is provided below.

[0100] In one embodiment, such as Figure 4 As shown, the collaborative robot system includes a collaborative robot 100 and a monitoring platform 200. The collaborative robot 100 includes a robot body 110, a controller 120, sensors 130, and communication equipment 140. The monitoring platform 200 includes a backend and an operation management center. The sensors 130 include a lubricating grease sensor, a vibration sensor, and an environmental sensor, corresponding to the lubricating grease detection device, vibration detection device, and environmental detection device mentioned above, respectively. The circles in the diagram represent different devices, and the overlapping parts of the circles indicate that the devices are interconnected. Figure 4 The collaborative robot 100 shown is connected to the robot body 110 via controller 120, sensor 130, and communication device 140. Sensor 130 and controller 120 are both connected to communication device 140. Communication device 140 is connected to a backend system, which in turn is connected to an operations management center, which is in turn connected to collaborative robot 100.

[0101] Specifically, the collaborative robot body 110 includes multiple joint modules, each of which may include a reducer assembly, a front end cover, and a lubricating grease sensor. The reducer assembly may be a harmonic reducer, and the lubricating grease sensor may be located between the front end cover and the harmonic reducer. The lubricating grease sensor can contact the lubricating grease in the harmonic reducer and detect its composition. The composition data is then uploaded to the backend via a communication device 140. The communication device 140 and the backend can be connected via wired or wireless network.

[0102] The collaborative robot body 110 can send status data to the backend via the communication device 140. The status data includes operational status data and wear status data. The backend can control the working status of the collaborative robot body 110 based on the operational status data and determine the service life of the collaborative robot body 110 based on the wear status data.

[0103] Specifically, the backend can analyze the lifespan of the lubricating grease based on lubricating grease testing data and determine whether the lubricating grease still has good lubrication properties. The analysis process may include estimating the lifespan of the lubricating grease and the harmonic reducer based on the detected metal powder content caused by harmonic reducer gear meshing friction or the content of deteriorated components in the lubricating grease. After obtaining the lubricating grease and harmonic reducer lifespans from the backend, the operations management center can generate maintenance information. This maintenance information can include specific maintenance recommendations. For example, it may recommend replacing the lubricating grease, replacing certain components, or suggesting a more suitable operating method to extend the current collaborative robot's lifespan.

[0104] A more suitable operating mode can be understood as follows: the joints of the collaborative robot body 110 typically have speed or acceleration limits. Frequent sharp turns in certain motion trajectories can affect the lifespan of the harmonic reducer. In this case, the operations management center can optimize the motion trajectory of the collaborative robot body 110 based on the site environment and work requirements. For example, the motion trajectory of the collaborative robot body 110 can be set to be smoother and the speed more uniform. The optimized motion trajectory is then sent to the controller 120 via the backend. The controller 120 controls the movement of the collaborative robot body 110 according to the optimized motion trajectory to reduce sudden acceleration or deceleration. Furthermore, different models of collaborative robot bodies typically have different load limits. The farther the load is from the end effector of the collaborative robot body, the smaller the load it can withstand. If the collaborative robot body is frequently operated at high speed and under full or overload conditions, it will also affect the lifespan of the collaborative robot body. In this case, the operations management center can propose a reasonable load configuration based on the collaborative robot body's instruction manual.

[0105] Vibration sensors can be used to detect the vibration parameters of the rotating actuators, i.e., the rotary joints, of the collaborative robot body 110. These vibration parameters are uploaded to the backend via communication device 140. The backend then sends the data to the operations management center. The operations management center uses the vibration parameters to determine whether the collaborative robot body 110 is in a suitable working condition and whether a malfunction requires shutdown and maintenance. Different models of the collaborative robot body 110 have different set vibration parameter thresholds. The vibration parameter thresholds also differ for different rotary joints of the same collaborative robot body 110. Determining whether the collaborative robot body 110 is in a suitable working condition may include checking whether the vibration parameters exceed the vibration parameter thresholds.

[0106] Environmental sensors are used to detect the environmental conditions within the area where the collaborative robot 110 is located, generate environmental condition data based on the environmental conditions, and send the environmental condition data to the backend via communication device 140. The environmental condition data may include temperature, humidity, noise, etc.

[0107] The controller 120 can upload the motion trajectory information, joint speed, acceleration, and other operational data of the collaborative robot body 110 to the backend via the communication device 140. After processing by the backend, the operation management center can determine whether the current working status of the collaborative robot body 110 is reasonable based on the kinematics of the collaborative robot. For example, it can determine whether the current motion trajectory of the collaborative robot body 110 is reasonable, or whether the end effector speed or acceleration value of the collaborative robot body 110 is reasonable.

[0108] The backend can include a server. The server can be a standalone server or a server cluster consisting of multiple servers. For example... Figure 5 As shown, the backend can include an account creation and permission distribution module, an exception or data push module, a data storage module, a data backup module, a data security module, and a data display module. The account creation and permission distribution module is used to create accounts and distribute corresponding permissions, enabling users to view only the status data of their own collaborative robot 100. The exception or data push module is used to generate a fault code based on the fault type of the collaborative robot 110 and push it to the operations management center when a malfunction occurs. This module also pushes the status data, working condition, and lifespan-related data of the collaborative robot 110 to the operations management center. The data storage module stores the status data and other related data of the collaborative robot 110, such as its working condition and lifespan. The data backup module backs up the status data and other related data of the collaborative robot 110. The data security module has data security policies to protect the collaborative robot 110 and user information, ensuring that data is not lost, stolen, or maliciously attacked. The data display module displays data and can be connected to a display screen. The display screen can be a regular LCD monitor, a large LED screen, or a multi-screen collaborative display. The displayed data may include, but is not limited to, the continuous running time of the collaborative robot body 110, the percentage of lubricating grease composition, vibration level, noise level, end effector trajectory, joint angular velocity / angular acceleration, and fault information when the collaborative robot body 110 malfunctions.

[0109] The backend can receive data viewing requests from users and display the status data of the collaborative robot 110 corresponding to the request. Specifically, the collaborative robot supplier can create accounts for users and assign corresponding permissions through the backend's account creation and permission distribution module. Users who need to view the real-time working status of the collaborative robot 110 can log in to the account provided by the collaborative robot supplier. After successful login, the backend can receive data viewing requests from users and display the status data of the collaborative robot 110 corresponding to the request. It can also simultaneously display the working status and lifespan of the collaborative robot 110 obtained after the backend analyzes and processes the status data.

[0110] The backend can also categorize and summarize the data collected from multiple collaborative robot bodies. Specifically, collaborative robot bodies with similar status data can be grouped into one category. For example, collaborative robot bodies with similar vibration parameters can be grouped into one category. After categorization and summarization, the backend can also calculate the proportion of each type of collaborative robot body. For example, the proportion of collaborative robot bodies can include the proportion of collaborative robots experiencing slight vibration, the proportion of collaborative robots requiring maintenance, and the proportion of collaborative robots that have been working continuously for a long time.

[0111] The operations management center generates maintenance information based on operational and wear data. It features fault handling, data analysis, and solution provision capabilities. Specifically, the operations management center first obtains relevant data from the backend, such as the status data and lifespan of the collaborative robot body 110. Status data may include vibration parameters, trajectory parameters, singularities, acceleration, duty cycle, temperature, and noise. Lifespan may include the lifespan of the reducer and other components. Then, the operations management center performs fault analysis, singularity analysis, vibration analysis, and trajectory optimization on the acquired data. Based on the analysis results, it generates maintenance information. Collaborative robot suppliers or integrators can refer to the maintenance information generated by the operations management center to determine the maintenance schedule for the collaborative robot body 110, propose more suitable operating plans for the collaborative robot body 110, decide on the iteration direction of collaborative robot products, or respond to users' relevant solutions.

[0112] The collaborative robot system in this embodiment includes a collaborative robot 100, a backend system, and an operation management center. The collaborative robot 100 includes a robot body 110, a controller 120, sensors 130, and communication devices 140. Sensors 130 or the controller 120 upload the status data of the robot body 110 to the backend system via the communication device 140. The backend system categorizes, calculates, and summarizes the data, then displays the relevant data of the robot body 110, enabling real-time monitoring of the robot body 110. This allows users to promptly detect faults in the robot body 110 and perform maintenance. Based on the backend data, the operation management center generates relevant maintenance information and can provide specific maintenance plans or solutions to optimize the robot body 110's operation for users or collaborative robot suppliers, thus extending the robot body 110's service life.

[0113] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A collaborative robot system, characterized in that, The collaborative robot system includes a collaborative robot and a monitoring platform, wherein the collaborative robot is communicatively connected to the monitoring platform. The collaborative robot sends status data to the monitoring platform; the status data includes operational status data and wear status data. The monitoring platform is used to control the working status of the collaborative robot based on the operating status data, and to obtain the service life of the collaborative robot based on the wear status data; If the current working condition of the collaborative robot is unreasonable, the working condition of the collaborative robot is controlled and adjusted to make the working condition of the collaborative robot reasonable; including: optimizing the motion trajectory of the collaborative robot so that the optimized motion trajectory avoids singularities and improves the working condition; The collaborative robot's joint module is equipped with a lubricating grease detection device, which is communicatively connected to the monitoring platform. The joint module includes a reducer assembly and a front end cover, and the lubricating grease detection device is disposed between the front end cover and the reducer assembly. The wear status data includes lubricating grease detection data. The lubricating grease detection device is used to detect the lubricating grease in the joint module, generate lubricating grease detection data, and send it to the monitoring platform. The monitoring platform is used to determine the service life of the collaborative robot based on the lubricating grease detection data; The monitoring platform is also used to generate maintenance information based on the operating status data and the wear status data; wherein, the maintenance information includes operating condition improvement information and component replacement information, the operating condition improvement information is determined based on the operating status data, and the component replacement information is determined based on the wear status data.

2. The collaborative robot system according to claim 1, characterized in that, The lubricating grease detection data includes data on the deteriorated components of the lubricating grease and data on the content of metal powder in the lubricating grease. The monitoring platform obtains the service life of the lubricating grease and the service life of the reducer assembly based on the data on the deteriorated components of the lubricating grease and the data on the content of metal powder in the lubricating grease.

3. The collaborative robot system according to claim 1, characterized in that, The collaborative robot is connected to a vibration detection device, which is communicatively connected to the monitoring platform. The operating status data includes vibration parameters. The vibration detection device is used to detect the vibration state of the collaborative robot, generate the vibration parameters based on the vibration state, and send the vibration parameters to the monitoring platform. The monitoring platform controls the working status of the collaborative robot based on the vibration parameters.

4. The collaborative robot system according to claim 1, characterized in that, The collaborative robot includes a collaborative robot body and a controller connected to the collaborative robot body. The controller is communicatively connected to the monitoring platform, and the operating status data includes the operating data of the collaborative robot body. The controller is used to send the operating data to the monitoring platform; the monitoring platform controls the working status of the collaborative robot body according to the operating data.

5. The collaborative robot system according to claim 1, characterized in that, The collaborative robot is connected to an environmental monitoring device, which is communicatively connected to the monitoring platform. The status data also includes environmental status data. The environmental detection device is used to detect the environmental status of the area where the collaborative robot is located, generate environmental status data based on the environmental status, and send the environmental status data to the monitoring platform.

6. The collaborative robot system according to claim 1, characterized in that, The monitoring platform is also used to classify, summarize, and display the status data.

7. The collaborative robot system according to claim 1, characterized in that, The monitoring platform is also used to receive data viewing requests and display the status data of the collaborative robot corresponding to the data viewing request.

8. A collaborative robot monitoring method, characterized in that, The method is implemented based on the collaborative robot system according to any one of claims 1-7, and includes: Acquire status data sent by the collaborative robot, including operational status data and wear status data; The working status of the collaborative robot is controlled based on the operating status data, and the service life of the collaborative robot is obtained based on the wear status data.

9. The collaborative robot monitoring method according to claim 8, characterized in that, After obtaining the status data sent by the collaborative robot, the process also includes: Maintenance information is generated based on the operating status data and the wear status data.

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