A robot collision detection method and system

Abstract

The present invention relates to the technical field of robot testing, and specifically discloses a robot collision detection method and system. The method includes receiving a robot model input by a user, splitting the robot model into sub-components with labels, and determining the safety levels of the respective sub-components; obtaining the work tasks of the robot, and determining the risk levels of the respective sub-components according to the work tasks; determining the monitoring levels of the respective sub-components based on the risk levels and the safety levels; installing acquisition devices based on the monitoring levels to obtain collision information. The present invention first analyzes the robot model to determine the mechanical parameters of each sub-component in the robot model, analyzes the working environment of the robot to determine the risk exposure of each sub-component in the robot model, and allocates monitoring resources according to the mechanical parameters and the risk exposure. On the one hand, the utilization rate of monitoring resources is improved, and on the other hand, the collision monitoring effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot testing, and specifically to a robot collision detection method and system. Background Art

[0002] Robots have broad application prospects due to their good human-machine collaboration performance. However, during the operation of robots, they often collide with surrounding objects, causing damage to the robots and the collided objects. When a robot collides, the state of each component needs to be obtained through experiments by the staff. After multiple collision experiments, the staff can divide the working area more appropriately, improving the safety of the staff.

[0003] However, in the existing robot collision testing process, the installation of the acquisition device is mostly at a preset position and cannot be installed according to the actual situation and the robot's own condition. Therefore, how to better allocate the acquisition resources is the technical problem that the technical solution of the present invention wants to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide a robot collision detection method and system to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A robot collision detection method, the method includes:

[0007] Receiving a robot model input by a user, splitting the robot model into sub-components with labels, determining the rated load of each sub-component, and determining the safety level of each sub-component according to the rated load;

[0008] Obtaining the work task of the robot, and determining the risk level of each sub-component according to the work task;

[0009] Inputting the risk level and the safety level into a preset mapping relationship to determine the monitoring level of each sub-component;

[0010] Installing an acquisition device based on the monitoring level, and obtaining collision information indexed by the labels of each sub-component based on the acquisition device.

[0011] As a further solution of the present invention: The step of receiving a robot model input by a user, splitting the robot model into sub-components with labels, determining the rated load of each sub-component, and determining the safety level of each sub-component according to the rated load includes:

[0012] Receiving a robot model input by a user, and querying each assembly with a function label in the robot sub-model;

[0013] Cluster the assembly according to the function tags and connection relationships to obtain sub-components with labels.

[0014] Obtain the load parameters and connection parameters of the sub-component based on the sub-component, and determine the safety level of the sub-component according to the load parameters and connection parameters.

[0015] As a further solution of the present invention: the steps of obtaining the load parameters and connection parameters of the sub-component based on the sub-component, and determining the safety level of the sub-component according to the load parameters and connection parameters include:

[0016] Read the sub-components in sequence, and calculate the stress of the sub-component in each direction with the sub-component as a whole.

[0017] Obtain the materials and dimensions of the parts in the sub-component, calculate the part strength of each part, and take the lowest part strength as the sub-component strength.

[0018] Determine the connection points of the sub-component in each direction, calculate the stress at the connection points, and obtain the connection method and the corresponding connection strength.

[0019] Generate a safety level based on the stress of the sub-component, the sub-component strength, the stress at the connection points, and the connection strength.

[0020] As a further solution of the present invention: the steps of generating a safety level based on the stress of the sub-component, the sub-component strength, the stress at the connection points, and the connection strength include:

[0021] Generate a material safety level based on the stress of the sub-component and the sub-component strength.

[0022] Generate a connection safety level based on the stress at the connection points and the connection strength.

[0023] Compare the material safety level and the connection safety level, and take the smaller value as the safety level of the sub-component.

[0024] As a further solution of the present invention: the steps of obtaining the work tasks of the robot and determining the risk levels of the sub-components according to the work tasks include:

[0025] Read the task list of the robot within a preset time period, and query the sub-component tables involved in each task in sequence.

[0026] Connect the sub-component tables corresponding to all tasks to obtain the total component table.

[0027] Traverse each sub-component in the total component table, and count the repetition times of each sub-component.

[0028] Compare the number of repetitions with a number of preset threshold values, and determine the risk level of each sub-component according to the comparison result.

[0029] As a further solution of the present invention: the step of installing the acquisition device based on the monitoring level and obtaining the collision information indexed by the label of each sub-component based on the acquisition device includes:

[0030] Query the type and corresponding quantity of the acquisition device according to the monitoring level, and generate a collision instruction containing collision parameters; the collision parameters include collision speed and collision mass; the acquisition device includes a force sensor and a motion sensor;

[0031] Receive the collision information obtained by the acquisition device, and convert the collision information into a vector according to the installation position of the acquisition device;

[0032] Statistically analyze all the collision information corresponding to each sub-component to obtain the collision information indexed by the sub-component label.

[0033] As a further solution of the present invention: the method includes:

[0034] Obtain robot images during the collision process in real time according to a preset sampling frequency; the robot images are sorted based on time;

[0035] Read the robot image before the collision as a reference image; perform contour recognition on the reference image, and assign color values to the recognized contours based on the four-color principle;

[0036] Perform contour recognition on each robot image in sequence to obtain sub-contours; assign values to the sub-contours according to the color value assignment result of the previous robot image;

[0037] Dynamically display the robot images with color values assigned according to time.

[0038] The technical solution of the present invention also provides a robot collision detection system, and the system includes:

[0039] A safety level determination module, configured to receive a robot model input by a user, split the robot model into sub-components with labels, determine the rated load of each sub-component, and determine the safety level of each sub-component according to the rated load;

[0040] A risk level determination module, configured to obtain the work task of the robot, and determine the risk level of each sub-component according to the work task;

[0041] A monitoring level determination module, configured to input the risk level and the safety level into a preset mapping relationship to determine the monitoring level of each sub-component;

[0042] The collision information acquisition module is used to install the acquisition device based on the monitoring level, and acquire the collision information indexed by the labels of each sub-component based on the acquisition device.

[0043] As a further solution of the present invention: the safety level determination module includes:

[0044] The assembly query unit is used to receive the robot model input by the user and query each assembly containing function labels in the robot sub-model;

[0045] The clustering unit is used to cluster the assemblies according to the function labels and connection relationships to obtain sub-components with labels;

[0046] The execution unit is used to obtain the load parameters and connection parameters of the sub-components based on the sub-components, and determine the safety level of the sub-components according to the load parameters and connection parameters.

[0047] As a further solution of the present invention: the risk level determination module includes:

[0048] The sub-component table query unit is used to read the task linked list of the robot within a preset time period and query the sub-component tables involved in each task in turn;

[0049] The sub-component table connection unit is used to connect the sub-component tables corresponding to all tasks to obtain the total component table;

[0050] The traversal and statistics unit is used to traverse each sub-component in the total component table and count the repetition times of each sub-component;

[0051] The comparison and processing unit is used to compare the repetition times with a preset number of times thresholds, and determine the risk level of each sub-component according to the comparison result.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first analyzes the robot model to determine the mechanical parameters of each sub-component in the robot model, analyzes the working environment of the robot to determine the risk exposure of each sub-component in the robot model, and allocates the monitoring resources according to the mechanical parameters and risk exposure. On the one hand, the utilization rate of the monitoring resources is improved, and on the other hand, the collision monitoring effect is improved. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0054] Figure 1 It is a flow block diagram of the robot collision detection method.

[0055] Figure 2 It is the first sub - process block diagram of the robot collision detection method.

[0056] Figure 3 It is the second sub - process block diagram of the robot collision detection method.

[0057] Figure 4 It is the third sub - process block diagram of the robot collision detection method.

[0058] Figure 5 It is the composition structure block diagram of the robot collision detection system.

[0059] Figure 6 It is the composition structure block diagram of the safety level determination module in the robot collision detection system.

[0060] Figure 7 It is the composition structure block diagram of the risk level determination module in the robot collision detection system. Specific embodiments

[0061] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] Embodiment 1

[0063] Figure 1 It is the flow chart of the robot collision detection method. In the embodiment of the present invention, a robot collision detection method includes steps S100 to S400:

[0064] Step S100: Receive the robot model input by the user, split the robot model into sub - components with labels, determine the rated load of each sub - component, and determine the safety level of each sub - component according to the rated load;

[0065] In the design process of the robot, a robot model will be established. The robot model is composed of many sub - components, and these sub - components are parts integrated together with a functional orientation and are often regarded as a whole in the model; judge the rated load of the whole, and the safety level of the sub - component can be determined according to the rated load;

[0066] Step S200: Obtain the work task of the robot, and determine the risk level of each sub - component according to the work task;

[0067] The safety of each sub-component in the robot is also related to its working environment. Sub-components that are exposed to the outside for a long time and come into frequent contact with the working area are more likely to be damaged and have a higher risk level, which need to be considered key points during the collision detection process.

[0068] Step S300: Input the risk level and the safety level into a preset mapping relationship to determine the monitoring level of each sub-component;

[0069] By statistically analyzing the risk level and the safety level, the monitoring level of a certain sub-component can be jointly determined, and the monitoring level affects the allocation of monitoring resources;

[0070] Step S400: Install acquisition devices based on the monitoring level, and obtain collision information indexed by the labels of each sub-component based on the acquisition devices;

[0071] Install acquisition devices in each sub-component according to the monitoring level. The acquisition devices include various sensors to obtain the actual state of the robot during the collision process; after the acquisition devices are installed, establish a collision scenario, receive the data obtained by each acquisition device, and count the obtained data according to the labels of the sub-components.

[0072] Figure 2 It is the first sub-process block diagram of the robot collision detection method. The steps of receiving the robot model input by the user, splitting the robot model into sub-components with labels, determining the rated load of each sub-component, and determining the safety level of each sub-component according to the rated load include Step S101 to Step S103:

[0073] Step S101: Receive the robot model input by the user and query each assembly with function labels in the robot model;

[0074] Step S102: Cluster the assemblies according to the function labels and connection relationships to obtain sub-components with labels;

[0075] Step S103: Obtain the load parameters and connection parameters of the sub-component based on the sub-component, and determine the safety level of the sub-component according to the load parameters and connection parameters.

[0076] First, the robot is disassembled into small assemblies one by one. These assemblies refer to the smallest assembly units. If an assembly is still composed of multiple assemblies, it still needs to be further disassembled. Then, each assembly is clustered according to the function labels of the disassembled assemblies, and sub-components with labels can be obtained. Finally, mechanical analysis is performed on these sub-components to determine the safety level of the sub-components. The process of the mechanical analysis mainly includes two aspects: one is the load aspect, and the other is the connection aspect. The relationship between the two is the "short board effect", that is, the safety level is determined by the more insecure aspect.

[0077] Further, the steps of obtaining the load parameters and connection parameters of the sub-component based on the sub-component and determining the safety level of the sub-component according to the load parameters and connection parameters include:

[0078] Read the sub-components in sequence, and calculate the stress of the sub-component in each direction with the sub-component regarded as a whole.

[0079] Obtain the materials and dimensions of the parts in the sub-component, calculate the part strength of each part, and take the lowest part strength as the sub-component strength.

[0080] Determine the connection points of the sub-component in each direction, calculate the stress at the connection points, and obtain the connection method and the corresponding connection strength.

[0081] Generate a safety level based on the stress of the sub-component, the sub-component strength, the stress at the connection points, and the connection strength.

[0082] The above content specifically describes the process of determining the safety level. First, calculate the stress of the sub-component in each direction. In this process, a load state needs to be set. Generally, we take the load state of the robot's weight-bearing as the load state to be analyzed. Then, calculate the strength of each part in the sub-component, and take the lowest part strength as the strength of the whole sub-component. Finally, obtain the connection method between the sub-component and other sub-components, and determine the connection parameters according to the connection method. The safety level of the sub-component is determined by the stress of the obtained sub-component, the sub-component strength, and the connection parameters.

[0083] Specifically, the steps of generating a safety level based on the stress of the sub-component, the sub-component strength, the stress at the connection points, and the connection strength include:

[0084] Generate a material safety level based on the stress of the sub-component and the sub-component strength;

[0085] Generate a connection safety level based on the stress at the connection points and the connection strength;

[0086] Compare the material safety level and the connection safety level, and take the smaller value as the safety level of the sub-component.

[0087] The above content specifically describes the process of selecting the safety level. The material safety level can be determined from the stress of the sub-component and the strength of the sub-component, and the connection safety level can be generated from the stress at the connection and the connection strength. The lower level is selected from the material safety level and the connection safety level as the safety level of the sub-component.

[0088] Figure 3 It is the second sub-process block diagram of the robot collision detection method. The steps of obtaining the work task of the robot and determining the risk level of each sub-component according to the work task include steps S201 to S204:

[0089] Step S201: Read the task list of the robot within a preset time period, and sequentially query the sub-component tables involved in each task;

[0090] Step S202: Connect the sub-component tables corresponding to all tasks to obtain a total component table;

[0091] Step S203: Traverse each sub-component in the total component table and count the repetition times of each sub-component;

[0092] Step S204: Compare the repetition times with a preset number of times thresholds, and determine the risk level of each sub-component according to the comparison result.

[0093] The above content describes the process of generating the risk level. First, traverse the tasks in the task list of the robot, query the sub-component tables corresponding to each task, and connect the sub-component tables to obtain a total component table; then, count the occurrence times of each sub-component in the total component table, and the importance of the sub-component in the process of the robot completing the task can be determined according to the occurrence times; the more the repetition times, the higher the importance of the sub-component and the higher the set risk level.

[0094] Figure 4 It is the third sub-process block diagram of the robot collision detection method. The steps of installing the acquisition device based on the monitoring level and obtaining the collision information indexed by the label of each sub-component based on the acquisition device include steps S401 to S403:

[0095] Step S401: Query the type and corresponding quantity of the acquisition device according to the monitoring level, and generate a collision instruction containing collision parameters; the collision parameters include collision speed and collision mass; the acquisition device includes a force sensor and a motion sensor;

[0096] Step S402: Receive the collision information obtained by the acquisition device, and convert the collision information into a vector according to the installation position of the acquisition device;

[0097] Step S403: Count all the collision information corresponding to each sub-component to obtain the collision information indexed by the sub-component label.

[0098] Steps S401 to S403 define the process of collecting collision information. Before generating a collision instruction, it is necessary to determine the collision parameters. After the collision parameters are obtained, collision information is acquired based on the acquisition device. The collision information is a scalar, which can be converted into a vector according to the installation position of the acquisition device; all the collision information is counted with the label of the sub-component as the index.

[0099] As a preferred embodiment of the technical solution of the present invention, the method includes:

[0100] Obtain the robot images during the collision in real time according to the preset sampling frequency; the robot images are sorted based on time;

[0101] Read the robot image before the collision as the reference image; perform contour recognition on the reference image and assign color values to the recognized contours based on the four-color principle;

[0102] Perform contour recognition on each robot image in sequence to obtain sub-contours; assign values to the sub-contours according to the color value assignment result of the previous robot image;

[0103] Dynamically display the robot images after color value assignment according to time.

[0104] In an example of the technical solution of the present invention, during the collision of the robot, robot images are continuously obtained, contour recognition is performed on the robot images, and then values are assigned to the contour recognition results. The sub-contours after assignment can clearly reflect the states of each sub-component and even parts during the collision, which is convenient for users to observe.

[0105] Embodiment 2

[0106] Figure 5 Fig. is the structural block diagram of the robot collision detection system. In the embodiment of the present invention, a robot collision detection system, the system 10 includes:

[0107] A safety level determination module 11, configured to receive the robot model input by the user, split the robot model into sub-components with labels, determine the rated load of each sub-component, and determine the safety level of each sub-component according to the rated load;

[0108] A risk level determination module 12, configured to obtain the work task of the robot and determine the risk level of each sub-component according to the work task;

[0109] A monitoring level determination module 13 is configured to input the risk level and the safety level into a preset mapping relationship to determine the monitoring levels of each sub-component;

[0110] A collision information acquisition module 14 is configured to install acquisition devices based on the monitoring levels, and acquire collision information indexed by the labels of each sub-component based on the acquisition devices.

[0111] Figure 6 It is a structural block diagram of a safety level determination module 11 in a robot collision detection system. The safety level determination module 11 includes:

[0112] An assembly query unit 111 is configured to receive a robot model input by a user and query each assembly containing function labels in the robot sub-model;

[0113] A clustering unit 112 is configured to cluster the assemblies according to the function labels and connection relationships to obtain sub-components with labels;

[0114] An execution unit 113 is configured to obtain the load parameters and connection parameters of a sub-component based on the sub-component, and determine the safety level of the sub-component according to the load parameters and connection parameters.

[0115] Figure 7 It is a structural block diagram of a risk level determination module 12 in a robot collision detection system. The risk level determination module 12 includes:

[0116] A sub-component table query unit 121 is configured to read the task linked list of a robot within a preset time period and sequentially query the sub-component tables involved in each task;

[0117] A sub-component table connection unit 122 is configured to connect the sub-component tables corresponding to all tasks to obtain a total component table;

[0118] A traversal and statistics unit 123 is configured to traverse each sub-component in the total component table and count the repetition times of each sub-component;

[0119] A comparison and processing unit 124 is configured to compare the repetition times with a plurality of preset times thresholds, and determine the risk levels of each sub-component according to the comparison results.

[0120] All functions that the robot collision detection method can achieve are completed by a computer device. The computer device includes one or more processors and one or more memories. At least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to implement the functions of the robot collision detection method.

[0121] The processor fetches instructions from the memory one by one, analyzes the instructions, and then completes corresponding operations according to the requirements of the instructions, generating a series of control commands to make each part of the computer act automatically, continuously and coordinately, becoming an organic whole, and realizing the input of the program, the input of data, as well as the operation and output of results. All arithmetic operations or logical operations generated in this process are completed by the arithmetic unit; the memory includes a read-only memory (ROM), and the read-only memory is used to store computer programs, and a protection device is provided outside the memory.

[0122] Exemplarily, the computer program can be divided into one or more modules, and one or more modules are stored in the memory and executed by the processor to complete the present invention. One or more modules can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0123] Those skilled in the art can understand that the above description of the service device is only an example and does not constitute a limitation on the terminal device. It may include more or fewer components than the above description, or combine certain components, or different components. For example, it may include input and output devices, network access devices, buses, etc.

[0124] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The above processor is the control center of the above terminal device, and uses various interfaces and lines to connect all parts of the entire user terminal.

[0125] The above-mentioned memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, the above-mentioned processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as an information collection template display function, a product information release function, etc.); the data storage area can store data created according to the use of the berth status display system (such as product information collection templates corresponding to different product categories, product information that different product providers need to release, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0126] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the modules / units in the above-mentioned embodiment system of the present invention, it can also be completed by instructing relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can realize the functions of the above-mentioned various system embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0127] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including that element.

[0128] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for robot collision detection, characterized in that The method includes: Receiving a robot model input by a user, splitting the robot model into sub-components with labels, determining the rated load of each sub-component, and determining the safety level of each sub-component according to the rated load; Obtaining the work task of the robot, and determining the risk level of each sub-component according to the work task; Inputting the risk level and the safety level into a preset mapping relationship to determine the monitoring level of each sub-component; Installing acquisition devices based on the monitoring level, and obtaining collision information indexed by the labels of each sub-component based on the acquisition devices; Receiving a robot model input by a user, splitting the robot model into sub-components with labels, determining the rated load of each sub-component, wherein the step of determining the safety level of each sub-component according to the rated load includes: Receiving a robot model input by a user, and querying each assembly with function labels in the robot model; Clustering the assemblies according to the function labels and connection relationships to obtain sub-components with labels; Obtaining the load parameters and connection parameters of the sub-component based on the sub-component, and determining the safety level of the sub-component according to the load parameters and connection parameters; Obtaining the load parameters and connection parameters of the sub-component based on the sub-component, wherein the step of determining the safety level of the sub-component according to the load parameters and connection parameters includes: Sequentially reading the sub-components, and calculating the stress of the sub-component in each direction as a whole; Obtaining the material and size of each part in the sub-component, calculating the part strength of each part, and taking the lowest part strength as the sub-component strength; Determining the joints of the sub-component in each direction, calculating the joint stress, and obtaining the connection method and the corresponding connection strength; Generating a safety level based on the stress of the sub-component, the sub-component strength, the joint stress, and the connection strength; Wherein the step of generating a safety level based on the stress of the sub-component, the sub-component strength, the joint stress, and the connection strength includes: Generating a material safety level based on the stress of the sub-component and the sub-component strength; Generating a connection safety level based on the joint stress and the connection strength; Comparing the material safety level and the connection safety level, and taking the smaller value as the safety level of the sub-component; Obtaining the work task of the robot, wherein the step of determining the risk level of each sub-component according to the work task includes: Reading the task list of the robot within a preset time period, and sequentially querying the sub-component tables involved in each task; Connecting the sub-component tables corresponding to all tasks to obtain a total component table; Traversing each sub-component in the total component table, and counting the repetition times of each sub-component; Comparing the repetition times with several preset times thresholds, and determining the risk level of each sub-component according to the comparison result; Installing acquisition devices based on the monitoring level, wherein the step of obtaining collision information indexed by the labels of each sub-component based on the acquisition devices includes: Querying the type and corresponding quantity of the acquisition devices according to the monitoring level, and generating a collision instruction containing collision parameters; the collision parameters include collision speed and collision mass; the acquisition devices include force sensors and motion sensors; Receiving the collision information obtained by the acquisition devices, and converting the collision information into a vector according to the installation position of the acquisition devices; Statistically analyze all collision information corresponding to each sub-component to obtain collision information indexed by sub-component labels.

2. The robot collision detection method according to claim 1, wherein, The method includes: Obtain robot images during the collision process in real time according to a preset sampling frequency; the robot images are sorted based on time; Read the robot image before the collision as a reference image; perform contour recognition on the reference image and assign color values to the recognized contours based on the four-color principle; Perform contour recognition on each robot image in sequence to obtain sub-contours; assign values to the sub-contours according to the color value assignment results of the previous robot image; Dynamically display the robot images with color values assigned according to time.

3. A robot collision detection system, characterized in that, The system includes: A safety level determination module for receiving the robot model input by the user, splitting the robot model into sub-components with labels, determining the rated load of each sub-component, and determining the safety level of each sub-component according to the rated load; A risk level determination module for obtaining the work tasks of the robot and determining the risk level of each sub-component according to the work tasks; A monitoring level determination module for inputting the risk level and safety level into a preset mapping relationship to determine the monitoring level of each sub-component; A collision information acquisition module for installing acquisition devices based on the monitoring level and obtaining collision information indexed by the labels of each sub-component based on the acquisition devices; Among them, the safety level determination module includes: An assembly query unit for receiving the robot model input by the user and querying each assembly with function labels in the robot sub-model; A clustering unit for clustering the assemblies according to function labels and connection relationships to obtain sub-components with labels; An execution unit for obtaining the load parameters and connection parameters of the sub-components based on the sub-components and determining the safety level of the sub-components according to the load parameters and connection parameters; Among them, the risk level determination module includes: A sub-component table query unit for reading the task linked list of the robot within a preset time period and querying the sub-component tables involved in each task in sequence; A sub-component table connection unit for connecting the sub-component tables corresponding to all tasks to obtain a total component table; A traversal and statistics unit for traversing each sub-component in the total component table and counting the repetition times of each sub-component; A comparison and processing unit for comparing the repetition times with several preset times thresholds and determining the risk level of each sub-component according to the comparison results.

Images