Furnace front quick separation robot teaching method and related equipment

Abstract

The invention discloses a stokehole quick separation robot teaching method and related equipment, and relates to the technical field of industrial robot motion control, and the method comprises the steps: obtaining the process precision requirement of a target process and the spatial constraint parameters of the work of a stokehole quick separation robot; according to the process precision requirement and the spatial constraint parameter, determining a working space category; determining a quantitative movement mode corresponding to the workspace category according to the workspace category; and an input signal of the man-machine interaction device is responded, and the moving process is executed according to the quantitative moving mode so as to complete teaching operation of the stokehole quick separation robot. By combining the process precision and the spatial constraint parameters, the quantitative movement mode is intelligently matched, high-precision, high-consistency and low-threshold standardized teaching operation can be realized, and the teaching efficiency and safety are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of industrial robot motion control, and more specifically, to a furnace-front quick-splitting robot teaching method and related equipment. Background Art

[0002] With the continuous advancement of intelligent manufacturing and industrial automation, furnace-front quick-separation robots are increasingly being used in high-temperature, heavy-load, and high-risk operations such as steel metallurgy and heavy industrial manufacturing due to their high efficiency, precision, and stability. As a prerequisite for ensuring the efficient execution of tasks by robots, the accuracy and convenience of teaching operations play a crucial role in the entire workflow. Through teaching, operators can guide the robot through motion planning for critical paths and operating points, thereby ensuring the accuracy and reliability of subsequent automatic operations.

[0003] However, traditional teaching methods typically rely on manual inching, joystick operation, or point-by-point path recording. These methods are not only inefficient but also require high operator experience and lack standardization and intelligent support. In complex environments with confined spaces and extremely high temperatures, such as those in front of furnaces, the teaching process is prone to problems such as unstable operating accuracy, large point deviations, and spatial collisions. Especially when working in tiny spaces or high-precision work points, fixed modes or linear movement methods often fail to meet the requirements, thus affecting the robot's overall teaching quality and actual operation performance. In other words, the relevant technologies suffer from low teaching efficiency, poor accuracy, and insufficient intelligence. Summary of the Invention

[0004] The Summary of the Invention section of this application introduces a series of simplified concepts that will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] The teaching method and related equipment of the furnace-front quick-splitting robot provided in this application can intelligently match the quantitative movement mode by combining process accuracy and space constraint parameters, and can achieve high-precision, high-consistency, low-threshold standardized teaching operations, significantly improving teaching efficiency and safety.

[0006] In the first aspect, the present application provides a teaching method for a furnace-front quick-splitting robot, comprising: obtaining the process accuracy requirements of a target process and the spatial constraint parameters of the furnace-front quick-splitting robot; determining a workspace category based on the process accuracy requirements and the spatial constraint parameters; determining a quantitative movement mode corresponding to the workspace category based on the workspace category; and executing a movement process according to the quantitative movement mode in response to an input signal of a human-computer interaction device to complete the teaching operation of the furnace-front quick-splitting robot.

[0007] In some embodiments, determining the workspace category based on the process accuracy requirement and the space constraint parameter includes: when the space constraint parameter is greater than or equal to a first preset threshold and the process accuracy requirement is less than a second preset threshold, determining the workspace category as a space-free category; when the space constraint parameter is less than the first preset threshold, or the process accuracy requirement is greater than or equal to the second preset threshold, determining the workspace category as a space-restricted category.

[0008] In some embodiments, determining the quantitative movement mode corresponding to the workspace category based on the workspace category includes: when the workspace category is the free space category, determining the quantitative movement mode to be the first step mode, wherein the movement step of the first step mode is a free step, and the free step is obtained by performing analog-to-digital conversion on the input signal collected by the joystick or the inching button; when the workspace category is the restricted space category, determining the quantitative movement mode to be the second step mode, wherein the movement step of the second step mode is a preset step, and the preset step is a pre-set fixed step.

[0009] In some embodiments, the second step length mode includes a first step length sub-mode, a second step length sub-mode and a third step length sub-mode, the moving step length of the first step length sub-mode is smaller than the moving step length of the second step length sub-mode, and the moving step length of the third step length sub-mode is a digital signal value input by the user; when the workspace category is the space-constrained category, determining that the quantitative movement mode is the second step length mode includes: when the workspace category is the space-constrained category, receiving a user input signal; and determining that the quantitative movement mode is the first step length sub-mode, the second step length sub-mode or the third step length sub-mode according to the user input signal.

[0010] In some embodiments, the teaching method of the furnace-front fast-splitting robot further includes: obtaining the user's limit reaction time and the limit movement speed of the furnace-front fast-splitting robot; and determining the first preset threshold and the second preset threshold based on the limit reaction time and the limit movement speed.

[0011] In some embodiments, the response to the input signal of the human-computer interaction device and the execution of the movement process according to the quantitative movement mode include: determining the movement direction of the furnace-front fast-splitting robot according to the input signal; determining the displacement parameters of the furnace-front fast-splitting robot according to the input signal and the quantitative movement mode, wherein the displacement parameters include at least one of a movement angle and a movement distance; and controlling the furnace-front fast-splitting robot to move according to the movement direction and the displacement parameters.

[0012] In some embodiments, the teaching method of the furnace-front quick-splitting robot also includes: obtaining real-time posture data of the end effector of the furnace-front quick-splitting robot, wherein the real-time posture data is obtained by fusing the joint encoder feedback with the spatial coordinate data of the end vision sensor; comparing the real-time posture data with the target spatial coordinates to generate position deviation data, wherein the position deviation data represents the offset between the actual operating point of the end effector and the target reference point due to mechanical structure deformation or physical displacement of the target reference point; when the position deviation data exceeds the preset tolerance range, a movement parameter correction prompt is issued.

[0013] On the second aspect, the present application also provides a teaching device for a furnace-front fast-splitting robot, comprising: a data acquisition unit for acquiring the process accuracy requirements of the target process and the spatial constraint parameters of the furnace-front fast-splitting robot; a category determination unit for determining the workspace category based on the process accuracy requirements and the spatial constraint parameters; a mode determination unit for determining a quantitative movement mode corresponding to the workspace category based on the workspace category; a teaching movement unit for responding to an input signal of a human-computer interaction device and executing a movement process according to the quantitative movement mode to complete the teaching operation of the furnace-front fast-splitting robot.

[0014] In a third aspect, the present application further provides an electronic device comprising: a memory and a processor, wherein the processor is configured to implement the steps of the furnace front quick separation robot teaching method described in the first aspect when executing the computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the furnace front quick separation robot teaching method described in the first aspect.

[0016] In a fifth aspect, the present application also provides a computer program product, including a computer program or computer executable instructions. When the computer program or computer executable instructions are executed by a processor, the furnace front fast separation robot teaching method provided in the embodiment of the present application is implemented.

[0017] In summary, this application first obtains the process accuracy requirements and space constraint parameters to ensure that the teaching process is based on actual production conditions rather than subjective judgment; by classifying and identifying the workspace, it can match the most appropriate movement method for each type of point, and realize differentiated "quantitative movement mode" selection, such as large-step rapid movement, small-step fine-tuning, type-in precise positioning, etc., so that the furnace-front fast-splitting robot can accurately reach high-precision points, which can significantly improve the teaching consistency and repeatability; by inputting instructions through the human-computer interaction interface, the optimal movement method can be automatically matched without the need for frequent manual mode switching, which simplifies the decision-making process and reduces the operator's experience dependence and learning curve. Novices can also quickly complete complex teaching tasks, and the quantitative movement mode standardizes and modularizes the robot's operating steps, which can reduce unnecessary fine-tuning and reciprocating operations and improve overall teaching efficiency; the teaching mode is automatically switched according to the space constraint level, especially in confined areas with small spaces. The use of fine-tuning or type-in movement methods can effectively avoid mechanical collisions caused by misoperation. To sum up, the teaching method of the furnace-front quick-splitting robot provided in this application combines process accuracy and spatial constraint parameters, intelligently matches quantitative movement patterns, and can achieve high-precision, high-consistency, low-threshold standardized teaching operations, significantly improving teaching efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0019] Figure 1 A flow chart of a teaching method for a furnace-front quick separation robot provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a furnace-front quick separation robot teaching device provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Terms in the specification, claims, and drawings of this application, such as "first," "second," "third," "fourth," and the like (if any), are used to distinguish between similar objects, rather than to describe a particular order or precedence. Therefore, it is understood that these terms can be used interchangeably where appropriate, so that the embodiments described can be implemented in a different order, unless otherwise specified in the drawings or descriptions. In addition, the terms "is" and "has" and any variations thereof in this application are intended to cover all possible constituent elements on a non-exclusive basis. For example, a process, method, system, product, or apparatus that includes several steps or units is not necessarily limited to the steps or units that are explicitly listed, but may also include other steps or units that are not explicitly listed, or steps or units that are inherent to the process, method, product, or apparatus.

[0023] In this application, a "module" or "unit" refers to a computer program or part of a computer program that has a specific function and works in conjunction with other related parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as processing circuits or memories), or a combination of the two. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be part of a larger module or unit.

[0024] The technical solutions in this application will be described in detail below in conjunction with the accompanying drawings in the embodiments. It should be noted that the embodiments described are only part of this application, not all embodiments. In the following description, the "some embodiments" mentioned are only a subset of all possible embodiments, which may be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0025] Figure 1 This is a flow chart of a method for teaching a furnace-front quick separation robot provided by an embodiment of the present application. Figure 1 The teaching method of the furnace front quick separation robot provided in the embodiment of the present application may include the following steps 101 to 104:

[0026] Step 101: Obtain the process accuracy requirements of the target process and the space constraint parameters of the furnace front fast separation robot;

[0027] In some examples, a furnace-front fast-splitting robot refers to an automated robot deployed in a furnace-front fast-splitting laboratory for performing tasks such as automatic sample distribution, precise sorting, and standardized processing; the furnace-front fast-splitting robot mainly performs directional processing on metallurgical samples of different forms, and has high-precision motion control, multi-axis collaborative operation, and rapid task response capabilities; the furnace-front fast-splitting robot of the embodiment of the present application can also complete tasks such as sampling, temperature measurement, slag and iron identification, and separation. The target process refers to the specific process flow that the furnace-front fast-splitting robot needs to participate in or assist in; for example, sample reception, sample preparation, quality judgment, instrument analysis, information labeling, and sample retention. The process accuracy requirement refers to the tolerance range of the target process to the operating error of the furnace-front fast-splitting robot, which is usually defined in terms of position error (±mm), angular deviation (±°), or time window (±s), and can be used to evaluate the degree of precision required for the operation. The spatial constraint parameters refer to the degree of restriction of the working space of the furnace-front fast-splitting robot, including the size of the physical space, the distribution of obstacles, the robot's posture, etc., which can be used to determine whether the conditions for large-scale free movement are met; the spatial constraint parameters can be obtained by obtaining the current working environment modeling data through lidar or 3D visual scanning, or by reading CAD models or factory layout drawings, or the operator can manually input the narrow space boundary values.

[0028] By implementing step 101, the process accuracy requirements and spatial constraint parameters are accurately obtained, which can ensure that the teaching operation is based on actual task requirements and real environmental conditions, avoid relying on the operator's experience and judgment, improve the pertinence and feasibility of the teaching from the source, and lay a solid foundation for subsequent action planning.

[0029] Step 102: Determine the workspace category based on process accuracy requirements and space constraint parameters;

[0030] In some examples, the workspace category is a gradation of the accuracy and physical range of the furnace-front quick-sorting robot's current workspace, used to guide the robot's movement strategy for teaching. By comparing the acquired process accuracy requirements and spatial parameters with the set accuracy and spatial thresholds, the workspace category to which the current teaching operation belongs can be determined.

[0031] By implementing step 102, the workspace is classified, which helps to identify the complexity of the operating environment, thereby realizing intelligent grading and differentiated management of the teaching method, and can improve the adaptability and flexibility of the teaching process, especially in narrow or dangerous areas, and can effectively avoid collisions and misoperations.

[0032] Step 103, determining a quantitative movement mode corresponding to the workspace category according to the workspace category;

[0033] In some examples, the quantitative movement mode standardizes the movement method used by the furnace-front quick-dispensing robot during the teaching process into a quantifiable step-size control strategy, including parameters such as movement distance and angle, to achieve precise and repeatable operation. Based on the workspace category determined in step 102, the most appropriate quantitative movement control method can be automatically matched to adapt to the control requirements of different scenarios. For example, if the workspace category is free, the free step-size mode is used, such as joystick control, to dynamically adjust the movement amplitude based on the pressure. If the workspace category is constrained, the fixed step-size mode is used to avoid misoperation, and sub-modes such as fine-tuning, normal, and keying can be selected based on user input.

[0034] By implementing step 103, the appropriate quantitative movement mode is intelligently selected according to different spatial environments, which can improve the efficiency and accuracy of the movement of the furnace-front quick-sorting robot, reduce the cost of human decision-making, and enhance the consistency and standardization level of teaching.

[0035] Step 104, in response to the input signal of the human-machine interaction device, executing the movement process according to the quantitative movement mode to complete the teaching operation of the furnace front fast separation robot;

[0036] In some examples, a human-machine interface (HMI) is a hardware and / or software interface used for operators to exchange information with robotic systems; the human-machine interface may include a physical controller (handheld terminal, teach pendant, operation panel, joystick, etc.), a software interface (touch screen, PC interface, virtual console, etc.); the input signal is an operation instruction issued by the user through the human-machine interaction device, which is used to control the movement behavior of the furnace-front fast-splitting robot; after receiving the input signal, the furnace-front fast-splitting robot parses the input according to the previously selected quantitative movement mode, calculates the control parameters such as the movement direction, step size, displacement angle, and then drives the furnace-front fast-splitting robot to move, so as to manually guide it to complete a specific movement trajectory, point setting or process flow setting process.

[0037] By implementing step 104 and combining human-computer interaction with automatic mode matching, the operator does not need to frequently switch operating modes manually, which simplifies the operating process and lowers the learning threshold. Even novices can efficiently complete complex teaching tasks. At the same time, the operation path is standardized by the quantitative movement control strategy, which can reduce unnecessary fine-tuning or repetitive actions and significantly improve the teaching efficiency and quality.

[0038] In summary, the embodiment of the present application first obtains the process accuracy requirements and space constraint parameters to ensure that the teaching process is based on actual production conditions rather than subjective judgment; by classifying and identifying the workspace, it can match the most appropriate movement mode for each type of point, and realize differentiated "quantitative movement mode" selection, such as large-step rapid movement, small-step fine-tuning, type-in precise positioning, etc., so that the furnace-front fast-splitting robot can accurately reach high-precision points, which can significantly improve the teaching consistency and repeatability; by inputting instructions through the human-computer interaction interface, the optimal movement mode can be automatically matched without the need for frequent manual mode switching, which simplifies the decision-making process and reduces the operator's experience dependence and learning curve. Novices can also quickly complete complex teaching tasks, and the quantitative movement mode standardizes and modularizes the robot operation steps, which can reduce unnecessary fine-tuning and reciprocating operations and improve overall teaching efficiency; the teaching mode is automatically switched according to the space constraint level, especially in confined areas with small spaces. The use of fine-tuning or type-in movement methods can effectively avoid mechanical collisions caused by misoperation. To sum up, the teaching method for the furnace-front quick-splitting robot provided in the embodiment of the present application combines process accuracy and space constraint parameters, intelligently matches the quantitative movement mode, and can achieve high-precision, high-consistency, and low-threshold standardized teaching operations, thereby significantly improving teaching efficiency and safety.

[0039] In some embodiments, the aforementioned step 102 may include: when the space constraint parameter is greater than or equal to a first preset threshold and the process accuracy requirement is less than a second preset threshold, determining the workspace category as a space-free category; when the space constraint parameter is less than the first preset threshold, or the process accuracy requirement is greater than or equal to the second preset threshold, determining the workspace category as a space-restricted category.

[0040] In some examples, the first preset threshold is used to measure the degree of spatial constraints in the furnace-front quick-sorting robot's working environment. It serves as the dividing line between ample space and insufficient space. This threshold can be set based on the robot's size, movement radius, and safety clearance. For example, if the extended radius of the robot's manipulator arm is 800mm, the first threshold is set to 900mm to ensure at least 100mm of margin to avoid collisions. The second threshold is used to determine the operational accuracy requirements of the task, distinguishing between "normal precision" and "high precision" operations. This threshold can be set based on the error tolerance of the target process. For example, if the maximum allowable deviation at a point is ±0.5mm, the second threshold is set to 0.5mm; exceeding this threshold indicates a high precision requirement. The spatial freedom category indicates that the furnace-front quick-sorting robot has free movement, flexible turning, and high reachability within its working area, with few or no obstacles in the environment. The criteria for determination are: the spatial constraint parameter ≥ the first preset threshold, and the process accuracy requirement < the second preset threshold. The space-constrained category refers to the situation where the movement space of the furnace-front fast-splitting robot is limited, there is a risk of physical interference, or the task requires high precision, and a teaching scenario with smaller step size and more precise control is needed.

[0041] Through the implementation of the above embodiment, two preset thresholds are set to divide the working environment into a free space category or a restricted space category, which can realize adaptive adjustment of the operation mode under environmental perception, improve scene adaptability and intelligent classification capabilities, and effectively prevent misoperation or collision caused by insufficient space.

[0042] In some embodiments, the aforementioned step 103 may include: when the workspace category is a free space category, determining that the quantitative movement mode is a first step mode, wherein the moving step length of the first step mode is a free step length, and the free step length is obtained by performing analog-to-digital conversion on the input signal collected by the joystick or the inching button; when the workspace category is a restricted space category, determining that the quantitative movement mode is a second step mode, wherein the moving step length of the second step mode is a preset step length, wherein the preset step length is a pre-set fixed step length.

[0043] In some examples, the first step mode is a flexible movement method suitable for free-space working environments. The furnace-front fast-pointing robot can move continuously in response to user input, and the movement speed and step length are dynamically adjusted with the input signal strength. For example, in the open furnace-front area, the greater the operator pushes the joystick, the faster the robot moves and the longer the displacement, which is suitable for quickly approaching the target point. The movement step length refers to the distance or angle moved by the robot in a single response to an input command, usually measured in millimeters (mm) or degrees (°). The movement step length can be divided into free step length and preset step length. The free step length is a continuously variable step length calculated by analog-to-digital conversion (ADC) of the amplitude and time of the analog control signal (such as the joystick or jog button). For example, if the joystick is tilted by 30%, the current step length calculated after analog-to-digital conversion is 8mm; if it is tilted by 100%, the corresponding maximum step length can reach 20mm, achieving dynamic response. The input signals collected by the joystick or inching button are input signals sent by the operator through control devices such as industrial handles, keyboards, and touch screens. They are used to indicate the movement direction and amplitude of the furnace-front quick-splitting robot. The second step mode is suitable for space-constrained categories. In this mode, the furnace-front quick-splitting robot can only move step by step at a strictly defined fixed distance to avoid misoperation or collision. The preset step size is a predefined, fixed distance that cannot be automatically changed. It is used to precisely control the robot's movement in situations with high precision requirements or space constraints. For example, in tight spaces, the step size can be set to 0.2mm to achieve point-by-point fine-tuning. For high-precision point positioning, the keyboard can be used to input "0.1mm" precise steps.

[0044] For example, when the robot recognizes that the workspace is a free space category, the control system will enable the first step length mode, dynamically calculate the free step length by reading the joystick tilt angle and performing analog-to-digital conversion, and achieve fast and flexible teaching operations; when it is identified as a confined space category, the system automatically switches to the second step length mode, allowing only fixed preset step lengths for control, thereby avoiding collisions or error accumulation of the robot in a confined environment.

[0045] Through the implementation of the above embodiments, a flexible rocker-type step length is used when the space is free, which is free and efficient; a fixed step length is used when the space is confined, which is stable and safe, effectively balancing efficiency and safety, ensuring that the furnace-front fast-splitting robot can always maintain precision control and efficient operation under different working conditions.

[0046] In some embodiments, the aforementioned second step length mode may include a first step length sub-mode, a second step length sub-mode and a third step length sub-mode, the moving step length of the aforementioned first step length sub-mode is smaller than the moving step length of the second step length sub-mode, and the moving step length of the third step length sub-mode is a digital signal value input by the user; when the workspace category is a space-constrained category, determining that the quantitative movement mode is the second step length mode may include: when the workspace category is a space-constrained category, receiving a user input signal; and determining that the quantitative movement mode is the first step length sub-mode, the second step length sub-mode or the third step length sub-mode according to the user input signal.

[0047] In some examples, the first step length submode is the submode with the smallest step length and highest precision within the second step length mode. It can be used for fine-tuning control in extremely confined spaces or for high-precision points. For example, the preset step length for the first step length submode can be 0.1mm or 0.2mm, selected by the user in the control interface. The medium step length option within the second step length mode is suitable for environments with limited but not particularly compact spaces, balancing precision and efficiency. For example, the preset step length for the first step length submode can be 1mm or 2mm, selected by the user in the control interface. The third step length submode allows users to customize the movement step length by entering a precise value to accommodate non-standard requirements or special task scenarios. For example, users can manually enter a specific step length value, such as 0.75mm, through a human-machine interface (HMI) such as a touch screen, keyboard, or voice. The user-entered digital signal value is the precise value provided by the user through the human-machine interface in the third step length submode and serves as the unit step length for robot movement. When the workspace category is constrained, the robot can automatically determine and switch to the appropriate submode based on the user's specific operations, such as selection and input, to ensure the most appropriate control method. The user input signal is an operation instruction sent by the user and received through a human-machine interface (HMI), and is used to select or set the currently used step size sub-mode.

[0048] Furthermore, after determining that the quantitative movement mode is the second step mode, the control methods of the first step sub-mode, the second step sub-mode and the third step sub-mode are all implemented through virtual step selection buttons provided by the human-computer interaction interface; the control method for the furnace-front quick-splitting robot is based on the reading of the real-time spatial position of the furnace-front quick-splitting robot, and a single pulse method is used to superimpose the step movement amount selected by the user on the target direction coordinate value, thereby realizing a one-time quantitative movement operation; compared with the traditional teaching method that relies on inching buttons or physical joysticks, the control method of the embodiment of the present application does not rely on continuous physical pressing or mechanical switching, avoids unexpected continuous displacement caused by improper operation, and effectively improves the positioning accuracy, safety and control stability of the furnace-front quick-splitting robot in a space-constrained environment.

[0049] It should be noted that in traditional industrial robot teaching, the switching and control methods of different step length modes usually rely on jog buttons or physical joystick operations. The operator needs to achieve step length movement by long pressing, short pressing, or controlling the joystick angle. This operation mode requires a high level of operator proficiency. If the jog button is pressed too long or the joystick fails to reset in time, it is easy to cause the robot to move excessively in a continuous single pulse manner. Especially in scenarios where space is limited or high-precision positioning is required, it is easy to cause collisions or repeated repositioning risks. However, the embodiment of the present application uses virtual buttons for selecting different step lengths and virtual buttons for operating directions preset in the human-machine interface screen. The operator can select and execute the step length by clicking the virtual buttons. Unlike the traditional jog method, the embodiment of the present application does not rely on continuous physical pressing behavior. Instead, it reads the real-time position of the furnace-front quick-distribution robot and superimposes the set movement amount on the coordinate value of the target direction in the form of a single pulse to achieve quantitative and controllable single displacement. Even if the operator continues to press the virtual button, it will not cause unexpected continuous movement behavior, thereby effectively avoiding the risk of collision and misoperation and improving the stability and accuracy of control.

[0050] For example, when the workspace is identified as a space-constrained category, the operator can select the high-precision first step sub-mode, the medium-precision second step sub-mode, or directly enter a specific value to enable the third step sub-mode through the human-computer interaction interface according to task requirements; after receiving the user input signal, the corresponding control logic is automatically matched, so that the robot can complete the teaching operation accurately, flexibly and safely in a limited space.

[0051] Through the implementation of the above embodiments, the movement mode under the category of restricted space is refined, and three sub-modes are provided: small-step fine-tuning (the first-step long sub-mode), medium-step regular (the second-step long sub-mode), and user-defined input (the third-step long sub-mode). A graphical interface is used to replace traditional hardware operations, which significantly improves the control granularity, security and user experience, and reduces the risk of misoperation and training threshold.

[0052] In some embodiments, the aforementioned furnace-front quick-splitting robot teaching method may further include: obtaining the user's limit reaction time and the limit movement speed of the furnace-front quick-splitting robot; and determining a first preset threshold and a second preset threshold based on the limit reaction time and the limit movement speed.

[0053] In some examples, the user's critical reaction time refers to the minimum time required for an operator to perceive a robot's movement or control command, reach their brain, and then effectively respond. It is usually expressed in milliseconds (ms) or seconds (s) and is a key indicator for measuring the safety and response efficiency of human-machine interaction. This critical reaction time is primarily used to ensure that the instructor can promptly stop abnormal robot movements during robot movement to avoid personal injury or equipment collisions. Considering the actual operation of a furnace-front quick-splitting robot, its movement typically requires the operator to press the middle gear button of the enabling device to energize the motor and enable movement. Once the enabling device is released or re-engaged, the furnace-front quick-splitting robot immediately powers off and stops moving. Therefore, the critical reaction time can be tested by triggering an indicator light to light up or the system to issue a prompt signal, and recording the time from the signal appearing to the user releasing or re-engaging the enabling button to obtain the user's critical reaction time. The shortest or average value can also be obtained from multiple tests. For example, if the user's average reaction time across three tests is 0.6 seconds, the critical reaction time can be set to 0.6 seconds, or the lowest value of 0.5 seconds can be used as a reference for setting the critical reaction time. The maximum moving speed of the furnace-front fast-splitting robot is the maximum moving speed that can be achieved within the controllable range, usually expressed in millimeters per second (mm / s). It is used to assess the upper limit of the speed allowed to move under safe conditions. The maximum moving speed can be provided by the furnace-front fast-splitting robot manufacturer or obtained through on-site testing, such as moving the furnace-front fast-splitting robot a specified distance in an obstacle-free, maximum power state and calculating the speed. The maximum speed setting value in the controller can also be read. For example, the maximum moving speed of the furnace-front fast-splitting robot under no-load conditions is 120 mm / s, and the maximum moving speed can be set to 120 mm / s. Taking redundancy and safety into consideration, the maximum moving speed can also be set to 100 mm / s. By multiplying the maximum reaction time by the maximum movement speed, the maximum allowable movement distance that the operator can effectively control in an emergency situation can be obtained, that is, the first preset threshold. Combined with the precision control strategy, the second preset threshold can be set by empirical value or calculated through the error analysis model; for example, if the user reaction time is 0.5 seconds and the robot speed is 100 mm / s, the first preset threshold is 50 mm; if the second preset threshold is set to 0.6% of the first preset threshold, the second preset threshold is 0.3 mm.

[0054] For example, the human-computer interaction test module can be used to first obtain the user's maximum reaction time (such as 0.5 seconds) and the robot's maximum movement speed (such as 100 mm / s), and calculate that the first preset threshold is 50 mm, representing the boundary of space-constrained judgment. Then, combined with the preset calculation model, the second preset threshold is determined, thereby providing an accurate basis for subsequent workspace classification and movement mode matching.

[0055] Through the implementation of the above embodiment, the threshold is dynamically set by analyzing the operator's maximum reaction time and the robot's maximum speed, which can ensure that the spatial classification results match the actual operating capabilities, adapt to different types of robots or different operators, enhance the universality of the method, and make the teaching process friendly to both novices and experienced workers.

[0056] In some embodiments, the aforementioned response to the input signal of the human-computer interaction device and the execution of the movement process in accordance with the quantitative movement mode may include: determining the movement direction of the furnace-front fast-splitting robot according to the aforementioned input signal; determining the displacement parameters of the furnace-front fast-splitting robot according to the input signal and the quantitative movement mode, wherein the displacement parameters may include at least one of the movement angle and the movement distance; and controlling the furnace-front fast-splitting robot to move according to the movement direction and the displacement parameters.

[0057] In some examples, the movement direction of the furnace-front quick-splitting robot refers to the overall movement direction achieved in three-dimensional space through the coordinated movement between the joint axes, usually including translational movement in the positive and negative directions along the X, Y, and Z axes, and the posture adjustment direction corresponding to the rotation direction around the X, Y, and Z axes (such as Rx, Ry, Rz); the movement direction can be sent by the operator through the human-computer interaction device. The directional control signal is parsed into a predefined space vector; for example, the operator pushes the joystick upward, and the system recognizes it as movement in the positive direction of Z; clicking the "rotate left" button is recognized as rotation around the negative direction of Z axis. By parsing the input signal and combining it with the current quantitative movement mode, the specific angle or distance that the furnace-front quick-splitting robot needs to move can be calculated. The displacement parameter is the specific "amount" that the end of the furnace-front fast-splitting robot needs to move, usually in the form of a moving distance (mm) and / or a moving angle (°). The moving angle is the angular change in the furnace-front fast-splitting robot's rotation on a certain axis, measured in degrees (°), and is often used for end-position adjustment, such as gripper rotation. The moving distance is the linear distance the furnace-front fast-splitting robot moves in a certain direction, measured in millimeters (mm), and is used for fine-tuning the end position or rapid positioning. Instructions can be issued to the furnace-front fast-splitting robot controller based on the parsed direction vector and the corresponding displacement (angle or distance) to drive its end effector to complete the corresponding physical movement. For example, if "Y+ direction, 10mm" is parsed, the motion control instruction is sent after the path is calculated, and the furnace-front fast-splitting robot moves smoothly 10mm in the positive direction of the Y axis.

[0058] Through the implementation of the above embodiments, combined with input signals, direction judgment and displacement parameter calculation, full-process movement instruction analysis and precise control are achieved, which can ensure that each operation can be completed in the correct direction and reasonable distance, significantly improving positioning accuracy and movement stability.

[0059] In some embodiments, the aforementioned teaching method for the furnace-front quick-splitting robot may further include: obtaining real-time posture data of the end effector of the furnace-front quick-splitting robot, wherein the real-time posture data is obtained by fusing the joint encoder feedback with the spatial coordinate data of the end vision sensor; comparing the real-time posture data with the target spatial coordinates to generate position deviation data, wherein the position deviation data represents the offset between the actual operating point of the end effector and the target reference point due to the deformation of the mechanical structure or the physical displacement of the target reference point; when the position deviation data exceeds the preset tolerance range, a movement parameter correction prompt is issued.

[0060] In some examples, real-time pose data refers to the three-dimensional coordinate and posture information of the end position and posture of the furnace-front fast-splitting robot currently achieved by the cooperation of each axis in the workspace, usually expressed as a position vector and posture quaternion or Euler angle in a Cartesian coordinate system; real-time pose data can be calculated by the robot control system based on the real-time feedback information of each joint encoder through a forward kinematics algorithm, and can be combined with visual sensors, laser ranging equipment or other auxiliary positioning devices for data fusion to improve the accuracy of pose estimation; in application scenarios where some space is limited or the structure is complex and sensors cannot be installed, the control system can rely solely on joint angles and kinematic models for estimation. The target spatial coordinates represent the coordinates of the end-point of the furnace-front quick-dispensing robot's intended arrival during the teaching process, encompassing both position and attitude. These coordinates can be manually recorded by the user in the teaching interface or automatically generated by the system based on the specific task process flow. If the actual target point cannot be directly measured, the operator can gradually approach the target physical position through continuous micro-movements and manually set these as new target spatial coordinates. If the actual target position can be accurately measured using sensing methods such as laser ranging, the user can directly input these coordinates into the robot control system to update the target point, reducing manual debugging. Position deviation data represents the difference between the current real-time pose and the target spatial coordinates, reflecting the degree of spatial deviation of the robot end-point relative to the desired position. This data can be composed of both position error and attitude error. A preset tolerance range defines the allowable error limit for teaching. If the position deviation data exceeds this range, a correction prompt is triggered, prompting the user to perform manual calibration or repeat the teaching process. It should be pointed out in particular that the change in position deviation data is often not caused by the change of robot program coordinates. Under the premise that the furnace-front fast-splitting robot is in a non-fault state, it will still execute according to the teaching program and move to the original set coordinate point; however, the real-time posture data of the end effector may change due to slight deformation of the hardware structure, or the target space coordinates due to the physical displacement of the work object. For example, the robot end may cause structural offset due to a slight collision, or the target fixture or workpiece installation position may be physically offset, resulting in the furnace-front fast-splitting robot reaching the position set by the program, but actually failing to accurately align with the process target point; since such offsets are usually random and uncertain, the actual target position after the change is often an unpredictable variable, so the teaching task cannot be completed directly by modifying the coordinates; this is why the furnace-front fast-splitting robot needs to gradually approach the new target position through multiple fine-tuning movements to confirm its spatial coordinates; in order to adapt to the operational requirements of this process, the embodiment of the present application is combined with multiple step movement modes with different resolutions to reduce the complexity and error risk of manual debugging.

[0061] Through the implementation of the above embodiment, the actual and target coordinates are compared, the deviation is automatically determined and correction prompts are given, the operation error can be corrected in time, the error accumulation can be prevented, and the intelligent verification capability of the teaching process can be improved. It is especially suitable for point control tasks with high precision requirements, and the teaching closed-loop and robustness are enhanced.

[0062] Furthermore, as an implementation of the aforementioned method embodiment, the present application also provides a furnace-front fast separation robot teaching device for implementing the aforementioned method embodiment. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this furnace-front fast separation robot teaching device embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in the embodiment of the present application can correspond to and implement all the contents of the aforementioned method embodiment. Figure 2 As shown, the furnace-front quick-splitting robot teaching device 20 includes: a data acquisition unit 201, a category determination unit 202, a mode determination unit 203 and a teaching movement unit 204, wherein the data acquisition unit 201 is used to obtain the process accuracy requirements of the target process and the space constraint parameters of the furnace-front quick-splitting robot; the category determination unit 202 is used to determine the workspace category according to the process accuracy requirements and the space constraint parameters; the mode determination unit 203 is used to determine the quantitative movement mode corresponding to the workspace category according to the workspace category; the teaching movement unit 204 is used to respond to the input signal of the human-computer interaction device and execute the movement process according to the quantitative movement mode to complete the teaching operation of the furnace-front quick-splitting robot.

[0063] In some embodiments, the category determination unit 202 is also used to determine that the workspace category is a space-free category when the space constraint parameter is greater than or equal to a first preset threshold and the process accuracy requirement is less than a second preset threshold; and to determine that the workspace category is a space-restricted category when the space constraint parameter is less than the first preset threshold, or the process accuracy requirement is greater than or equal to the second preset threshold.

[0064] In some embodiments, the mode determination unit 203 is also used to determine that the quantitative movement mode is the first step mode when the workspace category is the free space category, wherein the moving step length of the first step mode is the free step length, and the free step length is obtained by performing analog-to-digital conversion on the input signal collected by the joystick or the inching button; when the workspace category is the restricted space category, the quantitative movement mode is determined to be the second step mode, wherein the moving step length of the second step mode is the preset step length, wherein the preset step length is a pre-set fixed step length.

[0065] In some embodiments, the second step length mode includes a first step length sub-mode, a second step length sub-mode and a third step length sub-mode, the moving step length of the first step length sub-mode is smaller than the moving step length of the second step length sub-mode, and the moving step length of the third step length sub-mode is a digital signal value input by the user; the mode determination unit 203 is also used to receive a user input signal when the workspace category is a space-restricted category; and determine, based on the user input signal, that the quantitative movement mode is the first step length sub-mode, the second step length sub-mode or the third step length sub-mode.

[0066] In some embodiments, the data acquisition unit 201 is also used to obtain the user's maximum reaction time and the maximum moving speed of the furnace-front quick-splitting robot; the category determination unit 202 is also used to determine the first preset threshold and the second preset threshold based on the maximum reaction time and maximum moving speed.

[0067] In some embodiments, the teaching movement unit 204 is also used to determine the moving direction of the furnace-front fast-splitting robot based on the input signal; determine the displacement parameters of the furnace-front fast-splitting robot based on the input signal and the quantitative movement mode, wherein the displacement parameters include at least one of the moving angle and the moving distance; and control the furnace-front fast-splitting robot to move based on the moving direction and the displacement parameters.

[0068] In some embodiments, the teaching movement unit 204 is also used to obtain the real-time posture data of the end effector of the furnace-front quick-splitting robot, wherein the real-time posture data is obtained by fusing the joint encoder feedback with the spatial coordinate data of the end vision sensor; the real-time posture data is compared with the target spatial coordinates to generate position deviation data, wherein the position deviation data represents the offset between the actual operating point of the end effector and the target reference point due to the deformation of the mechanical structure or the physical displacement of the target reference point; when the position deviation data exceeds the preset tolerance range, a movement parameter correction prompt is given.

[0069] The present application also provides a computer-readable storage medium, which stores computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are executed by a processor, the processor will execute any step of the furnace front fast separation robot teaching method provided in the present application.

[0070] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); or it may be various devices including one or any combination of the above memories.

[0071] In some embodiments, computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0072] In some embodiments, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (for example, files storing one or more modules, subroutines, or code portions).

[0073] In some embodiments, computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0074] like Figure 3 As shown, the present application also provides an electronic device 30, including a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned furnace front fast separation robot teaching method is implemented.

[0075] The present application also provides a computer program product, which includes a computer program or computer-executable instructions, wherein the computer program or computer-executable instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and the processor executes the computer program or computer-executable instructions, causing the electronic device to perform any step of the method for teaching a furnace-front fast-sorting robot described above.

[0076] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A teaching method for a furnace-front quick separation robot, characterized in that: include: Obtain the process accuracy requirements of the target process and the space constraint parameters of the furnace front fast separation robot; Determining a workspace category according to the process accuracy requirements and the space constraint parameters; According to the workspace category, determining a quantitative movement mode corresponding to the workspace category; In response to the input signal of the human-machine interaction device, the movement process is executed according to the quantitative movement mode to complete the teaching operation of the furnace front fast separation robot.

2. The teaching method of the furnace front quick separation robot according to claim 1, characterized in that: Determining the workspace category according to the process accuracy requirement and the space constraint parameters includes: When the space constraint parameter is greater than or equal to a first preset threshold value and the process accuracy requirement is less than a second preset threshold value, determining that the workspace category is a space free category; When the space constraint parameter is less than the first preset threshold, or the process accuracy requirement is greater than or equal to the second preset threshold, the workspace category is determined to be a space-restricted category.

3. The teaching method for the furnace front quick separation robot according to claim 2, characterized in that: The determining, according to the workspace category, a quantitative movement mode corresponding to the workspace category includes: When the workspace category is the free space category, determining that the quantitative movement mode is the first step mode, wherein the movement step length of the first step mode is a free step length, and the free step length is calculated by performing analog-to-digital conversion on an input signal collected by a joystick or a jog button; When the workspace category is the space-limited category, the quantitative movement mode is determined to be a second step mode, wherein the movement step of the second step mode is a preset step, wherein the preset step is a pre-set fixed step.

4. The teaching method for the furnace front quick separation robot according to claim 3, characterized in that: The second step length mode includes a first step length sub-mode, a second step length sub-mode and a third step length sub-mode, the movement step length of the first step length sub-mode is smaller than the movement step length of the second step length sub-mode, and the movement step length of the third step length sub-mode is a digital signal value input by a user; When the workspace category is the space-restricted category, determining the quantitative movement mode to be the second step length mode includes: When the workspace category is the space-restricted category, receiving a user input signal; According to the user input signal, the quantitative movement mode is determined to be the first step length sub-mode, the second step length sub-mode or the third step length sub-mode.

5. The teaching method for the furnace front quick separation robot according to claim 2, characterized in that: The furnace front quick separation robot teaching method also includes: Obtaining the user's maximum reaction time and the maximum moving speed of the furnace-front quick-distribution robot; The first preset threshold and the second preset threshold are determined according to the limit reaction time and the limit movement speed.

6. The teaching method for a furnace-front quick separation robot according to any one of claims 1 to 5, characterized in that: The step of executing the movement process according to the quantitative movement mode in response to the input signal of the human-computer interaction device includes: Determining the moving direction of the furnace front quick separation robot according to the input signal; Determining a displacement parameter of the furnace-front quick-distribution robot according to the input signal and the quantitative movement pattern, wherein the displacement parameter includes at least one of a movement angle and a movement distance; According to the moving direction and the displacement parameters, the furnace front quick separation robot is controlled to move.

7. The teaching method for a furnace-front quick separation robot according to any one of claims 1 to 5, characterized in that: The furnace front quick separation robot teaching method also includes: Acquire real-time pose data of the end effector of the furnace-front quick separation robot, wherein the real-time pose data is obtained by fusing joint encoder feedback with spatial coordinate data of the end vision sensor; Comparing the real-time pose data with the target space coordinates to generate position deviation data, wherein the position deviation data represents the offset between the actual working point of the end effector and the target reference point caused by mechanical structure deformation or physical displacement of the target reference point; When the position deviation data exceeds a preset tolerance range, a movement parameter correction prompt is issued.

8. A furnace-front quick separation robot teaching device, characterized in that: include: The data acquisition unit is used to obtain the process accuracy requirements of the target process and the space constraint parameters of the furnace front fast separation robot; A category determination unit, configured to determine a workspace category according to the process accuracy requirement and the space constraint parameters; a mode determining unit, configured to determine, according to the workspace category, a quantitative movement mode corresponding to the workspace category; The teaching movement unit is used to respond to the input signal of the human-computer interaction device and execute the movement process according to the quantitative movement mode to complete the teaching operation of the furnace front fast separation robot.

9. An electronic device comprising: A memory and a processor, characterized in that the processor is used to implement the steps of the furnace front fast separation robot teaching method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the furnace front quick separation robot teaching method according to any one of claims 1 to 7 are implemented.

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