A method for high-precision positioning and intelligent operation guidance of a composite robot

Abstract

This invention discloses a high-precision positioning and intelligent operation guidance method for a composite robot, relating to the fields of robot motion control and trajectory planning. The method includes: acquiring chassis pose information and robotic arm joint pose information; inserting a handheld intelligent teaching pendant into the robotic arm's auxiliary positioning calibration device for joint calibration; calculating the composite robot's chassis pose information and robotic arm joint pose information; acquiring and calibrating the auxiliary positioning calibration device at the current workstation; calculating the deviation between the robotic arm's real-time operational pose and the standard coordinate position at the docking position and guiding the composite robot to adjust to the ready-to-operate state; acquiring the operation trajectory, inputting operation commands, and guiding other sensors to acquire the required information; generating the final robot operation path's true trajectory and operational posture, and reproducing and confirming the operation path. This invention can efficiently and directly unify the coordinate system during secondary positioning and multi-scene, multi-workstation switching, achieving real-time tracking and guidance of complex trajectories.

Description

Technical Field

[0001] This invention relates to the field of robot motion control and trajectory planning, and in particular to a method for high-precision positioning and intelligent operation guidance of a composite robot. Background Technology

[0002] Currently, the main technologies for solving the secondary localization problem of composite robots are visual guidance and laser guidance. Visual guidance involves using cameras mounted on the robot to collect environmental information, and then using image processing and analysis algorithms to achieve robot localization and navigation. Common technologies include ABB's Robotstudio software and FANUC's iRVision system. Visual guidance technology, based on the robot's hand-eye calibration system, can achieve high-precision localization, but it has high requirements for ambient lighting and landmarks.

[0003] Laser guidance: This method uses laser signals to measure the distance between the robot and surrounding objects, enabling localization and navigation. Laser guidance technology has lower requirements for ambient lighting and landmarks, but the equipment cost is higher. Generally, laser guidance works by scanning point cloud data of the surrounding environment with a lidar sensor, matching the point cloud data with a pre-stored environmental map to determine the robot's position on the map, and using matching algorithms such as ICP or NDT to determine the accurate location. It can also integrate data from the robot's own encoder, IMU, and other sensors, along with algorithms such as Kalman filtering and particle filtering to improve positioning accuracy. Typical products include Clearpath Robotics' Husky robot, which achieves centimeter-level positioning accuracy, and Fetch Robotics' Fetch robot, which uses laser, IMU, and vision fusion to achieve indoor positioning accuracy of ±3cm. However, laser-guided secondary localization is highly dependent on the environment; changes in lighting conditions can significantly affect accuracy. Furthermore, it involves significant computational loads and requires frequent point cloud registration.

[0004] Furthermore, after the composite robot completes its secondary localization, it faces the challenge of quickly and efficiently teaching complex trajectories (such as welding scenarios) to meet specific operational requirements or processes. Currently, teaching technologies for complex trajectory operations mainly include offline programming and online teaching. Offline programming involves simulating the real environment and task on a computer, writing and debugging the program, and then downloading the program to the robot controller for execution. Offline programming can achieve high-precision trajectory control, but requires specialized programming knowledge and skills. Online teaching involves directly operating the robot in a real environment using a handheld teach pendant, recording and reproducing the operational trajectory. Online teaching is simple and intuitive, but its accuracy is lower.

[0005] Therefore, those skilled in the art are dedicated to developing a high-precision positioning and intelligent operation guidance method for composite robots. Summary of the Invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is that the secondary positioning accuracy of the composite robot is insufficient and the scene adaptability is poor. When facing different scene workstation switching, the accuracy is poor and the teaching efficiency is low. The teaching efficiency is low and the degree of automation is low. The robotic arm on the composite robot can hardly meet the high precision requirements of assembly, loading and unloading and other operations.

[0007] To achieve the above objectives, the present invention provides a method for high-precision positioning and intelligent operation guidance of a composite robot, comprising the following steps:

[0008] Step 1: The positioning sensors on the chassis and robotic arm of the composite robot begin to collect the chassis pose information and the joint pose information of the robotic arm;

[0009] Step 2: Insert the handheld intelligent teaching pendant into the robotic arm's auxiliary positioning and calibration device for joint calibration;

[0010] Step 3: After the composite robot is in position, calculate the pose information of the composite robot chassis and the pose information of the robotic arm joints;

[0011] Step 4: The handheld intelligent teaching pendant collects and calibrates the auxiliary positioning calibration device at the current workstation;

[0012] Step 5: Based on the conversion relationship between the intelligent teaching and positioning guidance system and the robot arm base coordinates, and the real-time collected chassis pose information and robot arm joint pose information, calculate the deviation between the real-time pose of the robot arm and the standard coordinate position when the composite robot is docked, and guide the composite robot to adjust to the ready-to-work state.

[0013] Step 6: The operator uses a handheld smart teaching pendant to collect the work trajectory, inputs work instructions, and guides other sensors to collect the required information;

[0014] Step 7: Generate the final robot operation path's real trajectory and operation posture in a non-programming manner, and reproduce and confirm the operation path in virtual simulation.

[0015] Further, step 1 specifically includes: with the robotic arm fixed on the chassis of the composite robot, it moves to several preset calibration points. The calibration system collects the real-time joint pose data of the robotic arm. At the same time, a positioning device is fixedly installed on the robotic arm. By using the joint transformation relationship of the robotic arm itself, the transformation relationship between the intelligent teaching positioning guidance system and the base coordinate system of the robotic arm is established, thereby establishing the relationship between the intelligent teaching pendant and the composite robot in the global coordinate system.

[0016] Furthermore, step 2 specifically includes:

[0017] Step 2.1: The operator holds the intelligent teaching pendant and inserts it into the fastening and calibration device on the robotic arm. The intelligent teaching pendant automatically triggers the positioning and calibration process.

[0018] Step 2.2: After the composite robot has stopped and stabilized, the operator uses a top calibration tool to perform four-point calibration on the tool on the robotic arm to obtain the transformation relationship between the tool coordinate system and the robotic arm base coordinate system.

[0019] Furthermore, step 3 specifically includes: after the composite robot is in position, the calibration system reads the coordinate data of multiple positioning sensors on the composite robot chassis, and calculates the rotation and translation data generated by the rigid body transformation based on the rigid body distance of the sensors in the standard position and the rotation angle relative to the initial posture of the robotic arm, thus forming the deviation data of the chassis rigid body transformation.

[0020] Further, step 4 specifically includes: the operator holds the intelligent teaching pendant and inserts it into the auxiliary positioning and calibration device above the workstation table. The algorithm system collects the angle and position data of the workpiece coordinate system on the worktable in real time and performs conversion calculations with the intelligent teaching positioning and guidance system to obtain the conversion relationship between the workpiece coordinate system and the intelligent teaching positioning and guidance system. Finally, based on the conversion relationship between the coordinate system of the intelligent teaching pendant, the base coordinate system of the robotic arm, and its tool coordinate system, the conversion relationship between the workpiece coordinate system and the variable base coordinate system of the composite robot is obtained.

[0021] Furthermore, step 5 specifically includes:

[0022] Step 5.1: Based on the chassis pose deviation, and combined with the relationship between the base coordinate system of the robotic arm and the global coordinate system of the intelligent teaching and positioning guidance system, establish the multi-rigid-body deflection error calculation for the movement-docking of the composite robot, obtain the transformation relationship of the variable base coordinate system of the composite robot arm on the moving base, and use the inverse joint solution of the robotic arm to calculate the deviation of the pose of the robotic arm under the variable base coordinate system from the standard pose.

[0023] Step 5.2: Based on the chassis's final pose and the actual template pose of the robotic arm, and according to the transformation relationship of variable base coordinates, the transformation formula, and the inverse joint calculation of the robotic arm, calculate the distance and position that the robotic arm still needs to move after compensating for the deviation of the composite robot chassis. The calculation center transmits the deviation data to the controller of the robotic arm to guide the robotic arm to perform real-time compensation. At this time, both the composite robot and the robotic arm return to the initial pose of the template.

[0024] Furthermore, step 6 specifically includes:

[0025] Step 6.1: The operator holds the intelligent teaching pendant and places its tip at the starting point of the complex trajectory of the workpiece. By moving the tip of the teaching pendant along the trajectory of the workpiece, the operator draws a trajectory that covers the entire workpiece. At the same time, the operator controls the drawing speed to design the locations in the trajectory where more features need to be added. During the drawing process, when encountering areas with large curvature, the operator manually reduces the drawing speed to increase the amount and density of feature point data, thereby establishing better trajectory data information.

[0026] Step 6.2: The starting point and ending point of the intelligent teaching pendant when collecting the trajectory, as well as the teaching pendant attitude angle at the starting point, and the coordinates of the key path points set by the user using the intelligent teaching pendant buttons during the trajectory collection process, are used as reference data for the next step of the overall positioning algorithm system to perform fine positioning calculations and processing.

[0027] Step 6.3: The positioning algorithm system uses median filtering to filter outliers in all collected trajectory datasets, removing discrete points that are more than 5% away from the data. The system also re-averages the positions and orientation angles of all data points in areas with higher curvature and higher distribution density to reduce point set conflicts caused by curvature changes.

[0028] Furthermore, in step 7: the working posture includes the movement trajectory of the robotic arm, the positioning point data, and the robotic arm posture when working on the workpiece.

[0029] Furthermore, step 6.1 specifically includes:

[0030] When the robotic arm of the composite robot begins to perform a task, the line scan sensor installed on the robotic arm begins to scan the trajectory points based on the position and orientation of the trajectory start point.

[0031] Furthermore, step 6.2 specifically includes:

[0032] During the scanning process, the rate of change of the trajectory tangent under unit distance or step size is calculated. When the rate of change is less than 5%, a smooth transition is used to guide the movement of the robotic arm. When the rate of change is greater than 5%, it is considered an anomaly, and motion control is performed according to the previously calculated angle transition while meeting the requirements of feature point position constraints. The robotic arm moves to the starting point of the trajectory collected by the intelligent teaching pendant, and determines the accurate acquisition position required for acquiring the 3D point cloud data of the workpiece in one go by comparing the minimum bounding rectangle of the trajectory calculated by the acquired trajectory with the working distance and field of view of the 3D structured light vision sensor. After guiding the robotic arm to move to this position, the 3D structured light vision is triggered to scan. Based on the point cloud data obtained by scanning, the starting and ending points of the trajectory, as well as the feature points in the process, are referenced to segment the point cloud data of the trajectory from the overall spatial point cloud data, and higher precision point cloud data of the trajectory is calculated to optimize the actual path of the trajectory.

[0033] Compared with existing technologies, the beneficial effects of this invention are as follows: The high-precision positioning system based on optical capture positioning technology combined with multiple visual sensor fusion provided by this invention can achieve more efficient and direct secondary positioning of composite robots and rapid coordinate system unification when switching between multiple scenarios and workstations. When switching between different scenarios and tasks, the composite robot using the technology of this invention can quickly achieve precise positioning of ±2mm to ±5mm when moving between multiple points. After positioning, it can directly perform the next operation action, which is highly efficient and accurate. For specific scenario tasks, the actual trajectory can be drawn directly through the intelligent teach pendant, and then a coarse guide trajectory can be directly generated with the help of algorithms. This assists visual sensing and algorithm calculation of the precise positioning data of the trajectory, thereby guiding the robot to achieve real-time tracking and guidance of complex spatial trajectories without pre-collection or point teaching, which is highly efficient and intuitive.

[0034] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a preferred embodiment of the high-precision positioning and intelligent operation guidance method for a composite robot according to the present invention.

[0036] Figure 2 This is a schematic diagram of the system structure of a high-precision positioning and intelligent operation guidance method for a composite robot according to a preferred embodiment of the present invention;

[0037] In the diagram: 1. Intelligent teach pendant; 2. Composite robot chassis; 3. Composite robot integrated robotic arm; 4. Positioning sensor on the robotic arm; 5. Positioning sensor on the composite robot chassis; 6. End effector of the robotic arm; 7. Workstation; 8. Auxiliary positioning calibration device; 9. Workpiece; 10. Spatial positioning base station; 11. Other visual / force sensors; 12. Intelligent teach pendant fastening and calibration mechanism. Detailed Implementation

[0038] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0039] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0040] like Figure 1 As shown, this invention provides a high-precision positioning and intelligent operation guidance method for composite robots in multi-workstation and multi-scenario operations. The composite robot provides an intelligent guidance method when facing complex operation trajectories in specific operation scenarios.

[0041] like Figure 2 As shown, the hardware system of this method is based on a composite robot chassis 2 and a composite robot arm 3, a composite robot chassis positioning system 5 and a robot arm positioning sensor 4, a workstation 7 and an auxiliary positioning calibration device 8, a spatial positioning base station 10 and an intelligent teaching pendant 1, and an end effector 6 of the composite robot arm. This method is based on at least two optical capture positioning sensors 5 on the composite robot chassis 2, a positioning sensor 4 fixedly installed at the fifth or sixth joint of the integrated composite robot arm 3, a base station system 10 installed above the multi-workstation workspace, and an overall high-precision positioning algorithm system.

[0042] The specific steps of the high-precision positioning and intelligent operation guidance method are as follows:

[0043] (1) The composite robot first stops and stabilizes in front of the workstation 7 where it needs to be executed, according to the process flow requirements between different workstations. After stopping and stabilizing, the robotic arm integrated on the composite robot will stay in a fixed position and posture ready for calibration, ensuring that the operator can easily perform global calibration;

[0044] (2) After the composite robot stops and stabilizes, the operator uses a top calibration tool to perform four-point calibration on the tool on the robotic arm to obtain the transformation relationship between the tool coordinate system and the robotic arm base coordinate system.

[0045] (3) The operator holds the intelligent teaching pendant 1 and inserts it into the fastening calibration device 12 on the robotic arm. The intelligent teaching pendant 1 automatically triggers the positioning calibration process. With the robotic arm fixed on the composite robot chassis 2, it moves to several preset calibration points. The calibration system collects real-time joint pose data of the specific robotic arm (including xyz three-axis coordinates and pitch, yaw and roll three posture angles). At the same time, the device on the robotic arm establishes the transformation relationship between the intelligent teaching positioning guidance system and the base coordinates of the robotic arm, thereby establishing the relationship between the intelligent teaching pendant and the composite robot in the global coordinate system.

[0046] (4) At the same time, the calibration system reads the coordinate data of multiple positioning sensors on the composite robot chassis 2, and calculates the rotation and translation data generated by the rigid body transformation based on the rigid body distance of the sensor in the standard position and the rotation angle relative to the initial posture of the robotic arm, thus forming the deviation data of the chassis rigid body transformation.

[0047] (5) Based on the chassis pose deviation, and combined with the relationship between the base coordinate system of the robotic arm and the global coordinate system of the intelligent teaching and positioning guidance system, establish the multi-rigid body deflection error calculation of the composite robot's movement-docking, obtain the transformation relationship of the composite robot's robotic arm on the mobile base (composite robot chassis), and use the inverse joint solution of the robotic arm to calculate the robotic arm deviation transformation calculation formula under the variable base coordinate.

[0048] (6) The operator holds the intelligent teaching pendant 1 and inserts it into the auxiliary positioning calibration device 8 above the workbench 7. The algorithm system collects the angle and position data of the workpiece coordinate system on the workbench 7 in real time and performs conversion calculation with the positioning coordinate system of the intelligent teaching pendant 1 to obtain the conversion relationship between the workpiece coordinate system and the intelligent teaching positioning guidance system. Finally, based on the conversion formula between the coordinate system of the intelligent teaching pendant, the base coordinate system of the robotic arm and its tool coordinate system, the conversion relationship between the workpiece coordinate system and the robotic arm of the composite robot with variable base coordinates is obtained.

[0049] (7) Based on the chassis position and the actual template position of the robotic arm set by the composite robot system, and based on the complex transformation relationship of the variable base coordinates, the robotic arm deviation transformation calculation formula under the variable base coordinates and the transformation formula between the coordinate system of the intelligent teaching pendant, the base coordinate system of the robotic arm and its tool coordinate system, and combined with the multi-joint inverse solution of the robotic arm, the distance and position that the robotic arm still needs to move after compensating for the deviation of the composite robot chassis is calculated.

[0050] (8) The computing center transmits the deviation data to the controller of the robotic arm to guide the robotic arm to perform real-time compensation. At this time, both the composite robot and the robotic arm return to the initial posture of the template.

[0051] (9) The operator holds a smart teach pendant, places its tip at the starting point of the complex trajectory of the workpiece, and traces the entire trajectory by moving the tip of the teach pendant along the workpiece's trajectory. At the same time, the operator controls the tracing speed to design the locations in the trajectory where additional features need to be added;

[0052] (10) During the drawing process, when encountering areas with large curvature, the drawing speed is artificially reduced to increase the amount and density of feature point data, thereby establishing better trajectory data information;

[0053] (11) The algorithm system reduces the frequency of use of the handheld teaching pendant by using the starting point, ending point of the trajectory and the teaching pendant posture trajectory at the starting point, and accurately records the pose angle of the intelligent teaching pendant at the key points for use as a reference for fine positioning.

[0054] (12) The algorithm system uses median filtering and other methods to filter out the outliers of all the collected trajectory datasets, remove discrete points that are far away from more than 5% variance, and re-average the position and attitude angle of all data points in the area with higher curvature distribution density after statistical analysis, so as to reduce the point set conflict caused by curvature changes.

[0055] (13) Send the entire dataset to the corresponding variable register of the robotic arm control terminal via the socket protocol and in the specified port mode to ensure that the composite robot robotic arm can accurately obtain the corresponding trajectory;

[0056] (14) During the process of using the intelligent teaching pendant to draw trajectory data, according to the task and process requirements, specific robotic arm operation actions, such as grasping and placing, are set near specific feature points by using the buttons and button combinations on the intelligent teaching pendant.

[0057] (15) For specific welding tasks, when the robotic arm of the composite robot begins to perform the task, the line scan sensor installed on the robotic arm begins to scan the trajectory points based on the position and attitude of the trajectory starting point. During the scanning process, the rate of change of the trajectory tangent angle per unit distance or step length is calculated. When the rate of change is less than 5%, the robotic arm can be guided to move in a smooth transition manner. When the rate of change is greater than 5%, it can be identified as an anomaly. Under the requirement of meeting the position constraints of the feature points, the motion control is carried out according to the angle transition calculated in the previous step.

[0058] (16) The robotic arm moves to the starting point of the trajectory collected by the intelligent teaching device, and compares the working distance and field of view of the 3D structured light vision sensor with the minimum bounding rectangle of the trajectory calculated based on the collected trajectory to determine the accurate collection position required for collecting the 3D point cloud data of the workpiece in one go. After guiding the robotic arm to move to the position, the 3D structured light vision is triggered to scan.

[0059] (17) Based on the point cloud data obtained by scanning, the starting and ending points of the reference trajectory, as well as the feature points in the process, the point cloud data of the trajectory is separated from the overall spatial point cloud data, and the point cloud data of the trajectory with higher precision is calculated by ICP algorithms such as point cloud registration, so as to optimize the actual path of the trajectory.

[0060] (18) Generate the final running trajectory of the robotic arm. At the same time, based on the type of operation, such as welding / gluing, and the characteristics of the specific trajectory, identify the depth, width, shape, curvature and other information of the trajectory, determine the process requirements that best match the trajectory, such as bevel welding and multi-pass welding. Under intelligent decision-making, match the corresponding parameterized process configuration to the trajectory operation content to ensure that the robotic arm can directly perform operations according to the process requirements during operation.

[0061] (19) In the system interface, with the help of the global coordinate system and the virtual simulation model we built, we simulate the robot arm action under the final operation trajectory and operation logic obtained in step 18, and check whether the robot arm action interferes with or collides with the workpiece, table, and its own motion angle. When collision and other types of problems are found, we recalculate and evaluate the operation path by modifying the path and angle of the trajectory feature points in the interface, and finally verify the feasibility of the real operation requirements.

[0062] (20) Finally, drive the robotic arm to complete the specific work requirements on workstation 7. When it is necessary to go to the second workstation for work processing, the composite robot can be guided to workstation 7 and the above steps can be repeated.

[0063] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for high-precision positioning and intelligent operation guidance of a composite robot, characterized in that, The method includes the following steps: Step 1: The positioning sensors on the chassis and robotic arm of the composite robot begin to collect the chassis pose information and the joint pose information of the robotic arm; Step 2: Insert the handheld intelligent teaching pendant into the robotic arm's auxiliary positioning and calibration device for joint calibration; Step 3: After the composite robot is in position, calculate the pose information of the composite robot chassis and the pose information of the robotic arm joints. Specifically, after the composite robot is in position, the calibration system reads the coordinate data of multiple positioning sensors on the composite robot chassis. Based on the rigid body distance of the sensors in the standard position and the rotation angle relative to the initial posture of the robotic arm, the system calculates the rotation and translation data generated by the rigid body transformation, and forms the deviation data of the chassis rigid body transformation. Step 4: The operator holds the intelligent teaching pendant and collects and calibrates the auxiliary positioning calibration device at the current workstation. Specifically, the operator holds the intelligent teaching pendant and inserts it into the auxiliary positioning calibration device above the workstation table. The algorithm system collects the angle and position data of the workpiece coordinate system on the worktable in real time and performs conversion calculations with the intelligent teaching positioning guidance system to obtain the conversion relationship between the workpiece coordinate system and the intelligent teaching positioning guidance system. Finally, based on the conversion relationship between the coordinate system of the intelligent teaching pendant, the base coordinate system of the robotic arm, and its tool coordinate system, the conversion relationship between the workpiece coordinate system and the robotic arm of the composite robot with variable base coordinates is obtained. Step 5: Based on the transformation relationship between the intelligent teaching and positioning guidance system and the robot arm's base coordinates, and the real-time collected chassis pose information and robot arm joint pose information, calculate the deviation between the robot arm's real-time working pose and the standard coordinate position at the robot's docking position, and guide the robot to adjust to the ready-to-work state. Specifically, this includes: Step 5.1: Based on the chassis pose deviation, and combined with the relationship between the base coordinate system of the robotic arm and the global coordinate system of the intelligent teaching and positioning guidance system, establish the multi-rigid-body deflection error calculation for the movement-docking of the composite robot, obtain the transformation relationship of the variable base coordinate system of the composite robot arm on the moving base, and use the inverse joint solution of the robotic arm to calculate the deviation of the pose of the robotic arm under the variable base coordinate system from the standard pose. Step 5.2: Based on the chassis's final pose and the actual template pose of the robotic arm, and according to the transformation relationship of the variable base coordinates, the transformation formula, and combined with the inverse joint calculation of the robotic arm, calculate the distance and position that the robotic arm still needs to move after compensating for the deviation of the composite robot chassis; the calculation center transmits the deviation data to the controller of the robotic arm to guide the robotic arm to perform real-time compensation; at this time, both the composite robot and the robotic arm return to the initial pose of the template; Step 6: The operator uses a handheld smart teaching pendant to collect the work trajectory, inputs work instructions, and guides other sensors to collect the required information; Step 7: Generate the final robot operation path's real trajectory and operation posture in a non-programming manner, and reproduce and confirm the operation path in virtual simulation.

2. The high-precision positioning and intelligent operation guidance method for composite robots as described in claim 1, characterized in that, Step 1 specifically includes: with the robotic arm fixed on the chassis of the composite robot, it moves to several preset calibration points. The calibration system collects the real-time joint pose data of the robotic arm. At the same time, a positioning device is fixedly installed on the robotic arm. By using the joint transformation relationship of the robotic arm itself, the transformation relationship between the intelligent teaching positioning guidance system and the base coordinate system of the robotic arm is established, thereby establishing the relationship between the intelligent teaching pendant and the composite robot in the global coordinate system.

3. The high-precision positioning and intelligent operation guidance method for composite robots as described in claim 1, characterized in that, Step 2 specifically includes: Step 2.1: The operator holds the intelligent teaching pendant and inserts it into the fastening and calibration device on the robotic arm. The intelligent teaching pendant automatically triggers the positioning and calibration process. Step 2.2: After the composite robot has stopped and stabilized, the operator uses a top calibration tool to perform four-point calibration on the tool on the robotic arm to obtain the transformation relationship between the tool coordinate system and the robotic arm base coordinate system.

4. The high-precision positioning and intelligent operation guidance method for composite robots as described in claim 1, characterized in that, Step 6 specifically includes: Step 6.1: The operator holds the intelligent teaching pendant and places its tip at the starting point of the complex trajectory of the workpiece. By moving the tip of the teaching pendant along the trajectory of the workpiece, the operator draws a trajectory that covers the entire workpiece. At the same time, the operator controls the drawing speed to design the locations in the trajectory where more features need to be added. During the drawing process, when encountering areas with large curvature, the operator manually reduces the drawing speed to increase the amount and density of feature point data, thereby establishing better trajectory data information. Step 6.2: The starting point and ending point of the intelligent teaching pendant when collecting the trajectory, as well as the teaching pendant attitude angle at the starting point, and the coordinates of the key path points set by the user using the intelligent teaching pendant buttons during the trajectory collection process, are used as reference data for the next step of the overall positioning algorithm system to perform fine positioning calculations and processing. Step 6.3: The positioning algorithm system uses median filtering to filter outliers in all collected trajectory datasets, removing discrete points that are more than 5% away from the data. The system also re-averages the positions and orientation angles of all data points in areas with higher curvature and higher distribution density, thereby reducing point set conflicts caused by curvature changes.

5. The high-precision positioning and intelligent operation guidance method for composite robots as described in claim 1, characterized in that, In step 7: the working posture includes the movement trajectory of the robotic arm, positioning point data, and the robotic arm posture when working on the workpiece.

6. The high-precision positioning and intelligent operation guidance method for composite robots as described in claim 4, characterized in that, Step 6.1 specifically includes: When the robotic arm of the composite robot begins to perform a task, the line scan sensor installed on the robotic arm begins to scan the trajectory points based on the position and orientation of the trajectory start point.

7. The high-precision positioning and intelligent operation guidance method for composite robots as described in claim 4, characterized in that, Step 6.2 specifically includes: During the scanning process, the rate of change of the trajectory tangent under unit distance or step size is calculated. When the rate of change is less than 5%, a smooth transition is used to guide the movement of the robotic arm. When the rate of change is greater than 5%, it is considered an anomaly, and the motion control is performed according to the previously calculated angle transition while meeting the requirements of feature point position constraints. The robotic arm moves to the starting point of the trajectory collected by the intelligent teaching pendant, and determines the accurate acquisition position required for acquiring the 3D point cloud data of the workpiece in one go by comparing the minimum bounding rectangle of the trajectory calculated by the acquisition trajectory with the working distance and field of view of the 3D structured light vision sensor. After guiding the robotic arm to move to this position, the 3D structured light vision is triggered to scan. Based on the point cloud data obtained by scanning, the starting and ending points of the trajectory, as well as the feature points in the process, are referenced to segment the point cloud data of the trajectory from the overall spatial point cloud data, and higher precision point cloud data of the trajectory is calculated to optimize the actual path of the trajectory.

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