Control method and device for assembly robot

Abstract

This application relates to a control method and apparatus for an assembly robot. The method includes: performing multi-level self-calibration of the assembly robot's pose based on visual sensing information and laser displacement sensing information to obtain assembly robot pose determination information; locating the part positions and angles in the pose determination information based on the assembly robot's multi-point micro-scanning information to obtain assembly robot part determination information; planning an assembly movement path based on the assembly robot part determination information and the corresponding three-dimensional spatial information of the assembly robot; controlling the assembly robot to move based on the assembly robot part determination information and the assembly movement path, and obtaining robot micro-force feedback control information; adjusting the assembly movement path and assembly robot part determination information based on the robot micro-force feedback control information to determine the assembly scenario application information. This method enables assembly efficiency and quality to meet preset standards.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, and in particular to a control method and apparatus for an assembly robot. Background Technology

[0002] With the continuous advancement of automation and intelligent manufacturing, assembly robots are gradually replacing traditional manual operations and becoming indispensable production equipment on assembly lines. Traditional control methods rely heavily on pre-programmed trajectory planning and simple command responses. However, traditional assembly robot control methods have limitations in dealing with complex production needs and high-precision requirements, resulting in assembly efficiency and quality that are difficult to meet preset standards. Summary of the Invention

[0003] Therefore, it is necessary to provide a control method, device, computer equipment, computer-readable storage medium, and computer program product for an assembly robot that can enable assembly efficiency and assembly quality to meet preset standards, in order to address the above-mentioned technical problems.

[0004] In a first aspect, this application provides a control method for an assembly robot, including:

[0005] Based on the visual sensing information and laser displacement sensing information of the assembly robot, the assembly posture of the assembly robot is self-calibrated at multiple levels to obtain the posture determination information of the assembly robot.

[0006] Based on the multi-point micro-scanning information of the assembly robot, the position and angle of the part in the pose determination information of the assembly robot are located to obtain the part determination information of the assembly robot.

[0007] Based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot, the assembly movement path of the assembly robot is planned;

[0008] Based on the part determination information of the assembly robot and the assembly movement path, the assembly robot is controlled to move, and robot micro-force feedback control information is obtained.

[0009] Based on the robot's micro-force feedback control information, the assembly movement path and the assembly robot part determination information are adjusted to determine the assembly scenario application information of the assembly robot.

[0010] Secondly, this application also provides a control device for an assembly robot, comprising:

[0011] The robot self-calibration module is used to perform multi-level self-calibration of the assembly robot's assembly posture based on the visual sensing information and laser displacement sensing information of the assembly robot, so as to obtain the assembly robot posture determination information.

[0012] The part pose adjustment module is used to locate the part position and part angle in the pose determination information of the assembly robot based on the multi-point micro-scan information of the assembly robot, so as to obtain the part determination information of the assembly robot.

[0013] The movement path planning module is used to plan the assembly movement path of the assembly robot based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot.

[0014] The feedback information acquisition module is used to control the assembly robot to move based on the part determination information of the assembly robot and the assembly movement path, and to acquire robot micro-force feedback control information.

[0015] The assembly information optimization module is used to adjust the assembly movement path and the assembly robot part determination information based on the robot micro-force feedback control information, and to determine the assembly scenario application information of the assembly robot.

[0016] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0017] Based on the visual sensing information and laser displacement sensing information of the assembly robot, the assembly posture of the assembly robot is self-calibrated at multiple levels to obtain the posture determination information of the assembly robot.

[0018] Based on the multi-point micro-scanning information of the assembly robot, the position and angle of the part in the pose determination information of the assembly robot are located to obtain the part determination information of the assembly robot.

[0019] Based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot, the assembly movement path of the assembly robot is planned;

[0020] Based on the part determination information of the assembly robot and the assembly movement path, the assembly robot is controlled to move, and robot micro-force feedback control information is obtained.

[0021] Based on the robot's micro-force feedback control information, the assembly movement path and the assembly robot part determination information are adjusted to determine the assembly scenario application information of the assembly robot.

[0022] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0023] Based on the visual sensing information and laser displacement sensing information of the assembly robot, the assembly posture of the assembly robot is self-calibrated at multiple levels to obtain the posture determination information of the assembly robot.

[0024] Based on the multi-point micro-scanning information of the assembly robot, the position and angle of the part in the pose determination information of the assembly robot are located to obtain the part determination information of the assembly robot.

[0025] Based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot, the assembly movement path of the assembly robot is planned;

[0026] Based on the part determination information of the assembly robot and the assembly movement path, the assembly robot is controlled to move, and robot micro-force feedback control information is obtained.

[0027] Based on the robot's micro-force feedback control information, the assembly movement path and the assembly robot part determination information are adjusted to determine the assembly scenario application information of the assembly robot.

[0028] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0029] Based on the visual sensing information and laser displacement sensing information of the assembly robot, the assembly posture of the assembly robot is self-calibrated at multiple levels to obtain the posture determination information of the assembly robot.

[0030] Based on the multi-point micro-scanning information of the assembly robot, the position and angle of the part in the pose determination information of the assembly robot are located to obtain the part determination information of the assembly robot.

[0031] Based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot, the assembly movement path of the assembly robot is planned;

[0032] Based on the part determination information of the assembly robot and the assembly movement path, the assembly robot is controlled to move, and robot micro-force feedback control information is obtained.

[0033] Based on the robot's micro-force feedback control information, the assembly movement path and the assembly robot part determination information are adjusted to determine the assembly scenario application information of the assembly robot.

[0034] The aforementioned control method, device, computer equipment, storage medium, and computer program product for an assembly robot perform multi-level self-calibration of the assembly robot's pose based on visual sensing information and laser displacement sensing information to obtain assembly robot pose determination information; based on multi-point micro-scanning information of the assembly robot, the positions and angles of parts in the pose determination information are located to obtain assembly robot part determination information; based on the assembly robot part determination information and the corresponding three-dimensional spatial information of the assembly robot, the assembly robot's assembly movement path is planned; based on the assembly robot part determination information and the assembly movement path, the assembly robot is controlled to move, and robot micro-force feedback control information is obtained; based on the robot micro-force feedback control information, the assembly movement path and assembly robot part determination information are adjusted to determine the assembly scenario application information of the assembly robot.

[0035] By integrating multi-layered perception technologies such as visual sensing, laser displacement, and multi-point micro-scanning, highly accurate pose self-calibration is achieved. This enables the assembly robot to precisely acquire part position and angle information, ensuring that every step in the assembly process is completed within a micrometer-level error range. Simultaneously, based on a real-time micro-force feedback control system, the assembly path and part positioning information can be dynamically adjusted, allowing the robot to adapt to minute errors and environmental changes in complex assembly scenarios. This series of precise control and dynamic optimization processes enhances the stability and intelligence level of robot assembly. It not only meets complex production demands and high-precision requirements, achieving assembly efficiency and quality that meet preset standards, but also significantly improves assembly efficiency and effectively reduces rework and material waste caused by assembly deviations, thus achieving high-quality, low-cost intelligent assembly. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is an application environment diagram of the control method for an assembly robot in one embodiment;

[0038] Figure 2 This is a flowchart illustrating the control method for an assembly robot in one embodiment;

[0039] Figure 3 This is a flowchart illustrating a method for determining application information in an assembly scenario, as shown in one embodiment.

[0040] Figure 4 This is a flowchart illustrating the method for determining application information in an assembly scenario, as described in another embodiment.

[0041] Figure 5 This is a flowchart illustrating a method for acquiring robot micro-force feedback control information in one embodiment;

[0042] Figure 6 This is a flowchart illustrating the method for calculating the initial assembly physical parameters in one embodiment;

[0043] Figure 7 This is a flowchart illustrating the initial assembly physical parameter calculation method in another embodiment;

[0044] Figure 8 This is a flowchart illustrating a method for obtaining pose determination information of an assembly robot in one embodiment;

[0045] Figure 9 This is a flowchart illustrating a method for obtaining pose determination information for an assembly robot in another embodiment;

[0046] Figure 10 This is a structural block diagram of the control device for an assembly robot in one embodiment;

[0047] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The control method for an assembly robot provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on other network servers. Server 104 performs multi-level self-calibration of the assembly robot's assembly pose based on the visual sensing information and laser displacement sensing information obtained by the assembly robot from terminal 102, obtaining assembly robot pose determination information; it locates the part positions and angles in the assembly robot pose determination information based on the multi-point micro-scanning information of the assembly robot, obtaining assembly robot part determination information; it plans the assembly robot's assembly movement path based on the assembly robot part determination information and the corresponding three-dimensional spatial information of the assembly robot; it controls the assembly robot to move based on the assembly robot part determination information and the assembly movement path, obtaining robot micro-force feedback control information; and it adjusts the assembly movement path and assembly robot part determination information based on the robot micro-force feedback control information to determine the assembly robot's assembly scenario application information. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0050] In one exemplary embodiment, such as Figure 2 As shown, a control method for an assembly robot is provided, which can be applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 202 to 210. Wherein:

[0051] Step 202: Based on the visual sensing information and laser displacement sensing information of the assembly robot, perform multi-level self-calibration of the assembly robot's assembly pose to obtain the assembly robot's pose determination information.

[0052] Among them, visual sensing information can be image or video data collected by the robot's camera equipment, which is used to identify and analyze the appearance features, spatial position and relative posture of the assembly object.

[0053] Among them, laser displacement sensing information can be distance measurement data generated by laser displacement sensors, which is used to accurately measure the relative position and shape details between the robot and the assembly object.

[0054] Multi-level self-calibration can be a process of adjusting the robot's posture and position by refining it layer by layer, in order to reduce operational errors.

[0055] Among them, the assembly pose can be the precise position and posture that the robot needs to maintain before assembly in order to correctly operate the assembled parts.

[0056] Among them, the pose determination information of the assembly robot can be precise posture data after multi-level self-calibration, including the position and orientation of the robot in the assembly area.

[0057] Specifically, in the initial stage of the assembly process, the system acquires image data of the overall assembly area through the assembly robot's vision sensors, identifying the assembly objects and their spatial layout. Subsequently, a laser displacement sensor performs high-precision 3D measurements of the assembly objects' details, obtaining their surface morphology, spatial position, and relative distances between assembly components. By fusing these two types of sensor information, the system constructs a detailed pose model. During multi-level calibration, the system repeatedly compares and adjusts this model to eliminate posture errors and correct pose deviations of the robot itself, ultimately obtaining the assembly robot's pose determination information, laying a stable spatial foundation for the assembly operation.

[0058] Step 204: Based on the multi-point micro-scanning information of the assembly robot, locate the part position and part angle in the pose determination information of the assembly robot to obtain the part determination information of the assembly robot.

[0059] Among them, multi-point micro-scanning information can be high-precision position and angle data obtained by multi-point scanning on the surface of assembled parts using a micro-scanning device.

[0060] Among them, the part determination information of the assembly robot can be detailed data generated based on multi-point micro-scanning and calibration information, which includes the precise position and orientation of the part in space.

[0061] Specifically, after the pose determination information of the assembly robot is established, the system uses multi-point micro-scanning technology to accurately locate the position and angle of the parts in the assembly pose. Micro-scanning captures minute geometric features by detecting the microstructure of the part surface point by point, and feeds back the position information of each scan point to the system for high-precision angle and position calculation. The system uses a three-dimensional feature matching algorithm to compare the scan results with the predetermined part features in the model to eliminate positioning errors caused by minute part offsets, thereby generating part determination information for the assembly robot and providing specific position and attitude data for subsequent path planning.

[0062] Step 206: Based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot, plan the assembly movement path of the assembly robot.

[0063] The assembly movement path can be the motion route planned by the robot in performing the assembly task, which defines the robot's movement trajectory between different assembly positions.

[0064] Specifically, after acquiring the part identification information for the assembly robot, the system performs path planning based on the robot's current pose, the part's spatial position, and the overall 3D information of the assembly environment. This planning process analyzes the range of motion of the robot joints, the gaps between parts, and obstacles to be avoided during assembly, designing an efficient and safe motion path. The path planning algorithm dynamically adjusts the assembly speed and acceleration curves according to the robot's joint dynamic capabilities and obstacle avoidance requirements in space, ensuring smooth and natural movements and maintaining precise part positioning during assembly. This results in an assembly path that includes trajectory, speed, and acceleration, minimizing assembly errors caused by path deviations.

[0065] Step 208: Based on the part determination information and assembly movement path of the assembly robot, control the assembly robot to move and obtain the robot's micro-force feedback control information.

[0066] Among them, the robot micro-force feedback control information can be the contact force and action force feedback data obtained by the robot during the assembly process, which is used to monitor the force balance between parts in real time during the assembly process.

[0067] Specifically, based on the part identification information and assembly movement path of the assembly robot, the system initiates the robot's movement control, gradually guiding it to the assembly position and monitoring force feedback information in real time during the assembly process. During the part contact and fixing phases, the system's micro-force sensors accurately detect the contact pressure, sliding friction, and strain of the parts, thereby controlling the assembly force and preventing assembly failure due to excessive or insufficient force. Through the feedback mechanism, the system can detect subtle deviations in the assembly position and make adjustments, ensuring the robot remains stable during assembly and guaranteeing safe and efficient part assembly in complex scenarios.

[0068] Step 210: Based on the robot's micro-force feedback control information, adjust the assembly movement path and the part determination information of the assembly robot to determine the assembly scenario application information of the assembly robot.

[0069] Among them, the assembly scenario application information can be a set of optimized parameters derived from the actual assembly situation, including adjustment information such as pose, path, part position, force feedback, etc.

[0070] Specifically, during the assembly process, the system continuously receives micro-force feedback control information from the robot to determine whether the actual assembly effect matches the expected path and position. If the system detects deviations caused by abnormal force feedback or positional errors, it automatically fine-tunes the path and part positioning information. It uses a real-time updated path optimization algorithm to recalculate the robot's movement path, adjust assembly force, change assembly modes, and adjust assembly angles until the assembly force and position information reach an ideal state. Through this dynamic adjustment mechanism, the system achieves intelligent adaptation to the assembly scenario, enabling the robot to maintain high-precision assembly results under different environments and assembly requirements, ultimately generating assembly scenario application information adapted to the specific assembly scenario.

[0071] In the aforementioned control method for an assembly robot, the assembly robot's pose is self-calibrated at multiple levels based on its visual sensing information and laser displacement sensing information to obtain pose determination information. The part positions and angles within the pose determination information are located based on the robot's multi-point micro-scanning information to obtain part determination information. The assembly robot's movement path is planned based on the part determination information and its corresponding three-dimensional spatial information. The robot is then controlled to move based on the part determination information and the movement path, acquiring micro-force feedback control information. Finally, the movement path and part determination information are adjusted based on the micro-force feedback control information to determine the robot's assembly scenario application information.

[0072] By integrating multi-layered perception technologies such as visual sensing, laser displacement, and multi-point micro-scanning, highly accurate pose self-calibration is achieved. This enables the assembly robot to precisely acquire part position and angle information, ensuring that every step in the assembly process is completed within a micrometer-level error range. Simultaneously, based on a real-time micro-force feedback control system, the assembly path and part positioning information can be dynamically adjusted, allowing the robot to adapt to minute errors and environmental changes in complex assembly scenarios. This series of precise control and dynamic optimization processes enhances the stability and intelligence level of robot assembly. It not only meets complex production demands and high-precision requirements, achieving assembly efficiency and quality that meet preset standards, but also significantly improves assembly efficiency and effectively reduces rework and material waste caused by assembly deviations, thus achieving high-quality, low-cost intelligent assembly.

[0073] In one exemplary embodiment, such as Figure 3 As shown, based on the robot's micro-force feedback control information, the assembly movement path and the part determination information of the assembly robot are adjusted to determine the assembly scenario application information of the assembly robot, including steps 302 to 306. Wherein:

[0074] Step 302: Determine the robot assembly process assembly mode of the assembly robot based on the robot micro-force feedback control information.

[0075] Among them, the assembly mode of the robot assembly process can be an operation mode that the system automatically selects based on micro-force feedback data and assembly requirements during the assembly task. Different assembly modes (such as flexible mode and precision mode) correspond to different force control and motion accuracy, and are suitable for various situations in the assembly process.

[0076] Specifically, the system analyzes subtle mechanical changes during the assembly process in real time through micro-force feedback control information from the assembly robot. Combining the amplitude, direction, and duration of force data, it identifies whether the assembly state is stable or abnormal. Based on the fluctuations in assembly forces, the system determines the assembly difficulty and precision requirements, and automatically selects the appropriate robot assembly mode, such as a flexible mode or a precision mode, to handle different assembly conditions. The flexible mode is typically used for assemblies requiring greater tolerance, while the precision mode is suitable for high-precision, high-rigidity operating environments. The system determines the timing of mode switching based on the characteristics of the feedback data to ensure the assembly robot operates in the optimal mode throughout the process.

[0077] Step 304: Based on the robot's micro-force feedback control information and the robot's assembly process assembly mode, fine-tune the assembly force and assembly error of the assembly robot parts to obtain robot assembly fine-tuning information.

[0078] The robot assembly fine-tuning information can be a set of parameters used to make subtle adjustments to the position, angle, and force of parts during the assembly process. By dynamically correcting the feedback of mechanical and positional data, the system continuously optimizes the accuracy of the assembly operation, enabling the assembly to reach an ideal state.

[0079] Specifically, the system combines robot micro-force feedback control information with the robot assembly process mode. For minor force errors, positional deviations, and deviations in assembly force detected during assembly, the system precisely corrects the force during assembly by adjusting the force output and direction of the robot's end effector, ensuring stable contact of parts without damage. At this point, the system uses closed-loop control, comparing the feedback force signal with the expected value, continuously fine-tuning the contact force and positional offset of the robot's end effector, gradually eliminating minor assembly errors. Simultaneously, through an error evaluation algorithm, the system confirms that all assembly parameters meet the standard requirements and generates robot assembly fine-tuning information, recording the precise mechanical parameters and position of the current assembly.

[0080] Step 306: Perform multi-level precision calibration on the robot assembly fine-tuning information to obtain assembly scenario application information.

[0081] Multi-level precision calibration can be a process of gradually improving assembly accuracy. Through coarse, intermediate, and fine calibration operations, assembly errors are gradually reduced. Preliminary calibration significantly corrects position and orientation, intermediate calibration further optimizes details, and the final fine calibration uses high-precision data to ensure that the assembly achieves extremely high accuracy.

[0082] Specifically, the system performs multi-level precision calibration on the generated robot assembly fine-tuning information, proceeding sequentially from coarse to fine adjustments to ensure the final assembly accuracy meets requirements. In the initial calibration phase, the system first adjusts the pose and force with larger errors, gradually reducing the error range. In the intermediate calibration phase, by integrating calibration data from multiple micro-force feedbacks, the assembly parameters are further optimized to ensure the error remains within acceptable limits. In the final fine calibration phase, the system utilizes feedback information from high-precision sensors to minimize residual errors and, through multi-level data fusion algorithms, achieves final calibration of pose and force, obtaining application information for the assembly scenario.

[0083] In this embodiment, by utilizing robot micro-force feedback control information, the system can determine the mechanical state during the assembly process in real time and dynamically select the most suitable assembly mode. This adaptive assembly mode helps the robot precisely control the applied force in complex assembly tasks and fine-tune the force and error based on feedback during the assembly process, generating robot assembly fine-tuning information. Finally, through multi-level precision calibration, the assembly accuracy and stability are further optimized. The system ensures the precise execution of each assembly step, significantly reducing deviations and assembly defects. This adaptive adjustment mechanism based on real-time feedback greatly improves assembly efficiency and finished product quality, reduces the possibility of rework and material loss, and achieves efficient and reliable intelligent assembly.

[0084] In one exemplary embodiment, such as Figure 4 As shown, multi-level precision calibration is performed on the robot assembly fine-tuning information to obtain assembly scenario application information, including steps 402 to 406. Wherein:

[0085] Step 402: Perform reference point positioning calibration on the reference point information of the robot assembly fine-tuning information to obtain robot assembly reference point calibration information.

[0086] Among them, the benchmark positioning calibration can be achieved by using high-precision sensing equipment to accurately measure and adjust the reference positions in the assembly area, so as to ensure that the assembly robot can accurately identify and align these key positions as the starting reference for the assembly action.

[0087] Among them, the robot assembly reference point calibration information can be the precise position and posture data obtained after the assembly robot has been positioned and calibrated by the reference point, which is used to define the spatial coordinates of each reference point in the assembly task.

[0088] Specifically, in the first step of multi-level precision calibration, the system precisely locates and calibrates the reference point data in the robot assembly fine-tuning information. The reference point is the core reference position for all operations in the assembly task, determining the initial posture and direction of the assembly actions. The system uses high-precision displacement sensors and multi-angle imaging equipment to progressively scan and identify the positions of reference points within the assembly area, ensuring that these points are strictly aligned with preset standard coordinates. Through real-time comparison and feedback adjustments, the system enables the robot to achieve sub-millimeter error in positioning the reference points, thereby generating accurate robot assembly reference point calibration information. This ensures that the entire assembly process starts from a precise reference position, providing a stable starting point for subsequent force and spatial calibrations.

[0089] Step 404: Perform contact force equalization calibration on the contact force information of the robot assembly reference point calibration information to obtain robot assembly contact force calibration information.

[0090] Among them, contact force information can be force data applied by the robot to the part or contact surface during the assembly process, including details such as the magnitude, direction and distribution of the force.

[0091] Among them, contact force equalization calibration can be a fine adjustment of the force applied by the assembly robot based on the contact force information, to ensure that the force distribution is uniform and reasonable, and to avoid affecting the assembly quality due to excessive or insufficient force in some areas.

[0092] Among them, the robot assembly contact force calibration information can be the adjustment data after contact force equalization calibration, which includes optimized force distribution and force application parameters. It is used to guide the mechanical control of the robot in the assembly process and ensure that each contact point in the assembly is in an ideal force balance state.

[0093] Specifically, in the second step of multi-level precision calibration, the system performs equalization calibration on the contact force parameters in the assembly reference point calibration information to ensure that the contact force applied by the robot during assembly is evenly distributed. This process uses contact force sensors to monitor the force applied by the robot when assembling parts in real time, compares the mechanical data with the standard values ​​required for assembly, identifies areas of imbalance, and automatically adjusts them. The system meticulously adjusts the robot's force output to form a balanced and moderate pressure on the contact surface, avoiding misalignment, wear, or damage to assembly parts caused by uneven force distribution. The calibration process involves multiple fine adjustments until the contact force distribution meets the standard, generating robot assembly contact force calibration information, providing a stable mechanical basis for subsequent spatial calibration.

[0094] Step 406: Perform multi-vector calibration on the three-dimensional space of the robot assembly contact force calibration information to obtain assembly scenario application information.

[0095] Among them, multi-vector calibration can finely adjust the multi-directional motion trajectory and angle of the assembly robot in three-dimensional space to eliminate the remaining motion error.

[0096] Specifically, in the third step of multi-level precision calibration, this stage involves analyzing the various motion vectors of the assembly robot in three-dimensional space to perform final precision fine-tuning of the assembly path and angles. Specifically, the system uses spatial vector analysis to detect the robot's displacement and rotation accuracy in various directions. By progressively optimizing the synchronization and coordination of each motion axis, each vector conforms to the ideal spatial orientation. The calibration system uses multiple repeated tests and feedback adjustments to further optimize the robot's motion path and motion parameters, ensuring that its movements in three-dimensional space are free from any deviations or error accumulation. After this stage of multi-vector calibration, the system generates the final assembly scenario application information, ensuring that the robot can perform complex assembly tasks with high precision, achieving optimal assembly accuracy and stability in three-dimensional space.

[0097] In this embodiment, by performing reference point positioning, contact force balancing, and three-dimensional multi-vector calibration of the robot assembly fine-tuning information, the system can achieve multi-level and refined calibration during the assembly process. Reference point positioning calibration ensures precise alignment of the robot at key positions in space, contact force balancing calibration makes the force distribution more uniform, thereby avoiding damage to parts or assembly deviations, while three-dimensional multi-vector calibration further optimizes the robot's precision control in different directions. This complete calibration process enables higher precision and stability in assembly operations, significantly improves assembly quality, and effectively reduces assembly problems caused by uneven force distribution or posture deviations, achieving efficient and reliable intelligent assembly applications.

[0098] In one exemplary embodiment, such as Figure 5 As shown, based on the part determination information and assembly movement path of the assembly robot, the assembly robot is controlled to move, and the robot's micro-force feedback control information is obtained, including steps 502 to 506. Wherein:

[0099] Step 502: Determine the robot assembly start mode based on the part determination information and assembly movement path of the assembly robot.

[0100] Among them, the robot assembly start assembly mode can be an initial operation mode determined according to the requirements of the assembly task, the characteristics of the parts and the assembly path, which defines the operation strategy when the robot starts to execute the assembly task.

[0101] Specifically, the system performs a comprehensive analysis of the assembly task requirements based on the part identification information and preset assembly movement paths of the assembly robot, determining the part's geometry, material, and spatial constraints of the assembly area. By analyzing the assembly precision requirements, task difficulty, and environmental conditions, the system selects the most suitable robot assembly start mode, such as a flexible mode (suitable for small, fragile parts) or a precision mode (for high-precision assembly). The mode selection considers the robot's flexibility and stability, enabling it to respond to different assembly scenarios in the most appropriate way at the start of assembly, ensuring the smooth and efficient completion of the entire assembly process.

[0102] Step 504: Calculate the initial assembly physical parameters based on the robot assembly start-up assembly mode and the assembly robot part determination information.

[0103] The initial assembly physical parameters can be key physical data set when the assembly starts, including the magnitude, direction, speed and acceleration of the force required by the robot when it contacts the parts.

[0104] Specifically, after the robot assembly startup mode is determined, the system performs precise calculations of the assembly physical parameters based on this mode to ensure that the mechanical conditions of each assembly action meet the process requirements. Specifically, the system integrates information such as the dimensions of the parts to be assembled, the distance of the assembly path, and the posture of the robot's end effector to calculate the initial physical parameters, including the magnitude and direction of the applied force, contact velocity, and acceleration. This calculation takes into account the balance between force and velocity, preventing loosening due to insufficient force while avoiding damage to parts due to excessive force. The system inputs these physical parameters into the robot controller as guidance data for assembly startup, ensuring that the initial operation reaches the optimal state.

[0105] Step 506: Based on the initial assembly physical parameters, control the assembly robot to move and obtain robot micro-force feedback control information.

[0106] Specifically, based on the initial assembly physical parameters, the system instructs the assembly robot to move along the assembly path, precisely contacting and positioning the assembly parts. During the movement, the robot monitors the contact force with the parts in real time, acquiring minute displacement and contact force feedback data through micro-force sensors. This robot micro-force feedback control information provides the system with real-time mechanical feedback for the assembly operation, used to evaluate the actual assembly effect. If the feedback information indicates deviations in assembly force, angle, or position, the system will automatically adjust the assembly path or force, making real-time fine adjustments to ensure that the assembly process accuracy meets the predetermined standard. Through this feedback mechanism, the system continuously optimizes the assembly operation, enabling the robot to complete tasks stably and efficiently under different assembly conditions.

[0107] In this embodiment, by using the assembly robot's part determination information and movement path, the system can select the most suitable assembly start mode for the current task, thereby achieving an adaptive assembly strategy. Combining the assembly mode and part characteristics, the system further calculates the initial assembly physical parameters to ensure the robot has appropriate force, direction, and speed at startup, avoiding assembly deviations caused by improper operation. Subsequently, the system controls the robot's movement and acquires micro-force feedback control information in real time, achieving precise monitoring and dynamic adjustment of the assembly process. This process makes the assembly process more flexible and efficient, significantly improving assembly accuracy and quality, while reducing assembly defects caused by mechanical deviations or speed mismatches, laying the foundation for intelligent and reliable assembly operations.

[0108] In one exemplary embodiment, such as Figure 6 As shown, the initial assembly physical parameters are calculated based on the robot assembly start-up assembly mode and the assembly robot part determination information, including steps 602 to 606.

[0109] in:

[0110] Step 602: Calculate the robot's movement pose adjustment information based on the robot's assembly start mode and assembly movement path.

[0111] Among them, the motion pose adjustment information can be the detailed adjustment data of joint angles, actuator positions and motion directions calculated by the system to ensure that the robot maintains the best posture along the assembly path during the assembly task.

[0112] Specifically, the system analyzes the pose adjustment requirements for each key point based on the geometric characteristics of the assembly movement path (such as curvature, angle, and slope) and the requirements of the robot's assembly startup mode. The pose adjustment information includes the robot's joint angles, the end effector's attitude angles, and the direction of motion to ensure stable contact with the parts during movement, thus calculating the assembly robot's movement pose adjustment information. Using this information, the system can fine-tune each joint of the robot, ensuring that the robot operates in the optimal posture at every step along the path, avoiding pose deviations that could affect assembly accuracy.

[0113] Step 604: Based on the movement pose adjustment information, dynamically adjust the part determination information of the assembly robot to obtain the dynamic movement parameter information of the assembly robot.

[0114] Dynamic adjustment can be a continuous optimization based on real-time data during the assembly process. The system adjusts the robot's operating parameters (such as position, posture, force, etc.) in real time to adapt to instantaneous changes in the environment or different characteristics of the assembly path.

[0115] Among them, the dynamic parameter information of the assembly robot can be the operation data dynamically generated by the robot during the assembly process, including the adjusted pose, speed, contact angle and other information.

[0116] Specifically, based on the motion pose adjustment information, the system further dynamically updates the part determination information of the assembly robot to adapt to the needs of different path segments. During this process, the system comprehensively analyzes the real-time position and angle changes of the part, as well as path characteristics, and dynamically fine-tunes the robot's grasping and manipulation methods. For example, when the path undergoes a significant turn or angle change, the system recalculates the contact force and contact angle to ensure precise and stable control of the part by the robot under different poses, thereby determining the dynamic parameters of the assembly robot's movement. This dynamic parameter information enables the robot to smoothly transition between different path segments, ensuring precise part control even in complex environments.

[0117] Step 606: Introduce the historical error feedback compensation information of the assembly robot, and perform nonlinear adjustment on the dynamic parameters of the assembly robot's movement to obtain the initial assembly physical parameters.

[0118] Among them, the historical error feedback compensation information can be correction information generated by the system based on the deviation data accumulated in the robot's previous assembly process, which records the magnitude, direction and trend of common errors.

[0119] Nonlinear adjustment can be a high-precision optimization method applied by the system to address the irregularity of errors during the assembly process, and dynamically corrects mechanical and attitude parameters through complex algorithms.

[0120] Specifically, the system incorporates historical error feedback compensation information from robot assembly. Utilizing the deviation patterns recorded in the historical error data, a nonlinear compensation algorithm optimizes parameters such as force, direction, and speed, eliminating accumulated errors or path deviations caused by repetitive motion in the robot's dynamic movement parameters. During the adjustment process, the nonlinear adjustment gradually adjusts the mechanical conditions in the robot's dynamic movement parameters based on the fluctuation trend of historical errors to obtain parameter outputs that meet assembly accuracy requirements. Finally, the initial assembly physical parameters, after compensation and nonlinear optimization, are obtained, ensuring the robot can enter the assembly operation with optimal force, direction, and posture, significantly improving assembly accuracy and stability.

[0121] In this embodiment, by calculating the robot's pose adjustment information based on the assembly start mode and movement path, the system can achieve real-time precise adjustments during the assembly process. By dynamically optimizing the part's positioning parameters using pose adjustment information, the system can flexibly adapt to different operational requirements within the assembly path, ensuring accuracy at every step. Simultaneously, historical error feedback compensation information is introduced for non-linear adjustments, allowing the system to correct minor deviations caused by repetitive operations or environmental changes, thereby generating optimized initial assembly physical parameters. This process significantly improves the stability and accuracy of assembly operations, reduces the impact of cumulative errors, lays a solid foundation for high-quality and reliable assembly, and enhances the overall efficiency and accuracy of intelligent assembly.

[0122] In one exemplary embodiment, such as Figure 7 As shown, historical error feedback compensation information of the assembly robot is introduced to nonlinearly adjust the dynamic parameters of the assembly robot's movement, thereby obtaining the initial assembly physical parameters, including steps 702 to 704. Wherein:

[0123] Step 702: Define the assembly mode adaptation factor according to the robot assembly start assembly mode.

[0124] The assembly mode adaptation factor can be a set of adjustment coefficients defined for different assembly task requirements. These coefficients are used to adjust the robot's operational flexibility, precision, and force output when performing assembly tasks. This factor is set according to the characteristics of the assembly mode (such as flexible assembly mode or precision assembly mode) to ensure that the robot can automatically adapt to operational requirements when performing different tasks.

[0125] Specifically, an assembly mode adaptation factor is defined based on the robot's assembly startup mode (e.g., flexible assembly or precision assembly) to quantify and adapt the specific mode's requirements for operational flexibility, accuracy, and force output. The assembly mode adaptation factor is a set of adjustment coefficients used to adjust the adaptability of parameters during the nonlinear optimization process. For example, in flexible mode, the adaptation factor relaxes the tolerance for force control, allowing the robot to be more adaptable to minor deviations; while in precision mode, the adaptation factor enhances the sensitivity to position and force, ensuring accuracy at every step. By analyzing the task's operational requirements and accuracy level, the system automatically sets the various parameters of the adaptation factor, thereby accurately incorporating the characteristics of different assembly modes into the subsequent nonlinear adjustment process, enabling the robot to adapt to the current assembly environment after startup.

[0126] Step 704: Nonlinearly adjust the fused data of the assembly robot's dynamic movement parameters, assembly mode adaptation factor, and historical error feedback compensation information to obtain the initial assembly physical parameters.

[0127] Specifically, the nonlinear adjustment uses an assembly mode adaptation factor as a weight, making parameter adjustments more flexible and adaptable to the mechanical requirements of the current mode. Simultaneously, the system analyzes historical error feedback compensation information, using the frequency, amplitude, and trend of errors to compensate for dynamic parameters, gradually eliminating accumulated errors. During the adjustment process, the system applies a nonlinear optimization algorithm, using iterative calculations to minimize residual deviations in the assembly robot's dynamic movement parameters, especially optimizing the details of mechanical output and motion paths, ensuring that the final generated initial assembly physical parameters meet the precise standards of force, angle, and position required for the task. The final output dataset ensures the robot is in the most ideal mechanical and geometric state at the start of assembly, achieving high-precision and stable assembly.

[0128] Among them, the fused data of assembly robot movement dynamic parameter information, assembly mode adaptation factor and historical error feedback compensation information are nonlinearly adjusted to obtain the expression corresponding to the initial assembly physical parameters as follows:

[0129] P 调整 =F 非线性 ·(M 因子 ·V 动态 +E 反馈 )+P 初始

[0130]

[0131] Where, α f As the adaptation factor for strength, α v α is the adaptation factor for velocity. θ β is the adaptation factor for attitude angle. f β is the force historical error compensation coefficient. v β is the speed history error compensation coefficient. θ This is the historical error compensation coefficient for attitude angle. This represents the cumulative error in force applied over the past n operations. This is the cumulative error in speed over the past n operations. The cumulative error of the attitude angle over the past n operations is represented by tan h(k·x), which is the nonlinear adjustment function, where k is the sensitivity coefficient, x is the input value of the nonlinear adjustment function, and P is the input value of the nonlinear adjustment function. 装配 To determine information for assembling robot parts, Δ x Δ y and Δ z These represent the positional deviations in the x, y, and z directions during dynamic adjustment.

[0132] In this embodiment, by defining an assembly mode adaptation factor based on the robot's assembly start mode, the system can adapt to the needs of different assembly tasks and flexibly adjust the precision and mechanical characteristics of the robot's operation. Combining the assembly mode adaptation factor, dynamic movement parameter information, and historical error feedback compensation information, the system performs nonlinear adjustments to generate optimized initial assembly physical parameters. This integrated adjustment method enables the system to accurately respond to environmental changes and accumulated errors in the task, ensuring that each assembly operation meets the requirements in terms of force, angle, and position. This significantly improves the accuracy, stability, and adaptability of the assembly, ensuring high-quality assembly results and effectively reducing rework rates.

[0133] In one exemplary embodiment, such as Figure 8 As shown, based on the visual sensing information and laser displacement sensing information of the assembly robot, the assembly pose of the assembly robot is self-calibrated at multiple levels to obtain the pose determination information of the assembly robot, including steps 802 to 806. Wherein:

[0134] Step 802: Based on the visual sensing information and the laser displacement sensing information, adjust the actual three-dimensional coordinates of the assembly robot to the reference three-dimensional coordinates to obtain the macroscopic calibration information of the assembly robot.

[0135] Among them, the reference three-dimensional coordinates can be spatial reference points for the standard position and posture pre-set in the assembly task, defining the ideal starting position and orientation of the robot in the assembly area.

[0136] Among them, the macroscopic calibration information of the assembly robot can be overall position adjustment data generated based on visual and laser displacement sensing data, which includes the alignment information of the robot with the reference coordinates in three-dimensional space.

[0137] Specifically, the system acquires an overall image of the assembly area using a vision sensor to determine the relative position of the robot and its surrounding environment. Simultaneously, a laser displacement sensor collects actual 3D coordinate data to accurately identify the robot's current positional deviation and initiates the first adjustment. Combining the visual sensor information and laser displacement data, the system compares the robot's actual 3D coordinates with preset reference 3D coordinates, analyzing the differences in position and posture. Based on the comparison results, the system calculates the adjustment amount for each joint and instructs the robot to gradually adjust its position until its overall 3D coordinates are precisely aligned with the reference coordinates. This process generates macroscopic calibration information for the assembly robot, providing it with accurate initial positioning and ensuring optimal alignment with the assembly environment on a macroscopic scale.

[0138] Step 804: Based on the laser alignment information of the assembly robot, fine-tune the macroscopic calibration information of the assembly robot to obtain the microscopic calibration information of the assembly robot.

[0139] Among them, laser alignment information can be high-precision position data obtained through laser displacement sensors, which is used to detect and adjust the slight deviation between the robot and the reference position.

[0140] Among them, the micro-calibration information of the assembly robot can be fine calibration data achieved by laser alignment based on macro-calibration, which records the position adjustment results of the robot at the micro scale.

[0141] Specifically, the system further utilizes laser displacement sensors for fine-tuning, improving positioning accuracy by detecting minute displacement differences between the reference point and the robot. The errors detected by the laser sensors are typically at the sub-millimeter level. The system uses this data for more precise fine-tuning, calculating correction values ​​for each key point to eliminate residual minute deviations. This fine-tuning ensures precise alignment between the robot and the reference point by adjusting joint positions and end-effector posture, generating micro-calibration information for the assembly robot. After micro-calibration, the robot achieves extremely high accuracy in position and posture, laying a solid foundation for performing more delicate assembly tasks.

[0142] Step 806: Adjust the robot arm pose information in the micro-calibration information of the assembly robot to obtain the pose determination information of the assembly robot.

[0143] Among them, the robot arm pose information can be the specific position and angle data of the robot arm in three-dimensional space, including the angle of each joint and the position and orientation of the end effector.

[0144] Specifically, based on the micro-calibration information of the assembly robot, the system performs a final adjustment to the end effector pose of the robotic arm to ensure that the position and angle of the assembly tool in space fully meet the operational requirements. The system first calculates the final angle and position of each joint after fine-tuning, placing the end effector in the optimal position required for the task. Subsequently, the system refines the motion path of each joint, using real-time sensor data to ensure the dynamic stability of the robotic arm in space. The final generated assembly robot pose determination information encompasses the precise positioning and attitude of each joint, enabling the robot to execute assembly tasks error-free, achieving the target accuracy and operational stability.

[0145] In this embodiment, by integrating visual sensing information and laser displacement sensing information, the system can precisely adjust the actual three-dimensional coordinates of the assembly robot to the reference three-dimensional coordinates, completing macro-calibration and ensuring accurate positioning of the robot within the assembly area. Next, the macro-calibration data is fine-tuned using laser alignment information to achieve sub-millimeter-level micro-calibration, enabling extremely high precision in the robot's position and posture. Finally, the micro-calibrated robot arm pose information is adjusted to obtain optimal pose determination information. This multi-level calibration process significantly improves the robot's positioning accuracy and posture stability, ensuring precise execution of every operation step during assembly, reducing error accumulation and assembly deviations, and achieving reliable, stable, and high-quality assembly.

[0146] In one exemplary embodiment, such as Figure 9 As shown, the robot arm pose information in the micro-calibration information of the assembly robot is adjusted to obtain the pose determination information of the assembly robot, including steps 902 to 906. Wherein:

[0147] Step 902: Based on the micro-calibration information of the assembly robot, lock the six-axis degrees of freedom of the end effector of the assembly robot.

[0148] Among them, the six-axis degrees of freedom can be the six independent directions of motion of the end effector of the assembly robot in three-dimensional space, including linear movement along the three coordinate axes X, Y, and Z (positional degrees of freedom), and rotation about the X, Y, and Z axes (attitude degrees of freedom).

[0149] Specifically, the system analyzes the end-effector pose data from the micro-calibration information of the assembly robot, locking the six degrees of freedom of the end-effector (including its X, Y, and Z position coordinates in three-dimensional space and its rotation angles around the X, Y, and Z axes). This locking operation ensures that the end-effector does not shift in position or orientation during assembly, maintaining its accuracy. By locking the six-axis degrees of freedom of the end-effector, the system establishes fixed reference points, ensuring that the end-effector remains in a reference position and angle during adjustments to the other robot arm joints. This operation establishes a stable spatial reference for subsequent steps, ensuring the accuracy and reliability of the final assembly.

[0150] Step 904: Based on the locked six-axis degrees of freedom, calculate the optimized pose information of each of the other robotic arms according to the current pose information of each of the other robotic arms based on the micro-calibration information of the assembly robot.

[0151] The remaining robotic arms can be other joints and links in the assembly robot besides the end effector, and these joints coordinate with each other to support the precise operation of the end effector.

[0152] Among them, the optimized pose information can be the ideal pose data calculated based on the end-effector locking and the position requirements of the robotic arm, which includes the optimal position and angle required for each joint while meeting the task requirements.

[0153] Specifically, based on the six-axis degree of freedom locking of the end effector, the system uses kinematic models and inverse algorithms to analyze the distribution of the remaining joints of the robot arm (i.e., all joints except the end effector) in the current pose. The system calculates the optimal position and angle required for each joint while keeping the end effector position unchanged, in order to minimize the stress and load on the robot arm and ensure the balance of the entire arm structure. The system further considers the interaction between the joints and, through multiple iterations of optimization, obtains optimized pose information that enables the robot to have the best operating posture, providing support for the smooth and efficient movement of the robot arm.

[0154] Step 906: Adjust the poses of the remaining robotic arms according to the optimized pose information to obtain the pose determination information of the assembly robot.

[0155] Specifically, the system progressively adjusts the poses of the remaining robotic arms, adjusting the other joints based on optimization calculations to achieve optimal balance and stress distribution. Each joint adjustment strictly adheres to optimized angle and position settings, with real-time feedback and sensor monitoring ensuring precise accuracy. The final generated assembly robot pose determination information encompasses the adjustment results of all joints, defining the robot's optimal pose throughout the assembly process, enabling efficient and stable task execution and ensuring mechanical stability and assembly precision during operation.

[0156] In this embodiment, through precise positioning using micro-calibration information, the system first locks the six degrees of freedom of the assembly robot's end effector, ensuring its position and posture remain stable during assembly. Next, based on these locked six degrees of freedom, the system analyzes the current pose information of the remaining robot joints and calculates the optimal pose adjustment scheme to reduce joint load and improve overall coordination. Finally, the system adjusts the remaining robot joints according to the optimized pose information, generating the assembly robot's pose determination information. This process significantly improves the positioning accuracy and mechanical stability during robot assembly, ensuring high reliability and consistency of the end effector assembly operation, thereby achieving more precise and efficient assembly operations.

[0157] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0158] Based on the same inventive concept, this application also provides a control device for an assembly robot to implement the control method for the assembly robot described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the control device for the assembly robot provided below can be found in the limitations of the control method for the assembly robot described above, and will not be repeated here.

[0159] In one exemplary embodiment, such as Figure 10 As shown, a control device for an assembly robot is provided, including: a robot self-calibration module 1002, a part pose adjustment module 1004, a movement path planning module 1006, and a feedback information acquisition module 1008, wherein:

[0160] The robot self-calibration module 1002 is used to perform multi-level self-calibration of the assembly robot's assembly posture based on the visual sensing information and laser displacement sensing information of the assembly robot, so as to obtain the assembly robot's posture determination information.

[0161] The part pose adjustment module 1004 is used to locate the part position and part angle in the pose determination information of the assembly robot based on the multi-point micro-scanning information of the assembly robot, so as to obtain the part determination information of the assembly robot.

[0162] The movement path planning module 1006 is used to plan the assembly movement path of the assembly robot based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot.

[0163] The feedback information acquisition module 1008 is used to control the assembly robot to move based on the part determination information and assembly movement path of the assembly robot, and to obtain the robot's micro-force feedback control information.

[0164] The assembly information optimization module 1010 is used to adjust the assembly movement path and the part determination information of the assembly robot based on the robot's micro-force feedback control information, and to determine the assembly scenario application information of the assembly robot.

[0165] In one embodiment, the assembly information optimization module 1010 is further configured to determine the assembly mode of the robot assembly process based on the robot micro-force feedback control information; fine-tune the assembly force and assembly error of the assembly robot parts determination information based on the robot micro-force feedback control information and the robot assembly process assembly mode to obtain robot assembly fine-tuning information; and perform multi-level precision calibration on the robot assembly fine-tuning information to obtain assembly scenario application information.

[0166] In one embodiment, the assembly information optimization module 1010 is further used to perform reference point positioning calibration on the reference point information of the robot assembly fine-tuning information to obtain robot assembly reference point calibration information; to perform contact force equalization calibration on the contact force information of the robot assembly reference point calibration information to obtain robot assembly contact force calibration information; and to perform multi-vector calibration on the three-dimensional space of the robot assembly contact force calibration information to obtain assembly scenario application information.

[0167] In one embodiment, the feedback information obtaining module 1008 is further configured to determine the robot assembly start mode based on the assembly robot part determination information and the assembly movement path; calculate the initial assembly physical parameters based on the robot assembly start mode and the assembly robot part determination information; and control the assembly robot to move based on the initial assembly physical parameters to obtain robot micro-force feedback control information.

[0168] In one embodiment, the feedback information obtaining module 1008 is further configured to calculate the movement pose adjustment information of the assembly robot based on the robot assembly start assembly mode and assembly movement path; dynamically adjust the part determination information of the assembly robot based on the movement pose adjustment information to obtain the movement dynamic parameter information of the assembly robot; and introduce the historical error feedback compensation information of the assembly robot to perform nonlinear adjustment on the movement dynamic parameter information of the assembly robot to obtain the initial assembly physical parameters.

[0169] In one embodiment, the feedback information obtaining module 1008 is further used to define an assembly mode adaptation factor based on the robot assembly start assembly mode; and to perform nonlinear adjustment on the fused data of assembly robot movement dynamic parameter information, assembly mode adaptation factor and historical error feedback compensation information to obtain initial assembly physical parameters.

[0170] In one embodiment, the robot self-calibration module 1002 is further configured to fine-tune the macroscopic calibration information of the assembly robot based on the laser alignment information of the assembly robot to obtain the microscopic calibration information of the assembly robot; and to adjust the robot arm pose information in the microscopic calibration information of the assembly robot to obtain the pose determination information of the assembly robot.

[0171] In one embodiment, the robot self-calibration module 1002 is further configured to lock the six-axis degrees of freedom of the end effector of the assembly robot according to the micro-calibration information of the assembly robot; calculate the optimized pose information of each of the other manipulators based on the locked six-axis degrees of freedom and the current pose information of each of the other manipulators according to the micro-calibration information of the assembly robot; and adjust the pose of each of the other manipulators according to the optimized pose information to obtain the pose determination information of the assembly robot.

[0172] The various modules in the control device for the assembly robot described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0173] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores server data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a control method for an assembly robot.

[0174] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0175] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0176] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0177] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0178] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0179] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0180] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A control method for an assembly robot, characterized in that, The method includes: Based on the visual sensing information and laser displacement sensing information of the assembly robot, the assembly posture of the assembly robot is self-calibrated at multiple levels to obtain the posture determination information of the assembly robot. Based on the multi-point micro-scanning information of the assembly robot, the position and angle of the part in the pose determination information of the assembly robot are located to obtain the part determination information of the assembly robot. Based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot, the assembly movement path of the assembly robot is planned; Based on the assembly robot part determination information and the assembly movement path, determine the robot assembly start mode; Calculate the initial assembly physical parameters based on the robot assembly start-up assembly mode and the assembly robot part determination information; Based on the initial assembly physical parameters, the assembly robot is controlled to move and acquire robot micro-force feedback control information; wherein, the robot micro-force feedback control information is the contact force and action force feedback data acquired by the robot during the assembly process, which is used to monitor the force balance between parts in real time during the assembly process; Based on the robot's micro-force feedback control information, the assembly movement path and the assembly robot part determination information are adjusted to determine the assembly scenario application information of the assembly robot.

2. The method according to claim 1, characterized in that, The step of adjusting the assembly movement path and the assembly robot part determination information based on the robot's micro-force feedback control information, and determining the assembly scenario application information of the assembly robot, includes: Based on the robot micro-force feedback control information, the robot assembly process assembly mode of the assembly robot is determined; Based on the robot micro-force feedback control information and the robot assembly process assembly mode, the assembly force and assembly error of the assembly robot parts determination information are fine-tuned to obtain robot assembly fine-tuning information. Multi-level precision calibration is performed on the robot assembly fine-tuning information to obtain the assembly scenario application information.

3. The method according to claim 2, characterized in that, The process of performing multi-level precision calibration on the robot assembly fine-tuning information to obtain the assembly scenario application information includes: The reference point information of the robot assembly fine-tuning information is calibrated by reference point positioning to obtain robot assembly reference point calibration information. The contact force information of the robot assembly reference point calibration information is calibrated by performing contact force equalization calibration to obtain robot assembly contact force calibration information. Multi-vector calibration is performed on the three-dimensional space of the robot assembly contact force calibration information to obtain the assembly scenario application information.

4. The method according to claim 1, characterized in that, The step of calculating the initial assembly physical parameters based on the robot assembly start-up mode and the assembly robot part determination information includes: Based on the robot assembly start assembly mode and the assembly movement path, calculate the movement pose adjustment information of the assembly robot; Based on the movement pose adjustment information, the part determination information of the assembly robot is dynamically adjusted to obtain the dynamic movement parameter information of the assembly robot. By introducing the historical error feedback compensation information of the assembly robot, the dynamic parameters of the assembly robot's movement are nonlinearly adjusted to obtain the initial assembly physical parameters.

5. The method according to claim 4, characterized in that, The introduction of historical error feedback compensation information from the assembly robot, and the nonlinear adjustment of the assembly robot's movement dynamic parameters to obtain the initial assembly physical parameters, include: Based on the robot assembly start assembly mode, define the assembly mode adaptation factor; The initial assembly physical parameters are obtained by nonlinearly adjusting the fused data of the assembly robot's movement dynamic parameters, the assembly mode adaptation factor, and the historical error feedback compensation information.

6. The method according to claim 5, characterized in that, The fusion data of the assembly robot's dynamic movement parameters, the assembly mode adaptation factor, and the historical error feedback compensation information are nonlinearly adjusted to obtain the expression corresponding to the initial assembly physical parameters: ， ， in, As an adaptive factor of strength, As an adaptation factor for speed, For the attitude angle adaptation factor, This is the historical error compensation coefficient for force. This is the speed history error compensation coefficient. This is the historical error compensation coefficient for attitude angle. For the past Accumulated error in force during each operation For the past Accumulated speed error during each operation For the past Accumulated error in attitude angle during each operation It is a nonlinear adjustment function. This is the sensitivity coefficient. The input value is the non-linear adjustment function. To determine information for assembling robot parts. , as well as They are dynamically adjusted , as well as Positional deviation in direction.

7. The method according to claim 1, characterized in that, The step of performing multi-level self-calibration of the assembly robot's pose based on its visual sensing information and laser displacement sensing information to obtain the assembly robot's pose determination information includes: Based on the visual sensing information and the laser displacement sensing information, the actual three-dimensional coordinates of the assembly robot are adjusted to the reference three-dimensional coordinates to obtain the macroscopic calibration information of the assembly robot. Based on the laser alignment information of the assembly robot, fine-tuning is performed on the macroscopic calibration information of the assembly robot to obtain the microscopic calibration information of the assembly robot; The robot arm pose information in the micro-calibration information of the assembly robot is adjusted to obtain the pose determination information of the assembly robot.

8. The method according to claim 7, characterized in that, The step of adjusting the robot arm pose information in the micro-calibration information of the assembly robot to obtain the pose determination information of the assembly robot includes: Based on the micro-calibration information of the assembly robot, the six-axis degrees of freedom of the end effector of the assembly robot are locked. Based on the locked six degrees of freedom, the optimized pose information of each of the remaining robotic arms is calculated according to the current pose information of each of the remaining robotic arms based on the micro-calibration information of the assembly robot. The poses of the remaining robotic arms are adjusted based on the optimized pose information to obtain the pose determination information of the assembly robot.

9. A control device for an assembly robot, characterized in that, The device includes: The robot self-calibration module is used to perform multi-level self-calibration of the assembly robot's assembly posture based on the visual sensing information and laser displacement sensing information of the assembly robot, so as to obtain the assembly robot posture determination information. The part pose adjustment module is used to locate the part position and part angle in the pose determination information of the assembly robot based on the multi-point micro-scan information of the assembly robot, so as to obtain the part determination information of the assembly robot. The movement path planning module is used to plan the assembly movement path of the assembly robot based on the part determination information of the assembly robot and the corresponding three-dimensional spatial information of the assembly robot. The feedback information acquisition module is used to determine the robot assembly start assembly mode based on the assembly robot part determination information and the assembly movement path; Calculate the initial assembly physical parameters based on the robot assembly start-up assembly mode and the assembly robot part determination information; Based on the initial assembly physical parameters, the assembly robot is controlled to move and acquire robot micro-force feedback control information; wherein, the robot micro-force feedback control information is the contact force and action force feedback data acquired by the robot during the assembly process, which is used to monitor the force balance between parts in real time during the assembly process; The assembly information optimization module is used to adjust the assembly movement path and the assembly robot part determination information based on the robot micro-force feedback control information, and to determine the assembly scenario application information of the assembly robot.

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