Robot tool coordinate system parameter calibration method, device and equipment and medium

Abstract

The invention discloses a tool coordinate system parameter calibration method, device and equipment of a robot and a medium. The method comprises the following steps: acquiring a target tool and a target calibration object of a target robot, and collecting and generating a calibration position attitude data set; setting target initial data, processing the calibration position attitude data set to obtain a target position attitude parameter, calculating a verification angle of the target position attitude parameter based on the target homotopy function and the calibration position attitude data set, and judging whether the verification angle is smaller than a preset angle threshold; after it is judged that the verification angle is smaller than a preset angle threshold value, whether the target position attitude parameter meets a preset iteration condition or not is judged based on a homotopy function; and after judging that the target position attitude parameter meets a preset iteration condition, outputting the target position attitude parameter as a parameter calibration result matched with the target tool. Through the technical scheme of the invention, the calibration of the tool coordinate system parameters of the robot can be realized, the accuracy of the calibration result is improved, and the complexity and the working cost of the parameter calibration work are reduced.

Description

Technical Field

[0001] This invention relates to the field of parameter calibration, and in particular to a method, apparatus, device, and medium for calibrating the tool coordinate system parameters of a robot. Background Technology

[0002] With the rapid development of manufacturing and assembly technologies for large and complex components in aerospace, rail transportation, and other fields, industrial robots, due to their high repeatability and strong adaptability, have shown broad application prospects in precision assembly processes such as aircraft skin stringer bonding and composite material component processing. These large components are generally characterized by large size, high precision, diverse varieties, and small batch production. Offline programming technology can significantly improve assembly efficiency, but it is highly dependent on the robot's absolute positioning accuracy. Currently, the absolute positioning accuracy of industrial robots is typically only ±12mm, which is insufficient to meet the ±0.1mm assembly accuracy requirements of aerospace manufacturing, becoming a core bottleneck restricting their application in high-end manufacturing.

[0003] To address the issue of insufficient absolute positioning accuracy, precise calibration of the workpiece and tool coordinate systems is crucial. Existing technologies primarily employ two approaches: one is contact measurement, which uses contact sensors such as ruby ​​probes for end-effector calibration, utilizing a "ball-to-ball" contact exploration principle to achieve origin positioning and attitude determination of the tool coordinate system; the other is non-contact measurement, which uses a combination of laser trackers and vision cameras for positioning, acquiring point cloud data by scanning the workpiece surface, processing it, and extracting feature points to guide robot movement. However, contact measurement requires dedicated positioning fixtures to fix the workpiece in a defined position, leading to high fixture design costs and low changeover efficiency in multi-variety, small-batch production. While non-contact measurement offers high automation, the cost of laser trackers and high-resolution vision measurement equipment is extremely high, hindering widespread application in general manufacturing scenarios. Furthermore, traditional calibration algorithms often employ local optimization methods such as the Gauss-Newton method, which are sensitive to initial values ​​and prone to getting trapped in local optima, making it difficult to guarantee the stability and accuracy of the global solution.

[0004] In summary, existing methods for calibrating tool coordinate system parameters suffer from poor accuracy and high complexity and cost. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for calibrating the tool coordinate system parameters of a robot, which can solve the problems of poor accuracy of calibration results and high complexity and cost of parameter calibration in existing robot tool coordinate system parameter calibration methods.

[0006] In a first aspect, embodiments of the present invention provide a method for calibrating the tool coordinate system parameters of a robot, the method comprising: The target robot acquires its target tool and target calibration object, controls the target tool to contact the target calibration object in multiple postures, and collects and generates a calibration position and posture dataset. Set initial target data, and process the calibration position and attitude dataset based on the initial target data and the pre-constructed target homotopy function to obtain target position and attitude parameters. The initial target data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds. The verification angle of the target position and attitude parameters is calculated based on the target homotopy function and the calibration position and attitude dataset, and it is determined whether the verification angle is less than a preset angle threshold. After determining that the verification angle is less than a preset angle threshold, the target position attitude parameters are determined based on the homotopy function to see if they meet the preset iteration conditions. After determining that the target position and attitude parameters meet the preset iteration conditions, the target position and attitude parameters are output as the parameter calibration result matched with the target tool.

[0007] Secondly, embodiments of the present invention provide a tool coordinate system parameter calibration device for a robot, the device comprising: The posture data acquisition module is used to acquire the target tool and target calibration object of the target robot, control the target tool to contact the target calibration object in multiple postures, and collect and generate a calibration position posture dataset. The target data acquisition module is used to set initial target data, process the calibration position and attitude dataset based on the initial target data and a pre-constructed target homotopy function, and obtain target position and attitude parameters. The initial target data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds. An angle verification module is used to calculate the verification angle of the target position and attitude parameters based on the target homotopy function and the calibration position and attitude dataset, and to determine whether the verification angle is less than a preset angle threshold. The iterative judgment module is used to determine whether the target position attitude parameters meet the preset iterative conditions based on the homotopy function after determining that the verification angle is less than a preset angle threshold. The result output module is used to output the target position and attitude parameters as the parameter calibration result matching the target tool after determining that the target position and attitude parameters meet the preset iteration conditions.

[0008] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a tool coordinate system parameter calibration method for a robot according to any embodiment of the present invention.

[0009] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute a tool coordinate system parameter calibration method for a robot according to any embodiment of the present invention.

[0010] The technical solution of this invention acquires the target tool and target calibration object of the target robot, controls the target tool to contact the target calibration object in multiple postures, collects and generates a calibration position and posture dataset, then sets initial target data, processes the calibration position and posture dataset based on the initial target data and a pre-constructed target homotopy function to obtain target position and posture parameters, then calculates the verification angle of the target position and posture parameters based on the target homotopy function and the calibration position and posture dataset, and determines whether the verification angle is less than a preset angle threshold. After determining that the verification angle is less than the preset angle threshold, it determines whether the target position and posture parameters meet preset iteration conditions based on the homotopy function. After determining that the target position and posture parameters meet the preset iteration conditions, the target position and posture parameters are output as the parameter calibration result matched with the target tool. This solves the problems of poor accuracy of calibration results and high complexity and cost of parameter calibration in existing robot tool coordinate system parameter calibration methods. It realizes the calibration of robot tool coordinate system parameters, improves the accuracy of calibration results, and reduces the complexity and cost of parameter calibration.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for calibrating the tool coordinate system parameters of a robot according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a method for calibrating the tool coordinate system parameters of a robot according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a tool coordinate system parameter calibration device for a robot according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements a method for calibrating the tool coordinate system parameters of a robot according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, any variations of the terms "comprising" and "having" are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a robot tool coordinate system parameter calibration method provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of calibrating the parameters of the robot's tool coordinate system. The method can be executed by a robot tool coordinate system parameter calibration device, which can be implemented in hardware and / or software. The robot tool coordinate system parameter calibration device can be configured in a terminal or server with robot tool coordinate system parameter calibration function.

[0017] like Figure 1 As shown, the method includes: S110. Obtain the target tool and target calibration object of the target robot, control the target tool to contact the target calibration object in multiple postures, and collect and generate a calibration position posture dataset.

[0018] The target robot refers to an industrial robot system that performs calibration tasks, with a target tool to be calibrated mounted on its end flange. The target tool includes, but is not limited to, various end-effectors such as machining spindles, measuring probes, bonding actuators, or grinding devices, which are terminal devices connected to the robot body to achieve specific process operations. The target calibration object is usually a high-precision standard part with known geometric features. Furthermore, in this embodiment, a standard ball can be selected as the calibration reference target.

[0019] Specifically, the multi-pose contact refers to controlling the robot to move the target tool to touch the surface of the calibration object from multiple different spatial orientations and angles, forming a spatially evenly distributed layout of measurement points. Optionally, point contact can be achieved by using the spherical body at the end of the tool to contact the surface of a standard sphere, thereby effectively improving contact stability and measurement repeatability. For example, during the measurement process, the robot can move sequentially to 10 to 15 measurement points according to a preset hemispherical array trajectory.

[0020] The calibration position and attitude data includes the tool0 position coordinates and attitude parameters of each measurement point. The tool0 position coordinates are the three-dimensional Cartesian coordinates of the center of the robot's end flange, and the attitude parameters include end-effector attitude information expressed in rotation matrices or Euler angles.

[0021] S120. Set initial target data, and process the calibration position and attitude dataset based on the initial target data and the pre-constructed target homotopy function to obtain the target position and attitude parameters.

[0022] The target initial data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds.

[0023] S130. Calculate the verification angle of the target position and attitude parameters based on the target homotopy function and the calibration position and attitude dataset, and determine whether the verification angle is less than a preset angle threshold.

[0024] The verification angle for the target position and attitude parameters is calculated based on the target homotopy function and the calibration position and attitude dataset, including: calculating the tangent vector of the target homotopy function at the target position and attitude parameters to obtain a first tangent vector, and calculating the tangent vector of the homotopy function at the initial point coordinates to obtain a second tangent vector; calculating the angle between the first tangent vector and the second tangent vector to obtain the verification angle.

[0025] The verification angle is used to quantify the path deviation of the homotopy curve during tracking, preventing the tracking curve from sliding to nearby irrelevant solution branches due to the accumulation of prediction errors, and ensuring that the algorithm always converges along the correct solution path. Specifically, the verification angle is calculated as follows: First, by solving the first partial derivative of the target homotopy function with respect to the arc length parameter, the tangent vector at the point corresponding to the current target position attitude parameter is obtained as the first tangent vector. Further, the tangent vector of the homotopy function at the initial point coordinates is calculated as the second tangent vector. The tangent vector represents the instantaneous motion direction of the homotopy curve at that point, and its mathematical essence is the differential form of the homotopy equation. Optionally, based on the first and second tangent vectors, the spatial angle between them is obtained through vector dot product or cross product operations; this angle is the verification angle.

[0026] Furthermore, the verification angle is numerically compared with a preset angle threshold. For example, the angle threshold can be set as an empirical value within the range of 15° to 30°. If the verification angle is greater than or equal to the threshold, it is determined that the current tracking path has deviated abnormally, and the current prediction result should be abandoned and the initial step size parameter reduced before re-performing the prediction process. If the verification angle is less than the threshold, the tracking path is determined to be valid. Optionally, when the verification angle is less than a specific acceleration threshold (such as 5°), the initial step size parameter can be adaptively increased to improve the subsequent tracking speed in order to further improve the algorithm efficiency.

[0027] S140. After determining that the verification angle is less than a preset angle threshold, determine whether the target position attitude parameters meet the preset iteration conditions based on the homotopy function.

[0028] The method of determining whether the target position and attitude parameters meet the preset iteration conditions based on the homotopy function includes: calculating the homotopy parameter components of the target position and attitude parameters; determining whether the homotopy parameter components of the target position and attitude parameters have reached a convergence value; if the convergence value has not been reached, it is determined that the iteration conditions are not met, updating the initial step size parameter in response to the user's setting operation, updating the target position and attitude parameters to the initial point coordinates, and returning to the operation of processing the calibration position and attitude dataset based on the target initial data and the continuous homotopy algorithm to obtain the target position and attitude parameters according to the updated initial step size parameter and the initial point coordinates; if the convergence value has been reached, it is determined that the iteration conditions are met, the iteration is stopped, and the current target position and attitude parameters are output as the parameter calibration result matching the target tool.

[0029] Furthermore, the iteration condition is a homotopy parameter convergence criterion, used to determine whether the algorithm has tracked the target solution. Specifically, firstly, the homotopy parameter component t is extracted from the target position and attitude parameters. This component is the core parameter controlling the continuous deformation of the homotopy equation from the known solution to the target solution, and its value range is [0,1]. Further, it is determined whether the homotopy parameter component has reached a convergence value. For example, the convergence value is set to 0, corresponding to the hyperplane t=0. At this time, the homotopy function degenerates into the original nonlinear equation system F(X)=0, indicating that complete path tracking has been completed.

[0030] Furthermore, if the homotopy parameter component does not reach a convergence value (i.e., t>0), the iteration condition is not met. The initial step size parameter needs to be updated in response to the user's settings or the algorithm's adaptive mechanism, and the current target position attitude parameters are updated to the new initial point coordinates. Then, the algorithm returns to execute the prediction and correction processing flow in S120 to continue tracking downstream along the homotopy curve. Optionally, the step size adjustment strategy can adopt a halving reduction or an adaptive scaling method based on error prediction. If the homotopy parameter component reaches a convergence value (i.e., t=0 or approximately 0 within the tolerance range), the iteration condition is met. The algorithm terminates the iteration and outputs the current target position attitude parameters. These parameters are the tool center point coordinates and attitude parameters matched with the target tool, and are used as the final parameter calibration result in the robot control system.

[0031] Those skilled in the art should understand that the methods for calculating the homotopy parameter components, determining convergence based on the homotopy parameter components, and calculating the verification angle are all mature mathematical calculation methods. This embodiment will not elaborate on their specific calculation steps and principles.

[0032] S150. After determining that the target position and attitude parameters meet the preset iteration conditions, the target position and attitude parameters are output as the parameter calibration result matched with the target tool.

[0033] The parameter calibration result is essentially a complete pose description of the tool's center point relative to the robot's end flange coordinate system, specifically including two parts: three-dimensional spatial position coordinates and three-dimensional attitude angle parameters. Further, the three-dimensional spatial position coordinates represent the Cartesian coordinates of the target tool's center point in the robot's base coordinate system, which can be exemplarily represented as a (X,Y,Z) vector with a numerical accuracy down to the micrometer level. The three-dimensional attitude angle parameters represent the orientation of the target tool, and can optionally be described using Euler angles (such as RX, RY, RZ) or quaternions to fully define the tool's spatial attitude matrix, which is used for coordinate transformation calculations in subsequent kinematics solutions.

[0034] The technical solution of this invention involves acquiring a target tool and a target calibration object for a target robot, controlling the target tool to contact the target calibration object in multiple postures, collecting and generating a calibration position and posture dataset, setting initial target data, processing the calibration position and posture dataset based on the initial target data and a pre-constructed target homotopy function to obtain target position and posture parameters, calculating the verification angle of the target position and posture parameters based on the target homotopy function and the calibration position and posture dataset, and determining whether the verification angle is less than a preset angle threshold. If the verification angle is less than the preset angle threshold, determining whether the target position and posture parameters meet preset iteration conditions based on the homotopy function. If the target position and posture parameters meet the preset iteration conditions, outputting the target position and posture parameters as the parameter calibration result matched with the target tool. This achieves the calibration of the robot's tool coordinate system parameters, improves the accuracy of the calibration results, and reduces the complexity and cost of parameter calibration work.

[0035] Example 2 Figure 2 This is a flowchart of a robot tool coordinate system parameter calibration method provided in Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. In this embodiment, the method of processing the calibration position and attitude dataset based on the target initial data and the pre-constructed target homotopy function to obtain the target position and attitude parameters is refined.

[0036] like Figure 2 As shown, the method includes: S210. Obtain the target tool and target calibration object of the target robot, control the target tool to contact the target calibration object in multiple postures, and collect and generate a calibration position posture dataset.

[0037] S220. Set the initial target data, construct a nonlinear least squares model based on the calibration position and attitude dataset, and use the target homotopy function to transform the nonlinear least squares model into a homotopy least squares model.

[0038] The target initial data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds.

[0039] The target initial data is the set of prior parameters necessary to start the homotopy optimization algorithm, specifically including four core control variables: initial point coordinates, initial step size parameter, tolerance error parameter, and angle threshold. The initial point coordinates define the starting position for homotopy curve tracking, and can be exemplarily set as t0=1 and X0=(1,1,1,1,1,1), at which point the homotopy equation has a known solution and is easy to construct. The initial step size parameter controls the curve's forward distance in a single iteration. The tolerance error parameter is used to calibrate the convergence accuracy of Newton's iterative correction. The angle threshold is used to determine the degree of abrupt change in the tangent vector direction.

[0040] Furthermore, the nonlinear least squares model is a parameter optimization framework established based on the calibration position and attitude dataset. Its mathematical essence is to minimize the residual sum of squares function, where the residual is defined as the difference in Euclidean distance between the actual measured position and the theoretical position of the tool. Furthermore, the target homotopy function constructs a continuous deformation path by introducing a homotopy parameter t, transforming the solution process of the nonlinear least squares model into solving an initial value problem of a differential equation. The above transformation process significantly improves the robustness of the homotopy least squares model to initial errors and ensures global convergence.

[0041] S230. Based on the initial point coordinates and initial step size parameters in the target initial data, the homotopy least squares model is estimated using the Euler forward integration method to obtain the estimated position and attitude parameters.

[0042] Specifically, based on the initial point coordinates and initial step size parameters in the target initial data, the homotopy least squares model is predicted using the Euler forward integration method to obtain the predicted position and attitude parameters. This includes: analyzing the homotopy least squares model to obtain the current tracking point of the homotopy least squares model; calculating the tangent vector of the target homotopy function at the current tracking point; and calculating the predicted position and attitude parameters using the Euler forward integration method based on the initial step size parameters and the tangent vector.

[0043] The Euler forward integration method is an explicit first-order numerical integration method that uses the tangent vector information of the current point to perform linear extrapolation along the tangent direction of the curve to estimate the position of the next tracking point. Specifically, the prediction process includes three sub-steps: First, the homotopy least squares model is analyzed to read the tracking point coordinates (t, x) of the current homotopy curve; second, the tangent vector v of the target homotopy function at the current tracking point with respect to the arc length parameter s is calculated. This tangent vector satisfies the differential relation of the homotopy equation and represents the instantaneous motion direction of the curve; finally, based on the initial step size parameter δ and the tangent vector v, the predicted position and attitude parameters are calculated using Euler's formula.

[0044] Optionally, after obtaining the estimated position and attitude parameters, a boundary verification and adaptive step size adjustment mechanism needs to be implemented. Specifically, the homotopy parameter component t1 of the estimated position and attitude parameters is calculated. If t1 < 0, it indicates that the estimated point has crossed the target hyperplane, and an adjustment message should be generated to indicate that the step size has exceeded the limit. Furthermore, the user or the adaptive algorithm can reduce the initial step size parameter δ proportionally based on this information and re-execute the estimated integral to ensure that the tracking point is always within the effective solution domain. This mechanism effectively avoids tracking failure caused by improper step size settings.

[0045] Optionally, after calculating the estimated position and attitude parameters using the Euler forward integration method, the method further includes: calculating the homotopy parameter component of the estimated position and attitude parameters; if the homotopy parameter component of the estimated position and attitude parameters is less than 0, generating adjustment information based on the estimated position and attitude parameters and the homotopy parameter component and sending it to the user, so that the user can adjust the initial step size parameter in the target initial data based on the adjustment information.

[0046] Based on the above steps, if the iteration result still fails to meet the allowable error parameter within the preset maximum number of iterations, the Newton iteration is deemed non-convergent. In this case, the process must return to step S230, reduce the initial step size parameter, and re-execute the estimation and correction processing to avoid tracking failure due to excessively large step sizes. Finally, the final iteration result that meets the convergence condition is output as the target position and attitude parameter. This parameter accurately describes the pose relationship between the tool coordinate system and the robot's end effector flange, providing a reference guarantee for subsequent high-precision operations.

[0047] Those skilled in the art should understand that the method of calculating the predicted position and attitude parameters using the Euler forward integral method is an existing mathematical calculation method. This embodiment only introduces its calculation principle, and its specific calculation steps are not described in detail here.

[0048] S240. The estimated position and attitude parameters are corrected using the Newton-Raphson iteration method to obtain the target position and attitude parameters.

[0049] The process of correcting the estimated position and attitude parameters using Newton's iteration method to obtain the target position and attitude parameters includes: using the estimated position and attitude parameters as the initial values ​​for Newton's iteration to construct an iterative equation system based on the target homotopy function; solving the iterative equation system to obtain the correction increment; using the correction increment to iteratively update the estimated position and attitude parameters to obtain the iterative result until the iterative result satisfies the tolerance error parameter; and outputting the final iterative result as the target position and attitude parameters.

[0050] The Newton-Raphson iteration method is used to eliminate the accumulated error introduced by the linear approximation in the Euler forward integral method, ensuring that the tracking point accurately falls on the homotopy curve. Specifically, the correction process includes three progressive stages: First, using the estimated position and attitude parameters as the initial values ​​for the Newton-Raphson iteration, an iterative equation system based on the target homotopy function is constructed; second, the correction increment is obtained by solving this iterative equation system, and the estimated position and attitude parameters are iteratively updated until the convergence condition is met; finally, the final iteration result is output as the target position and attitude parameters.

[0051] Furthermore, the iterative equation system constructed based on the target homotopy function is mathematically essentially a system of nonlinear equations for solving the homotopy equation H(t,X)=0. For example, the Newton iteration scheme X is employed. {k+1} =X k -J -1 (X k )·H(X k ), where J(X) k ) is the Jacobian matrix of the homotopy function, representing the local linear approximation property of the function at the current point.

[0052] Based on the above steps, if the iterative result still fails to meet the allowable error parameter within the preset maximum number of iterations, the Newton iteration is deemed non-convergent. Furthermore, in this case, it is necessary to return to step S230, reduce the initial step size parameter, and re-execute the estimation and correction processing to avoid tracking failure due to an excessively large step size. Finally, the final iterative result that meets the convergence condition is output as the target position and attitude parameter. This parameter accurately describes the pose relationship between the tool coordinate system and the robot's end effector flange, providing a benchmark for subsequent high-precision operations.

[0053] Those skilled in the art should understand that the Newton iteration method is an existing mathematical calculation method. This embodiment only introduces its calculation principle, and its specific calculation steps are not described in detail.

[0054] S250. Calculate the verification angle of the target position and attitude parameters based on the target homotopy function and the calibration position and attitude dataset, and determine whether the verification angle is less than a preset angle threshold.

[0055] S260. After determining that the verification angle is less than a preset angle threshold, determine whether the target position attitude parameters meet the preset iteration conditions based on the homotopy function.

[0056] S270. After determining that the target position attitude parameters meet the preset iteration conditions, the target position attitude parameters are output as the parameter calibration result matched with the target tool.

[0057] The technical solution of this invention involves acquiring the target tool and target calibration object of the target robot, controlling the target tool to contact the target calibration object in multiple postures, collecting and generating a calibration position and posture dataset, then setting initial target data, constructing a nonlinear least squares model based on the calibration position and posture dataset, and using the target homotopy function to transform the nonlinear least squares model into a homotopy least squares model. Then, based on the initial point coordinates and initial step size parameters in the initial target data, the homotopy least squares model is predicted using the Euler forward integration method to obtain predicted position and posture parameters, and the predicted position and posture parameters are corrected using the Newton iteration method. The target position and attitude parameters are obtained. Then, based on the target homotopy function and the calibration position and attitude dataset, the verification angle of the target position and attitude parameters is calculated, and it is determined whether the verification angle is less than a preset angle threshold. After determining that the verification angle is less than the preset angle threshold, it is determined whether the target position and attitude parameters meet the preset iteration conditions based on the homotopy function. After determining that the target position and attitude parameters meet the preset iteration conditions, the target position and attitude parameters are output as the parameter calibration result matched with the target tool. This realizes the calibration of the robot's tool coordinate system parameters, improves the accuracy of the calibration results, and reduces the complexity and cost of parameter calibration work.

[0058] Example 3 Figure 3 This is a schematic diagram of a tool coordinate system parameter calibration device for a robot provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The posture data acquisition module 310 is used to acquire the target tool and target calibration object of the target robot, control the target tool to contact the target calibration object in multiple postures, and collect and generate a calibration position posture dataset. The target data acquisition module 320 is used to set the target initial data, and process the calibration position and attitude dataset based on the target initial data and the pre-constructed target homotopy function to obtain the target position and attitude parameters. The target initial data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds. Angle verification module 330 is used to calculate the verification angle of the target position and attitude parameters based on the target homotopy function and the calibration position and attitude dataset, and to determine whether the verification angle is less than a preset angle threshold. The iterative judgment module 340 is used to determine whether the target position attitude parameters meet the preset iterative conditions based on the homotopy function after determining that the verification angle is less than a preset angle threshold. The result output module 350 is used to output the target position and attitude parameters as the parameter calibration result matching the target tool after determining that the target position and attitude parameters meet the preset iteration conditions.

[0059] The technical solution of this invention involves acquiring a target tool and a target calibration object for a target robot, controlling the target tool to contact the target calibration object in multiple postures, collecting and generating a calibration position and posture dataset, setting initial target data, processing the calibration position and posture dataset based on the initial target data and a pre-constructed target homotopy function to obtain target position and posture parameters, calculating the verification angle of the target position and posture parameters based on the target homotopy function and the calibration position and posture dataset, and determining whether the verification angle is less than a preset angle threshold. If the verification angle is less than the preset angle threshold, determining whether the target position and posture parameters meet preset iteration conditions based on the homotopy function. If the target position and posture parameters meet the preset iteration conditions, outputting the target position and posture parameters as the parameter calibration result matched with the target tool. This achieves the calibration of the robot's tool coordinate system parameters, improves the accuracy of the calibration results, and reduces the complexity and cost of parameter calibration work.

[0060] Based on the above embodiments, the target data acquisition module 320 includes: The model transformation unit is used to construct a nonlinear least squares model based on the calibration position and attitude dataset, and to transform the nonlinear least squares model into a homotopy least squares model using the target homotopy function. The Euler integration unit is used to predict the homotopy least squares model based on the initial point coordinates and initial step size parameters in the target initial data, and to obtain the predicted position and attitude parameters. The Newton iteration unit is used to correct the estimated position and attitude parameters using the Newton iteration method to obtain the target position and attitude parameters.

[0061] Based on the above embodiments, the Euler integral unit includes: The model parsing unit is used to parse the homotopy least squares model to obtain the current tracking point of the homotopy least squares model; The tangent vector calculation unit is used to calculate the tangent vector of the target homotopy function at the current tracking point; The forward integration unit is used to calculate the estimated position and attitude parameters based on the initial step size parameters and the tangent vector using the Euler forward integration method.

[0062] Based on the above embodiments, the forward integration unit is further configured to: calculate the homotopy parameter component of the estimated position and attitude parameters after calculating the estimated position and attitude parameters using the Euler forward integration method; if the homotopy parameter component of the estimated position and attitude parameters is less than 0, generate adjustment information based on the estimated position and attitude parameters and the homotopy parameter component and send it to the user, so that the user can adjust the initial step size parameter in the target initial data based on the adjustment information.

[0063] Based on the above embodiments, the Newton iteration unit includes: The equation system construction unit is used to construct an iterative equation system based on the target homotopy function, using the estimated position and attitude parameters as the initial values ​​for Newton iteration. An iterative update unit is used to solve the iterative equation system to obtain the correction increment, and use the correction increment to perform iterative update operations on the estimated position and attitude parameters to obtain iterative results until the iterative results satisfy the allowable error parameters. The result output unit is used to output the final iteration result as the target position and attitude parameters.

[0064] Based on the above embodiments, the angle verification module 330 includes: The coordinate vector calculation unit is used to calculate the tangent vector of the target homotopy function at the target position attitude parameters to obtain the first tangent vector, and to calculate the tangent vector of the homotopy function at the initial point coordinates to obtain the second tangent vector; Angle calculation unit is used to calculate the angle between the first tangent vector and the second tangent vector to obtain the verification angle.

[0065] Based on the above embodiments, the iterative judgment module 340 includes: A component calculation unit is used to calculate the homotopy parameter components of the target position and attitude parameters; A convergence determination unit is used to determine whether the homotopy parameter components of the target position and attitude parameters have reached a convergence value. The initial parameter update unit is used to determine that the iteration condition is not met if the convergence value is not reached, update the initial step size parameter in response to the user's setting operation, update the target position attitude parameter to the initial point coordinate, and return to the operation of processing the calibration position attitude dataset based on the target initial data and the continuous homotopy algorithm to obtain the target position attitude parameter according to the updated initial step size parameter and the initial point coordinate. An iteration stopping unit is used to determine that the iteration condition is met if the convergence value is reached, stop the iteration, and output the current target position attitude parameters as the parameter calibration result matching the target tool.

[0066] The tool coordinate system parameter calibration device for a robot provided in this embodiment of the invention can execute the tool coordinate system parameter calibration method for a robot provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0067] Example 4 Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0068] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0069] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0070] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for calibrating the tool coordinate system parameters of a robot.

[0071] Accordingly, the method includes: The target robot acquires its target tool and target calibration object, controls the target tool to contact the target calibration object in multiple postures, and collects and generates a calibration position and posture dataset. Set initial target data, and process the calibration position and attitude dataset based on the initial target data and the pre-constructed target homotopy function to obtain target position and attitude parameters. The initial target data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds. The verification angle of the target position and attitude parameters is calculated based on the target homotopy function and the calibration position and attitude dataset, and it is determined whether the verification angle is less than a preset angle threshold. After determining that the verification angle is less than a preset angle threshold, the target position attitude parameters are determined based on the homotopy function to see if they meet the preset iteration conditions. After determining that the target position and attitude parameters meet the preset iteration conditions, the target position and attitude parameters are output as the parameter calibration result matched with the target tool.

[0072] In some embodiments, a robot tool coordinate system parameter calibration method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the robot tool coordinate system parameter calibration method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a robot tool coordinate system parameter calibration method by any other suitable means (e.g., by means of firmware).

[0073] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0074] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0075] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0076] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0077] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0078] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0079] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

Claims

1. A method for calibrating the tool coordinate system parameters of a robot, characterized in that, include: The target robot acquires its target tool and target calibration object, controls the target tool to contact the target calibration object in multiple postures, and collects and generates a calibration position and posture dataset. Set initial target data, and process the calibration position and attitude dataset based on the initial target data and the pre-constructed target homotopy function to obtain target position and attitude parameters. The initial target data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds. The verification angle of the target position and attitude parameters is calculated based on the target homotopy function and the calibration position and attitude dataset, and it is determined whether the verification angle is less than a preset angle threshold. After determining that the verification angle is less than a preset angle threshold, the target position attitude parameters are determined based on the homotopy function to see if they meet the preset iteration conditions. After determining that the target position and attitude parameters meet the preset iteration conditions, the target position and attitude parameters are output as the parameter calibration result matched with the target tool.

2. The method according to claim 1, characterized in that, Based on the initial target data and the pre-constructed target homotopy function, the calibration position and attitude dataset is processed to obtain the target position and attitude parameters, including: A nonlinear least squares model is constructed based on the calibration position and attitude dataset, and the nonlinear least squares model is transformed into a homotopy least squares model using the target homotopy function; Based on the initial point coordinates and initial step size parameters in the target initial data, the homotopy least squares model is predicted using the Euler forward integration method to obtain the predicted position and attitude parameters. The predicted position and attitude parameters are corrected using the Newton-Raphson iteration method to obtain the target position and attitude parameters.

3. The method according to claim 2, characterized in that, Based on the initial point coordinates and initial step size parameters in the target initial data, the homotopy least squares model is predicted using the Euler forward integration method to obtain the predicted position and attitude parameters, including: The homotopy least squares model is analyzed to obtain the current tracking point of the homotopy least squares model; Calculate the tangent vector of the target homotopy function at the current tracking point; Based on the initial step size parameter and the tangent vector, the estimated position and attitude parameters are calculated using the Euler forward integration method.

4. The method according to claim 3, characterized in that, After calculating the predicted position and attitude parameters using the Euler forward integration method, the following steps are also included: Calculate the homotopy parameter components of the estimated position and attitude parameters; If the homotopy parameter component of the estimated position and attitude parameters is less than 0, adjustment information is generated based on the estimated position and attitude parameters and the homotopy parameter component and sent to the user so that the user can adjust the initial step size parameter in the target initial data based on the adjustment information.

5. The method according to claim 2, characterized in that, The predicted position and attitude parameters are corrected using the Newton-Raphson iteration method to obtain the target position and attitude parameters, including: Using the estimated position and attitude parameters as the initial values ​​for Newton's iteration, an iterative equation system based on the target homotopy function is constructed. Solve the iterative equations to obtain the correction increment, and use the correction increment to iteratively update the estimated position and attitude parameters to obtain the iterative result until the iterative result satisfies the tolerance error parameter; The final iteration result is output as the target position and attitude parameters.

6. The method according to claim 1, characterized in that, The verification angles of the target position and attitude parameters are calculated based on the target homotopy function and the calibration position and attitude dataset, including: The first tangent vector is obtained by calculating the tangent vector of the target homotopy function at the target position attitude parameters, and the second tangent vector is obtained by calculating the tangent vector of the homotopy function at the initial point coordinates. Calculate the angle between the first tangent vector and the second tangent vector to obtain the verification angle.

7. The method according to claim 1, characterized in that, Determining whether the target position and attitude parameters satisfy a preset iteration condition based on the homotopy function includes: Calculate the homotopy parameter components of the target position and attitude parameters; Determine whether the homotopy parameter components of the target position attitude parameters have reached a convergence value; If the convergence value is not reached, it is determined that the iteration condition is not met. In response to the user's setting operation, the initial step size parameter is updated, the target position attitude parameter is updated to the initial point coordinates, and the operation of processing the calibration position attitude dataset based on the target initial data and the continuous homotopy algorithm is returned according to the updated initial step size parameter and initial point coordinates to obtain the target position attitude parameter. If the convergence value is reached, it is determined that the iteration condition is met, the iteration is stopped, and the current target position and attitude parameters are output as the parameter calibration result matching the target tool.

8. A tool coordinate system parameter calibration device for a robot, characterized in that, include: The posture data acquisition module is used to acquire the target tool and target calibration object of the target robot, control the target tool to contact the target calibration object in multiple postures, and collect and generate a calibration position posture dataset. The target data acquisition module is used to set the target initial data, process the calibration position and attitude dataset based on the target initial data and the pre-constructed target homotopy function, and obtain the target position and attitude parameters. The target initial data includes: initial point coordinates, initial step size parameters, tolerance error parameters, and angle thresholds. An angle verification module is used to calculate the verification angle of the target position and attitude parameters based on the target homotopy function and the calibration position and attitude dataset, and to determine whether the verification angle is less than a preset angle threshold. The iterative judgment module is used to determine whether the target position attitude parameters meet the preset iterative conditions based on the homotopy function after determining that the verification angle is less than a preset angle threshold. The result output module is used to output the target position and attitude parameters as the parameter calibration result matching the target tool after determining that the target position and attitude parameters meet the preset iteration conditions.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a tool coordinate system parameter calibration method for a robot according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a method for calibrating the tool coordinate system parameters of a robot according to any one of claims 1-7.

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