Laser ranging calibration method and system based on laser sensor and application of laser ranging calibration method and system

Abstract

The invention relates to the technical field of laser sensing, in particular to a laser ranging calibration method and system based on a laser sensor and application of the laser ranging calibration method and system. The method comprises the following steps: acquiring a space coordinate data set of a plurality of points on a wall surface through a plurality of groups of high-precision long-stroke laser sensors; according to the space coordinate data set, plane fitting based on singular value decomposition is carried out on the wall face, and therefore a virtual plane and normal vector data of the virtual plane are obtained; obtaining mechanical arm normal vector data of a current plane of a mechanical arm of the wall surface scraping equipment; normalizing the normal vector data of the mechanical arm, the virtual plane and the normal vector data of the virtual plane, calculating a rotation axis and a rotation angle between the mechanical arm and the virtual plane, and generating rotation matrix data by using a Rodrigue rotation formula; according to the method, accurate parallel alignment of the mechanical arm and the wall surface is achieved through singular value decomposition and the Rodrigues rotation formula, and the construction precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of laser sensing technology, and in particular to a laser ranging calibration method and system based on a laser sensor and applications thereof. Background Art

[0002] In the field of construction engineering and decoration, wall scraping is a common and important task. Traditional wall scraping that relies on manual operation is time-consuming and laborious, and is easily affected by factors such as operator experience and manual operation errors, resulting in inconsistent wall flatness and quality. Laser sensor is a sensor device that uses laser technology for ranging, detection and imaging. It is widely used in industrial automation, robotics, construction engineering, measurement and mapping, environmental monitoring and other fields. The core working principle of laser sensor is to calculate the distance, angle or other geometric features of the target object or surface by emitting a laser beam and receiving its reflected signal.

[0003] In this context, laser distance measurement calibration methods based on laser sensors have emerged to solve the key problems faced by automated wall scraping equipment during operation. However, traditional laser distance measurement calibration methods based on laser sensors often have the following problems: the actual wall surface often has irregularities such as local bumps, tilts or bends, and traditional equipment is difficult to accurately adapt to these complex surfaces; the equipment is difficult to accurately adjust the posture of the robotic arm in real time, and cannot maintain uniform contact and pressure distribution with the wall. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a laser ranging calibration method, system and application based on a laser sensor to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a laser ranging calibration method based on a laser sensor is applied to a wall scraping device, comprising the following steps:

[0006] Step S1: obtaining spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; performing plane fitting on the wall based on singular value decomposition according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data;

[0007] Step S2: obtaining the normal vector data of the robot arm of the wall scraping device at the current plane; normalizing the normal vector data of the robot arm and the virtual plane and its normal vector data, calculating the rotation axis and rotation angle between the two, and generating rotation matrix data using the Rodriguez rotation formula;

[0008] Step S3: adjusting the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitoring the posture and pressure of the robot arm in real time, so as to obtain posture pressure feedback data; using the regional processing strategy to dynamically adjust the posture of the robot arm based on the complex curved surface according to the posture pressure feedback data, so as to obtain the uniform force parallel state data;

[0009] Step S4: using the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform parallel state data, thereby obtaining laser distance measurement calibration data; performing accuracy verification processing on the laser distance measurement calibration data, thereby obtaining a distance measurement error distribution map;

[0010] Step S5: remove abnormal points from the distance measurement error distribution diagram according to a preset error threshold, and perform distance measurement result fitting based on the least squares method, so as to generate distance measurement model parameter data to achieve distance measurement and calibration of the wall scraping equipment.

[0011] The present invention collects spatial coordinate data of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors, and performs plane fitting based on singular value decomposition of these data, which can accurately capture the approximate plane structure of the wall. This fitting method helps to eliminate the irregularities caused by local roughness of the wall or measurement noise, making the fitted virtual plane smoother and more realistic, and providing an accurate geometric reference for subsequent robot arm adjustment. Such processing significantly improves the accuracy of wall detection and avoids the deviation caused by inaccurate plane in subsequent operations. By obtaining the normal vector of the virtual plane of the wall and the current normal vector of the robot arm, the two are normalized to ensure that they can still maintain consistency at different scales. Subsequently, the rotation axis and rotation angle between the two are calculated, and the rotation matrix data for posture adjustment is generated. This process ensures that the working surface of the robot arm is parallel to the wall through precise mathematical calculations, thereby avoiding uneven scraping or scratching the wall due to angle mismatch, and significantly improving the operating accuracy of the equipment. Real-time monitoring of the posture and pressure data of the robot arm can dynamically track the contact between the robot arm and the wall. Combined with the regional processing strategy for complex curved surfaces, the robot arm is fine-tuned according to real-time feedback data to ensure that the robot arm always maintains uniform force and a posture parallel to the wall during operation. This dynamic adjustment mechanism can not only adapt to complex situations such as uneven wall surfaces and curvature changes, but also avoid the problem of excessive or insufficient local force, ultimately ensuring the quality and consistency of the scraping operation. After the robot arm is adjusted in posture, the laser sensor performs multi-point scanning and ranging according to the uniform force parallel state data to obtain accurate ranging calibration data. Based on these calibration data, a ranging error distribution map can be generated to intuitively display the ranging errors at different positions. This analysis can not only verify the accuracy of laser ranging, but also provide a reliable basis for subsequent error correction, ensuring that the equipment can perform accurate measurement and scraping under various wall conditions. According to the ranging error distribution map, the abnormal points that exceed the preset error threshold are eliminated to ensure the accuracy of the ranging data. Subsequently, the least squares method is used to fit the remaining data to generate accurate ranging model parameters. This process can minimize the distance measurement error and improve the overall distance measurement accuracy and reliability by eliminating and fitting abnormal data. Ultimately, these distance measurement model parameters not only provide an efficient distance measurement and calibration solution for the device, but also further optimize the device's working performance, making it adaptable to more complex wall environments.

[0012] The present invention also provides a laser ranging calibration system based on a laser sensor, which is used to execute the above-mentioned laser ranging calibration method based on a laser sensor. The laser ranging calibration system based on a laser sensor comprises:

[0013] The spatial coordinate acquisition module is used to obtain spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; plane fitting based on singular value decomposition is performed on the wall according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data;

[0014] The robot arm posture correction module is used to obtain the robot arm normal vector data of the current plane of the robot arm of the wall scraping equipment; normalize the robot arm normal vector data and the virtual plane and its normal vector data, calculate the rotation axis and rotation angle between the two, and generate rotation matrix data using the Rodrigues rotation formula;

[0015] The posture adjustment and feedback module is used to adjust the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitor the posture and pressure of the robot arm in real time to obtain the posture pressure feedback data; the posture of the robot arm is dynamically adjusted based on the complex surface according to the posture pressure feedback data using the regional processing strategy, so as to obtain the uniform force parallel state data;

[0016] The laser distance measurement calibration module is used to use the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform force parallel state data, so as to obtain the laser distance measurement calibration data; perform accuracy verification processing on the laser distance measurement calibration data, so as to obtain the distance measurement error distribution map;

[0017] The error correction and model generation module is used to eliminate abnormal points in the ranging error distribution diagram according to a preset error threshold, and to fit the ranging results based on the least squares method, so as to generate ranging model parameter data to achieve ranging and calibration of the wall scraping equipment.

[0018] The present invention ensures the accuracy of spatial coordinate data through a high-precision laser sensor, can capture detailed information of each point on the wall, and reduce the impact caused by measurement errors. By performing plane fitting on the spatial coordinate data through singular value decomposition, a virtual plane model of the wall can be accurately established, and accurate normal vector data can be provided, laying the foundation for subsequent mechanical arm posture correction and distance calibration. By processing the data of the mechanical arm normal vector and the virtual plane normal vector, it is ensured that the working surface of the mechanical arm is parallel to the wall. The generated rotation matrix data enables the mechanical arm to make precise angle adjustments, thereby improving the accuracy of construction; by calculating the rotation axis and the rotation angle, the posture of the mechanical arm can be effectively corrected to align it with the virtual plane, reducing construction errors and improving the accuracy of the overall operation. By monitoring the posture and pressure of the mechanical arm in real time, the data of the working status can be instantly obtained, ensuring that the contact pressure between the mechanical arm and the wall is uniform, and improving the stability of the construction effect; by using the regional processing strategy and dynamic adjustment, local optimization can be performed for complex walls, so that the working status of the mechanical arm in different areas remains consistent, and the uniformity and quality of construction are improved. The adjusted laser sensor can perform high-precision multi-point scanning to ensure the accuracy of the distance data. Accuracy verification and the generation of error distribution maps help identify and correct potential distance measurement errors and improve the overall accuracy of the system; through accuracy verification processing, it can be ensured that the calibration of the laser distance measurement system meets actual needs, making the distance measurement capability of the wall scraping equipment more reliable. Eliminating abnormal points that cannot be corrected can effectively improve the overall quality of the distance measurement data and reduce the errors caused by abnormal data; fitting the distance measurement results by the least squares method can generate accurate distance measurement model parameters, provide accurate distance measurement and calibration functions for wall scraping equipment, and ensure that the construction effect meets the expected standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0020] Figure 1 It is a schematic diagram of the steps of the laser ranging calibration method based on the laser sensor of the present invention;

[0021] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0022] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides a laser ranging calibration method based on a laser sensor, which is applied to a wall scraping device. The method comprises the following steps:

[0027] Step S1: obtaining spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; performing plane fitting on the wall based on singular value decomposition according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data;

[0028] Step S2: obtaining the normal vector data of the robot arm of the wall scraping device at the current plane; normalizing the normal vector data of the robot arm and the virtual plane and its normal vector data, calculating the rotation axis and rotation angle between the two, and generating rotation matrix data using the Rodriguez rotation formula;

[0029] Step S3: adjusting the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitoring the posture and pressure of the robot arm in real time, so as to obtain posture pressure feedback data; using the regional processing strategy to dynamically adjust the posture of the robot arm based on the complex curved surface according to the posture pressure feedback data, so as to obtain the uniform force parallel state data;

[0030] Step S4: using the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform parallel state data, thereby obtaining laser distance measurement calibration data; performing accuracy verification processing on the laser distance measurement calibration data, thereby obtaining a distance measurement error distribution map;

[0031] Step S5: remove abnormal points from the distance measurement error distribution diagram according to a preset error threshold, and perform distance measurement result fitting based on the least squares method, so as to generate distance measurement model parameter data to achieve distance measurement and calibration of the wall scraping equipment.

[0032] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a laser ranging calibration method based on a laser sensor of the present invention. In this example, the laser ranging calibration method based on a laser sensor includes the following steps:

[0033] Step S1: obtaining spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; performing plane fitting on the wall based on singular value decomposition according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data;

[0034] The embodiment of the present invention scans the wall surface through multiple groups of high-precision long-stroke laser sensors to obtain spatial coordinate data sets of multiple points on the wall surface. Each laser sensor can provide the three-dimensional coordinates of the point to form a comprehensive spatial data set. Next, a data analysis tool (such as MATLAB or Python's NumPy library) is used to perform singular value decomposition (SVD) on the spatial coordinate data to determine the best fitting plane of the wall. Singular value decomposition decomposes the spatial coordinate data into a set of eigenvalues ​​and eigenvectors, and the normal vectors of the plane are determined by these eigenvectors. Finally, based on these normal vector data, a virtual plane model is generated to provide the necessary plane reference data for subsequent steps.

[0035] Step S2: obtaining the normal vector data of the robot arm of the wall scraping device at the current plane; normalizing the normal vector data of the robot arm and the virtual plane and its normal vector data, calculating the rotation axis and rotation angle between the two, and generating rotation matrix data using the Rodriguez rotation formula;

[0036] In an embodiment of the present invention, a built-in sensor or inertial measurement unit (IMU) is used on the robotic arm of the wall scraping device to obtain the normal vector data of the robotic arm of the current plane. Then, the normal vector data of the robotic arm and the normal vector data of the virtual plane are normalized using data processing software (such as MATLAB). The normalization process includes standardizing the length of the normal vector to 1 for comparison. Next, the rotation axis and rotation angle between the two are calculated. By performing dot product and cross product operations on the normal vector, the rotation matrix data is generated using the Rodriguez rotation formula, which is widely used in calculating rotation angles and axes. The generation of the rotation matrix can be implemented using the relevant functions in Python's SciPy library to ensure that the posture adjustment of the robotic arm can be accurately completed.

[0037] Step S3: adjusting the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitoring the posture and pressure of the robot arm in real time, so as to obtain posture pressure feedback data; using the regional processing strategy to dynamically adjust the posture of the robot arm based on the complex curved surface according to the posture pressure feedback data, so as to obtain the uniform force parallel state data;

[0038] The embodiment of the present invention uses the generated rotation matrix data to adjust the angle of the robotic arm through the motion control system of the robotic arm so that its working surface remains parallel to the wall. The posture and pressure data of the robotic arm are monitored in real time, and the real-time feedback data is collected using the posture sensor and the force sensor. The data is processed by the control system to calculate the posture pressure feedback data of the robotic arm. Based on these data, a regional processing strategy is applied to divide the wall into multiple grid sub-regions, and each region is locally analyzed. An algorithm (such as local weighted regression or smoothing) is used to dynamically adjust the posture of the robotic arm to ensure that an even force parallel state is achieved on a complex curved surface.

[0039] Step S4: using the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform parallel state data, thereby obtaining laser distance measurement calibration data; performing accuracy verification processing on the laser distance measurement calibration data, thereby obtaining a distance measurement error distribution map;

[0040] After the mechanical arm is adjusted to the equal force parallel state, the embodiment of the present invention uses the laser sensor thereon to perform multi-point scanning distance measurement of the wall. Each distance measurement is performed based on the adjusted equal force parallel state data to ensure the accuracy of the data. The distance measurement data is recorded and transmitted to the data processing system for accuracy verification. The accuracy verification includes statistical analysis and error analysis to generate a distance measurement error distribution map. These data are processed by analysis tools (such as MATLAB's statistical toolbox) to obtain a detailed error distribution map, which provides basic data for subsequent error correction.

[0041] Step S5: remove abnormal points from the distance measurement error distribution diagram according to a preset error threshold, and perform distance measurement result fitting based on the least squares method, so as to generate distance measurement model parameter data to achieve distance measurement and calibration of the wall scraping equipment.

[0042] According to the ranging error distribution diagram obtained in step S4, the embodiment of the present invention first removes abnormal points. This process uses a preset error threshold to identify and exclude abnormal data points that exceed the threshold, and uses an algorithm (such as RANSAC) to process the error data. Then, the least squares method is used to fit the ranging data after the abnormal points are removed to generate the final ranging model parameter data. The least squares method can be implemented through data analysis tools (such as Python's Scikit-learn library), and its purpose is to optimize the parameters of the model to minimize the fitting error. Finally, the generated ranging model parameter data will be used to achieve accurate ranging and calibration of wall scraping equipment, thereby improving the accuracy of construction.

[0043] The present invention collects spatial coordinate data of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors, and performs plane fitting based on singular value decomposition of these data, which can accurately capture the approximate plane structure of the wall. This fitting method helps to eliminate the irregularities caused by local roughness of the wall or measurement noise, making the fitted virtual plane smoother and more realistic, and providing an accurate geometric reference for subsequent robot arm adjustment. Such processing significantly improves the accuracy of wall detection and avoids the deviation caused by inaccurate plane in subsequent operations. By obtaining the normal vector of the virtual plane of the wall and the current normal vector of the robot arm, the two are normalized to ensure that they can still maintain consistency at different scales. Subsequently, the rotation axis and rotation angle between the two are calculated, and the rotation matrix data for posture adjustment is generated. This process ensures that the working surface of the robot arm is parallel to the wall through precise mathematical calculations, thereby avoiding uneven scraping or scratching the wall due to angle mismatch, and significantly improving the operating accuracy of the equipment. Real-time monitoring of the posture and pressure data of the robot arm can dynamically track the contact between the robot arm and the wall. Combined with the regional processing strategy for complex curved surfaces, the robot arm is fine-tuned according to real-time feedback data to ensure that the robot arm always maintains uniform force and a posture parallel to the wall during operation. This dynamic adjustment mechanism can not only adapt to complex situations such as uneven wall surfaces and curvature changes, but also avoid the problem of excessive or insufficient local force, ultimately ensuring the quality and consistency of the scraping operation. After the robot arm is adjusted in posture, the laser sensor performs multi-point scanning and ranging according to the uniform force parallel state data to obtain accurate ranging calibration data. Based on these calibration data, a ranging error distribution map can be generated to intuitively display the ranging errors at different positions. This analysis can not only verify the accuracy of laser ranging, but also provide a reliable basis for subsequent error correction, ensuring that the equipment can perform accurate measurement and scraping under various wall conditions. According to the ranging error distribution map, the abnormal points that exceed the preset error threshold are eliminated to ensure the accuracy of the ranging data. Subsequently, the least squares method is used to fit the remaining data to generate accurate ranging model parameters. This process can minimize the distance measurement error and improve the overall distance measurement accuracy and reliability by eliminating and fitting abnormal data. Ultimately, these distance measurement model parameters not only provide an efficient distance measurement and calibration solution for the device, but also further optimize the device's working performance, making it adaptable to more complex wall environments.

[0044] Preferably, step S1 comprises the following steps:

[0045] Step S11: performing sensor self-calibration processing based on laser power and receiving sensitivity on a plurality of pre-deployed high-precision long-stroke laser sensors, thereby obtaining sensor parameter calibration data;

[0046] Step S12: using multiple groups of high-precision long-stroke laser sensors to perform gridded multi-point scanning on the wall according to sensor parameter calibration data, thereby obtaining an original spatial coordinate data set;

[0047] Step S13: smoothing the original spatial coordinate data set using a moving average filtering algorithm to obtain a smoothed spatial coordinate data set;

[0048] Step S14: establishing a global coordinate system according to the original spatial coordinate data set, and converting the smoothed spatial coordinate data set into the global coordinate system to obtain a spatial coordinate data set under a unified coordinate system;

[0049] Step S15: performing plane fitting on the wall surface based on singular value decomposition according to the spatial coordinate data set, thereby obtaining a virtual plane and its normal vector data.

[0050] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0051] Step S11: performing sensor self-calibration processing based on laser power and receiving sensitivity on a plurality of pre-deployed high-precision long-stroke laser sensors, thereby obtaining sensor parameter calibration data;

[0052] The embodiment of the present invention performs self-calibration processing on multiple sets of pre-deployed high-precision long-stroke laser sensors to ensure their performance and data accuracy. During the self-calibration process, laser power and receiving sensitivity are used as calibration basis, and calibration is performed through standardized test targets and known distances. The laser sensor measures the laser power and received signal at different distances, and uses dedicated calibration software (such as the calibration toolbox in LabVIEW or MATLAB) to calculate the parameter calibration data of the sensor. These data include laser power attenuation and receiving sensitivity adjustment coefficients, ensuring that the sensor can accurately measure the spatial coordinates of the wall in practical applications.

[0053] Step S12: using multiple groups of high-precision long-stroke laser sensors to perform gridded multi-point scanning on the wall according to sensor parameter calibration data, thereby obtaining an original spatial coordinate data set;

[0054] After completing the self-calibration of the sensor, the embodiment of the present invention uses the calibrated laser sensor to perform grid multi-point scanning on the wall. The specific operation includes setting the grid parameters of the laser sensor scanning, such as the grid density and the scanning area, and performing system spatial point acquisition through the laser scanner. During data acquisition, the laser sensor divides the wall into multiple grid areas, and performs multi-point measurements in each grid area to obtain the original spatial coordinate data set. Use data processing tools (such as MATLAB's Data Acquisition Toolbox or Python's PySerial library) to record and store the collected data to generate a preliminary original spatial coordinate data set.

[0055] Step S13: smoothing the original spatial coordinate data set using a moving average filtering algorithm to obtain a smoothed spatial coordinate data set;

[0056] The embodiment of the present invention performs smoothing on the original spatial coordinate data set to reduce noise and improve data quality. A moving average filtering algorithm is used, which smoothes the data by calculating the sliding average of data points. In specific implementation, an appropriate window size is selected to slide and calculate the average value of each data point. The filtering process can be implemented by a filter function in MATLAB (such as filter or smoothdata) or a uniform_filter1d function in Python's SciPy library. After the moving average filtering process, the smoothed spatial coordinate data set obtained will more accurately reflect the actual spatial characteristics of the wall.

[0057] Step S14: establishing a global coordinate system according to the original spatial coordinate data set, and converting the smoothed spatial coordinate data set into the global coordinate system to obtain a spatial coordinate data set under a unified coordinate system;

[0058] The embodiment of the present invention establishes a global coordinate system and transforms the smoothed spatial coordinate data set into the coordinate system. First, the origin and axis of the global coordinate system are defined, and then the smoothed spatial coordinate data set is transformed into the global coordinate system using translation and rotation transformations. A transformation matrix can be used for coordinate transformation. In a specific operation, a mathematical software (such as MATLAB or Python's NumPy library) is used to calculate the coordinate transformation matrix and apply it to the smoothed data set. The transformed data set will be presented in the form of a spatial coordinate data set under a unified coordinate system, providing a standardized data basis for subsequent data processing and analysis.

[0059] Step S15: performing plane fitting on the wall surface based on singular value decomposition according to the spatial coordinate data set, thereby obtaining a virtual plane and its normal vector data.

[0060] The embodiment of the present invention performs wall plane fitting based on a spatial coordinate data set in a global coordinate system. The singular value decomposition (SVD) algorithm is used to perform plane fitting on the spatial coordinate data. First, a matrix containing spatial coordinate points is constructed, and the singular value decomposition algorithm (such as the svd function in MATLAB or the numpy.linalg.svd function in Python) is applied to decompose the matrix. Through SVD, the principal components are extracted, and the best fitting plane of the wall and its normal vector are calculated. The fitted virtual plane model and its normal vector data will be used in the subsequent robot arm correction and range calibration steps to ensure the accuracy and reliability of the plane model.

[0061] The present invention can ensure the consistency and accuracy of each sensor in actual work by performing self-calibration processing based on laser power and receiving sensitivity on multiple pre-deployed groups of high-precision long-stroke laser sensors. Since environmental factors (such as temperature and humidity) may affect the performance of the sensor, these external influences can be corrected through self-calibration to ensure that the measurement accuracy of each sensor is in the best state. The calibrated sensor parameters provide a basis for subsequent high-precision spatial coordinate measurement, greatly reduce measurement errors, and improve the reliability and consistency of the entire system. Using calibrated sensors to perform grid multi-point scanning, the original spatial coordinate data set of multiple points on the wall can be obtained. The grid scanning method ensures that each area on the wall is evenly covered and can capture the complex geometric shapes of the wall surface. This multi-point acquisition method provides rich measurement data, which helps to accurately construct a three-dimensional model of the wall and lays a solid foundation for subsequent data processing and fitting. The original spatial coordinate data set is smoothed by a moving average filtering algorithm, which can effectively remove noise and abnormal points that may exist in the measurement process. The filtered data set is smoother, retains the overall structural characteristics of the wall, and reduces the jitter caused by sensor errors or local irregularities of the wall. The smoothed dataset provides a more reliable input for subsequent geometric analysis and model building, and improves the accuracy of fitting and modeling. By establishing a global coordinate system based on the original spatial coordinate dataset and converting the smoothed dataset into this global coordinate system, all spatial coordinate data are processed in a unified reference frame. The establishment of a global coordinate system not only solves the relative position problem between the sensors, but also unifies the data of different measurement areas, making the data more comparable and consistent. This conversion provides a unified spatial reference for the overall geometric analysis of the wall, making the subsequent plane fitting and wall modeling more accurate. In a unified coordinate system, the plane fitting of the wall is performed using singular value decomposition (SVD), and the main plane of the wall and its normal vector data can be extracted. The singular value decomposition algorithm is very efficient in processing high-dimensional data and can accurately fit the overall plane of the wall. Especially for complex and irregular wall structures, the algorithm can eliminate the influence of local anomalies on the overall plane. This fitting result provides the overall geometric characteristics and direction information of the wall, providing an accurate reference for the attitude adjustment of the equipment, wall construction and other operations.

[0062] Preferably, step S15 comprises the following steps:

[0063] Step S151: performing abnormal coordinate detection and screening on the spatial coordinate data set according to a preset abnormal value detection threshold, thereby obtaining a spatial coordinate data cleaning set; and constructing a covariance matrix according to the spatial coordinate data cleaning set, and performing singular value decomposition on the covariance matrix, thereby obtaining wall space feature data, wherein the wall space feature data includes eigenvalues ​​and eigenvectors;

[0064] The embodiment of the present invention performs abnormal coordinate detection on the spatial coordinate data set according to a preset abnormal value detection threshold, and uses statistical methods (such as Z-score or box plot analysis) to identify and filter out abnormal data points. This process can be implemented by the isoutlier function in MATLAB or the scipy.stats.zscore function in Python. The spatial coordinate data cleaning set obtained after screening will not contain abnormal data. Then, the covariance matrix is ​​constructed using the cleaned data set to calculate the covariance between each pair of coordinate points. The construction and calculation of the covariance matrix can be completed by the cov function in MATLAB or the np.cov function in the NumPy library of Python. The covariance matrix is ​​subjected to singular value decomposition (SVD) to obtain wall space feature data, including eigenvalues ​​and eigenvectors. The SVD operation can be completed using the svd function of MATLAB or the numpy.linalg.svd function in Python, and these eigenvalues ​​and eigenvectors will be used for plane fitting of the wall.

[0065] Step S152: extracting a feature vector corresponding to the minimum feature value according to the magnitude relationship of the feature values ​​in the wall space feature data, and using it as the normal vector of the virtual plane, thereby obtaining initial normal vector data;

[0066] After acquiring the spatial feature data of the wall surface, the embodiment of the present invention extracts the eigenvector corresponding to the minimum eigenvalue according to the size relationship of the eigenvalues. The sorting and selection of eigenvalues ​​can be implemented by the sort function in MATLAB or the numpy.argsort function in Python. The eigenvector corresponding to the minimum eigenvalue will be used as the normal vector of the virtual plane. The extraction of this normal vector can be implemented by the SVD result of MATLAB or the SVD output in Python. The obtained initial normal vector data will provide the necessary direction information for plane fitting, laying the foundation for the subsequent calculation of the plane equation.

[0067] Step S153: Calculate the intercept term of the virtual plane according to the initial normal vector data and the cleaned set of spatial coordinate data, so as to obtain plane equation parameter data;

[0068] The embodiment of the present invention calculates the intercept term of the virtual plane based on the initial normal vector data and the cleaned set of spatial coordinate data. First, the initial normal vector and the point in the cleaned set of spatial coordinate data are substituted into the general form of the plane equation ax+by+cz=d to calculate the intercept term d. This process includes multiplying the components of the normal vector with the coordinate values ​​of the data points and summing them. The method can be implemented using the dot function in MATLAB or the numpy.dot function in Python. This will generate complete parameter data of the plane equation, which is used to describe the position and direction of the virtual plane.

[0069] Step S154: fitting the points in the spatial coordinate data set to the virtual plane based on the plane equation parameter data, thereby obtaining fitting error data;

[0070] The embodiment of the present invention uses the plane equation parameter data to calculate the distance from the point in the spatial coordinate data set to the virtual plane. According to the plane equation formula, the vertical distance from each data point to the plane is calculated, and the formula is: This calculation can be implemented through vectorized operations in MATLAB or the NumPy library in Python. The obtained fitting error data represents the deviation of each point from the virtual plane, providing basic data for subsequent error analysis.

[0071] Step S155: performing root mean square error and maximum deviation calculation and analysis on the fitting error data, thereby obtaining fitting quality evaluation index data;

[0072] The embodiment of the present invention performs root mean square error (RMSE) and maximum deviation calculation and analysis on the fitting error data. The root mean square error is obtained by calculating the square root of the mean of the sum of squares of all errors, and the formula is: The maximum deviation is the maximum value of all errors. These calculations can be done using the sqrt and mean functions in MATLAB or the np.sqrt and np.mean functions in Python's NumPy library. The resulting fit quality assessment index data will be used to evaluate the accuracy of the virtual plane fit and determine whether further adjustments are needed.

[0073] Step S156: If the fitting quality evaluation index meets the preset threshold, the virtual plane of the wall and its normal vector data are output; if not, return to step S151, adjust the outlier detection threshold, and re-execute steps S151 to S156.

[0074] The embodiment of the present invention checks whether the preset threshold is met based on the fitting quality evaluation index data obtained in step S155. If the fitting quality index meets the requirements, the virtual plane of the wall and its normal vector data are output. If the requirements are not met, return to step S151, adjust the outlier detection threshold, and re-execute steps S151 to S156. This process involves iterative optimization, using data analysis tools (such as MATLAB's loop control structure or Python's while loop) to adjust the threshold and reprocess the data until the fitting quality meets the standard.

[0075] The present invention screens the abnormal coordinates of the spatial coordinate data set by a preset abnormal value detection threshold, which can effectively eliminate the noise or erroneous data that may appear in the measurement process, and ensure the accuracy and reliability of the data set. The screened data set (spatial coordinate data cleaning set) is further used to construct a covariance matrix, and the overall distribution characteristics of the wall data can be captured through the covariance matrix. The singular value decomposition of the covariance matrix can effectively extract the spatial feature data of the wall, including eigenvalues ​​and eigenvectors, which reflect the geometric characteristics and structural characteristics of the wall. This step ensures that the wall feature data used in subsequent calculations have high-quality geometric descriptions and improves the accuracy of subsequent fitting calculations. According to the size relationship of the eigenvalues ​​in the wall space feature data, the eigenvector corresponding to the minimum eigenvalue is extracted as the normal vector of the virtual plane. The eigenvector corresponding to the minimum eigenvalue represents the normal direction of the wall data on the main plane in space. This extraction process effectively identifies the directionality of the main plane of the wall and ensures the calculation accuracy of the normal vector. By extracting the initial normal vector, the system can provide an accurate direction reference for the calculation of the subsequent plane equation, thereby ensuring the matching degree between the virtual plane and the actual shape of the wall. Based on the initial normal vector and the cleaned set of spatial coordinate data, the intercept term of the virtual plane, that is, the complete parameters of the plane equation, is calculated. The calculation of the intercept term enables the virtual plane to not only have the correct normal vector direction, but also match the overall position of the wall, thereby obtaining the complete plane equation parameters. The construction of the plane equation lays the foundation for the subsequent calculation of the distance between each point and the plane, making the wall model more accurate and able to correctly reflect the position and angle characteristics of the wall. Based on the plane equation parameters, the distance from each point in the spatial coordinate data set to the virtual plane is calculated to obtain the fitting error data. This process compares each measured point with the fitting plane to evaluate the degree of deviation between the real shape of the wall and the fitting plane. Through this step, the accuracy and representativeness of the fitting plane can be judged, which directly reflects the matching degree of the fitting plane to the geometric structure of the wall. This error calculation process provides an important basis for subsequent quality assessment. By calculating and analyzing the root mean square error (RMSE) and maximum deviation of the fitting error data, the accuracy of the fitting plane can be comprehensively evaluated. The root mean square error reflects the distribution of the overall error in the fitting process, while the maximum deviation reflects the most extreme error point. These two indicators provide a quantitative standard for the fitting quality, which can intuitively judge the accuracy and reliability of the current plane fitting. This analysis process can help the system determine whether further optimization or adjustment is needed to ensure the accuracy of the wall virtual plane model. According to the results of the fitting quality assessment, if the root mean square error and the maximum deviation both meet the preset accuracy threshold, it can be confirmed that the data of the current virtual plane and normal vector are accurate enough, and the final wall plane model is output. If the threshold is not met, the system will return to step S151, readjust the outlier detection threshold, and re-perform data screening, fitting calculation and other steps.This feedback loop ensures that the system has the ability to adaptively optimize, and can continuously improve the model accuracy during the data fitting process until it meets the set accuracy standards. Ultimately, it ensures that the wall plane fitting reaches the highest quality and adapts to various complex wall conditions.

[0076] Preferably, step S2 comprises the following steps:

[0077] Step S21: collecting the spatial coordinates of the current working surface of the robotic arm through multiple groups of laser sensors and an inertial measurement unit on the robotic arm, and converting them into spatial coordinate data of the robotic arm in the local coordinate system of the robotic arm;

[0078] Step S22: performing work surface plane fitting based on singular value decomposition according to the robot arm spatial coordinate data, and performing an initial normal vector of the current work plane of the robot arm, thereby obtaining initial normal vector data of the robot arm;

[0079] Step S23: normalizing the initial normal vector data of the robot arm, and calculating the angle and rotation axis with the virtual plane and its normal vector data, so as to obtain the normal vector rotation axis data;

[0080] Step S24: preliminarily adjusting the robotic arm according to the normal vector rotation axis data, and re-measuring the adjusted robotic arm posture to obtain corrected robotic arm normal vector data;

[0081] Step S25: Compare the robot arm normal vector data with the virtual plane and its normal vector data based on the rotation axis and rotation angle, and generate rotation matrix data using the Rodriguez rotation formula.

[0082] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0083] Step S21: collecting the spatial coordinates of the current working surface of the robotic arm through multiple groups of laser sensors and an inertial measurement unit on the robotic arm, and converting them into spatial coordinate data of the robotic arm in the local coordinate system of the robotic arm;

[0084] The embodiment of the present invention utilizes multiple groups of laser sensors and inertial measurement units (IMUs) on the robotic arm to collect the spatial coordinates of the current working surface of the robotic arm. The laser sensor is used to obtain the spatial coordinate data of multiple points on the working surface, and the IMU provides the posture data of the robotic arm. After the data collection is completed, the spatial coordinates measured by the laser sensor are converted to the local coordinate system of the robotic arm to ensure that the coordinate data is consistent with the actual working position and direction of the robotic arm. In specific operations, the laser sensor can read data through the control system, and the output data of the IMU is obtained through the serial port or CAN bus interface. These data can be converted and sorted through data processing tools (such as numpy or pandas libraries) in the MATLAB or Python programming environment to generate spatial coordinate data in the local coordinate system of the robotic arm.

[0085] Step S22: performing work surface plane fitting based on singular value decomposition according to the robot arm spatial coordinate data, and performing an initial normal vector of the current work plane of the robot arm, thereby obtaining initial normal vector data of the robot arm;

[0086] The embodiment of the present invention uses the singular value decomposition (SVD) method to perform plane fitting on the working surface according to the spatial coordinate data in the local coordinate system of the robot. First, a matrix containing the spatial coordinate data is constructed, and then SVD is applied to the matrix to obtain eigenvalues ​​and eigenvectors. The eigenvalue reflects the main variation direction of the data, and the eigenvector corresponding to the minimum eigenvalue represents the normal vector of the plane. This process can be completed using the svd function in MATLAB or the numpy.linalg.svd function in Python. The initial normal vector data of the working plane of the robot arm obtained will be used for the next step of posture correction and adjustment.

[0087] Step S23: normalizing the initial normal vector data of the robot arm, and calculating the angle and rotation axis with the virtual plane and its normal vector data, so as to obtain the normal vector rotation axis data;

[0088] The embodiment of the present invention normalizes the initial normal vector data of the robot arm to ensure that the normal vector has unit length. This step can be implemented using the norm function in MATLAB or the numpy.linalg.norm function in Python. Then, the normalized normal vector data of the robot arm is compared with the virtual plane and its normal vector data, and the angle and rotation axis between the two are calculated. The angle calculation is completed by the dot product of the vector, and the rotation axis is determined by the cross product of the vector. The relevant calculations can use the dot and cross functions in MATLAB or the numpy.dot and numpy.cross functions in Python. These calculation results will generate normal vector rotation axis data, providing a basis for the posture adjustment of the robot arm.

[0089] Step S24: preliminarily adjusting the robotic arm according to the normal vector rotation axis data, and re-measuring the adjusted robotic arm posture to obtain corrected robotic arm normal vector data;

[0090] The embodiment of the present invention makes a preliminary adjustment to the robot arm according to the normal vector rotation axis data so that its working surface is parallel to the wall. The posture of the robot arm can be adjusted by executing the corresponding motion command by the control system. After the adjustment, the posture of the robot arm is re-measured using a laser sensor and an IMU to obtain the corrected normal vector data of the robot arm. The data during the adjustment process can be recorded by a real-time control system and transmitted to a data processing platform (such as MATLAB or Python), and the normal vector data before and after the adjustment can be compared to evaluate the adjustment effect and make further corrections as necessary.

[0091] Step S25: Compare the robot arm normal vector data with the virtual plane and its normal vector data based on the rotation axis and rotation angle, and generate rotation matrix data using the Rodriguez rotation formula.

[0092] The embodiment of the present invention compares the corrected manipulator normal vector data with the virtual plane and its normal vector data to generate rotation matrix data. First, a rotation matrix is ​​constructed using the Rodrigues rotation formula based on the normal vector rotation axis and rotation angle. The rotation angle is calculated by the angle between the normal vectors, and the rotation axis is calculated by the cross product. The rotation matrix is ​​generated using the rodrigues function in MATLAB or the scipy.spatial.transform.Rotation module in Python. The rotation matrix data will be used for further posture adjustment and optimization to ensure that the working plane of the manipulator is completely parallel to the target wall.

[0093] The present invention collects the spatial coordinates of the current working surface of the manipulator in real time through multiple groups of laser sensors and inertial measurement units (IMUs) on the manipulator, and converts them into data in the local coordinate system of the manipulator. This process ensures accurate tracking of the position information of the working surface of the manipulator. The position information provided by the inertial measurement unit can compensate for the possible offset of the manipulator in different postures or operation processes, ensuring the accuracy and real-time performance of the measurement data; this step provides a basis for subsequent plane fitting and posture adjustment, ensuring the operation accuracy of the manipulator in a dynamic environment. By performing plane fitting based on singular value decomposition (SVD) on the spatial coordinate data of the working surface of the manipulator, the main plane of the current working surface can be accurately obtained, and the initial normal vector data of the manipulator can be obtained by plane fitting. This process helps to quickly analyze the geometric characteristics of the working surface of the manipulator, ensuring that the geometric relationship between the posture of the manipulator and its current working environment is accurately described. The extraction of the initial normal vector provides the necessary geometric reference for subsequent posture adjustment, making the adjustment process more efficient. The initial normal vector data of the manipulator is normalized to ensure the consistency of the normal vector at different scales. Subsequently, by comparing with the normal vector of the virtual plane, calculating the angle and rotation axis between the two, the direction and angle that the robot arm needs to adjust can be accurately determined. The normalization of the normal vector and the calculation process of the rotation axis ensure that the robot arm can quickly find the correct posture parallel to the wall without losing accuracy, avoiding unnecessary complex calculations, and thus improving the efficiency of posture adjustment. According to the normal vector rotation axis data obtained in the previous step, the robot arm is preliminarily adjusted in posture, and the corrected normal vector data of the robot arm is remeasured. This process ensures that the posture of the robot arm can quickly approach the parallel state of the virtual plane of the wall through real-time feedback adjustment. Through multiple measurements and adjustments, the system can gradually optimize the posture of the robot arm, improve the accuracy of posture adjustment, ensure that the robot arm can maintain a consistent posture with the wall during operation, and avoid uneven scraping or excessive errors. The corrected normal vector data of the robot arm is compared with the normal vector data of the virtual plane for rotation axis and angle, and the rotation matrix data is further generated using the Rodriguez rotation formula. This step provides a directional reference for further optimizing the posture of the robot arm through precise mathematical calculations, ensuring that the robot arm can remain completely parallel to the wall during operation. The generation of the rotation matrix can not only accurately adjust the posture of the robotic arm, but also provide a highly accurate mathematical tool for posture control in subsequent operations, greatly improving the operating performance and stability of the robotic arm in complex environments.

[0094] Preferably, step S25 comprises the following steps:

[0095] Step S251: performing dot product calculation on the normal vector data of the robot arm and the virtual plane and its normal vector data, thereby obtaining the cosine value of the angle between the two vectors;

[0096] The embodiment of the present invention performs dot product calculation on the normal vector data of the manipulator and the virtual plane and its normal vector data. In the specific operation, the two sets of normal vector data are first standardized to unit vectors to eliminate the influence of length on the calculation result. Then, the cosine value between the two unit vectors is calculated using the dot product formula. In MATLAB, the dot function can be used to calculate the dot product, and in Python, the numpy.dot function can be used. The calculation result provides the cosine value of the angle between the two normal vectors, which is further used to calculate the rotation angle.

[0097] Step S252: Calculate the angle between the two normal vectors using the cosine value of the angle between the two vectors, thereby obtaining rotation angle data;

[0098] The embodiment of the present invention uses the cosine value of the angle obtained in step S251 to calculate the angle between the two normal vectors; first, the cosine value of the angle is converted into the angle value by the arccosine function (acos). In MATLAB, the acos function can be used, and in Python, the numpy.arccos function can complete this step. The rotation angle data will provide the necessary information for the subsequent rotation matrix construction, ensuring that the rotation matrix can correctly represent the relative rotation between the two normal vectors.

[0099] Step S253: performing cross product calculation on the normal vector data of the robot arm and the virtual plane and its normal vector data to obtain a rotation axis vector;

[0100] The embodiment of the present invention performs a cross product calculation on the normal vector data of the manipulator and the virtual plane and its normal vector data to determine the rotation axis vector. The cross product calculation can be completed by the cross function of MATLAB or the numpy.cross function of Python. The cross product result provides the direction of the rotation axis, which is perpendicular to the plane where the two normal vectors are located. The rotation axis vector is a key parameter for constructing a rotation matrix, ensuring that the rotation matrix can accurately represent the rotation axis and rotation angle.

[0101] Step S254: constructing a rotation matrix according to the rotation angle data and the unit rotation axis vector using the Rodriguez rotation formula, thereby obtaining initial rotation matrix data;

[0102] The embodiment of the present invention constructs a rotation matrix using the Rodrigues rotation formula according to the rotation angle data in step S252 and the unit rotation axis vector in step S253. Rodrigues rotation formula (R = I + sin (θ) K + (1 - cos (θ)) K 2) can be implemented in MATLAB through a custom function, or in Python using the scipy.spatial.transform.Rotation module to construct the rotation matrix. The rotation matrix data calculated by the formula will provide a preliminary rotation matrix as the basis for adjusting the robot arm's posture.

[0103] Step S255: numerically optimizing the initial rotation matrix data to minimize the error between the normal vector data of the rotated manipulator and the virtual plane and its normal vector data, thereby obtaining the rotation matrix data.

[0104] The embodiment of the present invention minimizes the error between the normal vector data of the mechanical arm after rotation and the normal vector data of the virtual plane through numerical optimization. An optimization algorithm such as the Levenberg-Marquardt algorithm is used to optimize through the scipy.optimize.least_squares() function. The algorithm adjusts the rotation matrix parameters to minimize the error of the normal vector after rotation. The optimization process requires the definition of an error function, which calculates the difference between the normal vector after rotation and the target normal vector. After the optimization is completed, the adjusted rotation matrix data is obtained.

[0105] The present invention can directly obtain the cosine value of the angle between the two vectors by performing dot product calculation on the normal vector of the manipulator and the normal vector of the virtual plane. The dot product operation is a fast and concise way to calculate the directional relationship between the two vectors, and the cosine value reflects the relative directionality between the two. This step of calculation can quickly evaluate the angle difference between the current posture of the manipulator and the virtual plane, provide preliminary angle information, and lay the foundation for subsequent posture adjustment. The acquisition of the cosine value of the angle enables the system to more effectively judge the posture difference between the manipulator and the wall, which helps to optimize the adjustment direction. Based on the cosine value, the angle between the normal vector of the manipulator and the normal vector of the virtual plane is further calculated. The angle data directly reflects the degree of deviation between the posture of the manipulator and the plane of the wall. Through accurate angle calculation, the system can obtain the specific rotation angle that the manipulator needs to adjust, and provide an accurate angle reference for posture adjustment; this process ensures the accuracy of the rotation angle, avoids excessive or insufficient rotation that may occur in posture adjustment, so that the manipulator can be more accurately aligned with the wall. By performing cross product calculation on the normal vector of the manipulator and the normal vector of the virtual plane, the rotation axis vector between them can be obtained. The cross product calculation can accurately determine which axis the robot arm needs to rotate around for adjustment. This rotation axis vector provides the system with a directional basis for posture adjustment. The result of the cross product not only indicates the direction of rotation, but also ensures that there will be no misjudgment or misalignment during the rotation process, helping the robot arm to accurately adjust to a posture parallel to the wall. Based on the rotation angle data and the unit rotation axis vector, the initial rotation matrix is ​​constructed using the Rodrigues rotation formula. This formula can quickly generate a three-dimensional rotation matrix through the rotation angle and rotation axis, ensuring that the robot arm can adjust its posture along the correct axis and angle. The construction of the initial rotation matrix provides a mathematical basis for subsequent posture adjustment, ensuring that the adjustment process of the robot arm is both fast and accurate. The Rodrigues rotation formula is efficient and accurate in processing rotation operations, allowing the robot arm to accurately respond to the geometric characteristics of the wall. By numerically optimizing the initial rotation matrix, the rotation matrix is ​​further optimized with the goal of minimizing the error between the normal vector of the robot arm after rotation and the normal vector of the virtual plane. This optimization process ensures that the accuracy of the robot arm posture adjustment reaches the optimal state, so that the normal vector of the robot arm after rotation is completely consistent with the normal vector of the virtual plane or the error is minimized. The optimized rotation matrix provides high-precision parameters for the final posture adjustment, ensuring that the posture control of the robot arm during wall construction is more stable and precise, greatly improving the quality and reliability of the operation.

[0106] Preferably, step S3 comprises the following steps:

[0107] Step S31: Calculate the target posture of the robot arm according to the rotation matrix data, and adjust the posture of the robot arm so that the working surface of the robot arm is parallel to the wall through the motion control system of the robot arm, thereby obtaining initial adjustment posture data;

[0108] The embodiment of the present invention calculates the target posture of the robotic arm through rotation matrix data. First, the rotation matrix data is used to adjust the posture of the robotic arm to ensure that its working surface is parallel to the wall. The target posture data is input through the motion control system of the robotic arm, and the system adjusts the joint angles of the robotic arm according to these data. In a specific implementation, the control software of the robotic arm (such as ROS or a dedicated robotic arm control platform) can be used to apply the rotation matrix and perform posture adjustment operations. After the adjustment is completed, the posture sensor of the robotic arm feeds back the initial adjustment posture data, which provides a basis for subsequent posture correction.

[0109] Step S32: using the force sensor on the end effector of the robotic arm to monitor the contact pressure between the robotic arm and the wall in real time according to the initial adjustment posture data, and monitoring the current posture of the robotic arm through the posture sensor, so as to obtain posture pressure feedback data;

[0110] The embodiment of the present invention uses a force sensor on the end effector of the robotic arm to monitor the contact pressure between the robotic arm and the wall in real time. The force sensor provides real-time pressure data, and the current robotic arm posture is recorded by the posture sensor of the robotic arm. These data are transmitted through the feedback channel of the robotic arm control system and processed by data analysis software (such as MATLAB or Python's Pandas library). The posture pressure feedback data includes pressure value and posture information, which is used for subsequent local pressure analysis.

[0111] Step S33: using a regional processing strategy to divide the wall area into multiple grid sub-areas, and performing local analysis on the posture pressure feedback data of each sub-area, thereby obtaining local pressure distribution data;

[0112] The embodiment of the present invention uses a regional processing strategy to divide the wall area into multiple grid sub-areas for local analysis. The posture pressure feedback data of each sub-area is analyzed by a software tool (such as the Scikit-learn library in Python) to generate local pressure distribution data. These data help to understand the pressure situation in each area and provide a basis for posture parameter adjustment. In specific implementation, a data visualization tool (such as Matplotlib) can be used to draw a pressure distribution map of each sub-area for easy analysis and adjustment.

[0113] Step S34: performing posture parameter adjustment processing according to the local pressure distribution data, thereby obtaining posture adjustment instruction data;

[0114] The embodiment of the present invention performs posture parameter adjustment processing according to the local pressure distribution data. The posture parameters are adjusted using an optimization algorithm (such as a gradient descent algorithm or a least squares method) to optimize the overall pressure distribution. The scipy.optimize module in the SciPy library in Python can be used to implement the optimization process. The posture adjustment instruction data is generated according to the optimization results, and these instruction data will be used for further adjustment of the robot arm to ensure uniform pressure distribution.

[0115] Step S35: performing local fine adjustment of the robot arm based on dynamic adaptation of complex surfaces according to the posture adjustment instruction data, thereby obtaining real-time posture pressure data;

[0116] The embodiment of the present invention performs local fine adjustment of the mechanical arm for dynamic adaptation of complex curved surfaces according to the posture adjustment instruction data. The adjustment instruction is applied to the mechanical arm through the control system to implement local fine adjustment. The posture and pressure data of the mechanical arm are monitored in real time to ensure that the adjusted posture meets the requirements. The real-time control software and sensor feedback system of the mechanical arm can be used to realize dynamic adjustment, and the posture pressure data can be obtained and updated in real time.

[0117] Step S36: Calculate the pressure uniformity of each sub-area according to the real-time posture pressure data, so as to obtain the uniform force parallel state data.

[0118] The embodiment of the present invention uses real-time posture pressure data to calculate the pressure uniformity of each sub-area. By analyzing the pressure data of each sub-area, the pressure uniformity index is calculated. In a specific implementation, Python's statistical analysis library (such as NumPy or Pandas) can be used to perform uniformity calculations and generate uniform force parallel state data. These data are used to verify whether the adjustment of the robot arm has successfully achieved uniform pressure distribution, ensuring the quality and effect of wall treatment.

[0119] The present invention calculates the target posture of the robot arm according to the rotation matrix data to ensure that the working surface of the robot arm remains parallel to the wall. This process performs posture adjustment through the motion control system of the robot arm to ensure that the robot arm initially reaches the required posture position. The acquisition of the initial adjustment posture data enables the robot arm to quickly adapt to the geometric shape of the wall, thereby improving work efficiency. The effect of this step is that through the precise control of the rotation matrix, the robot arm can quickly achieve a parallel state with the wall, ensuring that the basic posture adjustment during the scraping process has high precision. The contact pressure between the robot arm and the wall is monitored in real time by the force sensor on the end effector of the robot arm, and the current posture of the robot arm is monitored by the posture sensor, so that the posture pressure feedback data can be obtained in time. This monitoring process ensures that during the operation, the system can instantly perceive the actual contact situation between the robot arm and the wall, especially the contact pressure. By monitoring the pressure and posture, the system can make timely adjustments when the robot arm is over-pressed or the posture deviates, thereby avoiding operational errors. The effect of this step is that through the real-time feedback mechanism, the robot arm can be flexibly adjusted under different environmental conditions to ensure uniform pressure distribution and protect the working surface. By dividing the wall into multiple grid sub-regions through the regional processing strategy and performing local analysis on the posture pressure feedback data of each sub-region, the pressure distribution of each region can be understood in detail. This local analysis helps to identify the working differences of the robot arm in different regions, such as the situation where the pressure in some regions is too high or too low. The acquisition of local pressure distribution data provides a targeted reference for subsequent posture adjustment to ensure that the pressure in each sub-region is more uniform. The effect of this strategy is that it can accurately adjust the operation of the robot arm in different regions, improve the overall uniformity, and avoid the phenomenon of excessive force or uneven scraping in some regions. According to the local pressure distribution data, the system can make targeted posture parameter adjustments and generate corresponding posture adjustment instruction data. Through this process, the system can respond quickly to the local uneven pressure situation and dynamically adjust the posture of the robot arm to ensure that the robot arm can adapt to the complex geometric shape of the wall. The effect of the posture parameter adjustment processing is that it can make precise fine-tuning according to the actual working state, ensuring that the robot arm can continue to operate in the optimal state, further improving the accuracy and precision of the operation. According to the posture adjustment instruction data, the robot arm can achieve dynamic adaptive adjustment to complex surfaces, and make fine adjustments in each sub-area to ensure that the posture and pressure in each area reach the ideal state. This local fine adjustment can respond to the complex geometric changes of the working surface in real time, ensuring that the robot arm has good adaptability to complex surfaces. The acquisition and processing of real-time posture pressure data enables the system to continuously optimize the operating state of the robot arm. The effect of this step is that the robot arm can achieve highly flexible dynamic adjustment, has a high degree of adaptability when dealing with complex wall surfaces, and avoids operational errors caused by irregular surfaces.By calculating the pressure uniformity of the real-time posture pressure data of each sub-area, the system can determine whether the operation of the robot arm in different areas is balanced to ensure the overall operation quality. The uniform parallel state data can reflect the pressure consistency of each area of ​​the wall, so that the system can further optimize the posture of the robot arm to ensure a consistent and smooth scraping effect. The effect of this step is that through the feedback of the uniform parallel state data, the system can adjust the robot arm to achieve the best working state, ensure that the scraping operation of the entire wall is uniform and smooth, and effectively improve the construction quality.

[0120] Preferably, step S4 comprises the following steps:

[0121] Step S41: calibrating the initial scanning position of the laser sensor on the adjusted robot arm based on the uniform parallel state data, and using the laser sensor to perform grid multi-point scanning distance measurement based on the calibrated reference plane, thereby obtaining original laser distance measurement data;

[0122] The embodiment of the present invention calibrates the initial scanning position of the laser sensor on the adjusted robotic arm based on the uniform parallel state data; first, the laser sensor is set to the calibration mode, and then the position of the sensor is adjusted according to the uniform parallel state data to ensure that its scanning of the wall is accurate. Next, the laser sensor is used to perform gridded multi-point scanning ranging to generate raw laser ranging data. The control software of the laser sensor (such as the SDK or API provided by the manufacturer) can be used to achieve accurate scanning and data acquisition. The grid scanning method ensures full coverage of the wall, thereby obtaining comprehensive ranging data.

[0123] Step S42: performing preliminary filtering and outlier elimination processing on the original laser ranging data, thereby obtaining preprocessed ranging data;

[0124] The embodiment of the present invention performs preliminary filtering and outlier removal processing on the original laser ranging data. First, a moving average filter or a median filter is applied to smooth the data and remove noise. Subsequently, a statistical method (such as the Z-score method) is used to detect and remove outliers. Data processing can be performed by the Scikit-learn library in Python, using the sklearn.preprocessing module for filtering and outlier processing. The processed data is preprocessed ranging data for subsequent calibration processing.

[0125] Step S43: fusing the pre-processed distance measurement data with the real-time attitude pressure data, and converting them into a global coordinate system, thereby obtaining calibration space coordinate data;

[0126] The embodiment of the present invention fuses the pre-processed ranging data with the real-time attitude pressure data and converts them into a global coordinate system; first, the pre-processed ranging data and the real-time attitude pressure data are synchronized, and the data of the two are combined through a data fusion algorithm (such as a Kalman filter). Then, the fused data is converted into a global coordinate system, usually through a coordinate transformation matrix. Data fusion and conversion can be implemented using the NumPy library in MATLAB or Python, and finally the calibration spatial coordinate data is obtained, providing the basis for calibration data.

[0127] Step S44: obtaining known calibration point data on the wall; performing initial distance measurement error calculation on the calibration space coordinate data and the known calibration point data, thereby obtaining initial distance measurement error data;

[0128] The embodiment of the present invention obtains the known calibration point data on the wall, and performs initial ranging error calculation on the calibration spatial coordinate data; the calibration point data can be obtained by pre-calibrating the position on the wall to ensure its accuracy. Then, the error between the calibration spatial coordinate data and the known calibration point data is calculated, usually using the Euclidean distance. The error calculation can be processed by the scipy.spatial.distance module in the SciPy library of Python to obtain the initial ranging error data, which provides a basis for the next step of sensor compensation.

[0129] Step S45: adjusting the internal parameters of the laser sensor according to the initial ranging error data, thereby generating sensor compensation parameters;

[0130] The embodiment of the present invention adjusts the internal parameters of the laser sensor according to the initial ranging error data; the least square method or other optimization algorithms (such as nonlinear least square method) are used to calculate the compensation parameters of the sensor according to the initial ranging error data. In specific implementation, the scipy.optimize module in the SciPy library of Python can be used to perform parameter optimization. The generated sensor compensation parameters will be used to adjust the ranging accuracy of the sensor, thereby improving the accuracy of the ranging result.

[0131] Step S46: Correcting the calibration space coordinate data according to the sensor compensation parameters to obtain laser ranging calibration data;

[0132] The embodiment of the present invention performs correction processing on the calibration space coordinate data according to the generated sensor compensation parameters; uses the compensation parameters to correct the distance measurement data, and adjusts the space coordinate data through a correction formula (such as a linear or nonlinear correction model). The correction processing can use the NumPy library in Python to perform matrix operations and data adjustments. The laser ranging calibration data finally obtained is used to improve the ranging accuracy and ensure the accuracy of the laser ranging results.

[0133] Step S47: Perform accuracy verification processing on the laser ranging calibration data to obtain a ranging error distribution map.

[0134] The embodiment of the present invention performs accuracy verification processing on the laser ranging calibration data to obtain a ranging error distribution map; first, the ranging error is calculated by comparing the calibration data with the ranging results of known calibration points. Then, the error distribution map is generated using the error data, and the error distribution map is drawn using a data visualization tool (such as Python's Matplotlib library). The accuracy verification process helps identify the ranging error distribution after calibration, thereby ensuring the accuracy and reliability of the laser ranging system.

[0135] The present invention calibrates the initial scanning position of the laser sensor on the mechanical arm based on the equal force parallel state data, ensures that it is parallel to the wall surface, and then performs grid multi-point scanning and ranging. This step helps to improve the accuracy of the laser sensor, ensures that the reference plane is consistent during each scan, and thus obtains high-precision original ranging data. Grid scanning not only increases the coverage area, but also ensures that the measurement results of different areas are consistent. The effect of this step is that through accurate scanning calibration, the error in laser ranging is minimized to the maximum extent, ensuring the reliability of subsequent data processing. The original laser ranging data is filtered to remove noise and interference, and the discrete data that may appear in the measurement process is eliminated by the outlier elimination algorithm. This process ensures the validity and consistency of subsequent data. The process of filtering and eliminating outliers reduces the system error and ensures that the data set is more accurate; its effect is that by effectively removing bad data, the quality of ranging data is improved, which lays a good foundation for subsequent ranging calibration and analysis. The pre-processed ranging data is fused with the real-time attitude pressure data, and converted into a global coordinate system to generate spatial coordinate data for calibration. The fusion of attitude pressure data can compensate for the ranging error caused by the change of the mechanical arm attitude. Converting to the global coordinate system can ensure that the data has a unified reference frame for further processing; the effect of this step is that it can effectively reduce the ranging error caused by the change of the robot arm posture, and improve the overall consistency and reliability of the measurement data. By obtaining the known calibration point data on the wall, the initial ranging error between the calibration space coordinate data and the calibration point is calculated. This error calculation helps the system quickly locate the deviation in the ranging, thereby providing a basis for subsequent compensation. The calibration point is used as a known reference to ensure the accuracy of the ranging error calculation. Its effect is that through accurate error calculation, reliable data information can be provided for the adjustment of the internal parameters of the sensor, thereby improving the accuracy of the entire ranging system. According to the initial ranging error data, the internal parameters of the laser sensor are adjusted to generate the compensation parameters of the sensor. This step can effectively correct the internal system error of the sensor and ensure that the subsequent ranging is more accurate. By optimizing the performance of the sensor, the accuracy of laser ranging can be significantly improved. The effect of this step is that through systematic parameter adjustment, the sensor can adapt to changes in complex measurement environments and provide more accurate ranging results. Using the compensation parameters of the sensor, the calibration space coordinate data is corrected to generate accurate laser ranging calibration data. This process ensures that the distance measurement data is more closely matched to the actual measurement environment after compensation, reducing systematic errors. The corrected data can reflect the true geometric characteristics of the wall. The effect of this step is that the application of compensation parameters makes the laser distance measurement data more accurate, ensuring the accuracy of subsequent data analysis and construction control. Finally, by verifying the accuracy of the laser distance measurement calibration data, a distance measurement error distribution map is generated. This verification process can show the accuracy of the laser distance measurement system in different areas and identify potential errors.The distance measurement error distribution diagram provides a strong reference for subsequent construction adjustments. The effect of this step is that through precise accuracy verification, the system can evaluate the overall distance measurement accuracy and optimize it according to the error distribution, ensuring the consistency and high accuracy of the final construction effect.

[0136] Preferably, step S47 includes the following steps:

[0137] Step S471: calculating the Euclidean distance between the point in the initial ranging error data and the corresponding calibration point according to the laser ranging calibration data, thereby obtaining point-to-point error data;

[0138] The embodiment of the present invention extracts the actual coordinates of each measurement point from the laser ranging calibration data and compares it with the known calibration point data. By calculating the Euclidean distance between these points, the point-to-point error data is obtained. Specifically, the scipy.spatial.distance.euclidean function in the SciPy library in Python can be used to calculate the Euclidean distance. The point-to-point error data obtained in this step provides basic data for subsequent error analysis.

[0139] Step S472: Calculate the root mean square error, maximum error, minimum error and error standard deviation of the point-to-point error data, and map them to the wall coordinate system, thereby generating a two-dimensional error distribution matrix;

[0140] The embodiment of the present invention uses the NumPy library in Python to calculate the root mean square error (RMSE), the maximum error, the minimum error, and the standard deviation of the error. Subsequently, by mapping these error data according to the coordinate system position of the wall, Matplotlib or a similar data visualization tool is used to generate a two-dimensional error distribution matrix, which is used to show the error distribution on the wall in detail.

[0141] Step S473: using an interpolation algorithm to convert the two-dimensional error distribution matrix into a continuous error distribution surface;

[0142] The embodiment of the present invention first selects an appropriate interpolation algorithm, such as spline interpolation or Kriging interpolation, and implements it through the scipy.interpolate module in the Python SciPy library. The two-dimensional error distribution matrix is ​​used as input, and the interpolation algorithm is applied to generate a continuous error distribution surface. This surface can provide more delicate and continuous error distribution information, which helps to deeply understand the change of errors on the entire wall surface.

[0143] Step S474: Visualizing the error distribution surface with color coding and performing systematic error pattern recognition to obtain a preliminary error distribution map;

[0144] The embodiment of the present invention uses Matplotlib or other visualization tools to color-code the error distribution surface, maps the error range with the color level, and generates an error distribution map. Then, the systematic error pattern is identified through visual analysis or an automated pattern recognition algorithm (such as a clustering algorithm in machine learning), thereby obtaining a preliminary error distribution map. This step helps identify the error distribution pattern and possible error sources on the wall.

[0145] Step S475: Identify the areas with drastic error changes based on the local error gradient on the preliminary error distribution map, perform high-density repeated measurements on the areas, and update the errors of the corresponding areas, thereby obtaining a ranging error distribution map.

[0146] The embodiment of the present invention identifies the areas where the error changes dramatically by calculating the local error gradient in the error distribution map. Then, these areas are repeatedly measured with high density to obtain more accurate error data. The NumPy and Matplotlib libraries in Python are used to calculate the error gradient and process the high-density measurement data. Finally, the error data of these areas are updated to generate a final ranging error distribution map, thereby providing more accurate ranging error information for further analysis and adjustment.

[0147] The present invention calculates the Euclidean distance between each point in the initial ranging error and the corresponding calibration point through laser ranging calibration data, thereby obtaining point-to-point error data. This method of directly calculating the error can accurately identify the difference between the measuring point and the calibration point, thereby clarifying the accuracy of the ranging system. The effect of this step is that the error of each measuring point can be clearly quantified, providing an accurate reference for subsequent data analysis and correction. The point-to-point error data is statistically calculated for the root mean square error (RMSE), maximum error, minimum error and error standard deviation, and these error values ​​are mapped to the wall coordinate system to generate a two-dimensional error distribution matrix. These statistical values ​​provide a detailed description of the overall error, which can help the system to fully understand the characteristics and distribution of the ranging error. The generated two-dimensional error distribution matrix can reflect the error size at different positions on the wall, which is helpful for more accurate error correction. Its effect is that through error statistics, the accuracy of laser ranging can be systematically evaluated, and a basis for further error correction and optimization is provided. The two-dimensional error distribution matrix is ​​converted into a continuous error distribution surface using an interpolation algorithm, thereby smoothing the discrete ranging error information into a continuous surface model. This process can more intuitively display the spatial distribution characteristics of the error on the wall, especially in some areas where the error is large, interpolation can help the system better identify and process. The effect is that by generating a continuous error surface, the system can more clearly show the changing trend of the ranging error, thereby effectively optimizing the laser ranging system. The error distribution surface is visualized by color coding, and the overall error distribution is analyzed using a systematic error pattern recognition algorithm. Color-coded visualization can help quickly identify areas with large errors, while error pattern recognition helps to find systematic error sources, such as fixed errors of sensors or environmental interference. The effect of this step is that through visualization and pattern recognition, potential problems in the measurement system can be quickly identified, providing a basis for subsequent corrections. Based on the error distribution map, areas with drastic error changes are identified, and areas with sudden error changes are found by calculating local error gradients. These areas may have large measurement deviations, so the system will perform high-density repeated measurements on them and update the error data of these areas to ensure that the final error distribution map is more accurate. The effect of this step is that it can perform more accurate detection and calibration of key areas, improve the overall measurement accuracy, ensure that the final distance measurement error distribution map can truly reflect the error situation of the wall, and provide high-precision data support for further construction.

[0148] Preferably, step S5 comprises the following steps:

[0149] Step S51: identifying the error peak point of the ranging error distribution graph, and performing abnormal data points exceeding the threshold range according to a preset error threshold, thereby obtaining error abnormal data points;

[0150] The embodiment of the present invention uses a data analysis tool (such as Python's NumPy library) to perform peak detection on the error distribution graph, and identifies the error peak point by calculating the local maximum value of the error value. Then, an error threshold is set, and points whose errors exceed this threshold are marked as abnormal data points. The numpy.where function can be used to implement threshold screening to obtain error abnormal data points, which will be used for further abnormal analysis and processing.

[0151] Step S52: performing local spatial analysis on the error abnormal data points based on isolated abnormal points or abnormal areas, and obtaining isolated abnormal point data and abnormal area data respectively;

[0152] The embodiment of the present invention uses a spatial clustering algorithm (such as DBSCAN or K-means) to classify abnormal data points into isolated points and clustered areas. The DBSCAN algorithm is particularly suitable for detecting isolated abnormal points and dense areas because it can find groups of abnormal points of any shape. By using Python's sklearn.cluster.DBSCAN module, these classifications can be performed and isolated abnormal point data and clustered abnormal area data can be generated to provide basic data for subsequent corrections.

[0153] Step S53: correcting the isolated abnormal point data by the interpolation method based on spatial proximity, thereby obtaining an interpolation correction data set; performing root cause analysis on the abnormal area data, and performing secondary collection of the ranging data of the area, thereby obtaining a regional resampled data set;

[0154] The embodiment of the present invention corrects the interpolation method based on spatial proximity for the isolated outlier data. An interpolation algorithm (such as nearest neighbor interpolation or spline interpolation) is selected to correct the data around the isolated outlier to obtain a more accurate interpolation corrected data set. The interpolation function in the scipy.interpolate module of Python can be used for the operation. At the same time, the root cause analysis of the data in the abnormal area is performed. By analyzing the characteristics and environmental impact of these areas, it may be necessary to collect the ranging data of the area again, and use high-density measurement technology to obtain a regional resampled data set. These operations provide the necessary information for correcting and updating the abnormal data.

[0155] Step S54: removing abnormal points that cannot be corrected according to the interpolation correction data set and the regional resampling data set, thereby generating an optimized ranging data set;

[0156] The embodiment of the present invention combines the interpolation correction data set with the regional resampling data set, and uses the data operation function in the pandas library of Python to screen and remove outliers. Outliers that cannot be corrected are identified by comparing with the corrected data. The optimized ranging data set can be generated, and the array operation function of the numpy library can be used to clean and optimize the data to ensure the accuracy and consistency of the final data set for fitting the subsequent ranging model.

[0157] Step S55: fitting the distance measurement results based on the least squares method to the optimized distance measurement data set, thereby generating distance measurement model parameter data to achieve distance measurement and calibration of the wall scraping equipment.

[0158] The embodiment of the present invention uses the least squares method to fit the optimized distance measurement data set. This can be achieved through the curve_fit function in the scipy.optimize module of Python to perform linear or nonlinear fitting. The least squares method fits the data points to the optimal model curve and calculates the model parameters. During the fitting process, it is necessary to input the independent variables and dependent variables of the distance measurement data set, and adjust the model parameters through the optimization algorithm to achieve accurate distance measurement and calibration of the wall scraping equipment. The distance measurement model parameter data finally generated will be used in actual distance measurement applications to ensure the high accuracy and stability of the equipment.

[0159] The present invention identifies the peak point of the error in the distance measurement error distribution diagram, and screens out the abnormal data points exceeding the threshold value according to the preset error threshold value. This process helps to quickly locate the extreme situation of the distance measurement error and identify the possible error source. The extraction of abnormal data points enables the system to focus on the area or point with the largest error, which is convenient for targeted analysis and correction. Its effect is that through accurate identification of abnormal data points, the problem area in the system can be quickly found, providing a basis for focusing on the subsequent processing steps. The identified abnormal data points are subjected to local spatial analysis based on isolated abnormal points or abnormal areas in pieces, so as to obtain isolated abnormal point data and abnormal area data in pieces respectively. This analysis process can identify single abnormal points and regional errors, and help the system distinguish different types of distance measurement errors. Isolated abnormal points may be caused by single point failures, while abnormal areas in pieces may indicate more extensive systemic problems. The effect of this step is that through the classification analysis of abnormal data, the system can adopt different correction strategies to improve the effectiveness and pertinence of overall data processing. The interpolation method based on spatial proximity is used to correct the data of isolated outliers to obtain an interpolation correction data set; the root cause analysis is performed on the data of the abnormal area, and the distance measurement data of the area is collected again to generate a regional resampling data set. The interpolation correction of isolated outliers fills the error by using the data of neighboring points, thereby improving the continuity of local data; and the resampling of the area can reacquire the actual distance measurement data of the area and correct the error in a larger range. The effect of this step is to optimize the accuracy of the data set and ensure the overall reliability of the distance measurement results through targeted processing of outliers and areas. According to the interpolation correction data set and the regional resampling data set, the outliers that cannot be corrected are eliminated to generate an optimized distance measurement data set. This process ensures that the final data set contains only effectively corrected distance measurement data, thereby improving the accuracy and consistency of the overall data. Eliminating outliers that cannot be corrected can reduce the error in the distance measurement results and improve the accuracy of the final calibration. The effect of this step is to ensure the high accuracy of the distance measurement system by optimizing the data set, so that the wall scraping equipment can be constructed and calibrated more accurately. The optimized distance measurement data set is fitted based on the least squares method to generate the distance measurement model parameter data. This process uses the least squares method to fit the distance measurement data into a mathematical model, which can effectively reduce the overall error and generate a reliable distance measurement model. The application of the least squares method ensures the best fitting state of the distance measurement results and provides high-precision distance measurement and calibration capabilities for wall scraping equipment; the effect of this step is to generate the final distance measurement model through precise fitting, ensuring the efficiency and accuracy of the distance measurement and calibration functions of the wall scraping equipment.

[0160] The present invention also provides a laser ranging calibration system based on a laser sensor, which is used to execute the above-mentioned laser ranging calibration method based on a laser sensor. The laser ranging calibration system based on a laser sensor comprises:

[0161] The spatial coordinate acquisition module is used to obtain spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; plane fitting based on singular value decomposition is performed on the wall according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data;

[0162] The robot arm posture correction module is used to obtain the robot arm normal vector data of the current plane of the robot arm of the wall scraping equipment; normalize the robot arm normal vector data and the virtual plane and its normal vector data, calculate the rotation axis and rotation angle between the two, and generate rotation matrix data using the Rodrigues rotation formula;

[0163] The posture adjustment and feedback module is used to adjust the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitor the posture and pressure of the robot arm in real time to obtain the posture pressure feedback data; the posture of the robot arm is dynamically adjusted based on the complex surface according to the posture pressure feedback data using the regional processing strategy, so as to obtain the uniform force parallel state data;

[0164] The laser distance measurement calibration module is used to use the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform force parallel state data, so as to obtain the laser distance measurement calibration data; perform accuracy verification processing on the laser distance measurement calibration data, so as to obtain the distance measurement error distribution map;

[0165] The error correction and model generation module is used to eliminate abnormal points in the ranging error distribution diagram according to a preset error threshold, and to fit the ranging results based on the least squares method, so as to generate ranging model parameter data to achieve ranging and calibration of the wall scraping equipment.

[0166] The present invention ensures the accuracy of spatial coordinate data through a high-precision laser sensor, can capture detailed information of each point on the wall, and reduce the impact caused by measurement errors. By performing plane fitting on the spatial coordinate data through singular value decomposition, a virtual plane model of the wall can be accurately established, and accurate normal vector data can be provided, laying the foundation for subsequent mechanical arm posture correction and distance calibration. By processing the data of the mechanical arm normal vector and the virtual plane normal vector, it is ensured that the working surface of the mechanical arm is parallel to the wall. The generated rotation matrix data enables the mechanical arm to make precise angle adjustments, thereby improving the accuracy of construction; by calculating the rotation axis and the rotation angle, the posture of the mechanical arm can be effectively corrected to align it with the virtual plane, reducing construction errors and improving the accuracy of the overall operation. By monitoring the posture and pressure of the mechanical arm in real time, the data of the working status can be instantly obtained, ensuring that the contact pressure between the mechanical arm and the wall is uniform, and improving the stability of the construction effect; by using the regional processing strategy and dynamic adjustment, local optimization can be performed for complex walls, so that the working status of the mechanical arm in different areas remains consistent, and the uniformity and quality of construction are improved. The adjusted laser sensor can perform high-precision multi-point scanning to ensure the accuracy of the distance data. Accuracy verification and the generation of error distribution maps help identify and correct potential distance measurement errors and improve the overall accuracy of the system; through accuracy verification processing, it can be ensured that the calibration of the laser distance measurement system meets actual needs, making the distance measurement capability of the wall scraping equipment more reliable. Eliminating abnormal points that cannot be corrected can effectively improve the overall quality of the distance measurement data and reduce the errors caused by abnormal data; fitting the distance measurement results by the least squares method can generate accurate distance measurement model parameters, provide accurate distance measurement and calibration functions for wall scraping equipment, and ensure that the construction effect meets the expected standards.

[0167] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0168] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A laser ranging calibration method based on a laser sensor, applied to wall scraping equipment, It is characterized in that The following steps are involved: Step S1: obtaining spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; performing plane fitting on the wall based on singular value decomposition according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data; Step S2: obtaining the normal vector data of the robot arm of the wall scraping device at the current plane; normalizing the normal vector data of the robot arm and the virtual plane and its normal vector data, calculating the rotation axis and rotation angle between the two, and generating rotation matrix data using the Rodriguez rotation formula; Step S3: adjusting the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitoring the posture and pressure of the robot arm in real time, so as to obtain posture pressure feedback data; using the regional processing strategy to dynamically adjust the posture of the robot arm based on the complex curved surface according to the posture pressure feedback data, so as to obtain the uniform force parallel state data; Step S4: using the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform parallel state data, thereby obtaining laser distance measurement calibration data; Perform accuracy verification processing on the laser ranging calibration data to obtain the ranging error distribution map; Step S5: remove abnormal points from the distance measurement error distribution diagram according to a preset error threshold, and perform distance measurement result fitting based on the least squares method, so as to generate distance measurement model parameter data to achieve distance measurement and calibration of the wall scraping equipment.

2. The laser ranging calibration method based on a laser sensor according to claim 1, It is characterized in that Step S1 includes the following steps: Step S11: performing sensor self-calibration processing based on laser power and receiving sensitivity on a plurality of pre-deployed high-precision long-stroke laser sensors, thereby obtaining sensor parameter calibration data; Step S12: using multiple groups of high-precision long-stroke laser sensors to perform gridded multi-point scanning on the wall according to sensor parameter calibration data, thereby obtaining an original spatial coordinate data set; Step S13: smoothing the original spatial coordinate data set using a moving average filtering algorithm to obtain a smoothed spatial coordinate data set; Step S14: establishing a global coordinate system according to the original spatial coordinate data set, and converting the smoothed spatial coordinate data set into the global coordinate system to obtain a spatial coordinate data set under a unified coordinate system; Step S15: performing plane fitting on the wall surface based on singular value decomposition according to the spatial coordinate data set, thereby obtaining a virtual plane and its normal vector data.

3. The laser ranging calibration method based on the laser sensor according to claim 2, It is characterized in that Step S15 includes the following steps: Step S151: performing abnormal coordinate detection and screening on the spatial coordinate data set according to a preset abnormal value detection threshold, thereby obtaining a spatial coordinate data cleaning set; and constructing a covariance matrix according to the spatial coordinate data cleaning set, and performing singular value decomposition on the covariance matrix, thereby obtaining wall space feature data, wherein the wall space feature data includes eigenvalues ​​and eigenvectors; Step S152: extracting a feature vector corresponding to the minimum feature value according to the magnitude relationship of the feature values ​​in the wall space feature data, and using it as the normal vector of the virtual plane, thereby obtaining initial normal vector data; Step S153: Calculate the intercept term of the virtual plane according to the initial normal vector data and the cleaned set of spatial coordinate data, so as to obtain plane equation parameter data; Step S154: fitting the points in the spatial coordinate data set to the virtual plane based on the plane equation parameter data, thereby obtaining fitting error data; Step S155: performing root mean square error and maximum deviation calculation and analysis on the fitting error data, thereby obtaining fitting quality evaluation index data; Step S156: If the fitting quality evaluation index meets the preset threshold, the virtual plane of the wall and its normal vector data are output; if not, return to step S151, adjust the outlier detection threshold, and re-execute steps S151 to S156.

4. The laser ranging calibration method based on a laser sensor according to claim 3, It is characterized in that Step S2 includes the following steps: Step S21: collecting the spatial coordinates of the current working surface of the robotic arm through multiple groups of laser sensors and an inertial measurement unit on the robotic arm, and converting them into spatial coordinate data of the robotic arm in the local coordinate system of the robotic arm; Step S22: performing work surface plane fitting based on singular value decomposition according to the robot arm spatial coordinate data, and performing an initial normal vector of the current work plane of the robot arm, thereby obtaining initial normal vector data of the robot arm; Step S23: normalizing the initial normal vector data of the robot arm, and calculating the angle and rotation axis with the virtual plane and its normal vector data, so as to obtain the normal vector rotation axis data; Step S24: preliminarily adjusting the robotic arm according to the normal vector rotation axis data, and re-measuring the adjusted robotic arm posture to obtain corrected robotic arm normal vector data; Step S25: Compare the robot arm normal vector data with the virtual plane and its normal vector data based on the rotation axis and rotation angle, and generate rotation matrix data using the Rodriguez rotation formula.

5. The laser ranging calibration method based on a laser sensor according to claim 4, It is characterized in that Step S25 includes the following steps: Step S251: performing dot product calculation on the normal vector data of the robot arm and the virtual plane and its normal vector data, thereby obtaining the cosine value of the angle between the two vectors; Step S252: Calculate the angle between the two normal vectors using the cosine value of the angle between the two vectors, thereby obtaining rotation angle data; Step S253: performing cross product calculation on the normal vector data of the robot arm and the virtual plane and its normal vector data to obtain a rotation axis vector; Step S254: constructing a rotation matrix according to the rotation angle data and the unit rotation axis vector using the Rodriguez rotation formula, thereby obtaining initial rotation matrix data; Step S255: numerically optimizing the initial rotation matrix data to minimize the error between the normal vector data of the rotated manipulator and the virtual plane and its normal vector data, thereby obtaining the rotation matrix data.

6. The laser ranging calibration method based on a laser sensor according to claim 5, It is characterized in that Step S3 includes the following steps: Step S31: Calculate the target posture of the robot arm according to the rotation matrix data, and adjust the posture of the robot arm so that the working surface of the robot arm is parallel to the wall through the motion control system of the robot arm, thereby obtaining initial adjustment posture data; Step S32: using the force sensor on the end effector of the robotic arm to monitor the contact pressure between the robotic arm and the wall in real time according to the initial adjustment posture data, and monitoring the current posture of the robotic arm through the posture sensor, so as to obtain posture pressure feedback data; Step S33: using a regional processing strategy to divide the wall area into multiple grid sub-areas, and performing local analysis on the posture pressure feedback data of each sub-area, thereby obtaining local pressure distribution data; Step S34: performing posture parameter adjustment processing according to the local pressure distribution data, thereby obtaining posture adjustment instruction data; Step S35: performing local fine adjustment of the robot arm based on dynamic adaptation of complex surfaces according to the posture adjustment instruction data, thereby obtaining real-time posture pressure data; Step S36: Calculate the pressure uniformity of each sub-area according to the real-time posture pressure data, so as to obtain the uniform force parallel state data.

7. The laser ranging calibration method based on a laser sensor according to claim 6, It is characterized in that Step S4 includes the following steps: Step S41: calibrating the initial scanning position of the laser sensor on the adjusted robot arm based on the uniform parallel state data, and using the laser sensor to perform grid multi-point scanning distance measurement based on the calibrated reference plane, thereby obtaining original laser distance measurement data; Step S42: performing preliminary filtering and outlier elimination processing on the original laser ranging data, thereby obtaining preprocessed ranging data; Step S43: fusing the pre-processed distance measurement data with the real-time attitude pressure data, and converting them into a global coordinate system, thereby obtaining calibration space coordinate data; Step S44: obtaining known calibration point data on the wall; performing initial distance measurement error calculation on the calibration space coordinate data and the known calibration point data, thereby obtaining initial distance measurement error data; Step S45: adjusting the internal parameters of the laser sensor according to the initial ranging error data, thereby generating sensor compensation parameters; Step S46: Correcting the calibration space coordinate data according to the sensor compensation parameters to obtain laser ranging calibration data; Step S47: Perform accuracy verification processing on the laser ranging calibration data to obtain a ranging error distribution map.

8. The laser ranging calibration method based on a laser sensor according to claim 7, It is characterized in that Step S47 includes the following steps: Step S471: calculating the Euclidean distance between the point in the initial ranging error data and the corresponding calibration point according to the laser ranging calibration data, thereby obtaining point-to-point error data; Step S472: Calculate the root mean square error, maximum error, minimum error and error standard deviation of the point-to-point error data, and map them to the wall coordinate system, thereby generating a two-dimensional error distribution matrix; Step S473: using an interpolation algorithm to convert the two-dimensional error distribution matrix into a continuous error distribution surface; Step S474: Visualizing the error distribution surface with color coding and performing systematic error pattern recognition to obtain a preliminary error distribution map; Step S475: Identify the areas with drastic error changes based on the local error gradient on the preliminary error distribution map, perform high-density repeated measurements on the areas, and update the errors of the corresponding areas, thereby obtaining a ranging error distribution map.

9. The laser ranging calibration method based on a laser sensor according to claim 7, It is characterized in that Step S5 includes the following steps: Step S51: identifying the error peak point of the ranging error distribution graph, and performing abnormal data points exceeding the threshold range according to a preset error threshold, thereby obtaining error abnormal data points; Step S52: performing local spatial analysis on the error abnormal data points based on isolated abnormal points or abnormal areas, and obtaining isolated abnormal point data and abnormal area data respectively; Step S53: correcting the isolated abnormal point data by the interpolation method based on spatial proximity, thereby obtaining an interpolation correction data set; performing root cause analysis on the abnormal area data, and performing secondary collection of the ranging data of the area, thereby obtaining a regional resampled data set; Step S54: removing abnormal points that cannot be corrected according to the interpolation correction data set and the regional resampling data set, thereby generating an optimized ranging data set; Step S55: fitting the distance measurement results based on the least squares method to the optimized distance measurement data set, thereby generating distance measurement model parameter data to achieve distance measurement and calibration of the wall scraping equipment.

10. A laser ranging calibration system based on laser sensor, It is characterized in that Used to perform the laser distance measurement calibration method based on a laser sensor as claimed in claim 1, the laser distance measurement calibration system based on a laser sensor comprises: The spatial coordinate acquisition module is used to obtain spatial coordinate data sets of multiple points on the wall through multiple groups of high-precision long-stroke laser sensors; plane fitting based on singular value decomposition is performed on the wall according to the spatial coordinate data sets, thereby obtaining virtual planes and their normal vector data; The robot arm posture correction module is used to obtain the robot arm normal vector data of the current plane of the robot arm of the wall scraping equipment; normalize the robot arm normal vector data and the virtual plane and its normal vector data, calculate the rotation axis and rotation angle between the two, and generate rotation matrix data using the Rodrigues rotation formula; The posture adjustment and feedback module is used to adjust the angle of the working surface of the robot arm parallel to the wall according to the rotation matrix data, and monitor the posture and pressure of the robot arm in real time to obtain the posture pressure feedback data; the posture of the robot arm is dynamically adjusted based on the complex surface according to the posture pressure feedback data using the regional processing strategy, so as to obtain the uniform force parallel state data; The laser distance measurement calibration module is used to use the laser sensor on the adjusted mechanical arm to perform multi-point scanning distance measurement according to the uniform force parallel state data, so as to obtain the laser distance measurement calibration data; perform accuracy verification processing on the laser distance measurement calibration data, so as to obtain the distance measurement error distribution map; The error correction and model generation module is used to eliminate abnormal points in the ranging error distribution diagram according to a preset error threshold, and to fit the ranging results based on the least squares method, so as to generate ranging model parameter data to achieve ranging and calibration of the wall scraping equipment.

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