A method and system for testing the docking accuracy of robot reflectors

Abstract

The present invention provides a test method and system for the docking accuracy of a robot reflector. A first laser rangefinder is installed on the robot to be tested, and a slide rail with a scale and a movable second laser rangefinder are set on the reflector. The laser rangefinder technology is used to automatically obtain the relative position data between the reflector and the robot. After the robot performs the docking task, the lateral distance, longitudinal distance, angle and angle deviation between the robot and the reflector are calculated using the measurement data. Through the data collection of multiple docking tasks and the generation of three-dimensional scatter plots, the error distribution is analyzed, and the robot control algorithm is optimized, thereby improving the docking accuracy. The method does not require manual intervention, automatically completes accuracy measurement and error analysis, and is highly efficient and accurate. The technology is suitable for robots of different models and can be widely used in multiple fields such as mechanical docking, automatic charging, and material handling, which significantly improves the accuracy and reliability of robot task execution.

Description

Technical Field

[0001] The invention belongs to the technical field of robots, and in particular relates to a method and system for testing the docking accuracy of a robot reflector. Background Art

[0002] With the rapid development of automation and robotics, LiDAR has been widely used in robot positioning, navigation, and environmental perception. LiDAR can provide high-precision distance measurement by emitting laser beams and receiving reflected signals, and is one of the indispensable core technologies in modern intelligent robot systems. However, despite the strong ranging capability of LiDAR, in practical applications, robots still face some challenges when performing complex docking tasks, especially in terms of docking accuracy and positioning accuracy.

[0003] In robot docking tasks, especially for robots that rely on LiDAR for positioning and mapping, their actual positions are usually determined by the control algorithm and motor drive. Therefore, there will be a certain error between the robot's position when performing the task and the theoretical calculated value. This error comes from a variety of factors, including sensor errors, algorithm deviations, changes in the external environment, etc. The accumulation of these errors will affect the accuracy of the robot's docking tasks, especially in scenarios that require high-precision docking. The errors may cause the robot to fail to dock accurately at the target position, affecting the completion of the task.

[0004] At present, when a robot docks with a target object, it usually relies on manual means to measure errors and correct positions. Manual measurement methods mainly include using a ruler or other tools to measure physical distances, or using a compass to measure angles. However, these methods not only consume a lot of time and manpower, but also have large errors. In particular, the accuracy of the measurement results of the compass is easily affected in an environment with strong magnetic field interference. In addition, manual intervention requires repeated measurements and adjustments for robots of different models, which increases the workload and complexity. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method and system for testing the docking accuracy of a robot reflector.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] In one aspect, the present invention provides a method for testing the docking accuracy of a robot reflector, comprising the following steps:

[0008] Installing a first laser rangefinder on the robot to be tested, wherein the first laser rangefinder is installed at the center position of the robot to be tested;

[0009] A slide rail with scales is installed on the reflective plate, and a movable second laser rangefinder is arranged on the slide rail;

[0010] Send a reflector docking command to the robot under test, and the robot under test performs the docking task according to the control algorithm;

[0011] After the docking is completed, reading the measurement value of the first laser rangefinder as the first measurement value;

[0012] Obtain the scale of the center of the reflector plane through the slide rail, and align the second laser rangefinder with the radar center of the robot to be tested;

[0013] Measuring the distance between the second laser rangefinder and the scale at the center of the reflector plane as the first distance, and reading the measurement value of the second laser rangefinder as the second measurement value;

[0014] Calculate the angle between the robot to be tested and the reflector according to the obtained first measurement value and second measurement value;

[0015] Obtain the lateral distance, longitudinal distance and angular deviation between the robot to be tested and the reflector according to the included angle, the first distance and the second measurement value;

[0016] Control the robot to be tested to perform multiple docking tasks in different working scenarios, and collect the lateral distance, longitudinal distance and angle deviation of each task;

[0017] Generate a three-dimensional scatter plot based on data from multiple tasks;

[0018] The error distribution is analyzed based on the three-dimensional scatter plot, and the control algorithm of the robot under test is optimized according to the error distribution.

[0019] Furthermore, the angle between the robot to be tested and the reflector is calculated based on the first measurement value and the second measurement value obtained, and the calculation formula is:

[0020]

[0021] Wherein, α is the angle between the robot to be measured and the reflective plate, b is the second measured value, and c is the first measured value.

[0022] Furthermore, the step of obtaining the lateral distance, longitudinal distance and angle deviation between the robot to be tested and the reflector according to the angle, the first distance and the second measurement value includes:

[0023] The first distance is used as the lateral distance between the robot to be tested and the reflector, the second measured value is used as the longitudinal distance between the robot to be tested and the reflector, and the included angle is used as the angular deviation between the robot to be tested and the reflector.

[0024] Furthermore, generating a three-dimensional scatter plot based on data from multiple tasks includes the following steps:

[0025] Collect the lateral distance, longitudinal distance and angular deviation data of each docking mission and organize these data into a data set;

[0026] Generate a 3D scatter plot using a data visualization tool based on the dataset.

[0027] Furthermore, generating a three-dimensional scatter plot using a data visualization tool according to the data set specifically includes:

[0028] The collected lateral distance, longitudinal distance and angle deviation data are homogenized and converted into a unified coordinate range;

[0029] A data visualization tool is used to use the normalized lateral distance, longitudinal distance, and angular deviation data as the X-axis, Y-axis, and Z-axis data in a three-dimensional coordinate system, respectively, to generate a three-dimensional scatter plot, in which each scatter point includes work scene information of the task corresponding to each scatter point data.

[0030] Furthermore, analyzing the error distribution based on the three-dimensional scatter plot and optimizing the control algorithm of the robot to be tested according to the error distribution specifically includes:

[0031] For each scattered point in the three-dimensional scatter plot, the distance between the scattered point and the origin is calculated, and the distance between the scattered point and the origin is used as the error of the task corresponding to the scattered point;

[0032] According to the calculated distance between each scattered point and the origin, the error size in each working scene is evaluated. If the scattered points in a working scene are far away from the origin, it means that the error of the robot control algorithm in this working scene is large. If the scattered points in a working scene are close to the origin, it means that the control accuracy in this working scene is high.

[0033] Count the average error, maximum error and standard deviation of error in each work scenario;

[0034] The control algorithm of the measuring robot is optimized according to the average error, maximum error and error standard deviation in each working scenario.

[0035] Another aspect of the present invention provides a test system for implementing any of the above-mentioned test methods, comprising: a first laser rangefinder, a slide rail with a scale, a reflector, a second laser rangefinder, a data acquisition module, a data processing module, and a control optimization module.

[0036] Furthermore, the first laser rangefinder is installed at the center position of the robot to be measured and is used to measure the first measurement value;

[0037] The slide rail with scale is installed on the reflective plate and is used to fix and move the second laser rangefinder;

[0038] The second laser rangefinder is used to measure a second measurement value;

[0039] The data acquisition module is used to read the first measurement value of the first laser rangefinder and the second measurement value of the second laser rangefinder;

[0040] The data processing module is used to obtain the included angle, the lateral distance, the longitudinal distance and the angular deviation according to the first measurement value, the second measurement value and the first distance.

[0041] Furthermore, the data processing module is further used to generate a scatter plot and analyze error distribution based on the scatter plot.

[0042] Furthermore, the control optimization module is used to optimize the control algorithm of the robot and improve the docking accuracy of the robot according to the error analysis results output by the data processing module.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] (1) The present invention installs a first laser rangefinder on the robot to be measured, and sets a slide rail with a scale on the reflector, and a movable second laser rangefinder is configured on the slide rail. This combination scheme enables the robot to accurately measure the relative position with the reflector when performing the docking task, avoiding the uncertainty and error of manual measurement. Through the automated laser distance measurement process, the present invention can provide more accurate measurement values, thereby improving the docking accuracy between the robot and the reflector.

[0045] (2) The present invention collects the measurement data of the first laser rangefinder and the second laser rangefinder through the data acquisition module, calculates the lateral distance, longitudinal distance, angle and angle deviation between the robot and the reflector through the data processing module, and generates a three-dimensional scatter plot through the data of multiple tasks to analyze the error distribution. This process realizes a comprehensive evaluation of the robot docking accuracy, and can optimize the control algorithm based on the error analysis results, thereby improving the control accuracy and stability of the robot.

[0046] (3) The present invention can intuitively display the distribution of errors by generating and analyzing a three-dimensional scatter plot of the collected task data. By calculating the distance between each data point and the origin, the error can be quantified, and the control algorithm can be optimized in a targeted manner according to the error size. This analysis method is more efficient and accurate than traditional manual measurement, and helps to quickly identify and correct the control deviation of the robot, significantly improving the docking accuracy of the robot in different working scenarios.

[0047] (4) Traditional robot docking tasks often rely on manual measurement and calibration, which is labor-intensive and prone to errors. The present invention reduces manual intervention and significantly improves work efficiency through fully automatic measurement and data processing. It can autonomously complete the test of the reflector docking task without human intervention, thereby reducing the difficulty of operation and the error rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the method of the present invention;

[0049] Figure 2 This is a schematic diagram of docking according to an embodiment of the present invention; DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0051] On the one hand, this embodiment provides a method for testing the docking accuracy of a robot reflector. Figure 1 As shown, the following steps are included:

[0052] Installing a first laser rangefinder on the robot to be tested, wherein the first laser rangefinder is installed at the center position of the robot to be tested;

[0053] A slide rail with scales is installed on the reflective plate, and a movable second laser rangefinder is arranged on the slide rail;

[0054] Send a reflector docking command to the robot under test, and the robot under test performs the docking task according to the control algorithm;

[0055] After the docking is completed, reading the measurement value of the first laser rangefinder as the first measurement value;

[0056] Obtain the scale of the center of the reflector plane through the slide rail, and align the second laser rangefinder with the radar center of the robot to be tested;

[0057] Measuring the distance between the second laser rangefinder and the scale at the center of the reflector plane as the first distance, and reading the measurement value of the second laser rangefinder as the second measurement value;

[0058] Calculate the angle between the robot to be tested and the reflector according to the obtained first measurement value and second measurement value;

[0059] Obtain the lateral distance, longitudinal distance and angular deviation between the robot to be tested and the reflector according to the included angle, the first distance and the second measurement value;

[0060] Control the robot to be tested to perform multiple docking tasks in different working scenarios, and collect the lateral distance, longitudinal distance and angle deviation of each task;

[0061] Generate a three-dimensional scatter plot based on data from multiple tasks;

[0062] The error distribution is analyzed based on the three-dimensional scatter plot, and the control algorithm of the robot under test is optimized according to the error distribution.

[0063] The angle between the robot to be tested and the reflector is calculated based on the first measurement value and the second measurement value obtained. The calculation formula is:

[0064]

[0065] Wherein, α is the angle between the robot to be measured and the reflective plate, b is the second measured value, and c is the first measured value.

[0066] The lateral distance, longitudinal distance and angle deviation between the robot to be tested and the reflector are obtained according to the included angle, the first distance and the second measurement value, including:

[0067] The first distance is used as the lateral distance between the robot to be tested and the reflector, the second measured value is used as the longitudinal distance between the robot to be tested and the reflector, and the included angle is used as the angular deviation between the robot to be tested and the reflector.

[0068] Generating a three-dimensional scatter plot based on data from multiple tasks includes the following steps:

[0069] Collect the lateral distance, longitudinal distance and angular deviation data of each docking mission and organize these data into a data set;

[0070] Generate a 3D scatter plot using a data visualization tool based on the dataset.

[0071] Using data visualization tools to generate a 3D scatter plot based on the data set specifically includes:

[0072] The collected lateral distance, longitudinal distance and angle deviation data are homogenized and converted into a unified coordinate range;

[0073] Use data visualization tools to use the normalized lateral distance, longitudinal distance, and angular deviation data as the X-axis, Y-axis, and Z-axis data in the three-dimensional coordinate system, respectively, to generate a three-dimensional scatter plot. Each point in the three-dimensional scatter plot includes the work scene information of the task corresponding to the point data.

[0074] Analyze the error distribution based on the three-dimensional scatter plot and optimize the control algorithm of the robot under test according to the error distribution, including:

[0075] For each scattered point in the three-dimensional scatter plot, the distance between the scattered point and the origin is calculated, and the distance between the scattered point and the origin is used as the error of the task corresponding to the scattered point;

[0076] According to the calculated distance between each scattered point and the origin, the error size in each working scene is evaluated. If the scattered points in a working scene are far away from the origin, it means that the error of the robot control algorithm in this working scene is large. If the scattered points in a working scene are close to the origin, it means that the control accuracy in this working scene is high.

[0077] Count the average error, maximum error and standard deviation of error in each work scenario;

[0078] The control algorithm of the measuring robot is optimized according to the average error, maximum error and error standard deviation in each working scenario.

[0079] On the other hand, this embodiment provides a test system for implementing any of the above test methods, including: a first laser rangefinder, a slide rail with a scale, a reflector, a second laser rangefinder, a data acquisition module, a data processing module, and a control optimization module.

[0080] A first laser rangefinder, installed at the center of the robot to be measured, for measuring a first measurement value;

[0081] A slide rail with scales, mounted on the reflective plate, for fixing and moving the second laser rangefinder;

[0082] a second laser rangefinder, for measuring a second measurement value;

[0083] A data acquisition module, used for reading a first measurement value of the first laser rangefinder and a second measurement value of the second laser rangefinder;

[0084] The data processing module is used to obtain the included angle, the lateral distance, the longitudinal distance and the angle deviation according to the first measurement value, the second measurement value and the first distance.

[0085] The data processing module is further used to generate a scatter plot and analyze error distribution based on the scatter plot.

[0086] The control optimization module is used to optimize the robot's control algorithm and improve the robot's docking accuracy based on the error analysis results output by the data processing module.

[0087] Embodiment 2:

[0088] The parts not mentioned in this embodiment are the same as those in Embodiment 1.

[0089] like Figure 2As shown, the implementation of the test method is divided into several modules: the robot to be tested; a removable storage medium such as a computer for issuing docking instructions and running data processing scripts; a data acquisition system: a laser rangefinder A fixed on the center of the test machine, a slide rail with a scale fixed on the plane of the reflector, and a laser rangefinder B that can move on the slide rail.

[0090] Terminal computer: connects to the machine to be tested via the http protocol and issues the reflector docking command;

[0091] Machine under test: After the robot under test completes the docking task, it waits for data to be collected;

[0092] Data acquisition module: After the robot completes docking, the value of A is read as c; the scale m at the center of the reflector plane is obtained through the scale rail device, and B is slid on the rail, and B is aligned with the center of the machine radar to check the distance between the laser rangefinder B and m, which is the y value. The value read by the laser rangefinder B is b;

[0093] Data processing module: The y, b, and c obtained by the acquisition module are written into the script, and the angle α between the machine and the reflector is obtained by the inverse trigonometric function:

[0094]

[0095] Thus, the machine's lateral y, longitudinal distance b and angle deviation α can be intuitively obtained.

[0096] This embodiment combines a laser rangefinder, a slide rail device, and a data acquisition and processing module to automatically perform position measurement and error analysis, thereby improving the accuracy of the robot docking task.

[0097] In this embodiment, a first laser rangefinder is installed on the robot to be tested. The laser rangefinder is installed at the center of the robot and is used to accurately measure the distance between the robot and the reflector. Due to the high precision of the laser rangefinder, the error problem caused by traditional manual measurement can be effectively avoided. Through this technical means, the robot can obtain the actual distance between the robot and the reflector, providing a relatively accurate initial measurement value, which provides a basis for subsequent error analysis and optimization.

[0098] A slide rail with scales is installed on the reflector, and a movable second laser rangefinder is set on the slide rail. The second laser rangefinder is used to measure the relative position between the reflector and the robot. The design of the slide rail can ensure that the second laser rangefinder can move smoothly and accurately on the reflector, so as to achieve accurate measurement of the relative position between the reflector and the robot. Through this design, the measurement system can not only achieve high-precision automatic measurement, but also avoid subjective errors and error accumulation in traditional manual measurement, ensuring the reliability and accuracy of the data.

[0099] When the robot to be tested performs the reflector docking task, the control system issues a task instruction according to a predetermined control algorithm, and the robot performs the docking task according to the instruction to reach the set target position. After the docking is completed, the measurement values ​​of the first laser rangefinder and the second laser rangefinder are read as key data in the reflector docking task. Through this automated data collection method, accurate reflector docking position data can be obtained, eliminating errors that may occur during manual operation and improving the execution efficiency and accuracy of the docking task.

[0100] In this embodiment, the second laser rangefinder is aligned with the scale at the center of the reflector plane through the slide rail device, and the distance between the second laser rangefinder and the scale is measured. This technical feature makes the relative position measurement between the reflector and the robot more accurate, avoids the error caused by the position deviation of the measuring point, and thus improves the accuracy of the docking process.

[0101] After the measurement is completed, the data acquisition module summarizes the measurement data collected by the first laser rangefinder and the second laser rangefinder, and calculates the angle, lateral distance, longitudinal distance and angle deviation through the data processing module. These data are key parameters for evaluating the docking accuracy between the robot and the reflector. Through real-time calculation of these parameters, the control system can adjust the robot's motion strategy in time to ensure that the robot docks with the reflector in the best posture during the docking process. The advantage of this technical feature is that it can provide real-time feedback and correction of the error between the robot and the reflector, significantly improving the stability and accuracy of the docking task.

[0102] Based on the data from multiple docking tasks, the data processing module will also generate a three-dimensional scatter plot. The lateral distance, longitudinal distance and angle deviation data of each task will be summarized into a data set, and the data processing module will use visualization tools to convert it into points in a three-dimensional coordinate system. Through the three-dimensional scatter plot, the distribution of errors can be intuitively analyzed and the possible sources of errors in different tasks can be identified. The visualization analysis of the three-dimensional scatter plot makes the error positioning more accurate, which helps the targeted optimization of the control algorithm and improves the global optimization capability of docking accuracy.

[0103] During the docking task, based on the error analysis of the three-dimensional scatter plot, the control optimization module will automatically adjust the robot control algorithm according to the distribution of errors. For each error point in the work scene, the control optimization module will calculate the distance between the point and the origin, and use this as an error indicator to quantitatively evaluate the errors in different task scenes. By counting the average error, maximum error and error standard deviation in different scenes, the optimization module can effectively identify work scenes with large errors, and adjust the control algorithm accordingly to reduce errors and improve the accuracy of robot docking. This technical feature can realize global error analysis and local optimization, further improving the stability and accuracy of the robot in practical applications.

[0104] Through the above technical means, this embodiment successfully achieves fully automatic precision optimization when the robot performs the reflector docking task. This embodiment not only overcomes the problems of large manual intervention and large errors in the prior art, but also makes the robot's docking task more accurate and fast through automatic measurement and data analysis, and can adaptively adjust the control strategy in different working environments. Through the technical solution of the present invention, the robot can efficiently complete the docking task without relying on human intervention, improve work efficiency, reduce operational risks, and show high stability and reliability in practical applications.

[0105] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0106] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for testing the docking accuracy of a robot reflector, characterized in that: The following steps are involved: Installing a first laser rangefinder on the robot to be tested, wherein the first laser rangefinder is installed at the center position of the robot to be tested; A slide rail with scales is installed on the reflective plate, and a movable second laser rangefinder is arranged on the slide rail; Send a reflector docking command to the robot under test, and the robot under test performs the docking task according to the control algorithm; After the docking is completed, reading the measurement value of the first laser rangefinder as the first measurement value; Obtain the scale of the center of the reflector plane through the slide rail, and align the second laser rangefinder with the radar center of the robot to be tested; Measuring the distance between the second laser rangefinder and the scale at the center of the reflector plane as the first distance, and reading the measurement value of the second laser rangefinder as the second measurement value; Calculate the angle between the robot to be tested and the reflector according to the obtained first measurement value and second measurement value; Obtain the lateral distance, longitudinal distance and angular deviation between the robot to be tested and the reflector according to the included angle, the first distance and the second measurement value; Control the robot to be tested to perform multiple docking tasks in different working scenarios, and collect the lateral distance, longitudinal distance and angle deviation of each task; Generate a three-dimensional scatter plot based on data from multiple tasks; The error distribution is analyzed based on the three-dimensional scatter plot, and the control algorithm of the robot under test is optimized according to the error distribution.

2. The method for testing the docking accuracy of a robot reflector according to claim 1, characterized in that: The angle between the robot to be tested and the reflector is calculated based on the first measurement value and the second measurement value obtained, and the calculation formula is: Wherein, α is the angle between the robot to be measured and the reflective plate, b is the second measured value, and c is the first measured value.

3. The method for testing the docking accuracy of a robot reflector according to claim 1, characterized in that: The method of obtaining the lateral distance, longitudinal distance and angular deviation between the robot to be tested and the reflector according to the included angle, the first distance and the second measurement value includes: The first distance is used as the lateral distance between the robot to be tested and the reflector, the second measured value is used as the longitudinal distance between the robot to be tested and the reflector, and the included angle is used as the angular deviation between the robot to be tested and the reflector.

4. The method for testing the docking accuracy of a robot reflector according to claim 1, characterized in that: The method of generating a three-dimensional scatter plot based on data of multiple tasks includes the following steps: Collect the lateral distance, longitudinal distance and angular deviation data of each docking mission and organize these data into a data set; Generate a 3D scatter plot using a data visualization tool based on the dataset.

5. The method for testing the docking accuracy of a robot reflector according to claim 4, characterized in that: Generating a three-dimensional scatter plot using a data visualization tool according to a data set specifically includes: The collected lateral distance, longitudinal distance and angle deviation data are homogenized and converted into a unified coordinate range; A data visualization tool is used to use the normalized lateral distance, longitudinal distance, and angular deviation data as the X-axis, Y-axis, and Z-axis data in a three-dimensional coordinate system, respectively, to generate a three-dimensional scatter plot, in which each scatter point includes work scene information of the task corresponding to each scatter point data.

6. The method for testing the docking accuracy of a robot reflector according to claim 1, characterized in that: Analyzing the error distribution based on the three-dimensional scatter plot and optimizing the control algorithm of the robot to be tested according to the error distribution specifically includes: For each scattered point in the three-dimensional scatter plot, the distance between the scattered point and the origin is calculated, and the distance between the scattered point and the origin is used as the error of the task corresponding to the scattered point; According to the calculated distance between each scattered point and the origin, the error size in each working scenario is evaluated; Count the average error, maximum error and standard deviation of error in each work scenario; The control algorithm of the measuring robot is optimized according to the average error, maximum error and error standard deviation in each working scenario.

7. A test system for implementing the test method according to any one of claims 1 to 6, characterized in that: include: A first laser rangefinder, a slide rail with scales, a reflective plate, a second laser rangefinder, a data acquisition module, a data processing module, and a control optimization module.

8. The robot reflector docking accuracy testing system according to claim 7, characterized in that: The first laser rangefinder is installed at the center of the robot to be measured and is used to measure the first measurement value; The slide rail with scale is installed on the reflective plate and is used to fix and move the second laser rangefinder; The second laser rangefinder is used to measure a second measurement value; The data acquisition module is used to read the first measurement value of the first laser rangefinder and the second measurement value of the second laser rangefinder; The data processing module is used to obtain the included angle, the lateral distance, the longitudinal distance and the angular deviation according to the first measurement value, the second measurement value and the first distance.

9. A robot reflector docking accuracy testing system according to claim 8, characterized in that: The data processing module is further used to generate a scatter plot and analyze error distribution based on the scatter plot.

10. The robot reflector docking accuracy testing system according to claim 7, characterized in that: The control optimization module is used to optimize the control algorithm of the robot and improve the docking accuracy of the robot according to the error analysis results output by the data processing module.

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