Coverage testing method, testing system, storage medium and computer program product of a robot sweeper

Abstract

The application discloses a coverage rate testing method and system of a sweeping robot, a storage medium and a computer program product, relates to the technical field of sweeping robots, and comprises the following steps: acquiring an image of a test site, and identifying a sweeping robot according to the image of the test site to determine the position of the sweeping robot relative to the vision of multiple cameras; in the case that the sweeping robot is in the visual area of the camera, determining the position coordinates of the sweeping robot in the image coordinate system of the test site based on the multiple cameras; in the case that the sweeping robot is in the visual blind area of the camera, determining the position coordinates of the sweeping robot in the image coordinate system of the test site based on multiple wireless positioning base stations; determining the motion trajectory of the sweeping robot according to the position coordinates; and determining the coverage rate of the sweeping robot according to the motion trajectory of the sweeping robot. The application aims to improve the accuracy of testing the coverage rate of the sweeping robot.

Description

Technical Field

[0001] This application relates to the field of robotic vacuum cleaner technology, and in particular to a method, testing system, storage medium, and computer program product for testing the coverage of a robotic vacuum cleaner. Background Technology

[0002] With the development of smart home and artificial intelligence technologies, robotic vacuum cleaners are widely used in homes and commercial environments due to their high degree of automation and cleaning efficiency. To evaluate their cleaning performance, it is usually necessary to record the movement trajectory of the robotic vacuum cleaner during operation and calculate its coverage of the floor area accordingly.

[0003] Currently, common methods for recording the trajectory and assessing the coverage of robotic vacuum cleaners mainly include camera-based visual recognition methods. While camera-based visual positioning methods offer high accuracy, they rely heavily on the camera's field of view. When the robotic vacuum cleaner enters areas such as under furniture, in corners, or other blind spots, its position information cannot be continuously acquired, leading to trajectory interruptions and consequently affecting the accuracy of coverage statistics. Summary of the Invention

[0004] The main purpose of this application is to provide a method, system, storage medium, and computer program product for testing the coverage of a robotic vacuum cleaner, aiming to improve the accuracy of testing the coverage of the robotic vacuum cleaner.

[0005] To achieve the above objectives, this application proposes a coverage testing method for a robotic vacuum cleaner, applied to a testing system. The testing system includes multiple wireless positioning base stations and multiple cameras, wherein the wireless positioning base stations are communicatively connected to the robotic vacuum cleaner, and includes:

[0006] Acquire images of the test site and identify the robotic vacuum cleaner based on the images of the test site to determine the position of the robotic vacuum cleaner relative to the vision of the multiple cameras;

[0007] When the robot vacuum cleaner is within the field of view of the camera, the position coordinates of the robot vacuum cleaner in the image coordinate system of the test site are determined based on the multiple cameras;

[0008] When the robot vacuum cleaner is within the blind spot of the camera, the position coordinates of the robot vacuum cleaner in the image coordinate system of the test site are determined based on multiple wireless positioning base stations.

[0009] The movement trajectory of the sweeping robot is determined based on the position coordinates;

[0010] The coverage rate of the sweeping robot is determined based on its movement trajectory.

[0011] In one embodiment, multiple cameras are divided into multiple groups, and each group of cameras is set up in multiple rooms. The acquisition of images of the test site includes:

[0012] Control each group of cameras to acquire multiple partial images of the room in which it is located;

[0013] Affine transformation is performed on multiple local images acquired by each group of cameras to determine complete images of multiple rooms.

[0014] Complete images of multiple rooms are stitched together according to the preset relative positions of the rooms to determine the image of the test site.

[0015] In one embodiment, the top of the robotic vacuum cleaner is provided with a QR code label, and determining the position coordinates of the robotic vacuum cleaner based on the multiple cameras includes:

[0016] Identify QR code labels present in images of the test site;

[0017] Adaptive binarization is performed on the QR code label to extract the effective information area of ​​the QR code label;

[0018] Based on the effective information area, identify the coordinates of the four corner points of the QR code label in the image coordinate system of the test site;

[0019] The position coordinates of the sweeping robot in the image coordinate system of the test site are calculated based on the coordinates of the four corner points of the QR code label.

[0020] In one embodiment, the testing system includes a communication tag attached to the robotic vacuum cleaner, and determining the location coordinates of the robotic vacuum cleaner based on multiple wireless positioning base stations includes:

[0021] Control N wireless positioning base stations located near the sweeping robot to send their respective coded signals to the communication tag;

[0022] Record the arrival times of N coded signals to the communication tag, and using one of the wireless positioning base stations as a reference point, calculate the difference between the arrival time of the reference point to the communication tag and the arrival times of the other wireless positioning base stations to the communication tag, so as to determine multiple arrival time differences;

[0023] The position coordinates of the sweeping robot in the image coordinate system of the test site are determined based on multiple arrival time differences; where N is greater than or equal to 3.

[0024] In one embodiment, determining the motion trajectory of the sweeping robot based on the position coordinates includes:

[0025] Based on the determined position coordinates of the multiple robotic vacuum cleaners, the position coordinates are connected sequentially according to their corresponding time sequence to determine the movement trajectory of the robotic vacuum cleaners.

[0026] In one embodiment, determining the coverage rate of the sweeping robot based on its movement trajectory includes:

[0027] Based on the width of the robotic vacuum cleaner, the motion trajectory is drawn in the form of a continuous path in the grayscale image;

[0028] Whenever the motion trajectory passes through a pixel position in the grayscale image, the grayscale value of that pixel position is increased by a fixed grayscale increment value;

[0029] The number of pixels with different grayscale values ​​in a grayscale image is counted, and the number of pixels with each different grayscale value is divided by the number of pixels that can be covered in the grayscale image to determine the single-time coverage rate and multiple-time coverage rate of the sweeping robot.

[0030] In one embodiment, after determining the movement trajectory of the sweeping robot based on the position coordinates, the method further includes:

[0031] Obtain the position coordinates of the sweeping robot at multiple consecutive time points;

[0032] If the position coordinates of the sweeping robot are the same at multiple consecutive time points, the position coordinates of the latest time point among the multiple time points are retained, and the position coordinates of the remaining time points are deleted from the sweeping robot's movement trajectory.

[0033] In addition, to achieve the above objectives, this application also proposes a testing system, the testing system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the coverage testing method for the sweeping robot described above; and multiple cameras and multiple wireless positioning base stations, the cameras and the wireless positioning base stations being electrically connected to the processor.

[0034] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the coverage testing method for the sweeping robot described above.

[0035] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the coverage testing method for a sweeping robot as described above.

[0036] The coverage testing method for the robotic vacuum cleaner of this application includes acquiring an image of a test site and identifying the robotic vacuum cleaner based on the image of the test site to determine the position of the robotic vacuum cleaner relative to multiple cameras; when the robotic vacuum cleaner is within the visible area of ​​the cameras, determining the position coordinates of the robotic vacuum cleaner in the image coordinate system of the test site based on the multiple cameras; when the robotic vacuum cleaner is within the visual blind zone of the cameras, determining the position coordinates of the robotic vacuum cleaner in the image coordinate system of the test site based on multiple wireless positioning base stations; determining the movement trajectory of the robotic vacuum cleaner based on the position coordinates; and determining the coverage rate of the robotic vacuum cleaner based on the movement trajectory of the robotic vacuum cleaner.

[0037] With this configuration, in practical applications, when the robotic vacuum cleaner enters the camera's blind spot (such as under furniture, in corners, etc.), it automatically switches to a positioning method based on wireless positioning base stations, avoiding trajectory interruption caused by visual recognition failure. Furthermore, when the robotic vacuum cleaner enters the camera's blind spot, it switches to a positioning method based on multiple cameras to improve the accuracy of locating the robotic vacuum cleaner's position coordinates. By combining camera visual positioning and wireless positioning base station positioning, this application selects the optimal positioning method in different scenarios, thereby achieving continuous tracking and accurate evaluation of the robotic vacuum cleaner's movement trajectory. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating an embodiment of the coverage testing method for the robotic vacuum cleaner of this application;

[0041] Figure 2 A flowchart illustrating another embodiment of the coverage testing method for the sweeping robot of this application;

[0042] Figure 3 A flowchart illustrating yet another embodiment of the coverage testing method for the sweeping robot of this application;

[0043] Figure 4 A flowchart illustrating another embodiment of the coverage testing method for the robotic vacuum cleaner of this application;

[0044] Figure 5A flowchart is provided for another embodiment of the coverage testing method for the sweeping robot of this application;

[0045] Figure 6 This is a flowchart illustrating another embodiment of the coverage testing method for the sweeping robot of this application.

[0046] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0048] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0049] With the development of smart home and artificial intelligence technologies, robotic vacuum cleaners are widely used in homes and commercial environments due to their high degree of automation and cleaning efficiency. To evaluate their cleaning performance, it is usually necessary to record the movement trajectory of the robotic vacuum cleaner during operation and calculate its coverage of the floor area accordingly.

[0050] Currently, common methods for recording the trajectory and assessing the coverage of robotic vacuum cleaners mainly include camera-based visual recognition methods. While camera-based visual positioning methods offer high accuracy, they rely heavily on the camera's field of view. When the robotic vacuum cleaner enters areas such as under furniture, in corners, or other blind spots, its position information cannot be continuously acquired, leading to trajectory interruptions and consequently affecting the accuracy of coverage statistics.

[0051] To address the technical problem that existing methods for evaluating the coverage of robotic vacuum cleaners rely on the field of view of cameras, this application proposes a coverage testing method, testing system, storage medium, and computer program product for robotic vacuum cleaners. Specific embodiments and implementation methods are as follows:

[0052] In one embodiment, reference Figure 1 The coverage testing method for a robotic vacuum cleaner is applied to a testing system, which includes multiple wireless positioning base stations and multiple cameras. The wireless positioning base stations are communicatively connected to the robotic vacuum cleaner, and the system includes:

[0053] Step S100: Acquire an image of the test site and identify the sweeping robot based on the image of the test site to determine the position of the sweeping robot relative to the vision of the multiple cameras;

[0054] It should be noted that this application acquires images of the test site using multiple cameras. The robotic vacuum cleaner has a QR code label on its top. The testing system determines the robotic vacuum cleaner's position relative to the camera's visual field by identifying the presence of the QR code label in the test site image. When the testing system does not recognize the QR code label in the image, it determines that the robotic vacuum cleaner is within the camera's blind spot; when the testing system recognizes the QR code label in the image, it determines that the robotic vacuum cleaner is within the camera's field of view.

[0055] In one embodiment, when the test site consists of multiple rooms, multiple cameras are divided into multiple groups, with each group of cameras set up in multiple rooms. The multiple cameras are evenly distributed at multiple locations within the rooms. Figure 2 The steps to obtain the test site image are as follows:

[0056] Step S110: Control each group of cameras to acquire multiple partial images of the room in which it is located;

[0057] It should be noted that a set of cameras (e.g., 4 cameras) is deployed in each room, arranged in a rectangle or ring, ensuring a certain overlap in the field of view between adjacent cameras. After acquiring images, the cameras need to perform preprocessing on the raw images, including distortion correction, grayscale conversion, and contrast enhancement, to improve the accuracy of subsequent feature matching and image stitching.

[0058] Step S120: Perform affine transformation processing on the multiple local images acquired by each group of cameras to determine the complete images of multiple rooms;

[0059] It should be noted that since there is a certain overlap in the field of view between adjacent cameras, image stitching can be performed within this overlap area. Specifically, this application can extract key points from each image and generate feature descriptors using algorithms such as SIFT, SURF, or ORB. Feature descriptor matching is then performed on the local images of adjacent cameras to obtain a set of matching point pairs. From these, a sufficient number of reasonably distributed point pairs (usually more than 3 sets of non-collinear points) are selected for calculating the affine transformation.

[0060] A feature descriptor is a mathematical representation of the region surrounding a keypoint in an image. It is a numerical vector used to describe information such as texture, edges, and orientation around that point. A matched point pair refers to a pair of keypoints with similar feature descriptors found in two images; they correspond to the same physical location in the scene.

[0061] Affine transformation is a linear transformation (such as rotation, scaling, and translation) in two-dimensional space, with the following equation:

[0062]

[0063] Where x and y are the original coordinates of a pixel in the local image, x1 and y1 are the transformed target coordinates of a pixel in the local image, A is a parameter controlling horizontal scaling and rotation, B is a parameter controlling diagonal stretching or tilting, C is a parameter controlling the overall left / right movement of the image, D is a parameter controlling the vertical stretching of the image, and T... x To control the parameters of vertical scaling or rotation of the image, T y These are parameters used to control the vertical movement of the image.

[0064] Using one of the adjacent local images as a reference, a position transformation is performed on the other local image of the adjacent local images. The coordinates of the matching point relative to the corresponding point in the reference local image are used as the target coordinates, and the coordinates of the matching point relative to the corresponding point in the other local image are used as the original coordinates. Since more than three sets of non-collinear points have been selected in the above steps, the three sets of original coordinates and target coordinates can be substituted into the above equations to calculate the multiple scale transformation parameters (A, B, C, D, T). x and T y Then, based on the aforementioned multiple scale transformation parameters, another local image is transformed (rotated, scaled, and translated) to fuse adjacent local images. By processing multiple local images sequentially through the above steps, a complete image of the room can be obtained.

[0065] Step S130: Stitch together the complete images of multiple rooms according to the preset relative positional relationship of the multiple rooms to determine the image of the test site.

[0066] It should be noted that the relative positions of the multiple rooms are known. Technicians only need to perform coordinate transformation on the images of each room according to their relative positions and then stitch them together into a large image to obtain the image of the test site.

[0067] In another embodiment, when the test site is a single room, the present application can obtain a complete image of the single room through the above steps S110 and S120, which will not be repeated here.

[0068] Step S200: When the sweeping robot is within the visible area of ​​the camera, determine the position coordinates of the sweeping robot in the image coordinate system of the test site based on the multiple cameras.

[0069] It should be noted that, when the robotic vacuum cleaner is within the visible area of ​​the camera, this application can locate the position coordinates of the robotic vacuum cleaner based on the image coordinate system of ArUco QR code recognition, for reference. Figure 3 The specific steps include:

[0070] Step S210: Identify the QR code labels present in the image of the test site;

[0071] Step S211: Adaptively binarize the QR code label to extract the effective information area of ​​the QR code label;

[0072] It should be noted that in practical applications, due to uneven lighting, occlusion, perspective distortion, and other factors in the testing environment, the overall quality of the QR code label in the image may be poor. Directly recognizing the QR code label may not accurately identify its valid information. Considering that the valid information area of ​​the QR code label consists of a black-and-white matrix of black and white squares on the label, as well as its black border, adaptive binarization can be performed on the image to convert it into a clear black-and-white binary image. This helps the testing system identify the valid information area of ​​the QR code label in the image, thereby improving the recognition accuracy.

[0073] Step S212: Based on the effective information area, identify the coordinates of the four corner points of the QR code label in the image coordinate system of the test site;

[0074] It should be noted that the coordinates of the four corner points represent the position information of the four corners of the QR code label within the image coordinate system of the test site. These corner coordinates can be used to calculate the center position and orientation of the QR code label. After recognizing the black border of the QR code label, the test system can determine the coordinates of the four corner points of the black border within the image coordinate system of the test site. The test system can also decode the black and white pixel matrix of the valid information area to identify the QR code's ID number. This ID number is used by the test system to identify the target identity of the robotic vacuum cleaner, and combined with its corner coordinates, enables continuous visual positioning and trajectory tracking of the robotic vacuum cleaner.

[0075] Step S213: Calculate the position coordinates of the sweeping robot in the image coordinate system of the test site based on the coordinates of the four corner points of the QR code label.

[0076] It should be noted that this application can average the coordinates of the four corner points of the QR code, and the average value is the center position coordinate of the QR code label. Since the QR code label is located on the top of the robot vacuum cleaner, the center position coordinate of the QR code label can be used as the position coordinate of the robot vacuum cleaner.

[0077] In addition, this application can also estimate the robot's true position coordinates in the image coordinate system of the test site by identifying the pixel coordinates of the robot in each camera image and using the triangulation principle. The specific steps are as follows: obtain the installation position information and camera parameters of multiple cameras; identify the pixel coordinates of the robot in the coordinate system of each frame of each camera image; and calculate the position coordinates of the robot in the image coordinate system of the test site based on the pixel coordinates and camera parameters using the triangulation principle.

[0078] Step S200 further includes: when the sweeping robot is in the visual blind spot of the camera, determining the position coordinates of the sweeping robot in the image coordinate system of the test site based on multiple wireless positioning base stations.

[0079] This application can determine the location coordinates of a robotic vacuum cleaner by measuring the time difference of signal arrival between different wireless positioning base stations. The testing system includes a communication tag, which is attached to the robotic vacuum cleaner. Figure 4 The specific steps are as follows:

[0080] Step S220: Control N wireless positioning base stations close to the sweeping robot to send their respective coded signals to the communication tag;

[0081] It should be noted that the communication tag is used to receive coded signals from multiple wireless positioning base stations and record their arrival times. These wireless positioning base stations are fixedly deployed in the test site and periodically transmit coded signals with their own IDs. Each wireless positioning base station's coded signal contains a unique identifier used to distinguish its source.

[0082] Step S221: Record the arrival times of N coded signals to the communication tag, and using one of the wireless positioning base stations as a reference point, calculate the difference between the arrival time of the reference point to the communication tag and the arrival times of the other wireless positioning base stations to the communication tag, so as to determine multiple arrival time differences.

[0083] Step S222: Determine the position coordinates of the sweeping robot in the image coordinate system of the test site based on the multiple arrival time differences; wherein, N is greater than or equal to 3.

[0084] Specifically, assuming the coordinates of the wireless positioning base station as the reference point in the world coordinate system are (x0, y0), and the coordinates of the other wireless positioning base stations in the world coordinate system are (x1, y1)...(xn, yn), and multiple arrival time differences are determined as t1, t2...t3, and the signal propagation speed is c (the speed of electromagnetic waves is 3×10^8 m / s), then a system of equations can be established:

[0085]

[0086] Where xi and yi represent the position coordinates of the i-th remaining wireless positioning base station in the world coordinate system, ti is the difference between the arrival time of the i-th remaining wireless positioning base station and the arrival time of the reference point, and x and y are the position coordinates of the sweeping robot in the world coordinate system.

[0087] Substituting the location coordinates of the wireless positioning base station (used as a reference point), the location coordinates of the other wireless positioning base stations, and multiple arrival time differences into the above equations yields multiple equations. These multiple equations are then used to calculate the position coordinates of the robot vacuum cleaner in the world coordinate system. Finally, the coordinates of the robot vacuum cleaner in the world coordinate system are mapped onto the image coordinate system of the test site to obtain the position coordinates of the robot vacuum cleaner in the image coordinate system of the test site.

[0088] In addition, this application can also determine the position coordinates of the robotic vacuum cleaner based on the weighted centroid method of RSSI. The specific steps are as follows: Measure the signal strength (RSSI) of the received signal transmitted by the communication tag using multiple wireless positioning base stations; process the multiple signal strengths (RSSI) according to the RSSI attenuation model to estimate the distance between the multiple wireless positioning base stations and the robotic vacuum cleaner; perform a weighted average of the position coordinates of the multiple wireless positioning base stations to determine the position coordinates of the robotic vacuum cleaner in the world coordinate system; then map the coordinates of the robotic vacuum cleaner in the world coordinate system onto the image coordinate system of the test site to obtain the position coordinates of the robotic vacuum cleaner in the image coordinate system of the test site. The weight of the weighted average is the reciprocal of the distance.

[0089] Step S300: Determine the movement trajectory of the sweeping robot based on the position coordinates;

[0090] It should be noted that the testing system can continuously collect the location coordinates of the robot vacuum cleaner at different points in time (whether from camera recognition or wireless base station), arrange them in chronological order, and connect them point by point to form a continuous path, thereby generating a motion trajectory map of the robot vacuum cleaner.

[0091] Step S400: Determine the coverage rate of the sweeping robot based on its movement trajectory.

[0092] It should be noted that coverage rate is the proportion of the area effectively cleaned or traversed by the robotic vacuum cleaner to the total test area, used to measure the cleaning efficiency of the robotic vacuum cleaner. This application can determine the coverage rate of the robotic vacuum cleaner based on an image overlay coverage evaluation method, referencing... Figure 5 Specific embodiments include:

[0093] Step S410: Based on the width of the sweeping robot, draw the motion trajectory in the form of a continuous path in the grayscale image;

[0094] It should be noted that robotic vacuum cleaners do not clean a single line, but rather clean the floor across a certain width. Recording only the center point would underestimate the cleaning area. Therefore, this application uses the width of the robotic vacuum cleaner to draw its trajectory, which more closely resembles actual cleaning behavior, helping to improve the accuracy of coverage determination and avoiding errors in coverage area calculation caused by single-pixel trajectories. The grayscale image is a preprocessed digitized two-dimensional image of the test area, ensuring that all pixels initially have a grayscale value of 0. This facilitates subsequent counting of pixels with different grayscale values, improving the accuracy of coverage calculation.

[0095] Step S420: Whenever the motion trajectory passes through a pixel position of the grayscale image, the grayscale value of that pixel position is increased by a fixed grayscale increment value;

[0096] It should be noted that a higher grayscale value indicates more cleaning cycles. Assuming a fixed grayscale increment of 85, when a pixel's grayscale value is zero, it means that the pixel is not covered; when a pixel's grayscale value is 85, it means that the pixel has been covered once; when a pixel's grayscale value is 170, it means that the pixel has been covered twice; and when a pixel's grayscale value is 255, it means that the pixel has been covered three times.

[0097] Step S430: Count the number of pixels with different gray values ​​in the grayscale image, and divide the number of pixels with each different gray value by the number of pixels that can be covered in the grayscale image to determine the single coverage rate and multiple coverage rate of the sweeping robot.

[0098] It should be noted that the number of pixels that can be covered refers to the total number of pixels in the grayscale image corresponding to the effective area that the robot vacuum cleaner can theoretically pass through and perform cleaning tasks. Before testing, the researchers calculated the test area area based on the total area of ​​the test site and the area of ​​obstacles (such as furniture, walls, fixed equipment, etc.), which is the total area of ​​the effective cleaning area of ​​the robot vacuum cleaner. This application can calculate the number of pixels that can be covered in the grayscale image based on the test area area.

[0099] It's important to note that single-pass coverage and multiple-pass coverage are used to evaluate the basic cleaning capabilities of a robotic vacuum cleaner in a test area. Single-pass coverage represents the percentage of the area that has been cleaned at least once, reflecting whether the robotic vacuum cleaner can quickly cover the entire test area. Users can use the single-pass coverage rate to determine whether the robotic vacuum cleaner has completed its basic cleaning task. When the single-pass coverage rate reaches a preset value (e.g., 95%), it indicates that the robotic vacuum cleaner's current path strategy is relatively reliable. A low single-pass coverage rate indicates that the robotic vacuum cleaner's path planning is unreasonable or its obstacle avoidance strategy is too conservative.

[0100] The multiple coverage rate represents the percentage of areas that have been swept twice or more. A higher multiple coverage rate indicates significant path overlap by the robot vacuum, requiring a readjustment of its path strategy. Users can adjust the robot vacuum's path strategy by analyzing areas with high multiple coverage rates and areas with low single coverage rates.

[0101] The coverage testing method for a robotic vacuum cleaner in this application includes acquiring images of a test site and identifying the robotic vacuum cleaner based on the images to determine its position relative to the camera's vision. When the robotic vacuum cleaner is within the camera's field of view, its position coordinates in the image coordinate system of the test site are determined based on multiple cameras. When the robotic vacuum cleaner is in the camera's blind spot, its position coordinates in the image coordinate system of the test site are determined based on multiple wireless positioning base stations. The movement trajectory of the robotic vacuum cleaner is determined based on the position coordinates. The coverage rate of the robotic vacuum cleaner is determined based on its movement trajectory. With this setup, in practical applications, when the robotic vacuum cleaner enters the camera's blind spot (such as under furniture or in a corner), this application automatically switches to a positioning method based on wireless positioning base stations, avoiding trajectory interruption due to visual recognition failure. Furthermore, when the robotic vacuum cleaner enters the camera's blind spot, it switches to a positioning method based on multiple cameras to improve the accuracy of locating the robotic vacuum cleaner's position coordinates. By combining camera visual positioning and wireless positioning base station positioning, this application selects the optimal positioning method in different scenarios, thereby achieving continuous tracking and accurate evaluation of the robotic vacuum cleaner's movement trajectory.

[0102] It is important to consider that when the robot vacuum cleaner is paused, the camera or wireless base station may continue to report the same coordinates. These repeated points do not reflect actual movement, but they will increase the amount of data and interfere with subsequent analysis.

[0103] In this regard, in one embodiment of this application, reference is made to Figure 6 After determining the movement trajectory of the sweeping robot based on the position coordinates, the method further includes:

[0104] Step S500: Obtain the position coordinates of the sweeping robot at multiple consecutive time points;

[0105] Step S600: When the position coordinates of the sweeping robot are the same at multiple consecutive time points, retain the position coordinates of the latest time point among the multiple time points, and delete the position coordinates of the remaining time points from the movement trajectory of the sweeping robot.

[0106] It is understandable that when the robot vacuum is paused, it will be in the same position at multiple consecutive points in time.

[0107] This application, when the location is the same at multiple time points, retains the location coordinates of the latest time point and deletes the location coordinates of the remaining time points. This not only eliminates redundant trajectory points when the robot is stationary, reducing data storage and computation, but also preserves the robot's actual stopping position (i.e., the last stopping point), thus preventing the loss of critical location information. With this setup, the robot's movement trajectory only includes points where its position changes. Therefore, the trajectory data obtained from testing more accurately reflects the robot's actual movement path, avoiding misjudgments of coverage frequency and improving the reliability of cleaning assessments.

[0108] This application provides a testing system, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the coverage testing method for the sweeping robot in Embodiment 1 above; and multiple cameras and multiple wireless positioning base stations, the cameras and the wireless positioning base stations being electrically connected to the processor.

[0109] The testing system provided in this application employs the coverage testing method for robotic vacuum cleaners described in the above embodiments, which solves the technical problem that existing methods for evaluating the coverage of robotic vacuum cleaners rely on the field of view of a camera. Compared with the prior art, the beneficial effects of the testing system provided in this application are the same as those of the coverage testing method for robotic vacuum cleaners provided in the above embodiments, and other technical features of this testing system are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0110] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0112] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the coverage testing method of the sweeping robot in the above embodiments.

[0113] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0114] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the coverage testing method for the above-described robotic vacuum cleaner, thereby solving the technical problem that existing methods for evaluating the coverage of robotic vacuum cleaners rely on the field of view of a camera. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the coverage testing method for robotic vacuum cleaners provided in the above embodiments, and will not be repeated here.

[0115] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the coverage testing method for a sweeping robot as described above.

[0116] The computer program product provided in this application can solve the technical problem that existing methods for evaluating the coverage of robotic vacuum cleaners rely on the field of view of a camera. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the robotic vacuum cleaner coverage testing method provided in the above embodiments, and will not be repeated here.

[0117] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A coverage test method of a robot cleaner, applied to a test system, the test system comprising a plurality of wireless positioning base stations and a plurality of cameras, the wireless positioning base stations being in communication connection with the robot cleaner, characterized in that, The method comprises: acquiring an image of a test site and identifying the sweeping robot based on the image of the test site to determine the position of the sweeping robot relative to the multiple cameras; when the sweeping robot is in the visual field of the cameras, determining the position of the sweeping robot in the image coordinate system of the test site based on the position coordinates of the four corner points on the two-dimensional code label on the top of the sweeping robot in the image coordinate system of the test site identified by the multiple cameras; when the sweeping robot is in the visual blind area of the cameras, determining the position of the sweeping robot in the image coordinate system of the test site based on the time difference of arrival of the encoded signals sent by the multiple wireless positioning base stations at the communication tag of the sweeping robot; connecting the position coordinates of the sweeping robot in the order of their corresponding time sequence to determine the motion trajectory of the sweeping robot according to the determined position coordinates of the sweeping robot; determining the coverage rate of the sweeping robot according to the motion trajectory of the sweeping robot. 2.The coverage test method of the robotic cleaner according to claim 1, wherein, The multiple cameras are divided into multiple groups, and each group of cameras is arranged in a plurality of rooms. The acquisition of the image of the test site comprises: controlling each group of cameras to acquire multiple local images of the room where the group of cameras is located; performing affine transformation processing on the multiple local images acquired by each group of cameras to determine the complete images of the multiple rooms; splicing the complete images of the multiple rooms according to the preset relative position relationship of the multiple rooms to determine the image of the test site. 3.The coverage test method of the robotic cleaner according to claim 1, wherein, The determination of the position of the sweeping robot in the image coordinate system of the test site based on the position coordinates of the four corner points on the two-dimensional code label on the top of the sweeping robot in the image coordinate system of the test site identified by the multiple cameras comprises: identifying the two-dimensional code label existing in the image of the test site; performing adaptive binaryzation on the two-dimensional code label to extract the effective information area of the two-dimensional code label; identifying the coordinates of the four corner points of the two-dimensional code label in the image coordinate system of the test site based on the effective information area; calculating the position coordinates of the sweeping robot in the image coordinate system of the test site according to the coordinates of the four corner points of the two-dimensional code label. 4.The coverage test method of the robotic cleaner according to claim 1, wherein, The test system comprises a communication tag arranged on the sweeping robot. The determination of the position of the sweeping robot in the image coordinate system of the test site based on the time difference of arrival of the encoded signals sent by the multiple wireless positioning base stations at the communication tag of the sweeping robot comprises: controlling N wireless positioning base stations close to the sweeping robot to send respective encoded signals to the communication tag; recording the times at which the N encoded signals arrive at the communication tag, and taking one of the wireless positioning base stations as a reference point to calculate the time difference between the time at which the reference point arrives at the communication tag and the times at which the remaining wireless positioning base stations arrive at the communication tag to determine multiple time differences of arrival; determining the position of the sweeping robot in the image coordinate system of the test site according to the multiple time differences of arrival; wherein N is greater than or equal to 3. 5.The coverage test method of the robotic cleaner according to claim 1, wherein, The determining the coverage of the sweeping robot according to the motion trajectory of the sweeping robot comprises: drawing the motion trajectory in the form of a continuous path in the gray-scale image based on the width of the sweeping robot; increasing the gray-scale value of a pixel position in the gray-scale image by a fixed gray-scale increment value each time the motion trajectory passes the pixel position; counting the number of pixels with different gray-scale values in the gray-scale image, and dividing the number of pixels with each different gray-scale value by the number of coverable pixels in the gray-scale image to determine the single coverage and the multiple coverage of the sweeping robot. 6.The coverage test method of the robotic cleaner according to claim 1, wherein, The determining the motion trajectory of the sweeping robot according to the position coordinates further comprises: obtaining the position coordinates of the sweeping robot at a plurality of continuous time points; in the case that the position coordinates of the sweeping robot at the plurality of continuous time points are all the same, retaining the position coordinates of the latest time point among the plurality of time points and deleting the position coordinates of the remaining time points from the motion trajectory of the sweeping robot.

7. A test system, characterized by The test system comprises a memory, a processor, a computer program stored on the memory and executable on the processor, and a plurality of cameras and a plurality of wireless positioning base stations, wherein the computer program is configured to implement the steps of the coverage test method of the sweeping robot according to any one of claims 1 to 6, and the cameras and the wireless positioning base stations are electrically connected to the processor.

8. A storage medium, characterized by The storage medium is a computer-readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the coverage test method of the sweeping robot according to any one of claims 1 to 6.

9. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps of the coverage test method of the sweeping robot according to any one of claims 1 to 6.

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