Cleaning method of cleaning robot and cleaning robot

Abstract

This application provides a cleaning method and a cleaning robot. The method includes: when cleaning a target area, if there are obstacles in the target area, determining the type of obstacle; if the obstacle is a ground-level obstacle, determining the placement angle of the ground-level obstacle, and adjusting the cleaning path of the cleaning robot based on the placement angle. In this solution, the cleaning robot can effectively avoid ground-level obstacles, fully preventing entanglement accidents, ensuring operational safety, and reducing safety hazards.

Description

Technical Field

[0001] This application relates to the field of robotics, and in particular to a cleaning method for a cleaning robot and a cleaning robot. Background Technology

[0002] With the improvement of people's living standards, cleaning robots with functions such as sweeping, mopping, and combined sweeping and mopping have been widely used in people's lives. Generally, when performing cleaning tasks, cleaning robots usually adjust their cleaning path in real time based on detected obstacles to achieve obstacle avoidance. In existing technology, cleaning robots mainly use two methods to detect obstacles: contact and non-contact. The principles of these two detection methods are as follows:

[0003] 1. The contact type involves installing collision sensors in front of and on the sides of the cleaning robot, which detects obstacles by colliding with them.

[0004] 2. Non-contact obstacle detection can be achieved using methods such as lidar, ultrasonic ranging, and monocular / binocular distance estimation;

[0005] (1) LiDAR and ultrasonic ranging involves installing radar and ultrasonic devices on the top of the cleaning robot, thereby detecting obstacles by continuously rotating and ranging.

[0006] (2) Monocular and binocular distance estimation involves installing monocular or binocular cameras on the cleaning robot, estimating distances through the monocular or binocular cameras, and detecting obstacles.

[0007] However, contact-based detection methods cannot react to ground-level obstacles such as cables and rags, making them ineffective at detecting such obstacles. Similarly, since lidar and other mechanical devices are mounted on top of the cleaning robot, this method is also ineffective at detecting ground-level obstacles. Furthermore, the detection performance of monocular and binocular cameras is significantly affected by light refraction, temperature, and camera error, resulting in poor detection and making it difficult to guarantee effective detection of ground-level obstacles.

[0008] This shows that when using existing technologies to detect obstacles, it is impossible to effectively identify ground-level obstacles, resulting in poor obstacle avoidance. This makes cleaning robots prone to entanglement accidents when performing cleaning tasks, posing a significant safety hazard. Summary of the Invention

[0009] The purpose of this application is to provide a cleaning method and a cleaning robot that can effectively avoid obstacles close to the ground, fully prevent the cleaning robot from getting entangled, and ensure the safety of the operation.

[0010] On the one hand, this application provides a cleaning method for a cleaning robot, including:

[0011] When cleaning the target area, if there are obstacles in the target area, determine the type of obstacle;

[0012] If the obstacle is a ground-level obstacle, determine the placement angle of the ground-level obstacle and adjust the cleaning path of the cleaning robot based on the placement angle.

[0013] In one embodiment, determining the type of obstacle includes:

[0014] Determine whether the height of the obstacle is greater than a preset height threshold;

[0015] If the height of an obstacle is greater than a preset height threshold, the obstacle type is a non-ground-hugging obstacle;

[0016] If the height of an obstacle is less than a preset height threshold, the obstacle type is a ground-level obstacle.

[0017] In one embodiment, the cleaning robot is equipped with a TOF sensor; before determining whether the height of the obstacle is greater than a preset height threshold, the method further includes:

[0018] Perform edge detection on obstacles to obtain their edge contour information;

[0019] Based on the depth map generated by the TOF sensor based on the target area, the position information of the obstacle edge contour is obtained, and a point cloud map is generated based on the depth map;

[0020] The height of the obstacle is determined based on the location information of the point cloud map and the edge contour of the obstacle.

[0021] In one embodiment, determining the placement angle of the ground-level obstacle includes:

[0022] Generate the minimum bounding rectangle of the ground-hugging obstacle, and use Hough test to determine whether the slope of the ground-hugging obstacle within the minimum bounding rectangle is greater than zero.

[0023] If the slope is greater than zero, the slope of the line connecting the lower left vertex and the upper right vertex of the smallest circumscribed rectangle is taken as the slope of the ground-hugging obstacle.

[0024] If the slope is less than zero, the slope of the line connecting the top left vertex and the bottom right vertex of the smallest circumscribed rectangle shall be taken as the slope of the ground-hugging obstacle.

[0025] Calculate the placement angle of the ground-level obstacle based on its slope.

[0026] In one embodiment, adjusting the cleaning path of the cleaning robot based on the placement angle includes:

[0027] If the placement angle is less than the first angle threshold, control the cleaning robot to rotate the first angle in a direction perpendicular to the original direction of travel;

[0028] If the placement angle is greater than or equal to the first angle threshold and less than the second angle threshold, control the cleaning robot to rotate the second angle in a direction perpendicular to the original direction of travel.

[0029] If the placement angle is greater than or equal to the second angle threshold and less than the third angle threshold, control the cleaning robot to rotate the third angle in a direction perpendicular to the original direction of travel.

[0030] Control the cleaning robot to move in the direction after the rotation angle;

[0031] Among them, the second angle threshold is greater than the first angle threshold, and the third angle threshold is greater than the second angle threshold; when the placement angle is a, the first angle is 90°, the second angle is 90°-a, and the third angle is a-90°.

[0032] In one embodiment, cleaning the target area includes:

[0033] Generate a bow-shaped cleaning path and control the cleaning robot to clean the target area based on the bow-shaped cleaning path.

[0034] In one embodiment, after controlling the cleaning robot to move in the direction following the rotation angle, the method further includes:

[0035] When the robot reaches the bow-shaped cleaning path, control it to continue cleaning the target area along the bow-shaped cleaning path.

[0036] In one embodiment, the cleaning robot is equipped with a TOF sensor; before determining the type of obstacle if an obstacle exists in the target area, the method further includes:

[0037] Based on preset coordinate values, the infrared image of the target area collected by the TOF sensor is divided into a detection area and a non-detection area. Based on the detection area, it is determined whether there are obstacles in the detection block at a preset distance from the cleaning robot.

[0038] In one embodiment, the method further includes:

[0039] Morphological erosion operations are performed on the detection area to smooth out interference factors in the detection block.

[0040] On the other hand, this application also provides a cleaning robot, including an image sensor, a processor, and a memory for storing processor-executable instructions; wherein the image sensor is used to identify obstacles, and the processor is configured to execute the cleaning method of the cleaning robot described above based on the identification results of the image sensor.

[0041] In this application, the cleaning robot can detect the presence of obstacles in the cleaning area in real time while performing cleaning tasks, and identify the type of obstacle when it is detected. Furthermore, when the obstacle is identified as a ground-level obstacle, the cleaning robot's cleaning path can be adjusted in real time by determining the obstacle's placement angle. Therefore, it can be seen that this application can effectively avoid ground-level obstacles by adjusting the cleaning robot's path in real time when they are detected, thus preventing entanglement accidents, ensuring the safety of cleaning operations, and reducing safety hazards. Simultaneously, by adjusting the cleaning path, the robot can clean the area around ground-level obstacles by moving close to them, ensuring effective cleaning of the work area and improving the user experience. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below.

[0043] Figure 1 This is a schematic diagram of the structure of a cleaning robot provided in one embodiment of this application;

[0044] Figure 2 A schematic flowchart illustrating a cleaning method using a cleaning robot according to an embodiment of this application;

[0045] Figure 3 This is a schematic diagram of the structure of a cleaning robot provided in another embodiment of this application;

[0046] Figures 4(a), 4(b), and 4(c) are schematic diagrams of infrared images obtained by scanning with a TOF sensor according to an embodiment of this application;

[0047] Figure 5 A schematic diagram of an infrared image obtained by performing morphological erosion operation on the detection area in FIG4(c) according to an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of a cleaning robot determining the type of obstacle according to an embodiment of this application;

[0049] Figure 7 This is a schematic diagram of an infrared image of a detection block containing obstacles obtained by a TOF sensor scan, provided in another embodiment of this application.

[0050] Figure 8 A cleaning robot provided in one embodiment of this application Figure 7A schematic diagram obtained after edge contour detection of obstacles in the image;

[0051] Figure 9 A cleaning robot provided in one embodiment of this application Figure 8 A schematic diagram obtained after morphological processing of the image in the image;

[0052] Figure 10 The cleaning robot provided in one embodiment of this application is based on Figure 7 A schematic diagram showing the location information of the obstacles obtained;

[0053] Figure 11 The cleaning robot provided in one embodiment of this application is based on Figure 7 The depth map generated from the detection block shown in the diagram;

[0054] Figure 12 The cleaning robot provided in one embodiment of this application is based on Figure 11 The point cloud map generated from the depth map in the image;

[0055] Figure 13 This is a detailed flowchart illustrating step S220 provided in one embodiment of this application;

[0056] Figure 14(a) shows a cleaning robot provided in an embodiment of this application. Figure 7 A schematic diagram showing the minimum bounding rectangle formed by the cables in the diagram;

[0057] Figure 14(b) shows a cleaning robot provided in another embodiment of this application. Figure 7 A schematic diagram showing the minimum bounding rectangle formed by the cables in the diagram;

[0058] Figure 15(a) is a schematic diagram of a cleaning robot provided in an embodiment of this application determining the slope of a ground-level obstacle based on Figure 14(a);

[0059] Figure 15(b) is a schematic diagram of a cleaning robot provided in an embodiment of this application determining the slope of a ground-hugging obstacle based on Figure 14(b);

[0060] Figure 16(a) is a schematic diagram of the cleaning robot adjusting its cleaning path based on the placement angle of ground-level obstacles provided in the first embodiment of this application;

[0061] Figure 16(b) is a schematic diagram of the cleaning robot adjusting its cleaning path based on the placement angle of ground-level obstacles provided in the second embodiment of this application;

[0062] Figure 16(c) is a schematic diagram of the cleaning robot adjusting its cleaning path based on the placement angle of ground-level obstacles provided in the third embodiment of this application;

[0063] Figure 17 This is a schematic diagram illustrating a cleaning robot cleaning a target area according to an embodiment of this application;

[0064] Figure 18 This is a schematic diagram illustrating a cleaning robot cleaning a target area according to another embodiment of this application;

[0065] Figure 19 A schematic diagram of the target area provided in an embodiment of this application;

[0066] Figure 20 A cleaning robot provided in one embodiment of this application Figure 19 A schematic diagram of the cleaning path for area 4 in the diagram;

[0067] Figure 21 A cleaning robot provided in one embodiment of this application Figure 19 A schematic diagram of the cleaning path for area 12 in the diagram;

[0068] Figure 22 A cleaning robot provided in one embodiment of this application Figure 19 The diagram shows the cleaning path for areas 6, 7, and 8.

[0069] Figure label:

[0070] 1-Cleaning robot; 10-Bus; 11-Processor; 12-Memory; 13-TOF sensor. Detailed Implementation

[0071] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0072] Similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0073] Please refer to Figure 1 This is a schematic diagram of the structure of a cleaning robot 1 provided in an embodiment of this application. Figure 1 As shown, the cleaning robot 1 includes: at least one processor 11 and a memory 12. Figure 1 Taking a processor 11 as an example. The processor 11 and the memory 12 are connected via a bus 10. The memory 12 stores instructions that can be executed by the processor 11. The instructions are executed by the processor 11 to enable the cleaning robot 1 to perform all or part of the process of the method in the following embodiments.

[0074] The memory 12 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0075] Specifically, the cleaning robot 1 in this application can be a sweeping robot, a mopping robot, a sweeping and mopping robot, or a window cleaning robot, etc. For ease of description, this application uses a mopping robot as an example to explain in detail the cleaning method of the cleaning robot 1 in the following embodiments of this application.

[0076] Please refer to Figure 2 This is a flowchart illustrating the cleaning method of a cleaning robot 1 provided in an embodiment of this application. Figure 2 As shown, the cleaning method of the cleaning robot 1 in this application includes the following steps S210-S220.

[0077] Step S210: When cleaning the target area, if there are obstacles in the target area, determine the type of obstacle.

[0078] The target area is the area where the cleaning robot 1 performs its cleaning tasks. When the cleaning robot 1 is a mopping robot, the target area is the floor area in the user's home. For example, the target area can be any combination of areas such as the kitchen floor, study floor, living room floor, bedroom floor, and bathroom floor in the user's home; for example, the target area can be only the living room floor, or the target area can be the bedroom floor and the bathroom floor.

[0079] When a user needs the cleaning robot 1 to clean a target area, they can send a cleaning command to the robot 1 via an application on a mobile device such as a smartphone. This command includes information about the target area that the robot 1 needs to clean. Upon receiving the command, the robot 1 parses the target area information, plans a cleaning path based on this information, and then cleans the target area according to the planned path. To ensure successful cleaning, the robot 1 continuously monitors the target area for obstacles and takes appropriate obstacle avoidance measures when encountered. Specifically, if an obstacle is detected, the robot can further identify the type of obstacle and implement corresponding obstacle avoidance measures based on the type of obstacle.

[0080] Step S220: If the obstacle is a ground-level obstacle, determine the placement angle of the ground-level obstacle and adjust the cleaning path of the cleaning robot 1 based on the placement angle.

[0081] In this application, the cleaning robot 1 categorizes obstacles into ground-level obstacles and non-ground-level obstacles. Ground-level obstacles refer to those that are relatively low and closely adhere to the ground. Examples of ground-level obstacles include long, narrow objects such as cables, shoelaces, and rags. Non-ground-level obstacles refer to those that are relatively high. Examples of non-ground-level obstacles include water cups, televisions, and refrigerators. The placement angle refers to the angle at which the ground-level obstacle is positioned relative to the ground. For example, during the cleaning process of the target area, the cleaning robot 1 stores an environmental map of the target area. This environmental map corresponds to a coordinate system, and the aforementioned placement angle refers to the angle at which the ground-level obstacle is positioned within this coordinate system.

[0082] If the cleaning robot 1 detects a ground-level obstacle in the target area during the cleaning process, it can adjust its cleaning path in real time, cleaning along the direction of the obstacle to effectively avoid it. If the cleaning robot 1 detects a non-ground-level obstacle in the target area, it will avoid it using conventional obstacle avoidance methods. After obstacle avoidance is completed, the robot continues cleaning the target area according to the planned cleaning path. The cleaning operation ends when the entire target area is cleaned.

[0083] As can be seen from the above, in this application, the cleaning robot 1 can adjust its cleaning path in real time when it detects ground-level obstacles in the work area. This allows the cleaning robot 1 to effectively avoid ground-level obstacles, fully preventing entanglement accidents, ensuring the safety of the cleaning operation, and reducing safety hazards. Furthermore, by adjusting the cleaning path, the cleaning robot 1 can clean the area around the ground-level obstacles by moving in a manner that closely follows them, thus ensuring the cleaning effect on the work area and improving the user experience.

[0084] Please refer to Figure 3 This is a structural schematic diagram of a cleaning robot 1 provided in another embodiment of this application. Figure 3 As shown, to facilitate obstacle identification in the target area, an image sensor can be installed on the cleaning robot 1. In this embodiment, the image sensor is a Time-of-Flight (TOF) sensor 13. Specifically, the TOF sensor 13 identifies obstacles by scanning images of the target area around the cleaning robot 1 in real time while the cleaning robot 1 is cleaning the target area, and uploading the scanned images to the cleaning robot 1. Furthermore, this allows the cleaning robot 1 to determine whether obstacles exist in the target area based on the images, and if obstacles exist, to determine the type of obstacle based on the display of the obstacles in the images. Specifically, the TOF sensor 13 can be installed on the front of the cleaning robot 1 at a height h above the ground. For example, h can be 2cm to 4cm; the emitted light wavelength of the TOF sensor 13 can be 800mm to 900mm; the detection distance of the TOF sensor 13 can be 0.1m to 0.4m; the angle between the TOF sensor 13 and the cleaning robot 1 in the horizontal direction (x direction in the figure) can be 55° to 65°; the angle between the TOF sensor 13 and the cleaning robot 1 in the vertical direction (y direction in the figure) can be 40° to 50°. Here, the detection distance refers to the ability of the TOF sensor 13 to accurately detect the distribution of obstacles within the target area, at a distance of 0.1m to 0.4m from the cleaning robot.

[0085] It is worth noting that x, y, and z in the figure represent the image coordinate system of the image obtained by the TOF sensor 13 after scanning the target area. When actually identifying obstacles, the cleaning robot 1, after receiving the image, needs to convert the image coordinate system into the environmental map coordinate system stored internally. The purpose of this coordinate system conversion is to unify the coordinate systems, facilitating the subsequent adjustment of the cleaning robot 1's cleaning path accuracy based on the placement angle of the ground-level obstacles.

[0086] In one embodiment, to ensure the accuracy of obstacle identification, the cleaning robot 1 can only identify whether an obstacle exists within a detection block at a preset distance from it. Specifically, this can be achieved through the following step S110: Step S110: Based on preset coordinate values, the infrared image (IR image) of the target area collected by the TOF sensor 13 is divided into a detection area and a non-detection area, and based on the detection area, it is determined whether an obstacle exists within a detection block at a preset distance from the cleaning robot 1. Furthermore, if an obstacle exists, the type of obstacle is determined.

[0087] The detection area can be set according to the safe distance d required for the robot to avoid obstacles. Specifically, when the safe distance is d, the detection area can be the area within a range of 1 from the cleaning robot.

[0088] It is worth noting that when assigning a value to d, the value of 2d must be less than the detection distance of the TOF sensor 13. For example, when the detection distance of the TOF sensor 13 is 0.1m to 0.4m, d can be 0.2m, in which case the detection area is the region within 0.2m to 0.4m from the cleaning robot.

[0089] After determining the distance information of the detection block, this distance information can be converted into preset image coordinate values ​​and stored in the cleaning robot 1. This allows the cleaning robot 1 to divide the infrared image of the target area acquired by the TOF sensor 13 into detection and non-detection areas based on the image coordinate values, and ultimately determine whether an obstacle exists within the detection block based on the display of obstacles within the detection area. Specifically, a mathematical model can be established between the detection distance of the TOF sensor 13 and the size of the infrared image acquired by the TOF sensor 13, and the distance information of the detection block can be converted into the image coordinate values ​​based on this mathematical model.

[0090] As shown in Figure 4(a), the distance between the elongated obstacle and the cleaning robot 1 in the infrared image uploaded by the TOF sensor 13 is d; as shown in Figure 4(b), the distance between the elongated obstacle and the cleaning robot 1 in the infrared image uploaded by the TOF sensor 13 is 2d; then, as shown in Figure 4(c), the detection area can be the area between the two horizontal lines in the figure; the non-detection area can be the area outside the two horizontal lines in the figure. For example, as shown in Figure 4(c), after dividing into detection area and non-detection area, the non-detection area can be blurred to ensure the accuracy of obstacle identification within the detection area.

[0091] In another embodiment, the detection area can also be determined based on the installation height of the TOF sensor 13; in this case, the detection area is a region within the range of d to 2d from the cleaning robot and with a height range of 0 to h.

[0092] By employing the above measures, the distance information of the detection block is mapped onto the infrared image transmitted by the TOF sensor 13, thereby dividing the infrared image transmitted by the TOF sensor 13 into detection and non-detection areas. The obstacle situation within the detection block is determined solely based on the display of the detection area. This effectively reduces noise interference and improves the accuracy of obstacle recognition.

[0093] In one embodiment, after dividing the infrared image transmitted by the TOF sensor 13 into regions, morphological erosion operations can be performed on the detection area to smooth out interference factors in the detection block. For example, interference factors can be floor patterns and interfering lines such as floor seams.

[0094] like Figure 5 As shown, it is a schematic diagram of the infrared image obtained after performing morphological erosion operation on the detection area in Figure 4(c), based on Figure 5 It can be seen that after the treatment, the cracks in the ground are almost completely filtered out.

[0095] By implementing the above measures, morphological erosion operations are performed on the detection area, reducing the impact of interference factors on obstacle recognition and further improving the accuracy of obstacle recognition.

[0096] It is worth noting that when using the above method to smooth out interference factors, it is necessary to reduce the loss of obstacle features. Based on this, the loss of obstacle features can be reduced by setting the filter kernel. For example, in this application, the filter kernel is selected as (2, 2).

[0097] In one embodiment, such as Figure 6 As shown, when the cleaning robot 1 performs step S210 above, if it detects an obstacle in the target area, it can determine the type of obstacle based on steps S310-S330. More specifically, if it detects an obstacle in the detection block, it can determine the type of obstacle based on steps S310-S330.

[0098] Step S310: Perform edge detection on the obstacle to obtain the edge contour information of the obstacle.

[0099] Among them, the edge contour information is the line information located at the edge of the obstacle. This edge contour information can reflect the basic shape of the obstacle in the target area.

[0100] In this step, when the cleaning robot 1 determines that an obstacle exists in the detection area based on the infrared image uploaded by the TOF sensor 13, it can perform edge detection on the obstacle within the detection area in the infrared image, thereby obtaining the edge contour information of the obstacle based on the edge detection results. For example, the cleaning robot 1 can obtain the edge contour information of the obstacle through Canny edge detection.

[0101] The edge contour information of the obstacle obtained in this step may contain noise. Therefore, morphological operations can be performed on the edge contour information to remove noise interference and obtain a clearer edge contour.

[0102] Specifically, after obtaining the edge contour information of the obstacle, the cleaning robot 1 can perform morphological operations on the aforementioned edge contour information.

[0103] Step S320: Based on the depth map generated by the TOF sensor 13 based on the target area, obtain the position information of the obstacle edge contour, and generate a point cloud map based on the depth map.

[0104] The depth map reflects the distance between the obstacle and the cleaning robot 1. The positional information of the obstacle's edge contour reflects the obstacle's location within the target area.

[0105] In this step, during the cleaning operation of the cleaning robot 1, the TOF sensor 13 uploads not only the infrared image it scans but also the depth map it scans. The infrared image and depth map are generated by the TOF sensor 13 scanning the target area around the cleaning robot 1, and the specific image size is determined by the scanning range of the TOF sensor 13. The depth image received by the cleaning robot 1 has the same number of pixels and the same size as the infrared image. Further, after obtaining the depth map uploaded by the TOF sensor 13, the cleaning robot 1 maps the obstacle edge contours detected in the infrared image to the depth map according to their pixel positions, thereby obtaining the positional information of the obstacle edge contours. Simultaneously, a point cloud map can be generated based on the intrinsic parameters of the TOF sensor 13 and the aforementioned depth map.

[0106] Step S330: Determine the height of the obstacle based on the positional information of the point cloud map and the edge contour of the obstacle.

[0107] The height of the obstacle refers to its actual height.

[0108] In this step, after obtaining the point cloud map, the positional information of the obstacle edge contours can be mapped onto the point cloud map, thereby accurately determining the obstacle's height based on the point cloud map. After determining the obstacle's height, the obstacle type can be determined by judging whether the obstacle's height is greater than a preset height threshold. Specifically, if the obstacle's height is greater than the preset height threshold, the obstacle type is determined to be a non-ground-hugging obstacle; if the obstacle's height is less than the preset height threshold, the obstacle type is determined to be a ground-hugging obstacle. For example, the preset height threshold can be 2.5cm to 3.5cm.

[0109] By implementing the above measures, the type of obstacle can be determined based on its height. This simplifies the determination process and improves the speed of determination while ensuring the accuracy of obstacle type identification.

[0110] The following is an example Figure 7 Using the example of a detection area containing a thermos cup, socks, and a ground cable, the specific working principle of the above obstacle type determination will be explained:

[0111] like Figure 7 As shown, when the cleaning robot 1 partitions the infrared image uploaded by the TOF sensor 13, and detects an obstacle within the detection block based on the detection area, it can determine the type of obstacle. Specifically, as... Figure 8 As shown, firstly, cleaning robot 1 performs edge contour detection on obstacles in the detection area to obtain the edge contour information of the obstacles in the detection area. Then, as... Figure 9 As shown, after obtaining the edge contour information of the obstacle, morphological operations can be performed on this information to segment the obstacle and obtain its clear outline. Then, as... Figure 10 As shown, the image obtained through the morphological operations described above can be mapped to... Figure 11 In the (depth map), thus in Figure 11 The system accurately pinpoints the exact location of obstacles. Figure 10 The boxes in the diagram are merely illustrative markers indicating the location of obstacles. Further, such as... Figure 11 and Figure 12 As shown, the cleaning robot 1 can generate a corresponding point cloud map based on the depth map generated from the detection block, and finally generate a point cloud map based on the point cloud map and... Figure 10 The location of the obstacles shown in the diagram is used to determine the height of the thermos cup, sock, and cable. After determining the height, the height of the thermos cup, water cup, and cable is compared with a preset height threshold. Based on the comparison result, the thermos cup is determined to be a non-ground-level obstacle, while the sock and cable are determined to be ground-level obstacles.

[0112] In one embodiment, such as Figure 13As shown, if the cleaning robot 1 determines that the obstacle is a ground-level obstacle, it can determine the placement angle of the ground-level obstacle through the following steps S410-S430, and adjust the cleaning path of the cleaning robot 1 based on the placement angle.

[0113] Step S410: Generate the minimum bounding rectangle of the ground-hugging obstacle, and use Hough detection to determine whether the slope of the ground-hugging obstacle within the minimum bounding rectangle is greater than zero, and determine the slope of the ground-hugging obstacle based on the judgment result.

[0114] In this step, when an obstacle is detected in the detection block, and the obstacle type is a ground-level obstacle, the minimum bounding rectangle of the ground-level obstacle can be generated. Specifically, the minimum bounding rectangle of the ground-level obstacle within the detection block can be generated based on the distribution of ground-level obstacles within the detection area in the infrared image uploaded by the TOF sensor 13. After generating the minimum bounding rectangle, Hough detection can be used to determine whether the slope of the ground-level obstacle located within the minimum bounding rectangle relative to the image coordinate system is greater than zero, and the slope of the ground-level obstacle is determined based on the determination result. Given the parameters of the TOF sensor 13 and the transformation relationship between different coordinate systems, this slope can be mapped to other coordinate systems.

[0115] Furthermore, when the slope of the ground-hugging obstacle is greater than zero, the slope of the line connecting the lower left vertex and the upper right vertex of the smallest bounding rectangle is taken as the slope of the ground-hugging obstacle. Specifically, the lower left vertex and the upper right vertex of the smallest bounding rectangle are relative to the cleaning robot 1, referring to the lower left vertex and the upper right vertex of the smallest bounding rectangle relative to the cleaning robot 1.

[0116] When the slope of a ground-hugging obstacle is less than zero, the slope of the line connecting the top-left vertex and the bottom-right vertex of the smallest bounding rectangle is taken as the slope of the ground-hugging obstacle. Specifically, the top-left and bottom-right vertices of the smallest bounding rectangle are relative to the cleaning robot 1, referring to the top-left and bottom-right vertices of the smallest bounding rectangle relative to the cleaning robot 1.

[0117] As shown in Figure 14(a), if the cleaning robot 1 is based on Figure 7 If the minimum bounding rectangle of the cable is generated and the slope of the cable is identified to be greater than zero, then as shown in Figure 15(a), the slope of the line connecting the lower left vertex and the upper right vertex of the minimum bounding rectangle is taken as the slope of the ground-hugging obstacle. Here, x and y are the horizontal and vertical axes in the image coordinate system, respectively. A slope greater than zero means that the angle between the cable and the x-axis in the figure is greater than 0° and less than 90°.

[0118] As shown in Figure 14(b), if the cleaning robot 1 is based on Figure 7When the cable generates a minimum bounding rectangle and the slope of the cable is less than zero, as shown in Figure 15(b), the slope of the line connecting the upper left and lower right vertices of the minimum bounding rectangle is taken as the slope of the ground-hugging obstacle. Here, x and y are the horizontal and vertical axes in the image coordinate system. A slope less than zero means that the angle between the cable and the x-axis in the figure is greater than 90° and less than 180°.

[0119] Step S420: Calculate the placement angle of the ground-level obstacle based on its slope.

[0120] In this step, after determining the slope of the ground-hugging obstacle, the placement angle of the ground-hugging obstacle relative to the image coordinate system can be calculated by angle transformation.

[0121] Step S430: Adjust the cleaning path of the cleaning robot 1 based on the placement angle.

[0122] In this step, after calculating the placement angle of the ground-level obstacle, the cleaning path of the cleaning robot 1 can be adjusted based on the placement angle. Specifically, if the placement angle is determined to be less than a first angle threshold, the cleaning robot 1 can be controlled to rotate a first angle in a direction perpendicular to the original direction of travel; if the placement angle is determined to be greater than or equal to the first angle threshold and less than a second angle threshold, the cleaning robot 1 can be controlled to rotate a second angle in a direction perpendicular to the original direction of travel; if the placement angle is determined to be greater than or equal to the second angle threshold and less than a third angle threshold, the cleaning robot 1 can be controlled to rotate a third angle in a direction perpendicular to the original direction of travel. After the angle rotation is completed, the cleaning robot 1 continues to travel in the direction after the rotation. Specifically, the second angle threshold is greater than the first angle threshold, and the third angle threshold is greater than the second angle threshold; when the placement angle is 'a', the first angle is 90°, the second angle is 90°-a, and the third angle is a-90°. For example, the first angle threshold can be 10°~20°; the second angle threshold can be 85°~95°; and the third angle threshold can be 175°~180°.

[0123] For example, as shown in Figure 16(a), if the placement angle 'a' of the ground-level obstacle is determined to be less than a first preset threshold, then the cleaning robot 1 is controlled to rotate 90° to the left or right in the figure; as shown in Figure 16(b), if the placement angle 'a' of the ground-level obstacle is determined to be greater than or equal to the first angle threshold and less than the second angle threshold, then the cleaning robot 1 is controlled to rotate 90°-a to the left or right in the figure; as shown in Figure 16(c), if the placement angle 'a' of the ground-level obstacle is determined to be greater than or equal to the second angle threshold and less than the third angle threshold, then the cleaning robot 1 is controlled to rotate a-90° to the left or right in the figure. It is worth noting that in this example, whether the cleaning robot 1 rotates to the left or right can be determined according to the cleaning sequence of the cleaning robot 1. For example, if the cleaning robot 1 has finished cleaning the left side of the figure, it can be controlled to rotate to the right in the figure; similarly, if the cleaning robot 1 has finished cleaning the right side of the figure, it can be controlled to rotate to the left in the figure.

[0124] By implementing the above measures, since ground-level obstacles such as cables are usually curved and their overall angle is difficult to determine, the placement angle of the cable is approximately calculated using the minimum circumscribed regular rectangle method. Based on the placement angle, the cleaning path of the cleaning robot 1 is adjusted, enabling the cleaning robot 1 to effectively avoid ground-level obstacles. This fully avoids entanglement accidents of the cleaning robot 1, ensures the safety of cleaning operations, and reduces safety hazards.

[0125] In one embodiment, such as Figure 17 As shown, when cleaning robot 1 performs step S210 to clean the target area, it can generate a bow-shaped cleaning path. After generating the path, it controls cleaning robot 1 to clean the target area based on the bow-shaped cleaning path. During the cleaning process, if an obstacle is found in the detection block within a preset range of cleaning robot 1, the type of obstacle is determined. Further, if the obstacle is determined to be a ground-level obstacle, and the placement angle of the ground-level obstacle is determined, the cleaning path of cleaning robot 1 can be adjusted based on the placement angle. The specific adjustment method is as described in step S430: based on the placement angle, cleaning robot 1 is controlled to rotate in the corresponding direction, and then cleaning robot 1 continues to move in the direction after the rotation angle. When cleaning robot 1 detects that it has moved to the bow-shaped cleaning path, it continues to clean the target area according to the bow-shaped cleaning path; this process is repeated, and obstacles encountered during subsequent movement are avoided using the above method until the cleaning work of the target area is completed.

[0126] By combining the bow-shaped cleaning path with the obstacle avoidance method for ground-level obstacles proposed in the above embodiments of this application, the cleaning robot 1 can achieve full coverage and all-round cleaning of the target area while avoiding entanglement accidents, thus fully ensuring the cleaning effect of the target area.

[0127] like Figure 17 and Figure 18 As shown, cleaning robot 1 is following... Figure 17 When cleaning the target area using the method shown, after cleaning one side of the obstacle that is touching the ground, you can move to the other side of the obstacle according to a certain strategy and clean that side. However, as... Figure 19 As shown, when cleaning the target area, the actual cleaning robot 1 first divides the target area into multiple cleaning zones, and then cleans each zone individually to clean the target area. Considering the above two factors, a more comprehensive cleaning strategy for the target area by the cleaning robot 1 can be proposed. The following section will combine... Figure 19 This cleaning strategy will be explained in detail:

[0128] like Figure 19 As shown, after dividing the target area into multiple cleaning zones, obstacles such as cables were found close to the ground in cleaning zones 4, 12, 6, 7, and 8. Zones without obstacles were cleaned using normal bow sweeping methods. Zones with obstacles were cleaned as follows:

[0129] 1. When a cable is detected within the cleaning area, and both ends of the cable are within the cleaning area (e.g., cable A is in cleaning areas 4 and 12), the non-cross-area cable cleaning plan is executed. First, the cleaning of one side of the cable is completed, and then the system moves to the uncleaned area to the other side of the cable and cleans along the cable again.

[0130] The specific plan is as follows:

[0131] Record the position H where the cleaning robot 1 first detects the cable and the position F where it last detects the cable. Calculate the distance d from H to the starting boundary and the distance l from F to the ending boundary based on the distance from the midpoint of the line in space. When the cleaning robot 1 completes obstacle avoidance cleaning (bow-shaped) along one side of the cable, compare H and F with the diameter of the robot body.

[0132] ①If l is greater than the diameter of the machine body, the cleaning robot 1 will go around the cable from point F to the uncleaned area to perform the cleaning task.

[0133] ②If l is smaller than the body diameter, but d is larger than the body diameter, then the cleaning robot 1 will go around the cable from point H to the uncleaned area to perform the cleaning task.

[0134] ③ If both l and d are smaller than the body diameter, then the cleaning robot 1 is allowed to bypass the cable across the area, first F and then H.

[0135] For example, such as Figure 20 As shown, for area 4, the distance l from F to the upper boundary is less than the diameter of the cleaning robot 1, but the distance d from H to the lower boundary is greater than the diameter. Therefore, the cleaning robot 1 first moves to H, and then goes around the cable to the uncleaned area to perform the cleaning task; Figure 21 As shown, for area 12, the distance l from F to the upper boundary is greater than the diameter of the cleaning robot 1. Therefore, the robot can directly go around the cable from the nearest point F to the uncleaned area to perform the cleaning task.

[0136] 2. If a cable is detected in the current cleaning area (position H of the first cable detection), and a cable is also detected at the boundary of an adjacent cleaning area, then the cleaning robot 1 continues to move along the cable to the adjacent cleaning area until the end of the cable F is detected in the adjacent area (i.e., the cable was detected at the time, but not detected again after the next downward movement of the bow sweeper), at which point the movement stops. At this point, the cleaning task on one side of the cable is completed. Then, based on the positions H and F of the first and last cable detections by the cleaning robot 1, the distance d from H to the starting boundary and the distance l from F to the ending boundary are calculated, and H and F are compared with the diameter of the robot body.

[0137] ①If l is greater than the diameter of the machine body, the cleaning robot 1 will go around the cable from point F to the uncleaned area to perform the cleaning task.

[0138] ②If l is smaller than the body diameter, but d is larger than the body diameter, then the cleaning robot 1 will go around the cable from point H to the uncleaned area to perform the cleaning task.

[0139] ③If both l and d are smaller than the body diameter, the cleaning robot 1 is allowed to continue to traverse the cable across the area, first F and then H.

[0140] For example, such as Figure 22 As shown, the cross-area cleaning mode is executed for areas 6, 7, and 8. The cleaning robot 1 detects the cable end in area 6 and records the position H of the cable end. The distance from H to the upper boundary of area 6 is d. When the cleaning robot 1 moves to the boundary between area 6 and area 7, it continues to sweep downwards into area 7 until the cable end is detected in area 8 and the position F of the cable end is marked. The distance from F to the lower boundary of area 8 is l. Since l is greater than the diameter of the cleaning robot 1, the cleaning robot 1 will go around the cable from the nearest point F to the uncleaned area and perform the cleaning task.

[0141] Through the above measures, while avoiding obstacles and staying close to the ground, the target area is cleaned in an all-round way, which fully avoids the occurrence of cleaning omissions, improves the cleaning effect of the cleaning robot 1, and enhances the user experience.

[0142] The apparatuses and methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatuses, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0143] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0144] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

Claims

1. A cleaning method for a cleaning robot, characterized in that, The method includes: When cleaning a target area, if there are obstacles in the target area, determine the type of the obstacles; If the obstacle is a ground-level obstacle, a minimum bounding rectangle of the ground-level obstacle is generated, and Hough detection is used to determine whether the slope of the ground-level obstacle within the minimum bounding rectangle is greater than zero. If the slope is greater than zero, the slope of the line connecting the lower left and upper right vertices of the minimum bounding rectangle is used as the slope of the ground-level obstacle. If the slope is less than zero, the slope of the line connecting the upper left and lower right vertices of the minimum bounding rectangle is used as the slope of the ground-level obstacle. Based on the slope of the ground-level obstacle, the placement angle of the ground-level obstacle is calculated. If the placement angle is less than a first angle threshold, the cleaning robot is controlled to rotate a first angle in a direction perpendicular to the original direction of travel. If the placement angle is greater than or equal to the first angle threshold and less than a second angle threshold, the cleaning robot is controlled to rotate a second angle in a direction perpendicular to the original direction of travel. If the placement angle is greater than or equal to the second angle threshold and less than a third angle threshold, the cleaning robot is controlled to rotate a third angle in a direction perpendicular to the original direction of travel. The cleaning robot is then controlled to travel in the direction after the rotation angle. Wherein, the second angle threshold is greater than the first angle threshold, and the third angle threshold is greater than the second angle threshold; when the placement angle is a, the first angle is 90°, the second angle is 90°-a, and the third angle is a-90°.

2. The cleaning method of the cleaning robot according to claim 1, characterized in that, The determination of the type of obstacle includes: Determine whether the height of the obstacle is greater than a preset height threshold; If the height of the obstacle is greater than a preset height threshold, the obstacle type is a non-ground-hugging obstacle; If the height of the obstacle is less than a preset height threshold, the obstacle type is a ground-level obstacle.

3. The cleaning method of the cleaning robot according to claim 2, characterized in that, The cleaning robot is equipped with a TOF sensor; Before determining whether the height of the obstacle is greater than a preset height threshold, the method further includes: Edge detection is performed on the obstacle to obtain its edge contour information; Based on the depth map generated by the TOF sensor based on the target area, the position information of the obstacle edge contour is obtained, and a point cloud map is generated based on the depth map; The height of the obstacle is determined based on the positional information of the point cloud map and the edge contour of the obstacle.

4. The cleaning method of the cleaning robot according to claim 1, characterized in that, The cleaning of the target area includes: Generate a bow-shaped cleaning path and control the cleaning robot to clean the target area based on the bow-shaped cleaning path.

5. The cleaning method of the cleaning robot according to claim 4, characterized in that, After controlling the cleaning robot to move in the direction following the rotation angle, the method further includes: When the robot travels to the bow-shaped cleaning path, it is controlled to continue cleaning the target area along the bow-shaped cleaning path.

6. The cleaning method of the cleaning robot according to claim 1, characterized in that, The cleaning robot is equipped with a TOF sensor; Before determining the type of obstacle if it exists in the target area, the method further includes: Based on preset coordinate values, the infrared image of the target area acquired by the TOF sensor is divided into a detection area and a non-detection area, and based on the detection area, it is determined whether the obstacle exists in the detection block at a preset distance from the cleaning robot.

7. The cleaning method of the cleaning robot according to claim 6, characterized in that, The method further includes: Morphological erosion operations are performed on the detection area to smooth out interference factors in the detection block.

8. A cleaning robot, characterized in that, include: Image sensors are used to identify obstacles; processor; Memory used to store processor-executable instructions; The processor is configured to perform the cleaning method of the cleaning robot as described in any one of claims 1-7 based on the recognition results of the image sensor.

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