Sweeping robot control method and apparatus, sweeping robot, and storage medium

Abstract

Embodiments of the present disclosure are applicable to the technical field of smart homes, and provide a sweeping robot control method and apparatus, a sweeping robot, and a storage medium. The method comprises: collecting environmental point cloud data in a current direction by means of a ranging sensor of the sweeping robot; determining environmental point cloud data intersection information on the basis of the environmental point cloud data and a currently established map tile of the sweeping robot; and on the basis of the environmental point cloud data intersection information, controlling the sweeping robot to perform a preset action, so as to collect environmental point cloud data in multiple directions.

Description

Control methods, devices, robotic vacuum cleaners, and storage media for robotic vacuum cleaners Cross-reference to related applications

[0001] This disclosure claims priority to Chinese Patent Application No. 2024115964107, filed on November 8, 2024, which is incorporated herein by reference in its entirety. Technical Field

[0002] This disclosure belongs to the field of smart home technology, and in particular relates to a control method, device, robot vacuum cleaner, and storage medium for a robot vacuum cleaner. Background Technology

[0003] During the cleaning process, robotic vacuum cleaners need to perceive their environment in order to perform cleaning tasks or avoid obstacles based on the perceived environment. When perceiving the environment, robotic vacuum cleaners can use ranging sensors to collect environmental point cloud data. Summary of the Invention

[0004] A first aspect of this disclosure provides a control method for a robotic vacuum cleaner, comprising:

[0005] The robot vacuum cleaner uses its ranging sensor to collect environmental point cloud data in the current direction.

[0006] Based on the environmental point cloud data and the map tiles currently built by the sweeping robot, determine the intersection information of the environmental point cloud data;

[0007] Based on the intersection information of the environmental point cloud data, the robot vacuum cleaner is controlled to perform preset actions to collect environmental point cloud data from multiple directions.

[0008] A second aspect of this disclosure provides a control device for a robotic vacuum cleaner, comprising:

[0009] A ranging sensor, installed on a robotic vacuum cleaner, is used to collect point cloud data of the environment in the current direction;

[0010] The determination module is used to determine the intersection information of the environmental point cloud data based on the environmental point cloud data and the map tiles currently built by the sweeping robot;

[0011] The execution module is used to control the sweeping robot to perform preset actions based on the intersection information of the environmental point cloud data, so as to collect environmental point cloud data from multiple directions.

[0012] A third aspect of this disclosure provides a robotic vacuum cleaner, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0013] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0014] A fifth aspect of this disclosure provides a computer program product that, when run on a robotic vacuum cleaner, causes the robotic vacuum cleaner to perform the method described in the first aspect. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 is a flowchart illustrating the steps of a control method for a sweeping robot provided in an embodiment of this disclosure.

[0017] Figure 2 is a schematic diagram of the scene in which a sweeping robot is located, according to an embodiment of this disclosure;

[0018] Figure 3 is a schematic diagram of the currently built map tiles of a sweeping robot provided in an embodiment of this disclosure;

[0019] Figure 4 is a schematic diagram of an environmental point cloud collected by a sweeping robot according to an embodiment of this disclosure;

[0020] Figure 5 is a schematic diagram of an environmental point cloud collected by another sweeping robot provided in an embodiment of this disclosure;

[0021] Figure 6 is a schematic diagram of a control device for a sweeping robot provided in an embodiment of this disclosure;

[0022] Figure 7 is a schematic diagram of a sweeping robot provided in an embodiment of this disclosure. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will recognize that this disclosure may be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0024] It should be understood that, when used in this disclosure and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the term “and / or” as used in this disclosure and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] As used in this disclosure and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0027] Furthermore, in the description of this disclosure and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this disclosure mean that one or more embodiments of this disclosure include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized.

[0029] When a robotic vacuum cleaner can use a ranging sensor to collect environmental point cloud data, the viewing angle of the ranging sensor is limited. Therefore, the environmental point cloud data collected by the ranging sensor cannot cover all surrounding areas, resulting in blind spots for the robotic vacuum cleaner.

[0030] Due to blind spots, the maps obtained by robotic vacuum cleaners during localization or mapping are distorted. In particular, when the robotic vacuum cleaner is in a confined area, its blind spots are further limited by its own field of vision and obstacles in the confined space, making it difficult to perceive an accurate environmental map.

[0031] If an accurate map cannot be obtained, the robot vacuum cleaner may not clean thoroughly or may not be able to avoid obstacles properly during operation.

[0032] The technical solutions of this disclosure will be illustrated below through specific embodiments.

[0033] Referring to Figure 1, a flowchart illustrating the steps of a control method for a sweeping robot provided in this embodiment of the present disclosure is shown, which may specifically include the following steps:

[0034] S101, the robot vacuum cleaner's ranging sensor collects environmental point cloud data in the current direction.

[0035] The method in this embodiment can be applied to a robotic vacuum cleaner, specifically executed by the control device on the robotic vacuum cleaner. The robotic vacuum cleaner is equipped with a ranging sensor. This ranging sensor may include, but is not limited to, 2D LiDAR, 2D / 3D ToF (Time of Flight) sensors, binocular cameras, and RGBD cameras (structured light depth cameras). Based on the ranging sensor, the robotic vacuum cleaner maps the cleaning environment. The map created by the robotic vacuum cleaner includes, but is not limited to, 2D grid maps, 3D grid maps, 2D point cloud maps, and 3D point cloud maps.

[0036] For example, the ranging sensor is a laser ranging sensor. The laser ranging sensor emits a laser beam. When the laser beam is reflected by an object in the cleaning environment, the receiver of the laser ranging sensor receives the reflected laser beam and determines the position of the reflection point based on the reflected laser beam. Based on the laser beam emitted by the ranging sensor, the positions of multiple reflection points are obtained, thus obtaining environmental point cloud data. The environmental point cloud data represents the surface position information of objects in the cleaning environment. The environmental point cloud data collected by the laser ranging sensor includes multiple point cloud data points, each corresponding to the contour points of a target object in the cleaning environment. Based on the environmental point cloud data, the robot vacuum cleaner identifies obstacles in the cleaning environment. In this embodiment, the three-dimensional environmental point cloud data collected by the laser ranging sensor is used as an example to illustrate the solution in this disclosure.

[0037] The method in this embodiment is applied to scenarios involving cleaning confined areas. In confined area scenarios, the ranging sensor, with its limited field of view, is easily obstructed by other obstacles within the area. Confined areas include room boundaries in multi-room scenarios, gaps between large furniture, etc. When cleaning in confined areas, due to the limited field of view of the ranging robot itself and the obstruction of obstacles, the collected environmental point cloud data has little correlation with existing map tiles.

[0038] S102, determine the intersection information of the environmental point cloud data based on the environmental point cloud data and the map tiles currently built by the sweeping robot.

[0039] Mapping for a robotic vacuum cleaner involves identifying and labeling new targets in the current environment, as well as expanding the existing map to include new areas. When labeling new targets, to ensure accuracy, the collected environmental point cloud data needs to be mapped onto the existing map tiles. When expanding a new area, the collected environmental point cloud data needs to be stitched together with the existing map tiles. This requires a sufficient number of anchor points for accurate stitching.

[0040] Therefore, when building a map based on environmental point cloud data and existing map tiles, a high degree of correlation between the environmental point cloud data and the existing map tiles is required. When the correlation between the environmental point cloud data and the existing map tiles is high, the robot vacuum cleaner can accurately determine its pose and mark unknown obstacles in the existing map tiles based on the collected environmental point cloud data, so that the robot vacuum cleaner can avoid obstacles.

[0041] The horizontal field of view of a ranging sensor is generally less than 360°. Therefore, the point cloud data collected by a laser ranging robot can only cover the area in the cleaning environment corresponding to the field of view. When the robot is in a restricted area, the field of view of the ranging sensor is also affected by obstacles, resulting in a very limited correlation between the point cloud data collected by the robot and the known map. During the process of expanding into a new area, there is a process of stitching point cloud data with the known map. Because there are too few point cloud data in the environmental point cloud data that fall within the currently built map tiles, the robot's posture is inaccurate, and the map is distorted.

[0042] Therefore, after the robotic vacuum cleaner collects environmental point cloud data, it first detects the correlation between the environmental point cloud data and the currently built map tiles. In this embodiment of the disclosure, the correlation between the environmental point cloud data and the currently built map tiles is determined by judging whether each point cloud data in the environmental point cloud data falls within the currently built map tiles.

[0043] The environmental point cloud data collected by the robotic vacuum cleaner is established based on the robot's coordinate system. To facilitate comparison, the collected environmental point cloud data and the currently built map tiles are transformed into the same coordinate system for comparison. For example, the currently built map tiles are established based on the global coordinate system. Therefore, the robotic vacuum cleaner can perform coordinate transformation on each point cloud data in the environmental point cloud data based on its current pose, thereby transforming the environmental point cloud data from the robotic vacuum cleaner's coordinate system to the global coordinate system, thus obtaining the coordinates of each point cloud data in the global coordinate system. During coordinate transformation, it can be based on the robotic vacuum cleaner's current pose or through any other arbitrary method; this embodiment does not limit the method of coordinate transformation.

[0044] After obtaining the global coordinates of each point cloud data point, it is determined whether each point cloud data point is located within the corresponding area of ​​the currently built map tile based on the global coordinates. Point cloud data points that fall outside the currently built map tile in the environment are called external point cloud data, and point cloud data points that fall within the currently built map tile in the environment are called internal point cloud data.

[0045] When determining whether the currently collected environmental point cloud data will cause map distortion, the intersection information between the currently collected environmental point cloud data and the environmental point cloud data of the currently built map tiles can be determined. In one possible implementation, the robot vacuum cleaner can use the ratio of the number of external point cloud data to the number of internal point cloud data as the environmental point cloud data intersection information. If the ratio of the number of external point cloud data to the number of internal point cloud data is too large, it indicates that there are too many external point cloud data falling outside the currently built map tiles and too few internal point cloud data falling within the currently built map tiles. Building a map based on the current environmental point cloud data will lead to map distortion.

[0046] In another possible implementation, the robotic vacuum cleaner can count the number of external point cloud data and the total amount of point cloud data. Then, the ratio of the number of external point cloud data to the total amount of point cloud data is used as the intersection information of the external point cloud data. If the ratio of the number of external point cloud data to the total amount of point cloud data is too large, it indicates that there are too many external point cloud data points outside the currently built map tiles, and too few internal point cloud data points within the currently built map tiles. Building a map based on the current environmental point cloud data will lead to map distortion.

[0047] S103, based on the intersection information of the environmental point cloud data, control the sweeping robot to perform preset actions to collect environmental point cloud data from multiple directions.

[0048] In one possible implementation, when the intersection information of environmental point cloud data is the ratio of the number of external point cloud data to the number of internal point cloud data, if the intersection information of environmental point cloud data is greater than a first value, it indicates that there is too much environmental point cloud data outside the currently built map tiles and insufficient internal point cloud data. When the robot vacuum cleaner builds a map based on the currently collected environmental point cloud data, it will cause map distortion. To obtain more environmental point cloud data, it is necessary to control the robot vacuum cleaner to perform preset actions, thereby changing the scanning angle of the ranging sensor to collect environmental point cloud data in multiple directions.

[0049] In one possible implementation, when the intersection information of environmental point cloud data is the ratio of the number of external point cloud data to the total number of point cloud data, if the intersection information of environmental point cloud data is greater than a preset second value, it indicates that map distortion will occur when mapping based on the collected environmental point cloud data, and more environmental point cloud data needs to be acquired. At this time, it is necessary to control the robot vacuum cleaner to rotate, thereby changing the scanning angle of the ranging sensor to collect environmental point cloud data in multiple directions.

[0050] The aforementioned preset actions can be rotational. When rotating, the robot vacuum can rotate at any angle or according to a preset angle. In one possible implementation, to improve positioning and mapping accuracy, the robot vacuum can rotate one or more times to collect more environmental point cloud data. In another possible implementation, the robot vacuum can walk along an arc, causing it to rotate during movement, changing its current direction and thus altering the scanning angle of the ranging sensor.

[0051] The robotic vacuum cleaner in this embodiment may be equipped with a ranging sensor. The robotic vacuum cleaner can collect environmental point cloud data through the ranging sensor. The robotic vacuum cleaner can determine the intersection information of the environmental point cloud data based on the environmental point cloud data and the currently built map tiles. Based on the intersection information, it can be determined whether mapping based on the currently collected environmental point cloud data will lead to map distortion. During mapping, the robotic vacuum cleaner can merge information from the environmental point cloud data into the currently built map tiles. During information merging, the environmental point cloud data and the currently built map tiles need to rely on anchor points for information stitching. This requires that there be enough point cloud data in the environmental point cloud data falling within the currently built map tiles. Based on the intersection information of the environmental point cloud data, it can be determined whether there is enough point cloud data falling within the currently built map tiles; therefore, it can be determined whether mapping based on the currently collected environmental point cloud data will lead to map distortion.

[0052] When the currently collected environmental point cloud data leads to map distortion, the robot vacuum can be controlled to perform preset actions, changing its direction to collect environmental point cloud data from multiple directions. Based on this multi-directional point cloud data, the robot vacuum can obtain a precise pose and a more accurate map. With this precise pose and more accurate map, the robot vacuum can clean more thoroughly and avoid obstacles more effectively.

[0053] In one possible implementation, excessive rotation during the cleaning process of a robotic vacuum cleaner may reduce cleaning efficiency. To avoid excessive rotation, the rotation points of the robotic vacuum cleaner need to be filtered. For example, a certain time interval, a certain distance, and / or a certain angular difference can be set between two rotations. Based on this, the robotic vacuum cleaner can determine whether to perform a rotation step before rotating.

[0054] The robot vacuum cleaner records its status information each time it rotates, including the time of rotation, the direction of rotation, and the position of the robot vacuum cleaner.

[0055] Therefore, before rotating, the robot vacuum cleaner reads the first state information of the previous rotation from the recorded information. The first state information includes the first time point, first position and / or first direction of the robot vacuum cleaner during the last rotation, and the second state information includes the second time point, second position and / or second direction of the robot vacuum cleaner at the current time point.

[0056] The state change values ​​of the sweeping robot are determined by the first state information and the second state information. The state change values ​​include the time difference between the first time point and the second time point, the distance between the first position and the second position, and / or the angle difference between the first direction and the second direction.

[0057] Then, based on the state change value, the robot vacuum cleaner is controlled to rotate at a preset angle to collect environmental point cloud data from multiple directions. For example, when the state change value satisfies at least one of the following conditions: time difference greater than a time threshold, distance value greater than a distance threshold, or angle difference greater than an angle threshold, the robot vacuum cleaner is controlled to rotate at a preset angle, thereby changing the scanning angle of the ranging sensor so that the ranging sensor can collect environmental point cloud data from multiple directions under the scanning angle.

[0058] For example, a time threshold of 1 minute, a distance threshold of 0.5 meters, and an angle threshold of 10 degrees can be set. If the robot vacuum's last rotation occurred at 5 minutes and 30 seconds, and the current second rotation occurred at 6 minutes and 50 seconds, the time difference is 1 minute and 20 seconds, at which point the robot vacuum can be directly controlled to rotate. Similarly, if the distance between the robot vacuum's last rotation position and its current second position is 1 meter, the robot vacuum can also be directly controlled to rotate. Likewise, if the angle difference between the robot vacuum's last rotation direction and its current second rotation direction is 20 degrees, the robot vacuum can also be directly controlled to rotate.

[0059] When the time difference between the last rotation of the robotic vacuum cleaner and the current time is 30 seconds, the distance between the first position of the last rotation and the current second position is 1 meter, and the angle difference between the first direction of the last rotation and the current second direction is 20 degrees, it indicates that the time difference is less than the time threshold, the distance value is greater than the distance threshold, and the angle difference is greater than the angle threshold. In this case, the robotic vacuum cleaner can be controlled to rotate. When the time difference between the last rotation of the robotic vacuum cleaner and the current time is 2 minutes, the distance between the first position of the last rotation and the current second position is 0.3 meters, and the angle difference between the first direction of the last rotation and the current second direction is 20 degrees, it indicates that the time difference is greater than the time threshold, the distance value is less than the distance threshold, and the angle difference is greater than the angle threshold. In this case, the robotic vacuum cleaner can be controlled to rotate. When the time difference between the last rotation of the robotic vacuum cleaner and the current time is 2 minutes, the distance between the first position of the last rotation and the current second position is 1 meter, and the angle difference between the first direction of the last rotation and the current second direction is 3 degrees, it indicates that the time difference is greater than the time threshold, the distance value is greater than the distance threshold, and the angle difference is less than the angle threshold. In this case, the robotic vacuum cleaner can be controlled to rotate.

[0060] When the time difference between the last rotation of the robot vacuum and the current time is 2 minutes, the distance between the first position of the last rotation and the second position of the current rotation is 1 meter, and the angle difference between the first direction of the last rotation and the second direction of the current rotation is 20 degrees, it indicates that the time difference is greater than the time threshold, the distance value is greater than the distance threshold, and the angle difference is greater than the angle threshold. At this time, the robot vacuum can be controlled to rotate.

[0061] When the time difference between the last rotation of the robot vacuum and the current time is detected to be 30 seconds, it indicates that the time difference is less than the time threshold. In this case, rotation can be determined based on the distance and angle difference. When the distance between the first position of the last rotation and the current second position is detected to be 0.2 meters, it indicates that the distance is less than the distance threshold. Angle difference can then be detected to determine whether selection is possible. If the angle difference between the first direction of the last rotation and the current second direction is detected to be 3 degrees, it indicates that the time elapsed since the last rotation is relatively short, the distance traveled by the robot vacuum is relatively short, and the rotation angle is relatively small. In this case, rotating would not result in significant differences between the collected environmental point cloud data and the environmental point cloud data collected in the previous selection. Therefore, the robot vacuum does not need to rotate.

[0062] Making judgments before rotation can reduce the number of rotations by the robot vacuum, thereby improving cleaning efficiency. On the other hand, rotating only when the state change value is relatively large can ensure that the new environmental point cloud data collected by the robot vacuum has a certain difference from the previously collected environmental point cloud data. This reduces the amount of external point cloud data that falls outside the previously built map tiles in the new environmental point cloud data, making it easier to optimize mapping based on the new environmental point cloud data.

[0063] To better illustrate the solutions in this disclosure, a specific embodiment is described below. Figure 2 is a schematic diagram of a scene where a robotic vacuum cleaner is located, as provided in an embodiment of this disclosure. As shown in Figure 2, the scene where the robotic vacuum cleaner is currently located is a restricted area. An obstacle area exists within this scene, creating a recessed cleaning area. The robotic vacuum cleaner can perform mapping during the cleaning process. Figure 3 is a schematic diagram of the currently built map tiles of the robotic vacuum cleaner. As shown in Figure 3, the currently built map tiles only contain a portion of the recessed cleaning area. Figure 4 is a schematic diagram of environmental point cloud data collected by a robotic vacuum cleaner, as provided in an embodiment of this disclosure. As shown in Figure 4, the robotic vacuum cleaner's current viewpoint is not significantly restricted, and all the collected environmental point cloud data falls within the currently built map tile area. This indicates a strong correlation between the collected environmental point cloud data and the currently built map tile area. Mapping based on the currently collected environmental point cloud data will not cause map distortion. Therefore, in the scenario shown in Figure 4, the robotic vacuum cleaner does not need to rotate. Figure 5 is a schematic diagram of the environmental point cloud collected by a robotic vacuum cleaner according to another embodiment of this disclosure. As shown in Figure 5, when the robotic vacuum cleaner is located in the recessed area, it is obstructed by obstacles at the recess, significantly limiting the viewing angle of the ranging sensor. Most of the environmental point cloud data it collects falls outside the currently constructed map tile area. Based on the point cloud in Figure 5, the robotic vacuum cleaner has difficulty identifying obstacles at the recessed area, easily leading to inaccurate maps. Therefore, in the scenario shown in Figure 5, the robotic vacuum cleaner can rotate to change the viewing angle of its ranging sensor, allowing it to collect point cloud data from more directions.

[0064] After acquiring new environmental point cloud data, the robot vacuum cleaner can update the existing map tiles based on the newly collected environmental point cloud data from multiple directions and the existing map tiles.

[0065] When there are many point cloud data points in the environmental point cloud data that fall within the currently built map tiles, the robot vacuum cleaner can identify new targets within the currently built map tiles based on these point cloud data points. Alternatively, the robot vacuum cleaner can stitch the environmental point cloud data and the currently built map tiles together, thereby adding new areas to the currently built map tiles and expanding them. After stitching the environmental point cloud data with the currently built map tiles, the robot vacuum cleaner's pose can be corrected.

[0066] Based on the method in this embodiment, the robotic vacuum cleaner can detect the correlation between the environmental point cloud data collected by the ranging sensor and the currently built map tiles in real time during operation. When there are too many external point cloud data falling outside the currently built map tiles, it indicates that the correlation between the environmental point cloud data and the currently built map tiles is weak. At this time, the robotic vacuum cleaner can be controlled to rotate, thereby changing the perspective of the ranging sensor and collecting environmental point cloud data from more directions. Based on the environmental point cloud data from more directions, the robotic vacuum cleaner can re-map, thereby optimizing the mapping effect. In addition, to avoid excessive rotation, the rotation position can be filtered before rotation, thereby avoiding excessive rotation that reduces cleaning efficiency. Based on the method in this embodiment, a more accurate map can be obtained, which enables better planning of the travel path, achieving more thorough cleaning and more accurate obstacle avoidance.

[0067] Based on the method in this embodiment, in scenarios where there are large-scale changes in laser observation, such as the boundaries between rooms of a multi-room user or the gaps between large pieces of furniture, the robot vacuum cleaner can build a map normally and correct the map of these areas.

[0068] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0069] Referring to Figure 6, a schematic diagram of a control device for a sweeping robot according to an embodiment of the present disclosure is shown, which may specifically include a ranging sensor 61, a determining module 62, and an execution module 63, wherein:

[0070] The ranging sensor 61 is installed on the robot vacuum cleaner to collect environmental point cloud data in the current direction;

[0071] The determination module 62 is used to determine the intersection information of the environmental point cloud data based on the environmental point cloud data and the map tiles currently built by the sweeping robot.

[0072] The execution module 63 is used to control the sweeping robot to perform preset actions based on the intersection information of the environmental point cloud data, so as to collect environmental point cloud data from multiple directions.

[0073] In one possible implementation, the determining module 62 includes:

[0074] The coordinate transformation submodule is used to transform the environmental point cloud data in the ranging sensor coordinate system to the global coordinate system to obtain the global coordinates of each point cloud data in the environmental point cloud data in the global coordinate system. The currently built map tiles are built based on the global coordinate system.

[0075] The environmental point cloud data intersection information determination submodule is used to determine the environmental point cloud data intersection information based on the global coordinates of each point cloud data and the currently built map tiles.

[0076] In one possible implementation, the above-mentioned environmental point cloud data intersection information determination submodule includes:

[0077] The internal and external point cloud data determination unit is used to determine, based on the global coordinates of each point cloud data, the internal point cloud data falling within the currently established map block and the external point cloud data falling outside the currently established map block from the environmental point cloud data.

[0078] An environmental point cloud data intersection information determination unit is used to use the ratio of the number of external point cloud data to the number of internal point cloud data as the environmental point cloud data intersection information.

[0079] In one possible implementation, the execution module 63 includes:

[0080] The first execution submodule is used to control the sweeping robot to perform a preset action to collect environmental point cloud data from multiple directions if the intersection information of the environmental point cloud data is greater than a first value.

[0081] In one possible implementation, the aforementioned environmental point cloud data intersection information determination submodule includes:

[0082] An external point cloud data determination unit is used to determine external point cloud data that falls outside the currently established map tile from the environmental point cloud data based on the global coordinates of each point cloud data.

[0083] A quantity statistics unit is used to determine the total amount of point cloud data in the environmental point cloud data, as well as the amount of external point cloud data;

[0084] The ratio calculation unit is used to divide the quantity of external point cloud data by the total amount of point cloud data as the intersection information of the environmental point cloud data.

[0085] In one possible implementation, the execution module 63 includes:

[0086] The second execution submodule is used to control the sweeping robot to perform a preset action to collect environmental point cloud data from multiple directions if the intersection information of the environmental point cloud data is greater than a second value.

[0087] In one possible implementation, the preset action is a rotational action, and the execution module 63 further includes:

[0088] The status information acquisition submodule is used to acquire the first status information of the sweeping robot during its last rotation and the second status information of the sweeping robot. The first status information includes the first time point, first position and / or first direction of the sweeping robot during its last rotation, and the second status information includes the current second time point, second position and / or second direction of the sweeping robot.

[0089] The state change value determination submodule is used to determine the state change value of the sweeping robot through the first state information and the second state information. The state change value includes the time difference between the first time point and the second time point, the distance between the first position and the second position, and / or the angle difference between the first direction and the second direction.

[0090] The environmental point cloud data acquisition submodule is used to control the sweeping robot to rotate at a preset angle according to the state change value in order to acquire environmental point cloud data in multiple directions.

[0091] In one possible implementation, the aforementioned environmental point cloud data acquisition submodule includes:

[0092] The judgment unit is used to control the sweeping robot to rotate at a preset angle and change the scanning angle of the ranging sensor when the state change value meets at least one of the following conditions, so that the ranging sensor can collect environmental point cloud data in multiple directions under the scanning angle. The conditions include the time difference being greater than a time threshold, the distance value being greater than a distance threshold, and the angle difference being greater than an angle threshold.

[0093] In one possible implementation, the above-mentioned device further includes:

[0094] The map update module is used to update the currently built map tiles based on the environmental point cloud data collected from multiple directions.

[0095] As the apparatus embodiments are basically similar to the method embodiments, they are described in a relatively simple manner. For relevant details, please refer to the description in the method embodiment section.

[0096] Figure 7 is a schematic diagram of the structure of a sweeping robot provided in an embodiment of this disclosure. As shown in Figure 7, the sweeping robot 7 of this embodiment includes: at least one processor 70 (only one is shown in Figure 7), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70. When the processor 70 executes the computer program 72, it implements the steps in any of the above-described method embodiments.

[0097] The robotic vacuum cleaner may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that Figure 7 is merely an example of the robotic vacuum cleaner 7 and does not constitute a limitation on the robotic vacuum cleaner 7. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0098] The processor 70 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0099] In some embodiments, the memory 71 can be an internal storage unit of the robotic vacuum cleaner 7, such as a hard drive or memory. In other embodiments, the memory 71 can be an external storage device of the robotic vacuum cleaner 7, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, the memory 71 can include both internal and external storage units of the robotic vacuum cleaner 7. The memory 71 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0100] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0101] This disclosure provides a computer program product that, when run on a robotic vacuum cleaner, enables the robotic vacuum cleaner to perform the steps described in the various method embodiments above.

[0102] If the control method for the aforementioned robotic vacuum cleaner is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0103] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] The embodiments described above are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A control method for a robotic vacuum cleaner, comprising: The robot vacuum cleaner uses its ranging sensors to collect environmental point cloud data in the current direction. Based on the environmental point cloud data and the map tiles currently built by the sweeping robot, determine the intersection information of the environmental point cloud data; Based on the intersection information of the environmental point cloud data, the robot vacuum cleaner is controlled to perform preset actions to collect environmental point cloud data from multiple directions.

2. The method as described in claim 1, wherein determining the intersection information of the environmental point cloud data based on the environmental point cloud data and the currently built map tiles of the sweeping robot includes: By converting the environmental point cloud data in the ranging sensor coordinate system to the global coordinate system, the global coordinates of each point cloud data in the environmental point cloud data in the global coordinate system are obtained, wherein the currently built map tile is built according to the global coordinate system; Based on the global coordinates of each point cloud data and the currently constructed map tiles, the intersection information of the environmental point cloud data is determined.

3. The method of claim 2, wherein determining the intersection information of the environmental point cloud data based on the global coordinates of each point cloud data and the currently constructed map tiles includes: Based on the global coordinates of each point cloud data, determine the internal point cloud data that falls within the currently established map block and the external point cloud data that falls outside the currently established map block from the environmental point cloud data. The ratio of the number of external point cloud data to the number of internal point cloud data is used as the intersection information of the environmental point cloud data.

4. The method of claim 3, wherein controlling the sweeping robot to perform a preset action based on the intersection information of the environmental point cloud data to collect environmental point cloud data from multiple directions includes: If the intersection information of the environmental point cloud data is greater than a first value, the robot vacuum cleaner is controlled to perform a preset action to collect environmental point cloud data from multiple directions.

5. The method of claim 2, wherein determining the intersection information of the environmental point cloud data based on the global coordinates of each point cloud data includes: Based on the global coordinates of each point cloud data, determine the external point cloud data that falls outside the currently established map tile from the environmental point cloud data; Determine the total amount of point cloud data in the environmental point cloud data, and the amount of external point cloud data; The ratio of the number of external point cloud data to the total number of point cloud data is used as the intersection information of the environmental point cloud data.

6. The method as described in claim 5, characterized in that, The step of controlling the sweeping robot to perform preset actions based on the intersection information of the environmental point cloud data to collect environmental point cloud data from multiple directions includes: If the intersection information of the environmental point cloud data is greater than the second value, the robot vacuum cleaner is controlled to perform a preset action to collect environmental point cloud data from multiple directions.

7. The method of any one of claims 1 to 6, wherein the preset action includes a rotation action, and controlling the sweeping robot to perform the preset action to collect environmental point cloud data in multiple directions includes: The system acquires first state information of the robot vacuum cleaner during its last rotation and second state information of the robot vacuum cleaner. The first state information includes the first time point, first position, and / or first direction of the robot vacuum cleaner during its last rotation, and the second state information includes the current second time point, second position, and / or second direction of the robot vacuum cleaner. The state change value of the sweeping robot is determined by the first state information and the second state information. The state change value includes the time difference between the first time point and the second time point, the distance between the first position and the second position, and / or the angle difference between the first direction and the second direction. The robot vacuum cleaner is controlled to rotate at a preset angle based on the state change value in order to collect environmental point cloud data from multiple directions.

8. The method of claim 7, wherein controlling the sweeping robot to rotate at a preset angle according to the state change value to collect environmental point cloud data in multiple directions includes: In response to the state change value satisfying at least one of the following conditions, the robot vacuum cleaner is controlled to rotate at a preset angle, changing the scanning angle of the ranging sensor. The ranging sensor collects environmental point cloud data in multiple directions under the scanning angle. The conditions include the time difference being greater than a time threshold, the distance value being greater than a distance threshold, and the angle difference being greater than an angle threshold.

9. The method according to any one of claims 1 to 8, further comprising: The currently constructed map tiles are updated based on the environmental point cloud data collected from multiple directions.

10. A control device for a robotic vacuum cleaner, comprising: A ranging sensor, installed on a robotic vacuum cleaner, is used to collect point cloud data of the environment in the current direction; The determination module is used to determine the intersection information of the environmental point cloud data based on the environmental point cloud data and the map tiles currently built by the sweeping robot; The execution module is used to control the sweeping robot to perform preset actions based on the intersection information of the environmental point cloud data, so as to collect environmental point cloud data from multiple directions.

11. A robotic vacuum cleaner, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

12. A computer program product, wherein when the computer program product is run on a robotic vacuum cleaner, the robotic vacuum cleaner causes the robotic vacuum cleaner to perform the method as described in any one of claims 1 to 9.

13. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 9.

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