Lifting and lowering method and apparatus, mobile robot, and storage medium

Abstract

A lifting and lowering method and apparatus, a mobile robot (100), and a storage medium. In the lifting and lowering method, on the basis that a mobile robot (100) is at a first position, first point cloud data returned from an obstacle can be collected by controlling a liftable device (11) to rotate, and then, the first point cloud data is used to determine whether a projection position of the first point cloud data belongs to a low-clearance area. Since the mobile robot (100) can collect the first point cloud data at the first position, the mobile robot (100) can determine in advance, at the first position, whether other positions are low-clearance areas, thereby enabling timely updating of low-clearance areas in a map. Accordingly, when the mobile robot (100) leaves or enters a low-clearance area, the liftable device (11) can be controlled to raise or lower, so as to smoothly pass through the low-clearance area, thereby improving the work efficiency of the mobile robot (100).

Description

A lifting method, device, mobile robot, and storage medium

[0001] Cross-references to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202411606856.3, filed on November 11, 2024, entitled "A Lifting Method, Apparatus, Mobile Robot and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to, but is not limited to, the field of lifting technology, and in particular to a lifting method, device, mobile robot, and storage medium. Background Technology

[0004] Currently, to address the cleaning capabilities of home robotic vacuum cleaners in low-ceilinged spaces, a laser distance sensor (LDS) solution has been proposed. To avoid collisions, intelligent algorithms are needed to effectively identify low-ceilinged spaces and proactively lower the LDS so the machine can pass through safely.

[0005] However, in identifying low-ceilinged spaces, the upward ranging device of the robot vacuum cleaner is usually used to measure distances, thereby determining which locations belong to low-ceilinged areas and which locations belong to non-low-ceilinged areas.

[0006] However, this method requires reaching the target location before it can identify whether the target location is a low-ceilinged space, which will affect the working efficiency of the robot vacuum cleaner. Summary of the Invention

[0007] In view of this, the present disclosure provides at least one lifting method, apparatus, mobile robot, and storage medium. These improvements can enhance the working efficiency of the mobile robot.

[0008] The technical solution of this disclosure embodiment is implemented as follows:

[0009] On one hand, this disclosure provides a lifting method applied to a mobile robot, the mobile robot including a lifting device, comprising:

[0010] With the mobile robot in its first position, control the rotation of the liftable components;

[0011] The system uses a liftable device to collect the first point cloud data returned by obstacles formed during the rotation process.

[0012] Based on the first point cloud data, determine whether the projection position of the first point cloud data belongs to the first region;

[0013] Raise or lower the liftable device when the mobile robot leaves or enters the first area.

[0014] On the other hand, embodiments of this disclosure provide a lifting device disposed in a mobile robot, the mobile robot including a lifting mechanism, comprising:

[0015] The control module is used to control the rotation of the liftable components when the mobile robot is in its first position.

[0016] The acquisition module is used to acquire the first point cloud data returned by obstacles formed during the rotation process through a lifting device;

[0017] The determination module is used to determine whether the projection position of the first point cloud data belongs to the first region based on the first point cloud data.

[0018] A lifting module is used to raise or lower the liftable device when the mobile robot leaves or enters the first area.

[0019] In another aspect, embodiments of this disclosure provide a mobile robot, including a memory and a processor. The memory stores a computer program that can run on the processor, and the processor executes the program to implement some or all of the steps in the above-described method.

[0020] This disclosure provides a lifting method, device, mobile robot, and storage medium. The mobile robot includes a lifting device, comprising: controlling the rotation of the lifting device when the mobile robot is in a first position; collecting first point cloud data returned by obstacles formed during the rotation through the lifting device; determining whether the projection position of the first point cloud data belongs to a first area based on the first point cloud data; and raising or lowering the lifting device when the mobile robot leaves or enters the first area. In other words, in this disclosure, based on the mobile robot being in a first position, controlling the rotation of the lifting device allows the collection of first point cloud data returned by obstacles. The first point cloud data is then used to determine whether the projection position of the first point cloud data belongs to a low-lying area. Since the mobile robot can collect first point cloud data at the first position, it can pre-determine whether other positions are low-lying areas, thus updating the low-lying areas on the map in a timely manner. Therefore, when the mobile robot leaves or enters a low-lying area, the lifting device can be raised or lowered to smoothly pass through the low-lying area, thereby improving the working efficiency of the mobile robot. Attached Figure Description

[0021] Figure 1 is a schematic diagram of the structure of an optional mobile robot provided in an embodiment of this disclosure;

[0022] Figure 2 is a flowchart illustrating an optional lifting method provided in an embodiment of this disclosure;

[0023] Figure 3a is a schematic diagram of an optional sweeping robot collecting point cloud data according to an embodiment of this disclosure;

[0024] Figure 3b is a schematic diagram of an optional sweeping robot collecting point cloud data according to an embodiment of this disclosure;

[0025] Figure 3c is a schematic diagram of an optional sweeping robot collecting point cloud data according to an embodiment of this disclosure;

[0026] Figure 4 is a flowchart illustrating an example of an optional lifting method provided in an embodiment of this disclosure;

[0027] Figure 5 is a schematic diagram of an optional lifting device provided in an embodiment of this disclosure;

[0028] Figure 6 is a schematic diagram of the structure of an optional mobile robot provided in an embodiment of this disclosure. Detailed Implementation

[0029] To make the technical solutions and advantages of this disclosure clearer, the technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limitations on this disclosure. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0030] In the following description, references to "some embodiments" describe a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first / second / third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of this disclosure only and is not intended to limit this disclosure.

[0032] To address the issue that the inability of the map generated by the upward ranging device of a mobile robot to be timely affects the accuracy of the map, this disclosure provides a lifting method. This method is applied to a mobile robot. Figure 1 is a schematic diagram of the structure of an optional mobile robot provided by this disclosure. As shown in Figure 1, the mobile robot 100 includes a lifting device 11, which is located on the upper shell of the mobile robot's body 12.

[0033] The liftable device 11 can rotate 360 ​​degrees around its mounting position on the upper shell of the body 12. In this embodiment, the component on the liftable device 11 used for collecting point cloud data is at a preset elevation angle to the horizontal position.

[0034] In addition to the aforementioned lifting device 11, the mobile robot 100 may also include other lifting devices. This disclosure does not limit the scope of such devices.

[0035] Based on the structure of the mobile robot in Figure 1, Figure 2 is a flowchart illustrating an optional lifting method provided in an embodiment of this disclosure. As shown in Figure 2, the lifting method may include:

[0036] S201: When the mobile robot is in the first position, control the rotation of the liftable device;

[0037] To improve the efficiency of mobile robots, they are typically pre-stored with map data, which includes information about whether each location is a low-lying area.

[0038] Therefore, based on this map data, the mobile robot can lower the liftable components when entering a low-lying area and raise them when leaving a low-lying area. This shows that the accuracy of the map data directly affects the working efficiency of the mobile robot.

[0039] Typically, this map data can be obtained using an upward ranging device. However, for upward ranging devices, it is only possible to determine whether the target location is a low-lying area once the target location is reached. Therefore, there will be a lag in the creation and updating of map data, causing the mobile robot to be unable to identify low-lying areas in a timely manner.

[0040] To improve the working efficiency of mobile robots, accurate map data is needed to enable the mobile robot to identify which locations are low-lying areas and which are not. In this embodiment, during the operation of the mobile robot, when the mobile robot is in the first position, the rotation of the lifting device is controlled. The mobile robot in this embodiment can be a sweeping robot for cleaning the floor or a robot for providing certain service functions. This embodiment does not limit the scope of the application.

[0041] The first position can be any position within the mobile robot's movable area. When the mobile robot moves to the first position, the lifting device is controlled to rotate. Here, the range of rotation for the lifting device is (0, 360). Generally, a full rotation, or 360 degrees, is selected. Taking the component on the lifting device used to collect point cloud data as a lidar as an example, the lifting device is controlled to rotate one full rotation while the lidar emits a laser pulse.

[0042] S202: Collects the first point cloud data returned by obstacles formed during rotation using a liftable device;

[0043] The above-mentioned S201 enables the mobile robot at the first position to control the rotation of the liftable device. During the rotation, the liftable device collects the first point cloud data returned by the obstacles in the rotation area through the component used to collect point cloud data.

[0044] Taking a lidar as an example, the component on a liftable device used to collect point cloud data. As the liftable device rotates, the lidar emits laser pulses. The area scanned by the laser pulses is called the rotating area. The point cloud data returned from the obstacles scanned by the laser pulses is defined as the first point cloud data. This first point cloud data represents the position of the surface of the obstacle scanned in space.

[0045] In a home environment, these obstacles can include walls, household appliances, coffee tables, and stools. It's important to note that because LiDAR sensors typically have a small elevation angle, they generally scan the underside of coffee tables and stools. These surfaces are usually located in low-lying areas.

[0046] It should be noted that, due to the different shapes and heights of obstacles, the arrangement of point cloud data returned by the laser pulse at different locations will be different. For example, the arrangement of point cloud data obtained by scanning a wall and the bottom of a stool will be different.

[0047] Figure 3a is a schematic diagram of an optional sweeping robot collecting point cloud data according to an embodiment of this disclosure. As shown in Figure 3a, the sweeping robot is in its current position. It controls the lifting device to rotate one revolution. The laser pulse sweeps the bottom surface of the stool. The line connecting the point cloud data returned from the bottom surface of the stool is usually a curve with continuous curvature, and the direction of the curve's convexity is away from the current position.

[0048] If the laser pulse emitted by the robot vacuum cleaner sweeps the wall, since the wall is usually a plane perpendicular to the ground, the resulting point cloud data of the wall will usually be a straight line.

[0049] S203: Based on the first point cloud data, determine whether the projection position of the first point cloud data belongs to the first region;

[0050] After obtaining the first point cloud data through S202, in S203, it can be determined whether the projection position of the first point cloud data belongs to the first region based on the first point cloud data.

[0051] It should be noted that although the first point cloud data mentioned above belongs to a point in space, the projection of the first point cloud data onto the ground can be called the projection position of the first point cloud data, which belongs to a point on the plane.

[0052] In this embodiment of the disclosure, based on the arrangement of the first point cloud data, for example, the curvature of the points on the connecting lines of the first point cloud data can be used to determine whether the projection position of the first point cloud data belongs to the first region. Alternatively, the curvature of the points on the connecting lines of the first point cloud data and the changes in the point cloud data collected as the mobile robot moves can also be used to determine whether the projection position of the first point cloud data belongs to the first region. Here, this embodiment of the disclosure does not limit this.

[0053] S204: Raise or lower the liftable device when the mobile robot leaves or enters the first area.

[0054] Once it is determined which locations belong to the first area and which belong to the second area, the first area can be called the low-lying area and the second area can be called the non-low-lying area, thus forming map data.

[0055] Therefore, when the mobile robot moves from the second area to the first area, the lifting device is lowered; when the mobile robot moves from the first area to the second area, the lifting device is raised.

[0056] In this way, as long as the map data can be determined in a timely manner and its accuracy can be guaranteed, the mobile robot can accurately determine when to raise and lower the liftable device, thereby improving the working efficiency of the mobile robot.

[0057] In order to determine whether the projection position of the first point cloud data belongs to the first region, in an optional embodiment, S203 may include:

[0058] Determine the sum of the distances between the projected position of the first point cloud data and the first position;

[0059] If the sum is less than the first preset threshold, the projection position of the first point cloud data is determined to belong to the first region.

[0060] Understandably, taking the control of a lifting device to rotate one revolution as an example, when the preset elevation angle of the laser pulse is small, the laser pulse will generally partially sweep the bottom surface of low obstacles. The first point cloud data obtained at this time is usually connected to form a curve. If the laser pulse sweeps the entire bottom surface of low obstacles, the first point cloud data is usually a circle. Since the preset elevation angle is small, the area of ​​the circle is small.

[0061] We can first determine the distance between the projection position of the first point cloud data and the first position, and then sum all the distances to get the sum value. If the first point cloud data is dense enough, this sum value is equivalent to the area of ​​the region formed by the line connecting the first point cloud data and the first position.

[0062] When the line connecting the first point cloud data is a circle, the sum is equal to the area of ​​the circle. When the first point cloud data is a rectangle, the sum is the area of ​​the rectangle. When the first point cloud data is a curve, the sum is the area of ​​the region formed by the curve, the endpoint of the curve, and the first position.

[0063] After determining the sum, it is compared with a first preset threshold. If the sum is less than the threshold, it indicates that the area formed by the lines connecting the first point cloud data and the first position is small. The first preset threshold is typically defined as the area formed by the lines connecting the projected positions of all point cloud data scanned to the bottom surface of low obstacles. Therefore, if the sum is less than the threshold, it means the laser pulse has completely covered the bottom surface of the low obstacle. Thus, if the sum is less than the first preset threshold, the projected position of the first point cloud data is determined to belong to the first region, i.e., the low-lying region.

[0064] Thus, by comparing the sum of the distances between the projection position of the first point cloud data and the first position with the first preset threshold, it is determined whether the projection position of the first point cloud data belongs to a low-lying area. This allows for the filtering out of all cases where the data is scanned to the bottom of low-lying obstacles, and quickly identifies a large area of ​​low-lying regions.

[0065] In addition, to determine whether the projection position of the first point cloud data belongs to the first region, in an optional embodiment, the above method may further include:

[0066] If the sum is greater than the first preset threshold, determine the curvature of the points on the connecting line of the first point cloud data;

[0067] Points on the line connecting points with curvature greater than the second preset threshold are identified as the first target point cloud data.

[0068] Based on the first target point cloud data, determine whether the projection position of the first target point cloud data belongs to the first region.

[0069] Understandably, the above-mentioned filtering process selects cases where all laser pulses are scanned onto the bottom surface of low obstacles. However, in actual scenarios, it is common for some laser pulses to scan onto the bottom surface of low obstacles, while others scan onto the surface of low obstacles or non-low obstacles. In this case, in this embodiment of the disclosure, if the sum is greater than the first preset threshold, there may be cases where some laser pulses are scanned onto the bottom surface of low obstacles. It is necessary to further confirm whether some laser pulses are scanned onto the bottom surface of low obstacles based on the first point cloud data.

[0070] Here, when the sum is greater than the first preset threshold, the curvature of the points on the connecting line of the first point cloud data is determined. That is, since the first point cloud data is generally discrete points, the discrete adjacent points are connected to form a connecting line. This connecting line can include at least two connecting lines. The curvature of the points on each connecting line is calculated. Here, for the target point, the target point and its adjacent points are defined as a circle, and the reciprocal of the radius of the circle is determined as the curvature of the target point. In this way, the curvature of each point can be calculated.

[0071] Since the point cloud data obtained when the laser pulse scans the bottom surface of a low obstacle is generally a curve, here, by setting a second preset threshold, the points on the line with curvature greater than the second preset threshold are found, and the points on the line with curvature greater than the second preset threshold are determined as the first target point cloud data.

[0072] It should be noted that even if the line connecting the first target point cloud data is a curve, it does not necessarily mean that the bottom surface of the low obstacle is scanned. Therefore, it is necessary to further determine whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data.

[0073] In this way, by selecting points on the line that meet the curvature requirements from the first point cloud data as the first target point cloud data, the point cloud data that may scan the bottom surface of low obstacles can be filtered out. Then, it is further determined whether the projection position of these point cloud data belongs to the low area. In this way, further judgment is made on the cases where the bottom surface of low obstacles is scanned, so that the identified low area is more accurate.

[0074] To determine whether the projection position of the first target point cloud data belongs to a low-lying area, in one optional embodiment, determining whether the projection position of the first target point cloud data belongs to a first area based on the first target point cloud data may include:

[0075] If the first target point cloud data meets the preset conditions, control the mobile robot to move to the second position;

[0076] With the mobile robot in the second position, control the rotation of the liftable device;

[0077] The second point cloud data returned by obstacles formed during the rotation is collected using a liftable device.

[0078] Determine the curvature of the points on the connecting lines of the first point cloud data, and determine the points on the connecting lines with curvature greater than the second preset threshold as the second target point cloud data;

[0079] If the second target point cloud data meets the preset conditions, determine whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data and the second target point cloud data.

[0080] Understandably, the process first determines whether the first target point cloud data meets the preset conditions. Only when the first target point cloud data meets the preset conditions is the second target point cloud data collected. Then, based on the first target point cloud data and the second target point cloud data, it is determined whether the projection position of the first target point cloud data belongs to a low-lying area.

[0081] To acquire the second target point cloud data, similar to acquiring the first target point cloud data, the mobile robot is first moved to the second position. That is, the mobile robot moves from the first position to the second position, and the second point cloud data is acquired in a similar manner to acquiring the first point cloud data at the first position. This process will not be elaborated further. After obtaining the second point cloud data, the process for obtaining the second target point cloud data is similar to determining the first target point cloud data from the first point cloud data; this process will also not be elaborated further.

[0082] After determining the second target point cloud data, it is determined whether the second target point cloud data meets the preset conditions. If it does, it is determined whether the projection position of the first target point cloud data belongs to a low-lying area based on the first target point cloud data and the second target point cloud data. If it does not meet the conditions, it means that the projection position of the first target point cloud data belongs to a low-lying area.

[0083] In this way, by collecting point cloud data of the second target at the new location by moving the mobile robot, that is, by using the point cloud data after the move and the point cloud data of the first target to determine the changes in the point cloud data as the location moves, it can be determined whether the point cloud data of the first target belongs to the low-lying area, thus enabling a more accurate determination of the low-lying area within the mobile robot's range of movement.

[0084] To determine whether the first target point cloud data meets preset conditions, in an optional embodiment, the above method may further include:

[0085] If the difference in curvature between adjacent points in the first target point cloud data all fall within the preset error range, and the center of the circle determined by three consecutive points in the first target point cloud data is located in the preset area, then the first target point cloud data is determined to meet the preset conditions.

[0086] Understandably, the curvature of adjacent points in the first target point cloud data is calculated to obtain all the differences. If all the differences fall within the preset error range, and the center of the circle determined by three consecutive points in the first target point cloud data is located in the preset region, where the preset region is: the area formed by the line connecting the projection positions of the first target point cloud data, the endpoint of the line connecting the first position, and the line connecting the first position.

[0087] In other words, not only is the curvature of the points on the connecting line of the first target point cloud data continuous, but the direction of the curve's convexity also deviates from the first position. At this point, it is highly likely that the first target point cloud data is point cloud data returned from scanning the bottom surface of a low obstacle. Therefore, the first target point cloud data is determined to meet the preset conditions. Further observation of the changes in the first target point cloud data as the mobile robot moves can be conducted to further determine whether the projection position of the first target point cloud data belongs to a low-lying area.

[0088] It should be noted that the method for determining whether the second target point cloud data meets the preset conditions is similar to the method for determining whether the first target point cloud data meets the preset conditions, and will not be elaborated here.

[0089] In this way, by judging whether the first target point cloud data meets the preset conditions, the first target point cloud data that meets the preset conditions can be further judged, thus obtaining a more accurate low-lying area.

[0090] For first target point cloud data that does not meet preset conditions, in an optional embodiment, the above method may further include:

[0091] If the first target point cloud data does not meet the preset conditions, the projection position of the first target point cloud data is determined to belong to the second region.

[0092] Understandably, if the first target point cloud data does not meet the preset conditions after judgment, it means that the first target point cloud data is not the point cloud data returned by the laser pulse scanning the bottom surface of the low obstacle. Therefore, it is determined that the projection position of the first target point cloud data does not belong to the first region, but to the second region, that is, the non-low obstacle region.

[0093] Thus, by determining that the projection position of the first target point cloud data that does not meet the preset conditions belongs to a non-low-lying area, the determined map data becomes more accurate.

[0094] In an optional embodiment, if the first target point cloud data does not meet the preset conditions, the above method may further include:

[0095] If at least one difference in the curvature of adjacent points in the first target point cloud data does not fall within a preset error range, the first target point cloud data is determined not to meet the preset conditions.

[0096] Understandably, by taking the difference in curvature between adjacent points in the first target point cloud data, and obtaining all the differences, if at least one of the differences does not fall within the preset error range, it indicates that the curvature of adjacent points in the first target point cloud data is discontinuous, and there may be points with abrupt changes in curvature. This indicates that the first target point cloud data is not point cloud data returned from the bottom surface of low obstacles. Therefore, it is determined that the first target point cloud data does not meet the preset conditions, and the projection position of the first target point cloud data belongs to a non-low area.

[0097] In this way, by judging whether the curvature is continuous, it can be determined whether the first target point cloud data does not meet the preset conditions, thereby identifying the non-low-lying areas and improving the accuracy of the map data.

[0098] In addition, for cases where the first target point cloud data does not meet the preset conditions, in an optional embodiment, the above method may further include:

[0099] If at least one center of a circle determined by three consecutive points in the first target point cloud data is outside a preset area, the first target point cloud data is determined not to meet the preset conditions.

[0100] Understandably, the center of the circle determined by three consecutive points in the first target point cloud data lies within a preset region. This preset region is defined as the area formed by the line connecting the projection positions of the first target point cloud data, the endpoints of this line, and the line connecting the first position. In other words, the convex direction of the curve formed by the connected first target point cloud data points towards the first position, indicating that the first target point cloud data is not point cloud data returned from the bottom surface of a low-lying obstacle. Therefore, it is determined that the first target point cloud data does not meet the preset condition, and thus, the projection position of the first target point cloud data belongs to a non-low-lying region.

[0101] In this way, by judging the position of the center of the circle determined by three consecutive points in the first target point cloud data, it can be determined whether the first target point cloud data does not meet the preset conditions, thereby identifying the non-low-lying areas and improving the accuracy of the map data.

[0102] To determine whether the projection position of the first target point cloud data belongs to a low-lying area based on changes in the point cloud data of a mobile robot, in one optional embodiment, determining whether the projection position of the first target point cloud data belongs to a first area based on the first target point cloud data and the second target point cloud data may include:

[0103] Based on the relative positional relationship between the second position and the first position, and according to the first target point cloud data and the second target point cloud data, it is determined whether the projection position of the first target point cloud data belongs to the first region.

[0104] Understandably, the relationship between the second position and the first position is first determined. This positional relationship can include being close to or far from each other. Here, being close to or far from each other is relative to the projection position of the first target point cloud data. After determining the relative positional relationship, the projection position of the first target point cloud data is then determined based on the first target point cloud data and the second target point cloud data to determine whether it belongs to a low-lying area.

[0105] The methods for determining whether the projected position of the first target point cloud data belongs to the first region differ depending on the relative positional relationship. However, they all involve comparing the number of points in the point cloud data. In other words, whether the first target point cloud data belongs to the low-lying region is determined by comparing whether the number of points in the point cloud data increases or decreases.

[0106] In this way, by changing the location and the point cloud data, it is determined whether the projection position of the first target point cloud data belongs to a low-lying area. Using a dynamic change method to determine whether the projection position of the first target point cloud data belongs to a low-lying area makes the judgment of low-lying areas more accurate, thereby improving the accuracy of map data.

[0107] Regarding the situation where the second location is far from the projection location of the first target point cloud data, in an optional embodiment, based on the relative positional relationship between the second location and the first location, and according to the first target point cloud data and the second target point cloud data, determining whether the projection location of the first target point cloud data belongs to the first region may include:

[0108] If the sum of the distances between the projection position of the first target point cloud data and the second position is greater than the sum of the distances between the projection position of the first target point cloud data and the first position, and the number of points in the second target point cloud data is less than the number of points in the first target point cloud data, then the projection position of the first target point cloud data is determined to belong to the first region.

[0109] Understandably, the sum of the distances between the projected position of the first target point cloud data and the second position is calculated, and the sum of the distances between the projected position of the first target point cloud data and the first position is calculated. The two are compared. If the former is greater than the latter, it means that the second position is far away from the projected position of the first target point cloud data.

[0110] So, for the case where the second position is far from the projection position of the first target point cloud data, compare the number of points in the first target point cloud data with the number of points in the second target point cloud data. If the number of points is less than the projection position of the second target point cloud data, it means that the number of points in the point cloud data has decreased when the second position is far from the projection position of the first target point cloud data. This change indicates that the first target point cloud data is the point cloud data returned by the laser pulse scanning the bottom surface of the low obstacle. Therefore, the projection position of the first target point cloud data belongs to the low area.

[0111] Based on Figure 3a above, Figure 3b is a schematic diagram of an optional sweeping robot collecting point cloud data according to an embodiment of this disclosure. As shown in Figure 3b, when the mobile robot moves away, the number of points in the second target point cloud data is less than the number of points in the first target point cloud data in Figure 3a.

[0112] In this way, by controlling the mobile robot to move away from the projection position of the first target point cloud data and by observing the changes in the midpoint of the point cloud data, it is possible to determine whether the projection position of the first target point cloud data belongs to a low-lying area, thus accurately determining whether the projection position of the first target point cloud data belongs to a low-lying area and improving the accuracy of the map data.

[0113] Regarding the situation where the second location is close to the projection location of the first target point cloud data, in one optional embodiment, based on the relative positional relationship between the second location and the first location, and according to the first target point cloud data and the second target point cloud data, determining whether the projection location of the first target point cloud data belongs to the first region includes:

[0114] If the sum of the distances between the projection position of the first target point cloud data and the second position is less than the sum of the distances between the projection position of the first target point cloud data and the first position, and the number of points in the second target point cloud data is greater than the number of points in the first target point cloud data, then the projection position of the first target point cloud data is determined to belong to the first region.

[0115] Understandably, the sum of the distances between the projected position of the first target point cloud data and the second position is calculated, and the sum of the distances between the projected position of the first target point cloud data and the first position is calculated. Then, the two are compared. If the former is less than the latter, it means that the second position is closer to the projected position of the first target point cloud data.

[0116] So, regarding the case where the second position is close to the projection position of the first target point cloud data, compare the number of points in the first target point cloud data with the number of points in the second target point cloud data. If the second position is greater than the projection position of the first target point cloud data, it means that the number of points in the point cloud data has increased. This change indicates that the first target point cloud data is the point cloud data returned by the laser pulse scanning the bottom surface of the low obstacle. Therefore, the projection position of the first target point cloud data belongs to the low area.

[0117] Based on Figure 3a above, Figure 3c is a schematic diagram of an optional sweeping robot collecting point cloud data according to an embodiment of this disclosure. As shown in Figure 3c, when the mobile robot approaches, the number of points in the second target point cloud data is more than the number of points in the first target point cloud data in Figure 3a.

[0118] In this way, by controlling the mobile robot to approach the projection position of the first target point cloud data and by observing the changes in the midpoint of the point cloud data, it is possible to determine whether the projection position of the first target point cloud data belongs to a low-lying area, thus accurately determining whether the projection position of the first target point cloud data belongs to a low-lying area and improving the accuracy of the map data.

[0119] In an optional embodiment, if the projection location of the first target point cloud data belongs to the first region, the above method may further include:

[0120] If the projection location of the first target point cloud data is determined to belong to the first region, then the first location is determined to be a restricted location.

[0121] Understandably, robots can navigate normally in low-ceilinged spaces, but due to installation errors in the pitch angle of lidar, laser pulses tend to hit the bottom of these spaces more often, resulting in ineffective observations. This, in turn, affects functions such as Simultaneous Localization and Mapping (SLAM) localization and obstacle avoidance. Therefore, it is necessary to identify areas where laser observation is limited.

[0122] Since the projection position of the first target point cloud data is determined to be in a low-lying area, and the first target point cloud data is collected by the mobile robot at the first position, that is, the laser pulse scans the bottom surface of the low obstacle at the first position, and the data collected at this position is unreliable. Therefore, the first position is considered a restricted position, and the point cloud data collected by the mobile robot at the restricted position through the lifting device is considered unreliable.

[0123] This allows the mobile robot to segment locations in the map data, identify which locations are restricted and which are unrestricted, and enable the mobile robot to recognize valid data.

[0124] The following examples illustrate the lifting method described in one or more of the above embodiments.

[0125] Figure 4 is a flowchart illustrating an example of an optional lifting method provided in this embodiment of the present disclosure. As shown in Figure 4, the lifting method may include:

[0126] S401: When the LiDAR at the first position of the robot vacuum reaches its highest position, the robot vacuum rotates 360 degrees to obtain the first point cloud data.

[0127] S402: The robot vacuum cleaner calculates the sum of the distances from the projected position of the first point cloud data to the first position;

[0128] S403: Determine if the sum of distances is less than the first preset threshold. If yes, proceed to S404; if no, proceed to S405.

[0129] S404: Determine that the projection location of the first point cloud data belongs to a low-lying area, and determine that the first location is a location with limited observation.

[0130] S405: Calculate the curvature of the points on the connecting line of the first point cloud data, select the curve with a curvature greater than the second preset threshold and a continuous curvature, and take the points on the curve whose convex direction is away from the first position as the first target point cloud data.

[0131] In other words, if there are points in the connection of the first point cloud data with a curvature greater than the second preset threshold, and the curvature of these points is continuous, and the curve formed by these points is a tangentially convex arc (concentric with the shape of a circular sweeping robot), then it is considered that there may be laser-limited or low-lying areas.

[0132] S406: Control the mobile robot to move to the second position and collect the second point cloud data to obtain the second target point cloud data;

[0133] The second target point cloud data was determined using the same method as the method used to collect the first point cloud data.

[0134] S407: If the second position is closer to the projection position of the first target point cloud data relative to the first position, determine whether the sum of the distances between the first target point cloud data and the second position is greater than the sum of the distances between the second target point cloud data and the first position. If yes, execute S409; if no, execute S410.

[0135] S408: If the second position is farther away from the projection position of the first target point cloud data relative to the first position, determine whether the sum of the distances between the first target point cloud data and the second position is less than the sum of the distances between the second target point cloud data and the first position? If yes, execute S409; if no, execute S410.

[0136] S409: Determine that the projection position of the first target point cloud data belongs to a low-lying area;

[0137] S410: Determine that the projection position of the first target point cloud data belongs to a non-low-lying area.

[0138] Here, the movement of the robot is used to determine whether the curve segment increases or decreases as the robot approaches or moves away. If so, the existence of a low-lying space is double-confirmed (if it is a real obstacle, the relative point cloud features will not change regardless of the distance of the robot. If the point cloud on the curve changes simultaneously through the movement of the robot, it can be determined that the robot has hit the lower surface of the low-lying space).

[0139] In this example, before the aircraft enters the low-lying space, the pitch angle of the LiDAR installation can be used to pre-project laser pulses onto the lower surface of the low-lying space. When the point cloud data returned by the emitted laser pulse forms the aforementioned curved feature, the projection position of the point cloud data on the curve is considered to belong to the low-lying space. Combined with the navigation logic, the LDS can be lowered in advance when entering the low-lying area.

[0140] Meanwhile, due to the installation tolerance of the pitch angle of the LiDAR installed on the robot vacuum cleaner, even if the robot vacuum cleaner enters a relatively spacious low space, the point cloud data collected by the LiDAR will become arc-shaped (i.e., the LiDAR reaches the lower surface of the low space). The LDS module still needs to descend to ensure better observation for subsequent SLAM localization, mapping and navigation obstacle avoidance.

[0141] In this example, the arc shape is identified by point cloud data collected by LiDAR, thereby identifying low-lying areas and laser-limited scenes. By analyzing the changes in point cloud features across consecutive frames, the low-lying area is identified, rather than ordinary obstacles. Unlike traditional height measurement for identifying low-lying spaces, this method identifies low-lying spaces by the shape of a 2D plane (the laser will hit the lower surface of the low-lying space at close range).

[0142] As can be seen, the solution adopted in this example is low-cost, requires no additional upward ranging sensors, and can thus identify low-ceilinged spaces; it identifies laser observation-limited scenarios based on the point cloud shape collected by the LiDAR and controls the lifting and lowering of the LDS; it can identify low-ceilinged areas in advance, without the body entering low-ceilinged spaces and then stopping to lower the LDS, thus improving the operating efficiency of the robot vacuum cleaner.

[0143] This reduces sensor data and costs; it enables early identification of low-ceilinged areas, improving operational efficiency; and it allows for identification of laser-limited scenarios, ensuring reliable point clouds for SLAM and navigation functions by reducing LDS (Local Light Source Diagram).

[0144] This disclosure provides a lifting method for a mobile robot. The mobile robot includes a lifting device located on its body shell. The method includes: when the mobile robot is in a first position, controlling the lifting device to rotate; collecting first point cloud data returned by obstacles formed during the rotation via the lifting device; determining whether the projection position of the first point cloud data belongs to a first area based on the first point cloud data; and raising or lowering the lifting device when the mobile robot leaves or enters the first area. In other words, in this disclosure, based on the mobile robot being in a first position, controlling the rotation of the lifting device allows the collection of first point cloud data returned by obstacles. The first point cloud data is then used to determine whether the projection position of the first point cloud data belongs to a low-lying area. Since the mobile robot can collect first point cloud data at the first position, it can determine in advance whether other positions are low-lying areas, thus updating the low-lying areas on the map in a timely manner. Therefore, when the mobile robot leaves or enters a low-lying area, it can control the lifting device to rise or fall to smoothly pass through the low-lying area, thereby improving the working efficiency of the mobile robot.

[0145] Based on the same concept as the foregoing embodiments, this disclosure provides a lifting device, which is disposed in a mobile robot. The mobile robot includes a lifting device located on the upper shell of the mobile robot. Figure 5 is a schematic diagram of an optional lifting device provided in this disclosure. As shown in Figure 5, the lifting device 500 includes: a control module 51, a data acquisition module 52, a determination module 53, and a lifting module 54, wherein:

[0146] The control module 51 is used to control the rotation of the liftable device when the mobile robot is in the first position; the acquisition module 52 is used to acquire the first point cloud data returned by the obstacle formed during the rotation through the liftable device; the determination module 53 is used to determine whether the projection position of the first point cloud data belongs to the first area based on the first point cloud data; the lifting module 54 is used to raise or lower the liftable device when the mobile robot leaves or enters the first area.

[0147] In an optional embodiment, the determining module 53 is configured to: determine the sum of the distances between the projection position of the first point cloud data and the first position; and determine that the projection position of the first point cloud data belongs to the first region if the sum is less than a first preset threshold.

[0148] In an optional embodiment, the device is further configured to: determine the curvature of points on the connecting line of the first point cloud data when the sum is greater than a first preset threshold; determine the points on the connecting line with curvature greater than a second preset threshold as the first target point cloud data; and determine whether the projection position of the first target point cloud data belongs to a first region based on the first target point cloud data.

[0149] In one optional embodiment, the device determines whether the projection position of the first target point cloud data belongs to a first region based on the first target point cloud data, including: controlling a mobile robot to move to a second position when the first target point cloud data meets preset conditions; controlling a lifting device to rotate when the mobile robot is in the second position; collecting second point cloud data returned by obstacles formed during the rotation through the lifting device; determining the curvature of points on the connecting lines of the first point cloud data, and identifying points on the connecting lines with curvature greater than a second preset threshold as second target point cloud data; and determining whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data and the second target point cloud data when the second target point cloud data meets preset conditions.

[0150] In an optional embodiment, the device is further configured to: determine that the first target point cloud data satisfies a preset condition when the difference in curvature between adjacent points in the first target point cloud data all fall within a preset error range and the center of the circle determined by three consecutive points in the first target point cloud data is located in a preset region; wherein, the preset region is: the region formed by the line connecting the projection positions of the first target point cloud data, the endpoint of the line connecting the first position and the line connecting the first position.

[0151] In an optional embodiment, the device is further configured to: determine that the projection position of the first target point cloud data belongs to the second region when the first target point cloud data does not meet the preset conditions.

[0152] In an optional embodiment, the device is further configured to: determine that the first target point cloud data does not meet the preset conditions if at least one of the differences in curvature between adjacent points in the first target point cloud data does not fall within a preset error range value.

[0153] In an optional embodiment, the device is further configured to: determine that the first target point cloud data does not meet a preset condition if at least one center of a circle determined by three consecutive points in the first target point cloud data is outside a preset area; wherein the preset area is: the area formed by the line connecting the projection positions of the first target point cloud data, the endpoint of the line connecting the first position and the line connecting the first position.

[0154] In one optional embodiment, the device determines whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data and the second target point cloud data, including: determining whether the projection position of the first target point cloud data belongs to the first region based on the relative positional relationship between the second position and the first position, according to the first target point cloud data and the second target point cloud data.

[0155] In one optional embodiment, the device determines whether the projection position of the first target point cloud data belongs to the first region based on the relative positional relationship between the second position and the first position, according to the first target point cloud data and the second target point cloud data. This includes determining that the projection position of the first target point cloud data belongs to the first region when the sum of the distances between the projection position of the first target point cloud data and the second position is greater than the sum of the distances between the projection position of the first target point cloud data and the first position, and when the number of points in the second target point cloud data is less than the number of points in the first target point cloud data.

[0156] In one optional embodiment, the device determines whether the projection position of the first target point cloud data belongs to the first region based on the relative positional relationship between the second position and the first position, according to the first target point cloud data and the second target point cloud data. This includes determining that the projection position of the first target point cloud data belongs to the first region when the sum of the distances between the projection position of the first target point cloud data and the second position is less than the sum of the distances between the projection position of the first target point cloud data and the first position, and when the number of points in the second target point cloud data is greater than the number of points in the first target point cloud data.

[0157] In an optional embodiment, the device is further configured to: determine that the first location is a restricted location if the projection position of the first target point cloud data belongs to the first region; wherein the point cloud data collected by the mobile robot at the restricted location through the liftable device is considered unreliable.

[0158] In practical applications, the control module 51, acquisition module 52, determination module 53 and lifting module 54 mentioned above can be implemented by a processor located on the lifting device 500, which can be a central processing unit (CPU), microprocessor unit (MPU), digital signal processor (DSP) or field programmable gate array (FPGA), etc.

[0159] Figure 6 is a schematic diagram of an optional mobile robot provided in an embodiment of this disclosure. As shown in Figure 6, this embodiment of the disclosure provides a mobile robot 600, including:

[0160] The processor 61 and the storage medium 62 storing processor-executable instructions; the storage medium 62 performs operations via the communication bus 63 in dependence on the processor 61, and when the instructions are executed by the processor, the lifting method described in one or more of the above embodiments is executed on the processor side.

[0161] It should be noted that in practical applications, the various components in the electronic device are coupled together via the communication bus 63. The communication bus 63 is understood to be used to achieve communication between these components. In addition to the data bus, the communication bus 63 also includes a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as communication bus 63 in Figure 6.

[0162] This disclosure provides a computer storage medium storing executable instructions. When the executable instructions are executed by one or more processors, the processors execute the lifting and lowering method described in one or more of the above embodiments.

[0163] The computer-readable storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.

[0164] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0165] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0166] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0167] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0168] The above description is merely an optional embodiment of this disclosure and is not intended to limit the scope of protection of this disclosure.

Claims

1. A lifting method applied in a mobile robot, the mobile robot including a lifting device, the method comprising: When the mobile robot is in its first position, the lifting device is controlled to rotate. The lifting device is used to collect the first point cloud data returned by obstacles formed during the rotation process; Based on the first point cloud data, determine whether the projection position of the first point cloud data belongs to the first region; The liftable device is raised or lowered when the mobile robot leaves or enters the first area.

2. The method according to claim 1, wherein, Determining whether the projection position of the first point cloud data belongs to the first region based on the first point cloud data includes: Determine the sum of the distances between the projection position of the first point cloud data and the first position; If the sum is less than a first preset threshold, the projection position of the first point cloud data is determined to belong to the first region.

3. The method according to claim 2, further comprising: If the sum is greater than the first preset threshold, the curvature of the points on the line connecting the first point cloud data is determined; Points on the line connecting points with curvature greater than the second preset threshold are identified as the first target point cloud data. Based on the first target point cloud data, determine whether the projection position of the first target point cloud data belongs to the first region.

4. The method according to claim 3, wherein, Determining whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data includes: If the first target point cloud data meets the preset conditions, control the mobile robot to move to the second position; When the mobile robot is in the second position, control the rotation of the lifting device; The lifting device is used to collect second point cloud data returned by obstacles formed during the rotation process; Determine the curvature of the points on the connecting line of the first point cloud data, and determine the points on the connecting line with curvature greater than the second preset threshold as the second target point cloud data; If the second target point cloud data meets the preset conditions, determine whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data and the second target point cloud data.

5. The method according to claim 4, further comprising: If the difference in curvature between adjacent points in the first target point cloud data all fall within a preset error range, and the center of the circle determined by three consecutive points in the first target point cloud data is located in a preset area, then the first target point cloud data is determined to meet the preset conditions. The preset area is defined as: the area formed by the line connecting the projection positions of the first target point cloud data and the line connecting the endpoints of the line to the first position.

6. The method according to claim 4, further comprising: If the first target point cloud data does not meet the preset conditions, the projection position of the first target point cloud data is determined to belong to the second region.

7. The method according to claim 6, further comprising: If at least one difference in the curvature of adjacent points in the first target point cloud data does not fall within a preset error range, the first target point cloud data is determined not to meet the preset conditions.

8. The method according to claim 6, further comprising: If at least one center of a circle determined by three consecutive points in the first target point cloud data is outside a preset area, the first target point cloud data is determined not to meet the preset condition. The preset area is defined as: the area formed by the line connecting the projection positions of the first target point cloud data and the line connecting the endpoints of the line to the first position.

9. The method according to any one of claims 4 to 8, wherein, The step of determining whether the projection position of the first target point cloud data belongs to the first region based on the first target point cloud data and the second target point cloud data includes: Based on the relative positional relationship between the second position and the first position, and according to the first target point cloud data and the second target point cloud data, it is determined whether the projection position of the first target point cloud data belongs to the first region.

10. The method according to claim 9, wherein, The step of determining whether the projection position of the first target point cloud data belongs to the first region based on the relative positional relationship between the second position and the first position, according to the first target point cloud data and the second target point cloud data, includes: If the sum of the distances between the projection position of the first target point cloud data and the second position is greater than the sum of the distances between the projection position of the first target point cloud data and the first position, and the number of points in the second target point cloud data is less than the number of points in the first target point cloud data, then the projection position of the first target point cloud data is determined to belong to the first region.

11. The method according to claim 9, wherein, The step of determining whether the projection position of the first target point cloud data belongs to the first region based on the relative positional relationship between the second position and the first position, according to the first target point cloud data and the second target point cloud data, includes: If the sum of the distances between the projection position of the first target point cloud data and the second position is less than the sum of the distances between the projection position of the first target point cloud data and the first position, and the number of points in the second target point cloud data is greater than the number of points in the first target point cloud data, then the projection position of the first target point cloud data is determined to belong to the first region.

12. The method according to any one of claims 1 to 11, further comprising: If it is determined that the projection position of the first target point cloud data belongs to the first region, then the first position is determined to be a restricted position; Point cloud data collected by the mobile robot in the restricted position via the lifting device is considered unreliable.

13. A lifting device, the device being disposed in a mobile robot, the mobile robot including a lifting mechanism, the device comprising: A control module is used to control the rotation of the liftable device when the mobile robot is in the first position; The acquisition module is used to acquire the first point cloud data returned by the obstacle formed during the rotation through the lifting device; The determining module is used to determine whether the projection position of the first point cloud data belongs to the first region based on the first point cloud data; A lifting module is used to raise or lower the liftable device when the mobile robot leaves or enters the first area.

14. A mobile robot comprising a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 12.

Images