A method for selecting an arching direction of a cleaning robot and a cleaning robot

Abstract

The application discloses a bow sweeping direction selection method of a cleaning robot and the cleaning robot, wherein the bow sweeping direction selection method comprises: walking along an edge of a to-be-cleaned area to obtain feature information of the to-be-cleaned area, the feature information at least comprising segmentation area information corresponding to each long bow sweeping edge direction determined based on a profile of the to-be-cleaned area; and selecting a long bow sweeping edge direction based on the feature information. When selecting the bow sweeping direction, the segmentation area information corresponding to the long bow sweeping edge direction is taken as a consideration factor of a long bow sweeping edge planning direction of a cleaning path, so that the influence of the segmentation area information on cleaning efficiency is reduced as much as possible, and the overall cleaning efficiency is improved. Therefore, when planning the path, the influence of the segmentation area information on the cleaning efficiency is fully considered, and the cleaning efficiency of the planned cleaning path can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household appliances, and in particular to a bow sweeping direction selection method for a cleaning robot and the cleaning robot. BACKGROUND

[0002] Cleaning robots such as sweeping robots and sweeping and mopping robots are gradually accepted by the public and become new favorites in the market.

[0003] In order to achieve comprehensive and efficient cleaning of a cleaning area, existing cleaning robots often perform cleaning path planning in advance. The cleaning path in the related art is often a bow-shaped path and a back-shaped path. The bow-shaped path is widely used due to its high coverage rate and high cleaning efficiency. At present, in the bow-shaped path planning method, a fixed side is usually used as a long bow sweeping side direction to perform cleaning path planning. For a square cleaning area, the cleaning efficiency is basically the same when any side is used as a long bow sweeping side direction to perform cleaning path planning. However, for cleaning robot use scenarios such as home environments, there are usually few square cleaning areas, and the cleaning area is usually a non-square cleaning area. For a non-square cleaning area, using a certain side as a long bow sweeping side direction may result in low cleaning efficiency.

[0004] Therefore, how to improve the cleaning efficiency of a cleaning robot becomes a technical problem to be solved. SUMMARY

[0005] To solve the technical problem of how to improve the cleaning efficiency of a cleaning robot in the background art, the present application provides a bow sweeping direction selection method for a cleaning robot and the cleaning robot.

[0006] According to a first aspect, the embodiments of the present application provide a bow sweeping direction selection method for a cleaning robot, including: walking along a side of a cleaning area to obtain feature information of the cleaning area, the feature information at least including segmentation region information corresponding to each long bow sweeping side direction determined based on an outline of the cleaning area; and selecting a long bow sweeping side direction based on the feature information.

[0007] Optionally, the step of selecting a long bow sweeping side direction based on the feature information includes: selecting a long bow sweeping side direction with the least number of segmentation regions as a long bow sweeping side planning direction of the cleaning path.

[0008] Optionally, the feature information includes a side length of an outline side of the cleaning area, and the step of selecting a long bow sweeping side direction based on the feature information includes: selecting a long bow sweeping side direction based on the side length of the outline side and the number of segmentation regions.

[0009] Optionally, each contour edge corresponds to a long-arc sweeping direction; and the selecting the long-arc sweeping direction based on the length of the contour edge and the number of the segmented areas comprises: prioritizing the long-arc sweeping directions corresponding to the contour edges according to the length of the contour edge and the number of the segmented areas, wherein the longer the length of the contour edge and the fewer the number of the segmented areas, the higher the priority of the corresponding long-arc sweeping direction as the long-arc sweeping planning direction of the cleaning path; and determining the long-arc sweeping planning direction of the cleaning path based on the priority.

[0010] Optionally, each contour edge corresponds to a long-arc sweeping direction; and the selecting the long-arc sweeping direction based on the length of the contour edge and the number of the segmented areas comprises:

[0011] Optionally, the selecting the long-arc sweeping direction based on the length of the contour edge and the number of the segmented areas comprises: selecting the long-arc sweeping direction corresponding to the longest contour edge or the second longest contour edge in the long-arc sweeping directions with the number of the segmented areas less than the preset number as the long-arc sweeping planning direction of the cleaning path; or selecting the long-arc sweeping direction with the least number of the segmented areas in the long-arc sweeping directions corresponding to the longest contour edge or the second longest contour edge as the long-arc sweeping planning direction of the cleaning path.

[0012] Optionally, the segmented area information comprises: a coverage path of the arch-shaped path covering cleaning the segmented area, and a detour path of the cleaning robot jumping between different segmented areas; and the calculating the total cleaning time corresponding to each long-arc sweeping direction based on the set traveling speed and the coverage path and the detour path corresponding to each long-arc sweeping direction comprises: calculating the total cleaning time corresponding to each long-arc sweeping direction based on the set traveling speed and the coverage path and the detour path corresponding to each long-arc sweeping direction.

[0013] Optionally, the feature information further comprises: traveling data obtained by the cleaning robot walking along the edge, the traveling data comprising: bumping degrees of each traveling direction; and the calculating the total cleaning time corresponding to each long-arc sweeping direction based on the set traveling speed and the coverage path and the detour path corresponding to each long-arc sweeping direction comprises: calculating the total cleaning time corresponding to each long-arc sweeping direction based on the bumping degrees of each traveling direction, the set traveling speed and the coverage path and the detour path corresponding to each long-arc sweeping direction.

[0014] Optionally, the method for obtaining the bumping degree comprises: determining the bumping degree of each traveling direction based on the Z-axis acceleration in the walking along the edge.

[0015] Optionally, the set traveling speed comprises: a short-arc sweeping speed less than a long-arc sweeping speed.

[0016] According to a second aspect, the embodiments of the present application provide a cleaning robot, comprising a processor, a memory, and stored on the memory are execution instructions, which are configured to enable the cleaning robot to perform the arch sweeping direction selection method of any one of the first aspect when executed by the processor.

[0017] In the embodiments of the present application, when selecting the arch sweeping direction, the feature information of the to-be-cleaned area is acquired when walking along the edge of the to-be-cleaned area, for example, the segmentation region information corresponding to each long arch sweeping edge direction determined based on the profile of the to-be-cleaned area. Since there are segmentation regions in the arch cleaning path, the cleaning robot will make detours, which may cause the cleaning efficiency of the cleaning robot to decrease. The more segmentation regions, the more detours, and the lower the cleaning efficiency. When selecting the arch sweeping direction, the segmentation region information corresponding to the long arch sweeping edge direction is considered as a factor for planning the long arch sweeping edge direction of the cleaning path, so as to reduce the influence of segmentation regions on cleaning efficiency and improve the overall cleaning efficiency. Therefore, when planning the path, the influence of segmentation region information on cleaning efficiency is fully considered, which can improve the cleaning efficiency of the planned cleaning path.

[0018] Further, when selecting the long arch sweeping edge direction, the long arch sweeping edge direction with the least number of segmentation regions can be selected as the long arch sweeping edge planning direction of the cleaning path. The fewer the number of segmentation regions in the cleaning path, the fewer the detour routes, the less the calculation of parameter acquisition, route planning and the like, and the higher the continuity of the cleaning operation of the cleaning robot, and thus the higher the overall cleaning efficiency.

[0019] Further, the length of the profile edge corresponding to the long arch sweeping edge and the number of segmentation regions are comprehensively considered to select the long arch sweeping edge planning direction of the cleaning path. The number of segmentation regions affects the time consumption of detour when switching between segmentation regions, and the length of the edge corresponding to the long arch sweeping edge direction affects the average speed of performing the cleaning operation. Therefore, the length of the edge corresponding to the long arch sweeping edge and the number of segmentation regions can be considered as a whole to select the long arch sweeping edge planning direction of the cleaning path, which can comprehensively consider more factors to plan the cleaning path, so as to achieve the purpose of the highest global arch sweeping efficiency.

[0020] Further, the longest profile edge and the second longest edge can be selected as the long arch sweeping edge direction when selecting the long arch sweeping edge direction, which reduces the problem that the overall cleaning process is more fragmented and the average running speed of the cleaning operation is smaller due to selecting a shorter profile edge as the long arch sweeping edge direction, improves the efficiency of the cleaning path, and at the same time, the cleaning process is more complete.

[0021] Further, the bumping degree of each direction is acquired, and when the total cleaning time length corresponding to each long arc sweep edge is performed, the total cleaning time length corresponding to each arc sweep edge direction is calculated based on the bumping degree of each travel direction, the set travel speed, and the coverage path and the round path corresponding to each arc sweep edge direction, so that the influence of the bumping degree on the cleaning efficiency of each cleaning path can be comprehensively considered, and the cleaning efficiency is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0023] Figure 1 is an exemplary cleaning path planning schematic diagram in the related art;

[0024] Figure 2 is a flowchart of an exemplary arc sweep direction selection method of a cleaning robot according to an embodiment of the application;

[0025] Figure 3 is an exemplary cleaning path planning schematic diagram according to an embodiment of the application;

[0026] Figure 4 is an exemplary along-edge data point trajectory schematic diagram of a cleaning robot according to an embodiment of the application;

[0027] Figure 5 is an exemplary fitted along-edge continuous trajectory schematic diagram according to an embodiment of the application;

[0028] Figure 6 is an exemplary straight line fitted along-edge trajectory schematic diagram according to an embodiment of the application;

[0029] Figure 7 is an exemplary angle corrected along-edge trajectory schematic diagram according to an embodiment of the application;

[0030] Figure 8 is an exemplary wall along-edge trajectory schematic diagram according to an embodiment of the application;

[0031] Figure 9 is an exemplary structure schematic diagram of a cleaning robot according to an embodiment of the application. DETAILED DESCRIPTION

[0032] In order to have a clearer understanding of the technical features, objectives and effects of the application, the specific embodiments of the application will be described with reference to the accompanying drawings, in which the same reference numerals represent the same or similar components having the same function.

[0033] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods have not been described in detail in order to avoid obscuring the present application.

[0034] As described in the background section, as shown in Figure 1 The arch-shaped cleaning path can include long arch sweep edges A and short arch sweep edges B. The long arch sweep edges A are generally straight lines parallel to the contour edges, as the main arch sweep direction and path of the cleaning robot. The short arch sweep edges B are shorter straight lines perpendicular to the long arch sweep edges, or arc lines connecting between two long arch sweep edges, as the connecting arch sweep path for the cleaning robot to turn or U-turn. The running speed and path proportion of the cleaning robot in the long arch sweep edge direction are greater than those in the short arch sweep edge direction. Therefore, in order to improve efficiency, the longest edge in the cleaning area is often taken as the long arch sweep edge direction for arch-shaped cleaning path planning in the related art.

[0035] However, the inventors have found that in some cleaning areas, taking the longest contour edge as the long arch sweep edge direction often fails to achieve the highest cleaning efficiency, especially for complex home and office scenes. The cleaning area in a complex environment is often not a normal square or rectangle, but has many concave and convex regions.

[0036] When the cleaning robot plans the arch sweep direction according to the longest contour edge, the cleaning path is divided by the concave and convex regions, forming multiple divided regions. The cleaning robot needs to jump to another divided region after completing the cleaning of a certain divided region. This process may introduce redundant detour paths, increasing the moving time. In order to ensure accurate arrival at another divided region, the cleaning robot may need to perform data collection and comparison, route planning and other operations during the detour to another divided region, which may cause a speed drop, further increasing the moving time, and thus increasing the total cleaning time, causing a significant decrease in cleaning efficiency.

[0037] Moreover, due to the interruption of continuous cleaning operations, data collection and comparison, route planning and other operations may introduce more uncertain factors, further reducing the cleaning efficiency. Therefore, the more divided regions in the planned cleaning path, the lower the cleaning efficiency.

[0038] Based on this, the embodiments of the present application provide an arch sweep direction selection method for a cleaning robot, as shown in Figure 2 The method can include the following steps:

[0039] S10. Walking along the edge of the to-be-cleaned area to obtain feature information of the to-be-cleaned area. As an exemplary embodiment, the feature information of the to-be-cleaned area can include segmentation region information corresponding to each long-arc-sweep edge direction determined based on the contour of the to-be-cleaned area. As an exemplary embodiment, the segmentation region information can include the number of regions in which the cleaning robot continuously sweeps along the preset long-arc-sweep edge direction when cleaning the to-be-cleaned area. As shown in Figure 1 FIG. 1, segmentation regions C and D are both regions in which the cleaning robot can complete cleaning in one arc-sweep (referred to as a connected region). In this case, Figure 1 when the cleaning robot cleans the longest contour edge as the long-arc-sweep edge direction, the number of connected regions is 2, Figure 3 when the cleaning robot cleans the second longest edge as the long-arc-sweep edge direction, the number of connected regions is 1.

[0040] As an exemplary embodiment, the segmentation region can also be obtained by the cleaning robot itself. As an exemplary embodiment, the cleaning robot performs edge walking or cleaning to extract the contour of the to-be-cleaned area. The cleaning robot can obtain a plurality of long-arc-sweep edge directions based on the contour edge, perform cleaning path simulation for each long-arc-sweep edge direction, and obtain the number of regions in which the cleaning robot continuously sweeps along each long-arc-sweep edge direction during simulation, thereby obtaining the number of segmentation regions corresponding to each long-arc-sweep edge direction.

[0041] S20. Selecting a long-arc-sweep edge direction based on the feature information. As an exemplary embodiment, after obtaining the number of segmentation regions corresponding to each long-arc-sweep edge direction, the number of segmentation regions can be used as a consideration factor when selecting a long-arc-sweep edge direction. As an exemplary embodiment, the number of segmentation regions in the feature information can be used as at least one of the selection basis when selecting a long-arc-sweep edge direction. When planning a route, the cleaning robot can fully consider the impact of detours caused by segmentation regions on overall cleaning efficiency, which helps to improve overall cleaning efficiency.

[0042] In the embodiment of the present application, when the arch sweeping direction is selected, the feature information of the to-be-cleaned area obtained by walking along the edge of the to-be-cleaned area, for example, the number of areas with continuous arch sweeping paths when the cleaning robot completes the cleaning of the to-be-cleaned area along the preset long arch sweeping edge direction, will cause the cleaning robot to detour when there are segmented areas in the arch cleaning path, which may cause the cleaning efficiency of the cleaning robot to decrease. The more segmented areas, the more detours, and the lower the cleaning efficiency. Therefore, the number of segmented areas is taken as a factor affecting the efficiency, and when the long arch sweeping edge direction is selected, the number of segmented areas corresponding to the long arch sweeping edge direction is taken as a consideration factor for planning the long arch sweeping edge direction of the cleaning path, so as to reduce the influence of the number of segmented areas on the cleaning efficiency and improve the overall cleaning efficiency. Therefore, when the path is planned, the influence of the number of segmented areas on the cleaning efficiency is fully considered, which can improve the cleaning efficiency of the planned cleaning path.

[0043] As an exemplary embodiment, the long arch sweeping direction can be selected based on the number of segmented areas. Since there are segmented areas in the cleaning route corresponding to the long arch sweeping edge direction, the cleaning robot needs to jump to another segmented area after completing the cleaning of a certain segmented area, which may introduce an extra detour path, increase the travel time of the cleaning robot, and in the process of detouring, data collection and comparison, route planning and other operations may be required, which may greatly reduce the speed compared to the speed of traveling along the long arch sweeping edge, causing a significant decrease in cleaning efficiency. In addition, since the continuous cleaning operation needs to be interrupted for data collection and comparison, route planning and other operations, more uncertain factors may be introduced, further reducing the cleaning efficiency. Therefore, the more segmented areas there are in the planned cleaning path, the lower the cleaning efficiency.

[0044] Based on the above factors, in order to improve the efficiency, when the long arch sweeping edge direction is selected, the long arch sweeping edge direction with the least number of segmented areas can be selected as the long arch sweeping edge planning direction of the cleaning path. The fewer the number of segmented areas in the cleaning path, the fewer the detour routes, the fewer the parameters collection, route planning and other calculations required, and the higher the continuity of the cleaning operation of the cleaning robot, and thus the higher the overall cleaning efficiency. Therefore, the long arch sweeping edge direction with the least number of segmented areas can be used as the long arch sweeping edge planning direction of the cleaning path, and the cleaning path of the cleaning robot is planned.

[0045] In the embodiment, the influence of the segmented area information on the cleaning efficiency can be illustrated by the following specific embodiments:

[0046] The split area information can include an arch-shaped path covering cleaning the split area, and a bypass path when the cleaning robot jumps between different split areas. The cleaning robot needs to run through all the covering paths and bypass paths corresponding to the split areas when completing the cleaning of one to-be-cleaned area. The total cleaning time is the covering path time T1 running on the covering path and the bypass path time T2 running on the bypass path. To improve the efficiency of the cleaning robot, the total cleaning time of the cleaning robot completing the cleaning of the to-be-cleaned area needs to be reduced.

[0047] In the embodiment, the length of the covering path and the length of the bypass path of all the split areas corresponding to each long-arch sweeping direction can be calculated respectively, and the total cleaning time corresponding to each long-arch sweeping direction is calculated based on the set running speed and the length of the covering path and the length of the bypass path. The long-arch sweeping side direction corresponding to the total cleaning time with the shortest time is selected as the long-arch sweeping side planning direction of the cleaning path. As an exemplary embodiment, generally, the more the number of split areas, the longer the bypass path, and the longer the bypass time. Therefore, the fewer the number of split areas, the shorter the total cleaning time, and the higher the overall cleaning efficiency.

[0048] Referring to the exemplary embodiments shown in Figure 1 and Figure 3 , the cleaning path in Figure 3 is obtained by taking the shorter side as the long-arch sweeping side direction, the entire to-be-cleaned area is one connected area, and the cleaning robot can clean it at one time without bypassing. However, in Figure 1 , the longest profile side is taken as the long-arch sweeping side direction to obtain the cleaning path, and the cleaning robot needs to jump to area D after completing the cleaning of area C. This process may introduce redundant bypass paths, parameter collection, line planning, and other calculations, increase the bypass time, and result in a large increase in the total cleaning time, thereby reducing the cleaning efficiency of the cleaning path in Figure 3 .

[0049] As an optional embodiment, the profile map information of the to-be-cleaned area can be obtained, and the concave-convex deformation area of the to-be-cleaned area is determined based on the profile map. The deformation direction of the concave-convex deformation area, i.e., the “concave” and / or “convex” direction, is determined according to the concave-convex deformation area, for example, in Figure 1 and Figure 3 , the deformation direction of the concave-convex deformation area is the “concave” direction. The direction with the most concave-convex deformation is taken as the long-arch sweeping side planning direction of the cleaning path.

[0050] Specifically, when the to-be-cleaned region is deformed in a concave-convex manner, a segmented region is often divided out, and in the direction of the concave-convex deformation as the long-arc edge-sweeping direction, the cleaning path is not segmented by the concave-convex deformation region. Therefore, the more the concave-convex deformation in a certain direction, the more the concave-convex deformation region contained in the connected region cleaned in one cleaning, and the less the number of cleaning paths segmented by the concave-convex deformation, and the less the number of corresponding detour times and distances, so that the cleaning efficiency can be improved.

[0051] As another optional embodiment, when the selection of the long-arc edge-sweeping direction is based on the number of segmented regions and / or the direction of the concave-convex deformation, the long-arc edge-sweeping planning direction of the cleaning path can also be adjusted by the total length of the detour path. For example, there can be two or more long-arc edge-sweeping directions corresponding to a difference in the number of segmented regions that is less than a preset value, for example, the number of segmented regions corresponding to two or more long-arc edge-sweeping directions is greater than 1, and the difference in the number of segmented regions is one, two, or the same, and when the difference is small, the total length of the detour path in the cleaning path corresponding to the long-arc edge-sweeping direction can be calculated, and the long-arc edge-sweeping direction with the smallest total length of the detour path can be selected as the long-arc edge-sweeping planning direction of the cleaning path.

[0052] When the arc-sweeping direction is selected, other factors can also affect the selection, for example, the profile information of the to-be-cleaned region, the travel data of the cleaning robot along the edge, and other characteristic information of the to-be-cleaned region other than the segmented region information can affect the cleaning efficiency of the cleaning path. As an optional embodiment, the factors in the characteristic information of the to-be-cleaned region that can affect the selection of the arc-sweeping direction will be described below:

[0053] Generally, the arc-shaped path can be divided into two parts, one part is the long-arc edge-sweeping, and the other part is the short-arc edge-sweeping. The longer the length of the profile edge corresponding to the long-arc edge-sweeping, the longer the single long-arc edge-sweeping. Generally, the operating speed of the cleaning robot in the long-arc edge-sweeping direction is the largest, and in the short-arc edge-sweeping, the distance is short, and turning, U-turn, and acceleration and deceleration operations need to be performed in a short distance, so the speed of the short-arc edge-sweeping is smaller. Therefore, the longer the length of the profile edge corresponding to the long-arc edge-sweeping direction, that is, the longer the long-arc edge-sweeping or the larger the proportion of the long-arc edge-sweeping in the arc-shaped path, the higher the average speed during the cleaning operation, and the average speed of the arc-shaped path is relatively higher.

[0054] Based on the above factors, in an optional embodiment, the length of the long-arc-sweeping-side corresponding to the contour side and the number of segmented regions need to be considered comprehensively to select the long-arc-sweeping-side planning direction of the cleaning path. Therefore, the feature information of the to-be-cleaned region obtained can further include the length of the contour side of the to-be-cleaned region. The number of segmented regions determines the time-consuming of the detour when switching between segmented regions, and the length of the long-arc-sweeping-side direction has an impact on the average speed of the arc-shaped path, so the length of the long-arc-sweeping-side corresponding to the contour side and the number of segmented regions can be considered as a whole to select the long-arc-sweeping-side planning direction of the cleaning path, and more factors can be considered for planning the cleaning path to achieve the purpose of the highest global arc-sweeping efficiency.

[0055] As an optional implementation, the long-arc-sweeping-side directions corresponding to the contour sides can be prioritized according to the length of the contour side and the number of segmented regions, wherein the longer the length of the contour side and the fewer the number of segmented regions, the higher the priority of the corresponding long-arc-sweeping-side direction as the long-arc-sweeping-side planning direction of the cleaning path, and the long-arc-sweeping-side planning direction of the cleaning path is determined based on the priority. As an optional embodiment, the priorities of the long-arc-sweeping-side directions corresponding to the length of the contour side and the number of segmented regions can be different. Generally, because the detour is more likely to cause longer time-consuming, the priority of the long-arc-sweeping-side direction corresponding to the number of segmented regions is higher than that of the long-arc-sweeping-side direction corresponding to the length of the contour side, and through the prioritization, the long-arc-sweeping-side direction corresponding to a relatively small number of segmented regions and a relatively long contour side can be used as the long-arc-sweeping-side planning direction of the cleaning path.

[0056] As another optional embodiment, in the actual cleaning process, the contour side with the smaller length is used as the long-arc-sweeping-side direction, the overall appearance is more fragmented, and the average speed of the arc-shaped path is greater when the long-arc-sweeping-side direction corresponds to the longer contour side. Therefore, in order to achieve higher efficiency of the cleaning path and better integrity, in the embodiment, the longest contour side and the second longest contour side can be selected as the long-arc-sweeping-side direction.

[0057] Specifically, the long-arc-sweeping-side direction corresponding to the longest contour side or the second longest contour side among the long-arc-sweeping-side directions with the number of segmented regions less than a preset number can be selected as the long-arc-sweeping-side planning direction of the cleaning path; or the long-arc-sweeping-side direction with the least number of segmented regions among the long-arc-sweeping-side directions corresponding to the longest contour side or the second longest contour side can be selected as the long-arc-sweeping-side planning direction of the cleaning path.

[0058] As another optional embodiment, the total cleaning time of the cleaning path can also be calculated by setting the speed of the long-arc-sweeping-side direction, the speed of the short-arc-sweeping-side direction, and the speed of the detour path, and the cleaning path with the shortest total cleaning time can be selected as the final cleaning path.

[0059] Specifically, in the embodiment, the running speed of the long-arc sweeping side can be set as v, and the running speed of the short-arc sweeping side can be set as v / 2 due to the short distance. In addition, the short-arc sweeping side also needs to complete turning, U-turn, acceleration and deceleration and other operations, so the overall running speed of the cleaning robot in the short-arc sweeping side is less than v / 2, for example, can be v / 4.

[0060] The coverage path length of each long-arc sweeping side direction is calculated respectively.

[0061]

[0062] T1 is the coverage path length, and ∑D long ∑D represents the length sum of all long-arc sweeping sides in the arc path. short ∑D represents the length sum of all short-arc sweeping sides in the arc path.

[0063] The turning path length of the cleaning robot corresponding to each long-arc sweeping side direction is calculated respectively.

[0064]

[0065] Wherein, T2 is the turning length, and ∑D is the turning length sum.

[0066] The first cleaning total time T is calculated based on the coverage path length and the turning path length. 总

[0067] T 总 = T1+T2

[0068] In the embodiment, the long-arc sweeping side direction corresponding to the shortest time in the first cleaning total time calculated based on the length of the comprehensive contour side and the number of segmented areas can be selected as the planning direction of the cleaning path.

[0069] When constructing the contour of the area to be cleaned, edge walking needs to be performed, and real-time collection of environmental data is performed, and the collected data is matched with the preset data in the pre-constructed environmental map. However, the environmental data collected during bumping is often inaccurate and is usually excluded as abnormal data. Generally, the smaller the bumping degree, the less the excluded data, and the higher the matching degree of the collected data with the preset data; the greater the bumping degree, in order to obtain effective and representative environmental information, a large amount of bumping data is often excluded, resulting in a large amount of loss of effective data, which may affect the subsequent cleaning process. Therefore, the more bumpy the path is, the higher the random noise is, the more the loss of environmental data collected by the cleaning robot is, which may result in a lower environmental observation matching degree of the cleaning robot, a larger positioning deviation of the cleaning robot, and a larger running path deviation, thereby reducing the cleaning efficiency. ​

[0070] Therefore, the feature information of the area to be cleaned can further include travel data obtained by the cleaning robot walking along the edge, the travel data including: the bumping degree of each travel direction. In the calculation of the total cleaning time, the bumping degree of each travel direction is taken as one of the time-consuming factors, and based on the bumping degree of each travel direction, the set travel speed, and the coverage path and the round path corresponding to each arc-sweeping edge direction, the total cleaning time corresponding to each arc-sweeping edge direction is calculated. Wherein,

[0071]

[0072] Wherein, B long is the bumping degree of the long arc-sweeping edge direction, B short is the bumping degree of the short arc-sweeping edge direction, D i is the length of a certain round path, Bi is the bumping degree of the corresponding round path Di direction, and n is the number of round paths.

[0073] In this embodiment, each long arc-sweeping edge direction in the general arc path is basically the same, and the short arc-sweeping edge direction is also basically the same, so when the bumping degree factor is introduced, the bumping degree of all long arc-sweeping edge directions can adopt the same bumping degree, and the bumping degree of all short arc-sweeping edge directions adopts the same bumping degree. Since the direction of the round path is not necessarily the same, the time length of each round direction under the influence of the bumping degree in that direction can be calculated respectively and accumulated to obtain the time length of all round paths.

[0074] As an optional embodiment, the bumping degree corresponding to the direction of each long arc-sweeping edge, each short arc-sweeping edge and each round path can be obtained respectively, and the time length of each long arc-sweeping edge, each short arc-sweeping edge and each round path can be calculated based on the bumping degree. The sum of the time length of each long arc-sweeping edge, each short arc-sweeping edge and each round path is taken as the total cleaning time.

[0075] As an exemplary embodiment, before the cleaning robot cleans the area to be cleaned, it needs to run along the edge for one round to record the contour information of the maximum outer edge of the area to be cleaned. When obtaining the contour information, the bumping degree of each contour edge is calculated as the bumping degree of each long arc-sweeping edge direction. For example, the average bumping degree of the contour edge can be calculated as the average bumping degree of the long arc-sweeping edge direction.

[0076] Specifically: obtain the Z-axis acceleration of a certain contour edge, and calculate the bumping degree of the current contour edge based on the following formula:

[0077]

[0078]

[0079] represents the Z-axis acceleration at a certain moment; Dis is the total length of a certain profile edge, represents the average bumping degree of a certain profile edge. If The greater the value is, the higher the bumping degree of the profile edge in the corresponding arching edge direction is.

[0080] As an exemplary embodiment, the process of obtaining the feature information of the area to be cleaned can include: data acquisition by the sensor, map extraction and construction based on the collected data.

[0081] Specifically, the sensor can include an internal sensor and an external sensor, wherein the internal sensor collects the body data of the cleaning robot, which usually includes gyroscope data, accelerometer data, odometer data, collision data, wall-following data, and other data for representing the state of the cleaning robot body. The external sensor collects the environmental data of the robot, and common sensors include laser sensors, vision sensors, depth sensors, etc. The collected data includes environmental profile data, environmental image data, depth information within a certain range, and other data that can be used to represent the environmental information around the cleaning robot.

[0082] Specifically, the odometer information can be fused with the gyroscope data and acceleration information to generate a rough self-positioning. By fusing with the external sensor data, more accurate positioning information can be generated. For example, by combining the laser sensor data with the rough positioning information, the laser SLAM algorithm can be used to generate an environmental profile map; and / or, by combining the vision sensor with the rough positioning information, more accurate positioning information can be generated. This positioning information, combined with the wall-following data and collision data, can generate a simple profile map.

[0083] The map construction can include the construction of a map of an unknown area and the update of a map of a known area.

[0084] For an unknown area, the cleaning robot starts from a certain point, collects laser information or image information, etc. environmental information data through the sensor, determines the nearest wall from itself based on the environmental information data, and moves towards it. After a collision occurs, the starting point is recorded. Based on the edge-following sensor, the cleaning robot cleans or moves around the outermost edge of the area to be cleaned, returns to the starting point, stops the edge-following action, records the profile information of the maximum outer edge of the environment and the bumping condition of the ground, and constructs an environmental map using the SLAM method to extract a profile map.

[0085] For the known area, the cleaning robot uses the known map, finds the nearest wall from itself, and moves to it. After collision, it is recorded as the starting point. The edge sensor is used to clean or move around the outermost edge of the area to be cleaned. After returning to the starting point, the edge action is stopped, and the maximum outer edge profile information of the environment and the bumps of the ground are recorded. The environment map is updated using the SLAM method, and the profile map is extracted.

[0086] The extraction of the profile map is described below with specific examples:

[0087] When extracting and constructing the map based on the collected data, due to structural errors, control errors, sensor errors, environmental disturbances, etc. during the tracking of the wall by the cleaning robot, it is often impossible to walk a perfect straight line, and there is usually a phenomenon of snake-like movement. In order to reduce the storage amount of the map and improve the robustness of the subsequent algorithm, the walking trajectory can be fitted to obtain the profile trajectory. Specifically:

[0088] During the edge movement, data is continuously collected, so, as shown in Figure 4 , the original edge profile trajectory obtained from the walking trajectory can be an irregular trajectory composed of multiple data points. When the sensor collects edge data and collision information data, a timestamp can be attached to the collected data, so, Figure 4 the data points in are collision or edge data with timestamps, representing the actual edge trajectory of the cleaning robot. In order to extract a profile map more suitable for cleaning path planning, in this embodiment, the data points can be sequentially connected in timestamp order to form a continuous trajectory graph, as shown in Figure 5 .

[0089] After obtaining the continuous trajectory graph, the trajectory is often not a straight line, so the trajectory can be straightened by straight line fitting methods, such as least squares fitting straight line, principal component analysis fitting straight line, etc. The trajectory after straight line fitting is shown in Figure 6 .

[0090] Usually, due to the walking trajectory or data collection errors during the mapping and positioning of the cleaning robot, the trajectory graph after straight line fitting often has an angle skew. In order to better conform to the actual situation of the area to be cleaned and reduce errors, in this embodiment, the trajectory after straight line fitting can also be angle-corrected and trajectory-adjusted. Specifically, if the angle between the straight lines is less than a threshold value from a certain regular angle, such as a regular angle of 90°, 135°, etc. The threshold value can be determined according to the model of the cleaning robot, motion error, and system error. After angle correction between the straight lines, the trajectory diagram after angle correction can be obtained, as shown in Figure 7 .

[0091] After the angle correction, by counting all the angles, it is counted whether there are a few acute and obtuse angles and most of the right angles. And according to the SLAM map constructed by laser or vision, it is compared whether the points of acute and obtuse angles are right angles or angles close to right angles in the SLAM map, and the trajectory is further adjusted along the wall according to the corresponding angle in the angle statistics or the SLAM map. For details, see Figure 8 The edge trajectory along the wall after the adjustment is shown in the edge trajectory along the wall after the adjustment, and the edge trajectory along the wall after the adjustment is taken as the contour map of the to-be-cleaned area.

[0092] As an exemplary embodiment, after obtaining the contour map, the average bump parameter of each contour edge can be determined based on the timestamp in the data point and the contour map, and the average bump parameter of each contour edge is calculated as one of the parameters of the contour map. After obtaining the contour map, the feature information of the to-be-cleaned area can be obtained based on the contour map and the parameters in the contour map, such as the number of segmented areas, the length of the contour edge, and the average bump degree.

[0093] Those skilled in the art should understand that the values and value ranges in the above examples are only exemplary examples for understanding, and the protection scope in the present embodiment is not limited to the values and value ranges in the above examples.

[0094] Figure 9 A structure diagram of a cleaning robot according to an embodiment of the present application.

[0095] As Figure 9 shown, the present application also provides a cleaning robot, which includes a processor, a memory, and an execution instruction stored on the memory, the execution instruction being set to enable the cleaning robot to perform the above-mentioned cleaning robot arc scanning direction selection method when executed by the processor. Optionally, it also includes a memory and a bus, and in addition, the cleaning robot also allows other hardware required by the business.

[0096] As an exemplary embodiment, the robot vacuum cleaner can also include sensors, specifically, it can include internal sensors and external sensors, wherein the internal sensors collect the body data of the cleaning robot, which usually includes gyroscope data, accelerometer data, odometer data, collision data, wall-following data, and other data for representing the state of the cleaning robot body. The external sensors collect the environmental data of the robot vacuum cleaner, and common sensors include laser sensors, vision sensors, depth sensors, etc., and the collected data includes environmental contour data, environmental image data, depth information within a certain range, and other environmental information that can be used to represent the environment around the cleaning robot.

[0097] In some embodiments of the present application, the cleaning robot can include a sweeping robot, a sweeping and mopping integrated robot, and the like.

[0098] Optionally, the cleaning robot further comprises a memory and a bus, and can further comprise other hardware required by the business. The memory can include a memory and a non-volatile memory, and provide the processor with execution instructions and data. Exemplarily, the memory can be a high-speed random access memory (RAM), and the non-volatile memory can be at least one disk memory.

[0099] The bus is used to connect the processor, the memory and the network interface to each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, and the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For the convenience of representation, Figure 9 Only one bidirectional arrow is used in the figure, but this does not mean that there is only one bus or one type of bus.

[0100] In a feasible implementation of the above cleaning robot, the processor can first read the corresponding execution instructions from the non-volatile memory into the memory and then run, or first obtain the corresponding execution instructions from other devices and then run. When the processor executes the execution instructions stored in the memory, the bow sweeping direction selection method of any one of the cleaning robots described in the present disclosure can be implemented.

[0101] Those skilled in the art can understand that the above-mentioned cleaning robot arch sweeping direction selection method can be applied in a processor or implemented by means of a processor. Exemplarily, the processor is an integrated circuit chip with the ability to process signals. In the process of the processor executing the above-mentioned cleaning robot arch sweeping direction selection method, each step of the above-mentioned cleaning robot arch sweeping direction selection method can be completed by integrated logic circuits in hardware form or instructions in software form in the processor. Further, the above-mentioned processor can be a general-purpose processor, such as a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, microprocessors, and other any conventional processors.

[0102] Those skilled in the art can also understand that the steps of the above-mentioned cleaning robot arch sweeping direction selection method embodiments of the present disclosure can be executed by a hardware decoding processor or a combination of hardware and software modules in the decoding processor. Among them, the software module can be located in a random memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register and other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the execution of the steps in the above-mentioned cleaning robot arch sweeping direction selection method embodiments.

[0103] So far, the technical solutions of the present disclosure have been described in combination with the foregoing embodiments, but those skilled in the art can easily understand that the protection scope of the present disclosure is not limited to these specific embodiments. Those skilled in the art can split and combine the technical solutions in the above-mentioned embodiments, or make equivalent changes or replacements to related technical features, without deviating from the technical principles of the present disclosure. Any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principles of the present disclosure will fall within the protection scope of the present disclosure.

[0104] The various embodiments in the specification are described in progressive manner, and the same or similar parts among the various embodiments can be mutually referred to, and each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0105] The above merely describes the embodiments of the present application, and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for selecting the sweeping direction of a cleaning robot, characterized in that, The method comprises: walking along the edge of the area to be cleaned to obtain feature information of the area to be cleaned, the feature information comprising at least segmentation region information corresponding to each long-arc edge cleaning direction determined based on the profile of the area to be cleaned, and the length of the profile edge of the area to be cleaned; selecting a long-arc edge cleaning direction based on the feature information, comprising: prioritizing the long-arc edge cleaning directions corresponding to the profile edges according to the length of the profile edge and the number of segmentation regions, wherein the longer the length of the profile edge and the fewer the number of segmentation regions, the higher the priority of the corresponding long-arc edge cleaning direction as the long-arc edge planning direction of the cleaning path, and determining the long-arc edge planning direction of the cleaning path based on the priority.

2. The bowing direction selection method of the cleaning robot according to claim 1, wherein, The method of selecting a long-arc edge cleaning direction based on the feature information comprises: selecting a long-arc edge cleaning direction with the least number of segmentation regions as the long-arc edge planning direction of the cleaning path.

3. The bowing direction selection method of claim 1, wherein, The method of selecting a long-arc edge cleaning direction based on the feature information comprises: selecting a long-arc edge cleaning direction based on the length of the profile edge and the number of segmentation regions.

4. The bowing direction selection method of claim 3, wherein, Each profile edge corresponds to a long-arc edge cleaning direction. The method of selecting a long-arc edge cleaning direction based on the length of the profile edge and the number of segmentation regions comprises: selecting a long-arc edge cleaning direction corresponding to the longest profile edge or the second longest profile edge in the profile edges as the long-arc edge planning direction of the cleaning path, among the long-arc edge cleaning directions with a number of segmentation regions less than a preset number; or selecting a long-arc edge cleaning direction with the least number of segmentation regions as the long-arc edge planning direction of the cleaning path, among the long-arc edge cleaning directions corresponding to the longest profile edge or the second longest profile edge in the profile edges.

5. The bowing direction selection method of claim 1, wherein, The segmentation region information comprises: a coverage path for cleaning the segmentation region by an arc-shaped path, and a detour path for the cleaning robot to jump between different segmentation regions; calculating a total cleaning time corresponding to each long-arc edge cleaning direction based on the set travel speed, and the coverage path and the detour path corresponding to each long-arc edge cleaning direction; selecting a long-arc edge cleaning direction corresponding to the shortest total cleaning time as the long-arc edge planning direction of the cleaning path.

6. The bowing direction selection method of claim 5, wherein, The feature information further comprises: travel data obtained by the cleaning robot walking along the edge, the travel data comprising: bumping degrees of each travel direction; The method of calculating a total cleaning time corresponding to each long-arc edge cleaning direction based on the set travel speed, and the coverage path and the detour path corresponding to each long-arc edge cleaning direction comprises: calculating a total cleaning time corresponding to each long-arc edge cleaning direction based on the set travel speed, the coverage path and the detour path corresponding to each long-arc edge cleaning direction, and the bumping degrees of each travel direction.

7. The bowing direction selection method of claim 6, wherein, if the number of the obstacles is equal to or greater than a predetermined number, the bowing direction is selected to be the direction in which the bowing is performed in the direction in which the cleaning robot moves. The method of obtaining the bumping degrees comprises: determining the bumping degrees of each travel direction based on the Z-axis acceleration in the edge walking.

8. The bowing direction selection method of the cleaning robot according to any one of claims 5 to 7, wherein, The set travel speed comprises: a short-arc edge speed less than a long-arc edge speed.

9. A cleaning robot, characterized in that, The cleaning robot comprises a processor, a memory, and an execution instruction stored on the memory, the execution instruction being configured to enable the cleaning robot to perform the method of selecting an arc cleaning direction of the cleaning robot according to any one of claims 1-8 when executed by the processor.

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