WO2024016839A1 - 二次清洁方法、装置、清洁机器人及存储介质 - Google Patents

二次清洁方法、装置、清洁机器人及存储介质 Download PDF

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Publication number
WO2024016839A1
WO2024016839A1 PCT/CN2023/096950 CN2023096950W WO2024016839A1 WO 2024016839 A1 WO2024016839 A1 WO 2024016839A1 CN 2023096950 W CN2023096950 W CN 2023096950W WO 2024016839 A1 WO2024016839 A1 WO 2024016839A1
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WO
WIPO (PCT)
Prior art keywords
cleaning
partition
secondary cleaning
target
robot
Prior art date
Application number
PCT/CN2023/096950
Other languages
English (en)
French (fr)
Inventor
李蒙
谭国炎
孙乾
任娟娟
班玥
叶力荣
Original Assignee
深圳银星智能集团股份有限公司
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Publication of WO2024016839A1 publication Critical patent/WO2024016839A1/zh

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Classifications

    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
    • A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
    • A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
    • A47L11/4061—Steering means; Means for avoiding obstacles; Details related to the place where the driver is accommodated
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/60—Intended control result
    • G05D1/648—Performing a task within a working area or space, e.g. cleaning
    • G05D1/6482—Performing a task within a working area or space, e.g. cleaning by dividing the whole area or space in sectors to be processed separately
    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
    • A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
    • A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
    • A47L11/4011—Regulation of the cleaning machine by electric means; Control systems and remote control systems therefor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/24—Arrangements for determining position or orientation
    • G05D1/246—Arrangements for determining position or orientation using environment maps, e.g. simultaneous localisation and mapping [SLAM]
    • G05D1/2464—Arrangements for determining position or orientation using environment maps, e.g. simultaneous localisation and mapping [SLAM] using an occupancy grid
    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
    • A47L2201/00—Robotic cleaning machines, i.e. with automatic control of the travelling movement or the cleaning operation
    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
    • A47L2201/00—Robotic cleaning machines, i.e. with automatic control of the travelling movement or the cleaning operation
    • A47L2201/04—Automatic control of the travelling movement; Automatic obstacle detection
    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
    • A47L2201/00—Robotic cleaning machines, i.e. with automatic control of the travelling movement or the cleaning operation
    • A47L2201/06—Control of the cleaning action for autonomous devices; Automatic detection of the surface condition before, during or after cleaning
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2105/00—Specific applications of the controlled vehicles
    • G05D2105/10—Specific applications of the controlled vehicles for cleaning, vacuuming or polishing
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2107/00—Specific environments of the controlled vehicles
    • G05D2107/40—Indoor domestic environment
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2109/00—Types of controlled vehicles
    • G05D2109/10—Land vehicles

Definitions

  • the present invention relates to the technical field of smart home, and in particular to a secondary cleaning method, device, cleaning robot and storage medium.
  • the cleaning robot cleans the area by area, and returns to the charging base after cleaning. If it encounters obstacles during work, such as closed doors, moving people or pets, walls of narrow entrances, etc. It may cause the cleaning robot to leave the current partition and go to the next partition for cleaning, but the current partition has not been cleaned. After completing the cleaning of other partitions, the cleaning robot returns to the charging base position and will not clean the unfinished partitions a second time. Cleaning, resulting in some areas being missed for cleaning and the cleaning quality being low.
  • the main purpose of the present invention is to provide a secondary cleaning method, device, cleaning robot and storage medium to solve the problem of areas being missed for cleaning and improve cleaning quality.
  • a first aspect of the present invention provides a secondary cleaning method, which includes: obtaining a grid map of a target area, the grid map including at least one initial cleaning partition; determining whether there is an initial cleaning partition that satisfies secondary cleaning conditions, and if Yes, the initial cleaning partition that meets the secondary cleaning conditions is determined as the secondary cleaning partition, and at least one secondary cleaning partition is obtained.
  • the secondary cleaning condition is that there are marks along the wall in the initial cleaning partition and no cleaning marks are filled. Full; determine whether there is a secondary cleaning partition that the cleaning robot can reach in the at least one secondary cleaning partition according to the preset obstacle information database, which is used to record real-time obstacles identified during the cleaning process Information; if there is a secondary cleaning partition that the cleaning robot can reach, navigate to the target partition for cleaning.
  • the target partition is the secondary cleaning partition that the cleaning robot can reach and has the shortest distance.
  • it is determined according to a preset obstacle information database whether there is a secondary cleaning partition that the cleaning robot can reach in the at least one secondary cleaning partition It includes: planning the navigation route of the cleaning robot to each secondary cleaning partition in the at least one secondary cleaning partition to obtain at least one candidate navigation route; calling the preset obstacle information library to query the at least one candidate Real-time obstacle information on the navigation route; judging whether there is at least one barrier-free navigation route in the at least one candidate navigation route according to the real-time obstacle information on the at least one candidate navigation route; if there is, then removing the at least one obstacle-free navigation route
  • the secondary cleaning area corresponding to the barrier-free navigation route is determined as the secondary cleaning area that the cleaning robot can reach.
  • the navigation route of the cleaning robot to each of the at least one secondary cleaning partition is planned to obtain at least one candidate navigation.
  • the route includes: determining at least one candidate point according to the current position point of the cleaning robot, the candidate point being the contour point with the shortest straight line distance between the secondary cleaning partition and the current position point; planning the current position point to each Navigation routes between candidate points, get at least one navigation route.
  • the target partition is the area that the cleaning robot can reach.
  • Arrive at the secondary cleaning partition with the shortest distance including: if there is a secondary cleaning partition that the cleaning robot can reach, navigate to the target partition; when the navigation ends, determine whether the navigation is successful, and the successful navigation refers to the cleaning robot Reach the end of the planned navigation route; if the navigation is successful, control the cleaning robot to clean the target partition; if the navigation is unsuccessful, determine whether the cleaning robot is located in the target partition, and if so, control the The cleaning robot cleans the target partition, and if not, selects the target partition again or ends cleaning.
  • navigating to the target partition includes: if there is a secondary cleaning partition that the cleaning robot can reach. partition, then determine whether the secondary cleaning partition that the cleaning robot can reach is unique; if so, determine the only secondary cleaning partition that the cleaning robot can reach as the target partition, and navigate to the target partition; if not, then Determine the secondary cleaning partition closest to the cleaning robot as the target partition, and navigate to the target partition.
  • the secondary cleaning partition closest to the cleaning robot as the target partition if not, determine the secondary cleaning partition closest to the cleaning robot as the target partition, and navigate to the target partition, including: if If the secondary cleaning partition that the cleaning robot can reach is not unique, calculate the Manhattan distance between the current position point of the cleaning robot and the candidate point of each secondary cleaning partition to obtain at least one candidate Manhattan distance.
  • the point is the contour point with the shortest straight line distance between the secondary cleaning partition and the current position point; filter out the shortest Manhattan distance from the multiple candidate Manhattan distances; use the secondary cleaning corresponding to the shortest Manhattan distance
  • the partition is identified as the target partition and navigated to the target partition.
  • the sixth implementation manner of the first aspect of the present invention before obtaining the grid map of the target area, it also includes: establishing an obstacle information database, and the obstacle information database is used to record the cleaning operation of the cleaning robot.
  • the obstacle information includes the location of the obstacle, a picture of the obstacle, a picture of the surrounding environment of the obstacle, and the status of the obstacle; when the cleaning robot recognizes a new obstacle When the new obstacle information is added to the obstacle information database; when the cleaning robot recognizes that the position of the target obstacle that has been recorded in the obstacle information database has changed, The target obstacle information in the obstacle information database is updated.
  • a second aspect of the present invention provides a secondary cleaning device, including: an acquisition module, used to acquire a grid map of a target area, where the grid map includes at least one initial cleaning partition; and a determination module, used to determine whether there are If the initial cleaning partition of the secondary cleaning condition exists, the initial cleaning partition that meets the secondary cleaning condition is determined as the secondary cleaning partition, and at least one secondary cleaning partition is obtained.
  • the secondary cleaning condition is within the initial cleaning partition.
  • a judgment module is used to judge whether there is a secondary cleaning partition that the cleaning robot can reach in the at least one secondary cleaning partition according to the preset obstacle information database, and the obstacle
  • the object information database is used to record real-time obstacle information identified during the cleaning process
  • the navigation module is used to navigate to the target partition for cleaning if there is a secondary cleaning partition that the cleaning robot can reach, and the target partition is the cleaning The secondary cleaning zone with the shortest distance that the robot can reach.
  • the judgment module includes: a planning unit for navigating the cleaning robot to each secondary cleaning partition in the at least one secondary cleaning partition.
  • the route is planned to obtain at least one candidate navigation route;
  • the calling unit is used to call the preset obstacle information database to query the real-time obstacle information on the at least one candidate navigation route;
  • the first judgment unit is used to determine the real-time obstacle information on the at least one candidate navigation route according to the The real-time obstacle information on the at least one candidate navigation route is used to determine whether there is at least one barrier-free navigation route in the at least one candidate navigation route;
  • the determination unit is configured to, if present, determine the at least one barrier-free navigation route corresponding to the The secondary cleaning zone is determined as the secondary cleaning zone that the cleaning robot can reach.
  • the planning unit is specifically configured to: determine at least one candidate point according to the current position point of the cleaning robot, and the candidate point is the second cleaning partition and the location point of the cleaning robot.
  • the contour points with the shortest straight line distance between the current position points are calculated; a navigation route between the current position point and each candidate point is planned to obtain at least one navigation route.
  • the navigation module includes: a navigation sub-module, used to navigate to the target partition if there is a secondary cleaning partition that the cleaning robot can reach; a judgment sub-module. Module, used to determine whether the navigation is successful when the navigation ends.
  • the successful navigation means that the cleaning robot reaches the end point of the planned navigation route; the control submodule is used to control the cleaning robot to control the cleaning robot if the navigation is successful.
  • the target partition is cleaned; the judgment control submodule is used to determine whether the cleaning robot is located in the target partition if the navigation is unsuccessful; if so, control the cleaning robot to clean the target partition; if not, Then select the target partition again or end cleaning.
  • the navigation sub-module includes: a second judgment unit, configured to judge whether the cleaning robot can reach a secondary cleaning partition if there is a second cleaning partition that the cleaning robot can reach. Whether the secondary cleaning partition that can be reached is unique; the first navigation unit is used to determine the only secondary cleaning partition that the cleaning robot can reach as the target partition, and navigate to the target partition; if so, the second navigation unit, If not, determine the secondary cleaning partition closest to the cleaning robot as the target partition, and navigate to the target partition.
  • a second judgment unit configured to judge whether the cleaning robot can reach a secondary cleaning partition if there is a second cleaning partition that the cleaning robot can reach. Whether the secondary cleaning partition that can be reached is unique; the first navigation unit is used to determine the only secondary cleaning partition that the cleaning robot can reach as the target partition, and navigate to the target partition; if so, the second navigation unit, If not, determine the secondary cleaning partition closest to the cleaning robot as the target partition, and navigate to the target partition.
  • the second navigation unit is specifically configured to: if the secondary cleaning partition that the cleaning robot can reach is not unique, calculate the cleaning zone of the cleaning robot.
  • the Manhattan distance between the current position point and the candidate point of each secondary cleaning partition is used to obtain at least one candidate Manhattan distance.
  • the candidate point is the contour point with the shortest straight line distance between the secondary cleaning partition and the current position point. ; Filter out the shortest Manhattan distance from the plurality of candidate Manhattan distances; determine the secondary cleaning partition corresponding to the shortest Manhattan distance as the target partition and navigate to the target partition.
  • a building module is also included before the acquisition module.
  • the building module is specifically used to: establish an obstacle information database, and the obstacle information database is used to record Real-time obstacle information recognized by the cleaning robot during the cleaning process.
  • the obstacle information includes the location of the obstacle, a picture of the obstacle, a picture of the surrounding environment of the obstacle, and the status of the obstacle; when the cleaning robot recognizes a new When an obstacle is added, the information of the newly added obstacle is added to the obstacle information database; when the cleaning robot recognizes the position of the target obstacle that has been recorded in the obstacle information database, an error occurs.
  • the information of the target obstacle in the obstacle information database is updated.
  • a third aspect of the present invention provides a cleaning robot, including: a memory and at least one processor, instructions stored in the memory; the at least one processor calls the instructions in the memory, so that the cleaning robot The robot performs the secondary cleaning method described above.
  • a fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium Instructions are stored that, when run on the computer, cause the computer to perform the above-mentioned secondary cleaning method.
  • a grid map of the target area is obtained.
  • the grid map includes at least one initial cleaning partition. It is judged whether there is an initial cleaning partition that satisfies the secondary cleaning conditions. If it exists, it will satisfy the secondary cleaning conditions.
  • the initial cleaning partition of the cleaning conditions is determined as the secondary cleaning partition, and at least one secondary cleaning partition is obtained.
  • the secondary cleaning condition is that there are marks along the wall in the initial cleaning partition and are not filled with cleaning marks.
  • the information library determines whether there is a secondary cleaning partition that the cleaning robot can reach in the at least one secondary cleaning partition.
  • the obstacle information library is used to record real-time obstacle information identified during the cleaning process.
  • the target partition is the secondary cleaning partition that the cleaning robot can reach and has the shortest distance.
  • the partitions that require secondary cleaning are identified from the initial cleaning partitions according to the secondary cleaning conditions to avoid missing areas that have not been cleaned, and the nearest secondary cleaning partition that the cleaning robot can reach is selected to improve cleaning efficiency. After the cleaning robot reaches the secondary cleaning partition, it cleans the area to improve the cleaning quality of the target area.
  • Figure 1 is a schematic diagram of an embodiment of the secondary cleaning method in the embodiment of the present invention.
  • Figure 2 is a simplified schematic diagram of a grid map in an embodiment of the present invention.
  • Figure 3 is a schematic diagram of an embodiment of a grid map partition in an embodiment of the present invention.
  • Figure 4 is a schematic diagram of another embodiment of the secondary cleaning method in the embodiment of the present invention.
  • Figure 5 is a schematic diagram of the first secondary cleaning partition generation scenario in the embodiment of the present invention.
  • Figure 6 is a schematic diagram of the second secondary cleaning partition generation scenario in the embodiment of the present invention.
  • Figure 7 is a schematic diagram of the third secondary cleaning partition generation scenario in the embodiment of the present invention.
  • Figure 8 is a schematic diagram of candidate points for secondary cleaning partitions in the embodiment of the present invention.
  • Figure 9 is a schematic diagram of an area to assist in explaining the cleaning method in the embodiment of the present invention.
  • Figure 10 is a schematic diagram of an embodiment of the secondary cleaning device in the embodiment of the present invention.
  • Figure 11 is a schematic diagram of another embodiment of the secondary cleaning device in the embodiment of the present invention.
  • Figure 12 is a schematic diagram of a cleaning robot according to an embodiment of the present invention.
  • Embodiments of the present invention provide a secondary cleaning method, device, cleaning robot and storage medium, find the secondary cleaning partition according to the secondary cleaning conditions, and navigate the cleaning robot to the reachable and nearest secondary cleaning partition for cleaning. , improve cleaning efficiency and cleaning quality.
  • the execution subject of the present invention is the cleaning Cleaning robots include sweeping robots, mopping robots, vacuum cleaner robots or other robots with floor cleaning functions.
  • An embodiment of the secondary cleaning method in the embodiment of the present invention includes:
  • the raster map includes at least one initial cleaning partition
  • the grid map is a digital image created by the cleaning robot based on the real indoor environment during the cleaning process.
  • the smallest unit in the grid map is a grid, and each grid corresponds to an area of the real indoor environment.
  • the cleaning robot assigns a gray value to each grid, and uses the gray value to determine whether the actual environment corresponding to the grid is occupied by obstacles. Obstacles include walls, electrical cabinets, and furniture. As shown in Figure 2, commonly used grayscale values are 0 and 255, where 0 is black and is used to indicate that the area is occupied by obstacles, and 255 is white and is used to indicate that the area is not occupied by obstacles.
  • the cleaning robot partitions the unoccupied areas in the grid map.
  • it also provides a partition rule: the cleaning robot identifies the unoccupied areas at the top, bottom, left and right end of the grid map. Occupied grid, generating a first horizontal line through the uppermost grid and a second horizontal line through the lowermost grid, a first vertical line through the leftmost grid, and a second vertical line through the rightmost grid Vertical lines, cut the closed area surrounded by the first horizontal line, the second horizontal line, the first vertical line and the second vertical line horizontally and vertically.
  • the smallest partition obtained after cutting is the initial cleaning partition.
  • Each initial cleaning partition is Contains unoccupied grids.
  • the number of transverse and longitudinal cuts can be determined based on the actual length and width of the closed area, and the closed area can be divided into two sections based on the number of transverse and longitudinal cuts. Cut equally in several dimensions, and obtain multiple initial clean partitions after cutting.
  • M 1 and width M 2 are the length and width of the closed area respectively
  • N 1 and N 2 are the preset length and width respectively
  • M 1 ⁇ N 1 , M 2 ⁇ N 2 the number of transverse cuts K 1 in the closed area takes the integer part of M 1 /N 1
  • the number of longitudinal cuts K 2 in the closed area takes the integer part of M 2 /N 2 .
  • This partitioning rule obtains the required number of initial cleaning partitions by adjusting the preset length and width values.
  • the target area can be divided into a number of , Appropriately sized partitions and an appropriate number enable the cleaning robot to more accurately locate the location of the cleaning robot when the last cleaning was interrupted during the second cleaning, preventing the cleaned area from being cleaned again or the uncleaned area due to inaccurate positioning The phenomenon of being missed for cleaning.
  • Appropriate size can avoid the situation where the cleaning robot cannot enter due to the narrow partition, improve the positioning accuracy of the cleaning robot, and improve the cleaning quality of the target area.
  • the preset length and width can be set to the same or close value, then the number of horizontal cutting and the number of vertical cutting of the target area are equal, and the target area is divided into two dimensions: length and width. Make equal portions.
  • the 102 Determine whether there is an initial cleaning partition that meets the secondary cleaning conditions. If there is, determine the initial cleaning partition that meets the secondary cleaning conditions as the secondary cleaning partition, and obtain at least one secondary cleaning partition.
  • the secondary cleaning condition is initial cleaning. There are markings along the wall in the partition that are not filled with clean markings;
  • edge cleaning refers to cleaning along the edge of the initial cleaning partition.
  • area cleaning refers to cleaning along the wall. Cleaning is carried out in an arcuate path in the area surrounded by marks.
  • cleaning marks, wall markings and cleaning marks can be two marks with different colors or two marks with different shapes.
  • the cleaning robot scans each initial cleaning partition on the raster map. If any of the following three situations occurs in the initial cleaning partition, the initial cleaning partition will be determined as a secondary cleaning partition:
  • the clean partition is not filled with clean marks.
  • initial cleaning partition 1 has only part of the edge along the wall with markings
  • initial cleaning partition 2 has all edges along the wall with markings and is filled with cleaning marks
  • initial cleaning partition 3 has all edges with along the wall markings. Wall marks but no cleaning marks.
  • Initial cleaning partition 4 has markings along the wall on part of the edge and cleaning marks in the partition but the partition is not filled with cleaning marks.
  • Initial cleaning partition 5 has markings along the wall on all edges and cleaning marks in the partition but the partition is not filled with cleaning marks. Not filled with clean marks. Therefore, the initial cleaning partition 1, the initial cleaning partition 3, the initial cleaning partition 4 and the initial cleaning partition 5 are determined as secondary cleaning partitions.
  • the obstacle information database is used to record real-time obstacle information identified during the cleaning process
  • the obstacle information database records real-time obstacle information detected by the cleaning robot through lidar or infrared sensors during its movement. Obstacles refer to objects that appear in unoccupied positions in the grid map.
  • the cleaning robot senses When an obstacle is detected, it is judged whether the obstacle has been recorded in the obstacle information database. If not, the position of the obstacle is obtained, the camera is turned on to take pictures of the obstacle and the surrounding environment, and the position of the obstacle and the photo of the obstacle are , the surrounding environment picture of the obstacle and the status of the obstacle are recorded in the obstacle information database, and the status of the obstacle is set to "exist”; when the cleaning robot recognizes that the obstacle recorded in the obstacle information database is not at its original location , set the status of the obstacle to "does not exist".
  • the cleaning robot obtains its own position point and the candidate point closest to the cleaning robot in each secondary cleaning partition. Based on the position point and the candidate point of each secondary cleaning partition, it obtains the candidate navigation route to each secondary cleaning partition.
  • Call Preset obstacle information database query whether there are obstacles on each candidate navigation route. If there are obstacles on the target candidate navigation route, the cleaning robot cannot reach the target and reach the secondary cleaning partition corresponding to the candidate navigation route; if the target candidate navigation route has obstacles If there are no obstacles on the navigation route, the cleaning robot can reach the secondary cleaning partition corresponding to the target candidate navigation route.
  • the first is that dynamic obstacles are at the entrance; the second is that low obstacles are near or on the edge of the entrance. Due to the hardware structure, the lidar cannot identify low obstacles. Obstacles, identify low obstacles as occupied areas on the grid map, causing the entrance to become smaller, resulting in the same situation as the third one; the third type, the entrance is narrow, the entrance size is the same as the diameter of the cleaning robot, or If it is slightly larger, due to the motion control error of the cleaning robot, the cleaning robot may sometimes be unable to pass through the entrance.
  • the cleaning robot arrives at the entrance; when the candidate point of the secondary cleaning partition is at the non-entrance, the cleaning robot passes through the entrance. Therefore, it is determined whether the cleaning robot can reach the secondary cleaning
  • the necessary condition for partitioning is that there are no obstacles at the entrance of the secondary cleaning partition. If there are obstacles in the target candidate navigation route but the obstacles are not at the entrance, then the cleaning robot and the target candidate navigation route corresponding to the target secondary cleaning partition are judged again. Is there another barrier-free navigation route? If so, use the other barrier-free navigation route as target 2. New candidate navigation route for the second clean zone.
  • the target area has 4 secondary cleaning partitions: secondary cleaning partition 1, secondary cleaning partition 2, secondary cleaning partition 3 and secondary cleaning partition 4.
  • the cleaning robot plans navigation routes to the four secondary cleaning partitions. Obtain the corresponding candidate navigation route 1, candidate navigation route 2, candidate navigation route 3 and candidate navigation route 4, call the preset obstacle information database, and query whether there are obstacles on the four candidate navigation routes. After query, the candidate navigation route There are no obstacles on candidate navigation route 1 and candidate navigation route 2. There are obstacles on candidate navigation route 3 and candidate navigation route 4, but the obstacles on candidate navigation route 3 are at the entrance of secondary cleaning zone 3 and on candidate navigation route 4. The obstacle is not at the entrance of the secondary cleaning zone 4.
  • the cleaning robot determines whether there is a barrier-free navigation route to the secondary cleaning zone 4.
  • the cleaning robot After judging that there is a barrier-free navigation route 4, the cleaning robot takes the barrier-free navigation route 4 as a new candidate. Navigation route 4. Therefore, the secondary cleaning partition 1, the secondary cleaning partition 2 and the secondary cleaning partition 4 are the secondary cleaning partitions that the cleaning robot can reach.
  • the target partition is the secondary cleaning partition that the cleaning robot can reach and has the shortest distance.
  • the secondary cleaning partition that the cleaning robot can reach is called the reachable secondary cleaning partition.
  • the cleaning robot selects the candidate points closest to the cleaning robot on each reachable secondary cleaning partition and calculates the distance from each candidate point to the cleaning robot.
  • Distance There are many ways to calculate distance, such as calculating Euclidean distance, navigation distance or Manhattan distance.
  • the Manhattan distance from each candidate point to the cleaning robot is calculated to obtain at least one Manhattan distance.
  • the reachable secondary cleaning partition corresponding to the unique Manhattan distance is used as the target partition, and the cleaning robot navigates to the target partition for cleaning; when there are two or more Manhattan distances, the shortest Manhattan is selected.
  • the reachable secondary cleaning partition corresponding to the distance is used as the target partition, and the cleaning robot navigates to the target partition for cleaning.
  • Two methods are provided for selecting candidate points.
  • the first one is centered on the cleaning robot, using the Breadth First Search algorithm (Breadth First Search, BFS) to scan all points on the raster map, and the first scanned point that belongs to the target can be scanned.
  • the point that reaches the secondary cleaning partition is used as a candidate point whose target reaches the secondary cleaning partition.
  • the cleaning robot sends out acoustic signals or light signals to the target-reachable secondary cleaning zone for detection, and uses the point corresponding to the first received feedback signal as a candidate point for the target-reachable secondary cleaning zone.
  • the partitions that require secondary cleaning are identified from the initial cleaning partitions according to the secondary cleaning conditions to avoid missing areas that have not been cleaned, and the nearest secondary cleaning partition that the cleaning robot can reach is selected to improve cleaning efficiency. After the cleaning robot reaches the secondary cleaning partition, it cleans the area to improve the cleaning quality of the target area.
  • Another embodiment of the secondary cleaning method in the embodiment of the present invention includes:
  • the obstacle information database is used to record real-time obstacle information recognized by the cleaning robot during the cleaning process.
  • the obstacle information includes the location of the obstacle, pictures of the obstacles, pictures of the surrounding environment of the obstacles, and obstacles. status.
  • the cleaning robot recognizes a new obstacle, it obtains the position of the obstacle, turns on the camera to take pictures of the obstacle and the surrounding environment, sets the status of the new obstacle to "exist", and sets the position, Photos of obstacles, pictures of the surrounding environment of the obstacles and the status of the obstacles are recorded in the obstacle information database.
  • the rule for judging whether there are new obstacles is that an obstacle is detected at an unoccupied position in the raster map and the obstacle is
  • the object is not recorded in the obstacle information database; when the cleaning robot recognizes that the position of the target obstacle recorded in the obstacle information database has changed, it updates the information about the target obstacle in the obstacle information database.
  • the update content can be Target
  • the update of the obstacle status can also be the deletion of the target obstacle information. For example, when the cleaning robot recognizes that the target obstacle is not at its original location, it can set the status of the target obstacle to "does not exist", or it can set the target obstacle to "does not exist".
  • the object information is deleted from the obstacle information database.
  • the accuracy of the obstacle information database requires tracking the obstacles recognized by the cleaning robot, and adding a counter for each obstacle recorded in the obstacle information database.
  • the initial value of the counter is set to 0, and it passes during the movement of the cleaning robot.
  • the sensor updates the obstacle information database.
  • the cleaning robot's sensors include lidar, infrared sensors or cameras.
  • the obstacle's status is set to "does not exist” or the obstacle's information is deleted; When it is still at its original position, keep the status of the obstacle as "exist” and add 1 to the obstacle counter. Monitor each obstacle with the status "exist”.
  • the value displayed on the obstacle counter is greater than the set value, When setting the value, the obstacle is determined as a fixed obstacle, the grid map is updated, and the fixed obstacle information in the obstacle information database is deleted.
  • the cleaning robot recognizes new obstacle A and obstacle B, sets the status of the two obstacles to "exist” and adds the information of obstacle A and the obstacle
  • the information of B is recorded in the obstacle information database.
  • a counter with an initial value of 0 is added to obstacle A and obstacle B.
  • the cleaning robot detects that obstacle A is not at the recorded position and obstacle B remains. At the recorded position, therefore, set the status of obstacle A to "does not exist" and increase the counter of obstacle B by 1.
  • the value on the counter of obstacle B is 31, which is greater than the set value. Determine obstacle B as a fixed obstacle, update the grid at the location of obstacle B in the grid map to an occupied state, and delete the information about obstacle B in the obstacle information database.
  • the grid map is a digital image created by the cleaning robot based on the real indoor environment during the cleaning process.
  • the smallest unit in the grid map is a grid, and each grid corresponds to an area of the real indoor environment.
  • the cleaning robot assigns a gray value to each grid, and uses the gray value to determine whether the actual environment corresponding to the grid is occupied by obstacles.
  • Obstacles include walls, electrical cabinets, and furniture.
  • commonly used grayscale values are 0 and 255, where 0 is black and is used to indicate that the area is occupied by obstacles, and 255 is white and is used to indicate that the area is not occupied by obstacles.
  • the cleaning robot partitions the unoccupied areas in the grid map.
  • it also provides a partition rule: the cleaning robot identifies the unoccupied areas at the top, bottom, left and right end of the grid map. Occupied grid, generating a first horizontal line through the uppermost grid and a second horizontal line through the lowermost grid, a first vertical line through the leftmost grid, and a second vertical line through the rightmost grid Vertical lines, cut the closed area surrounded by the first horizontal line, the second horizontal line, the first vertical line and the second vertical line horizontally and vertically.
  • the smallest partition obtained after cutting is the initial cleaning partition.
  • Each initial cleaning partition is Contains unoccupied grids.
  • the number of transverse and longitudinal cuts can be determined based on the actual length and width of the closed area, and the closed area can be divided into two sections based on the number of transverse and longitudinal cuts. Cut equally in several dimensions, and obtain multiple initial clean partitions after cutting.
  • M 1 and width M 2 are the length and width of the closed area respectively
  • N 1 and N 2 are the preset length and width respectively
  • M 1 ⁇ N 1 , M 2 ⁇ N 2 the number of transverse cuts K 1 in the closed area takes the integer part of M 1 /N 1
  • the number of longitudinal cuts K 2 in the closed area takes the integer part of M 2 /N 2 .
  • This partitioning rule obtains the required number of initial cleaning partitions by adjusting the preset length and width values.
  • the target area can be divided into a number of , Appropriately sized partitions and appropriate numbers enable the cleaning robot to be more accurate during secondary cleaning Locate the position of the cleaning robot when the last cleaning was interrupted to avoid the phenomenon that the cleaned area is cleaned again or the uncleaned area is missed due to inaccurate positioning. Appropriate size can avoid the inability of the cleaning robot due to narrow partitions. Enter the situation to improve the positioning accuracy of the cleaning robot and improve the cleaning quality of the target area.
  • the preset length and width can be set to the same or close value, then the number of horizontal cutting and the number of vertical cutting of the target area are equal, and the target area is divided into two dimensions: length and width. Make equal portions.
  • the cleaning robot When the initial cleaning partition includes a small area such as a doorway, the cleaning robot will have dynamic obstacles blocking the doorway after the current partition along the wall, causing some areas in the initial cleaning partition to be missed for cleaning, as shown in Figure 6 It shows that the current initial cleaning partition is blocked by obstacles after the door along the wall, resulting in the internal slash area being missed for cleaning.
  • the cleaning robot When the cleaning robot was navigating, the cleaning robot once passed the area along the edge, but due to obstacles blocking the doorway, it was missed for cleaning. As shown in Figure 7, the cleaning robot is in the initial cleaning partition C and wants to navigate to a certain point in the initial cleaning partition F. It passes through the initial cleaning partition E. After the cleaning robot completes the initial cleaning partition F, it then navigates to the initial cleaning When partition E was being cleaned, the doorways of initial cleaning partition F and initial cleaning partition E were blocked, causing initial cleaning partition E to be missed for cleaning.
  • edge cleaning refers to cleaning along the edge of the initial cleaning partition.
  • area cleaning refers to cleaning along the wall. Cleaning is carried out in an arcuate path in the area surrounded by marks.
  • cleaning marks are added on the grid map every preset time or preset travel distance. Marks along the wall and cleaning marks can be of two colors. Different marks can also be two marks with different shapes.
  • the cleaning robot scans each initial cleaning partition on the raster map. If any of the following three situations occurs in the initial cleaning partition, the initial cleaning partition will be determined as a secondary cleaning partition:
  • the clean partition is not filled with clean marks.
  • initial cleaning partitions there are 5 initial cleaning partitions in the target area.
  • initial cleaning partition 1 has only part of the edge along the wall with markings
  • initial cleaning partition 2 has all edges along the wall with markings and is filled with cleaning marks
  • initial cleaning partition 3 has all edges with along the wall markings.
  • Wall marks but no cleaning marks initial cleaning partition 4 has markings along the wall on part of the edge and cleaning marks in the partition but the partition is not filled with cleaning marks
  • initial cleaning partition 5 has markings along the wall on all edges and cleaning marks in the partition but the partitions are not filled with cleaning marks Not filled with clean marks. Therefore, the initial cleaning partition 1, the initial cleaning partition 3, the initial cleaning partition 4 and the initial cleaning partition 5 are determined as secondary cleaning partitions.
  • the obstacle information database is used to record real-time obstacle information identified during the cleaning process
  • the candidate point is the contour point with the shortest straight line distance between the secondary cleaning partition and the current position point. As shown in Figure 8, plan the current position point to each candidate point.
  • Navigation routes between, obtain at least one navigation route, call the preset obstacle information database to query real-time obstacle information on at least one candidate navigation route, obtain information about obstacles with a status of "exist", and based on at least one candidate navigation route Determine whether there is at least one barrier-free navigation route in at least one candidate navigation route based on the obstacle information existing on the navigation route. If there is, determine the secondary cleaning partition corresponding to the at least one barrier-free navigation route as the secondary cleaning partition that the cleaning robot can reach. .
  • the first is that dynamic obstacles are at the entrance; the second is that low obstacles are near or on the edge of the entrance. Due to the hardware structure, the lidar cannot identify low obstacles. Obstacles, identify low obstacles as occupied areas on the grid map, causing the entrance to become smaller, resulting in the same situation as the third one; the third type, the entrance is narrow, the entrance size is the same as the diameter of the cleaning robot, or If it is slightly larger, due to the motion control error of the cleaning robot, the cleaning robot may sometimes be unable to pass through the entrance.
  • the cleaning robot arrives at the entrance; when the candidate point of the secondary cleaning partition is at the non-entrance, the cleaning robot passes through the entrance. Therefore, it is determined whether the cleaning robot can reach the secondary cleaning
  • the necessary condition for partitioning is that there are no obstacles at the entrance of the secondary cleaning partition. If there are obstacles in the target candidate navigation route but the obstacles are not at the entrance, then the cleaning robot and the target candidate navigation route corresponding to the target secondary cleaning partition are judged again. Whether there is another barrier-free navigation route in the interval, if so, use the other barrier-free navigation route as a new candidate navigation route for the target secondary cleaning partition.
  • the target area has 4 secondary cleaning partitions: secondary cleaning partition 1, secondary cleaning partition 2, secondary cleaning partition 3 and secondary cleaning partition 4.
  • the cleaning robot plans navigation routes to the four secondary cleaning partitions. Obtain the corresponding candidate navigation route 1, candidate navigation route 2, candidate navigation route 3 and candidate navigation route 4, call the preset obstacle information database, and query whether there are obstacles on the four candidate navigation routes. After query, the candidate navigation route There are no obstacles on candidate navigation route 1 and candidate navigation route 2. There are obstacles on candidate navigation route 3 and candidate navigation route 4, but the obstacles on candidate navigation route 3 are at the entrance of secondary cleaning zone 3 and on candidate navigation route 4. The obstacle is not at the entrance of the secondary cleaning zone 4.
  • the cleaning robot determines whether there is a barrier-free navigation route to the secondary cleaning zone 4.
  • the cleaning robot After judging that there is a barrier-free navigation route 4, the cleaning robot takes the barrier-free navigation route 4 as a new candidate. Navigation route 4. Therefore, the secondary cleaning partition 1, the secondary cleaning partition 2 and the secondary cleaning partition 4 are the secondary cleaning partitions that the cleaning robot can reach.
  • the target partition is the secondary cleaning partition that the cleaning robot can reach and has the shortest distance
  • the cleaning robot can reach determines the secondary cleaning area that the cleaning robot can reach. Whether the cleaning partition is unique. If the secondary cleaning partition that the cleaning robot can reach is unique, determine the only secondary cleaning partition that the cleaning robot can reach as the target partition, and navigate to the target partition; if the secondary cleaning partition that the cleaning robot can reach is If the partition is not unique, then calculate the distance between the current position point of the cleaning robot and the candidate point of each secondary cleaning partition.
  • the distance such as calculating Euclidean distance, navigation distance or Manhattan distance.
  • the Manhattan distance from each candidate point to the cleaning robot is calculated, and at least one candidate Manhattan distance is obtained.
  • the candidate point is the contour point with the shortest straight line distance between the secondary cleaning partition and the current position point, and is screened from multiple candidate Manhattan distances. Find the shortest Manhattan distance, determine the secondary cleaning partition corresponding to the shortest Manhattan distance as the target partition, and navigate to the target partition.
  • Two methods are provided for selecting candidate points.
  • the first one is centered on the cleaning robot, using the Breadth First Search algorithm (Breadth First Search, BFS) to scan all points on the raster map, and the first scanned point that belongs to the target can be scanned.
  • the point that reaches the secondary cleaning partition is used as a candidate point whose target reaches the secondary cleaning partition.
  • the cleaning robot sends out acoustic signals or light signals to the target-reachable secondary cleaning zone for detection, and uses the point corresponding to the first received feedback signal as a candidate point for the target-reachable secondary cleaning zone.
  • the cleaning robot When the cleaning robot reaches the end point according to the planned navigation route, the navigation ends, and the navigation is successful; when the cleaning robot encounters obstacles on the way to the target partition, the navigation ends, and the navigation is unsuccessful.
  • the first one is to find the wall markings and cleaning marks in the target partition.
  • the cleaning robot moves to the end position of the cleaning mark, and starts from the end position to clean the area along the arcuate path; when the target partition
  • the cleaning robot moves to the corner position, starting from the corner position and cleaning the area along the wall along an arcuate path; when there are some markings along the wall in the target partition, the cleaning robot moves to the markings along the wall.
  • the end position continue cleaning along the edge from the end position, and then clean the area according to the arcuate path after the edge cleaning is completed.
  • the second method is to find the wall markings and cleaning marks in the target partition. For the part with wall markings, according to the wall markings, first clean the area along the arcuate path according to the wall markings. If there are along wall markings, After cleaning the area, it is found that there are still uncleaned parts in the current partition. The uncleaned parts will be cleaned along the edges first, and then the area along the wall will be cleaned according to the arcuate path. As shown in Figure 9: The missed cleaning areas include the parts in the diagonal frame that have been cleaned along the edge and the parts in the vertical line frame that have not been cleaned along the edge. If it can be entered from the entrance, the area in the diagonal frame that has been cleaned along the edge should be cleaned first. When cleaning the arcuate area, after the diagonal frame area is cleaned, clean the vertical frame area along the edge first, and then clean the area according to the arcuate path.
  • step 204 determines whether there is a secondary cleaning partition that the cleaning robot can reach. If so, select the next target partition. If not, end the cleaning.
  • the cleaning robot itself has a certain size. In order to improve the cleaning efficiency, when part of the cleaning robot enters the target partition, it is equivalent to the cleaning robot being located in the target partition. Therefore, here is an additional method to determine whether the cleaning robot is located in the target partition. Rules within the target partition:
  • N is a preset positive integer value to obtain the target expanded area. If all cleaning robots are located in the target expanded area, it is determined that the cleaning robot is located in the target partition, and the target partition is cleaned. , the cleaning method is as described in step 207, which will not be repeated here; if all the cleaning robots are not located in the target expansion area, return to step 204 to determine whether there is a secondary cleaning partition that the cleaning robot can reach. If so, then Select the next target partition, if not, end cleaning.
  • the partitions that require secondary cleaning are identified from the initial cleaning partitions according to the secondary cleaning conditions to avoid missing areas that have not been cleaned.
  • the latest status of obstacles is ensured by maintaining the obstacle information database, and the cleaning robot is accurately selected. It can reach the nearest secondary cleaning partition to improve cleaning efficiency, judge the result of the cleaning robot navigating to the secondary cleaning partition, and perform cleaning, select the next secondary cleaning partition or end cleaning operations based on the judgment results, maximizing the Complete the cleaning of the secondary cleaning partition and improve the cleaning quality of the target area.
  • the secondary cleaning method in the embodiment of the present invention is described above.
  • the secondary cleaning device in the embodiment of the present invention is described below. Please refer to Figure 10.
  • One embodiment of the secondary cleaning device in the embodiment of the present invention includes:
  • the acquisition module 1001 is used to acquire a grid map of the target area, where the grid map includes at least one initial cleaning partition;
  • the determination module 1002 is used to determine whether there is an initial cleaning partition that meets the secondary cleaning conditions. If there is, determine the initial cleaning partition that meets the secondary cleaning conditions as the secondary cleaning partition, and obtain at least one secondary cleaning partition.
  • the cleaning condition is that there are marks along the wall in the initial cleaning zone and are not filled with cleaning marks;
  • Determination module 1003 is used to determine whether there is a secondary cleaning partition that the cleaning robot can reach in at least one secondary cleaning partition according to a preset obstacle information database.
  • the obstacle information database is used to record real-time obstacles identified during the cleaning process. information;
  • the navigation module 1004 is used to navigate to the target partition for cleaning if there is a secondary cleaning partition that the cleaning robot can reach.
  • the target partition is the secondary cleaning partition that the cleaning robot can reach and has the shortest distance.
  • the partitions that require secondary cleaning are identified from the initial cleaning partitions according to the secondary cleaning conditions to avoid missing areas that have not been cleaned, and the nearest secondary cleaning partition that the cleaning robot can reach is selected to improve cleaning efficiency. After the cleaning robot reaches the secondary cleaning partition, it cleans the area to improve the cleaning quality of the target area.
  • Another embodiment of the secondary cleaning device in the embodiment of the present invention includes:
  • the acquisition module 1001 is used to acquire a grid map of the target area, where the grid map includes at least one initial cleaning partition;
  • the determination module 1002 is used to determine whether there is an initial cleaning partition that meets the secondary cleaning conditions. If there is, determine the initial cleaning partition that meets the secondary cleaning conditions as the secondary cleaning partition, and obtain at least one secondary cleaning partition.
  • the cleaning condition is that there are marks along the wall in the initial cleaning zone and are not filled with cleaning marks;
  • Determination module 1003 is used to determine whether there is a secondary cleaning partition that the cleaning robot can reach in at least one secondary cleaning partition according to a preset obstacle information database.
  • the obstacle information database is used to record real-time obstacles identified during the cleaning process. information;
  • the navigation module 1004 is used to navigate to the target partition for cleaning if there is a secondary cleaning partition that the cleaning robot can reach.
  • the target partition is the secondary cleaning partition that the cleaning robot can reach and has the shortest distance.
  • the judgment module 1003 includes:
  • the planning unit 10031 is used to plan the navigation route of the cleaning robot to each of the at least one secondary cleaning partition, and obtain at least one candidate navigation route;
  • the calling unit 10032 is used to call the preset obstacle information database to query the real-time obstacle information on at least one candidate navigation route;
  • the first judgment unit 10033 is used to judge whether there is at least one barrier-free navigation route in the at least one candidate navigation route based on the real-time obstacle information on the at least one candidate navigation route;
  • the determination unit 10034 is configured to determine the secondary cleaning partition corresponding to at least one barrier-free navigation route as the secondary cleaning partition that the cleaning robot can reach, if it exists.
  • planning unit 10031 is specifically used for:
  • the candidate point is the contour point with the shortest straight line distance between the secondary cleaning partition and the current position point; plan the navigation route between the current position point and each candidate point, and get At least one navigation route.
  • the navigation module 1004 includes:
  • Navigation submodule 10041 used to navigate to the target partition if there is a secondary cleaning partition that the cleaning robot can reach;
  • the judgment sub-module 10042 is used to judge whether the navigation is successful when the navigation ends.
  • Successful navigation means that the cleaning robot reaches the end point of the planned navigation route;
  • Control submodule 10043 used to control the cleaning robot to clean the target partition if the navigation is successful
  • the judgment control submodule 10044 is used to judge whether the cleaning robot is located in the target partition if the navigation is unsuccessful. If so, control the cleaning robot to clean the target partition. If not, select the target partition again or end cleaning.
  • navigation submodule 10041 includes:
  • the second judgment unit 100411 is used to determine whether the secondary cleaning partition that the cleaning robot can reach is unique if there is a secondary cleaning partition that the cleaning robot can reach;
  • the first navigation unit 100412 is used to, if so, determine the only secondary cleaning partition that the cleaning robot can reach as the target partition, and navigate to the target partition;
  • the second navigation unit 100413 is used to determine the secondary cleaning partition closest to the cleaning robot as the target partition if not, and navigate to the target partition.
  • the second navigation unit 100413 is specifically used for:
  • the secondary cleaning partition that the cleaning robot can reach is not unique, calculate the Manhattan distance between the current position point of the cleaning robot and the candidate points of each secondary cleaning partition, and obtain at least one candidate Manhattan distance, and the candidate point is the secondary cleaning
  • the contour point with the shortest straight line distance between the partition and the current position point filter out the shortest Manhattan distance from multiple candidate Manhattan distances; determine the secondary cleaning partition corresponding to the shortest Manhattan distance as the target partition and navigate to the target partition.
  • the secondary cleaning device also includes a building module 1005, which is specifically used for:
  • the obstacle information database is used to record real-time obstacle information recognized by the cleaning robot during the cleaning process.
  • the obstacle information includes the location of the obstacle, pictures of the obstacles, pictures of the surrounding environment of the obstacles, and obstacles. state; when the cleaning robot recognizes a new obstacle, the new obstacle information is added to the obstacle information database; when the cleaning robot recognizes the position of the target obstacle that has been recorded in the obstacle information database When changes occur, the target obstacle information in the obstacle information database is updated.
  • the partitions that need to be cleaned for the second time are identified from the initial cleaning partitions according to the secondary cleaning conditions to avoid missing areas that have not been cleaned.
  • the latest status of the obstacles is ensured by maintaining the obstacle information database and is accurate. Select the nearest secondary cleaning partition that the cleaning robot can reach to improve cleaning efficiency, judge the result of the cleaning robot navigating to the secondary cleaning partition, and perform cleaning, select the next secondary cleaning partition, or end cleaning based on the judgment results , to complete the cleaning of the secondary cleaning partition to the maximum extent and improve the cleaning quality of the target area.
  • FIG. 12 is a schematic structural diagram of a cleaning robot provided by an embodiment of the present invention.
  • the cleaning robot 1200 may vary greatly due to different configurations or performance, and may include one or more central processing units (CPUs) 1210 (eg, one or more processors) and memory 1220, one or more storage media 530 (eg, one or more mass storage devices) storing applications 1233 or data 1232.
  • the memory 1220 and the storage medium 1230 may be short-term storage or persistent storage.
  • the program stored in the storage medium 1230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the cleaning robot 1200 .
  • the processor 1210 may be configured to communicate with the storage medium 1230 and execute a series of instruction operations in the storage medium 1230 on the cleaning robot 1200 .
  • the cleaning robot 1200 may also include one or more power supplies 1240, one or more wired or wireless network interfaces 1250, one or more input and output interfaces 1260, and/or, one or more operating systems 1231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD and more.
  • operating systems 1231 such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD and more.
  • the present invention also provides a cleaning robot.
  • the computer device includes a memory and a processor.
  • Computer-readable instructions are stored in the memory. When the computer-readable instructions are executed by the processor, they cause the processor to execute the steps in the above embodiments. Steps of the secondary cleaning method.
  • the present invention also provides a computer-readable storage medium.
  • the computer-readable storage medium can be a non-volatile computer-readable storage medium.
  • the computer-readable storage medium can also be a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium, and when the instructions are run on the computer, they cause the computer to perform the steps of the secondary cleaning method.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the technical solution of the present invention is essentially or contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program code. .

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Abstract

一种二次清洁方法、装置、清洁机器人及存储介质，用于提高清洁质量。二次清洁方法包括：获取目标区域的栅格地图，栅格地图包括至少一个初始清洁分区（101）；判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满（102）；根据预置的障碍物信息库判断至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区（103）；若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，目标分区为清洁机器人能够到达且距离最短的二次清洁分区（104）。

Description

二次清洁方法、装置、清洁机器人及存储介质
本申请要求2022年07月21日向中国国家知识产权局递交的申请号为202210873679.X，申请名称为“二次清洁方法、装置、清洁机器人及存储介质”的在先申请的优先权，上述在先申请的内容以引入的方式并入本文本中。
技术领域
本发明涉及智能家居技术领域，尤其涉及一种二次清洁方法、装置、清洁机器人及存储介质。
背景技术
随着科技的发展，一些智能家用设备逐渐进入大众的生活，例如清洁机器人，可以帮助人们减少家务负担，维护良好的生活环境。
在现有的技术中，清洁机器人对区域进行分区清洁，清洁完毕后回到充电座位置，如果在工作时碰到障碍物，例如关上的门、移动的人或宠物、狭窄入口的墙壁等，可能会导致清洁机器人离开当前分区去往下一个分区进行清洁，但当前分区尚未清洁完毕，在完成其他分区的清洁工作后清洁机器人回到充电座位置，不会对未清洁完毕的分区进行二次清洁，造成部分区域被遗漏清洁的情况，清洁质量低。
申请内容
本发明的主要目的在于提供一种二次清洁方法、装置、清洁机器人及存储介质，用于解决区域被遗漏清洁的问题，提高清洁质量。
本发明第一方面提供了一种二次清洁方法，包括：获取目标区域的栅格地图，所述栅格地图包括至少一个初始清洁分区；判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足所述二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，所述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，所述障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区。
可选的，在本发明第一方面的第一种实现方式中，所述根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，包括：对清洁机器人到所述至少一个二次清洁分区中每个二次清洁分区的导航路线进行规划，得到至少一条候选导航路线；调用所述预置的障碍物信息库查询所述至少一条候选导航路线上的实时障碍物信息；根据所述至少一条候选导航路线上的实时障碍物信息判断所述至少一条候选导航路线中是否存在至少一条无障碍导航路线；若存在，则将所述至少一条无障碍导航路线对应的二次清洁分区确定为所述清洁机器人能够到达的二次清洁分区。
可选的，在本发明第一方面的第二种实现方式中，所述对清洁机器人到所述至少一个二次清洁分区中每个二次清洁分区的导航路线进行规划，得到至少一条候选导航路线，包括：根据清洁机器人的当前位置点确定至少一个候选点，所述候选点为二次清洁分区与所述当前位置点之间具有最短直线距离的轮廓点；规划所述当前位置点到每个候选点之间的导航路线，得到至少一条导航路线。
可选的，在本发明第一方面的第三种实现方式中，所述若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区，包括：若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区；当导航结束时，判断是否导航成功，所述导航成功是指所述清洁机器人到达规划导航路线的终点；若导航成功，则控制所述清洁机器人对所述目标分区进行清洁；若导航不成功，则判断所述清洁机器人是否位于所述目标分区内，若是，则控制所述清洁机器人对所述目标分区进行清洁，若否，则再次选择目标分区或结束清洁。
可选的，在本发明第一方面的第四种实现方式中，所述若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区，包括：若存在清洁机器人能够到达的二次清洁分区，则判断所述清洁机器人能够到达的二次清洁分区是否唯一；若是，则将所述清洁机器人能够到达的唯一二次清洁分区确定为目标分区，并导航至目标分区；若否，则将距离所述清洁机器人最近的二次清洁分区确定为目标分区，并导航至目标分区。
可选的，在本发明第一方面的第五种实现方式中，所述若否，则将距离所述清洁机器人最近的二次清洁分区确定为目标分区，并导航至目标分区，包括：若所述清洁机器人能够到达的二次清洁分区不唯一，则计算所述清洁机器人的当前位置点与每个二次清洁分区的候选点之间的曼哈顿距离，得到至少一个候选曼哈顿距离，所述候选点为二次清洁分区与所述当前位置点之间具有最短直线距离的轮廓点；从所述多个候选曼哈顿距离中筛选出最短的曼哈顿距离；将所述最短的曼哈顿距离对应的二次清洁分区确定为目标分区并导航至目标分区。
可选的，在本发明第一方面的第六种实现方式中，在获取目标区域的栅格地图之前，还包括：建立障碍物信息库，所述障碍物信息库用于记录清洁机器人在清洁过程中识别到的实时障碍物信息，所述障碍物信息包括障碍物的位置、障碍物的图片、障碍物的周边环境图片和障碍物的状态；当所述清洁机器人识别到新增的障碍物时，将所述新增的障碍物的信息添加至所述障碍物信息库中；当所述清洁机器人识别到已经记录于所述障碍物信息库中的目标障碍物的位置发生变化时，将所述障碍物信息库中目标障碍物的信息进行更新。
本发明第二方面提供了一种二次清洁装置，包括：获取模块，用于获取目标区域的栅格地图，所述栅格地图包括至少一个初始清洁分区；确定模块，用于判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足所述二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，所述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；判断模块，用于根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，所述障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；导航模块，用于若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区。
可选的，在本发明第二方面的第一种实现方式中，所述判断模块包括：规划单元，用于对清洁机器人到所述至少一个二次清洁分区中每个二次清洁分区的导航路线进行规划，得到至少一条候选导航路线；调用单元，用于调用所述预置的障碍物信息库查询所述至少一条候选导航路线上的实时障碍物信息；第一判断单元，用于根据所述至少一条候选导航路线上的实时障碍物信息判断所述至少一条候选导航路线中是否存在至少一条无障碍导航路线；确定单元，用于若存在，则将所述至少一条无障碍导航路线对应的二次清洁分区确定为所述清洁机器人能够到达的二次清洁分区。
可选的，在本发明第二方面的第二种实现方式中，所述规划单元具体用于：根据清洁机器人的当前位置点确定至少一个候选点，所述候选点为二次清洁分区与所述当前位置点之间具有最短直线距离的轮廓点；规划所述当前位置点到每个候选点之间的导航路线，得到至少一条导航路线。
可选的，在本发明第二方面的第三种实现方式中，所述导航模块包括：导航子模块，用于若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区；判断子模块，用于当导航结束时，判断是否导航成功，所述导航成功是指所述清洁机器人到达规划导航路线的终点；控制子模块，用于若导航成功，则控制所述清洁机器人对所述目标分区进行清洁；判断控制子模块，用于若导航不成功，则判断所述清洁机器人是否位于所述目标分区内，若是，则控制所述清洁机器人对所述目标分区进行清洁，若否，则再次选择目标分区或结束清洁。
可选的，在本发明第二方面的第四种实现方式中，所述导航子模块包括：第二判断单元，用于若存在清洁机器人能够到达的二次清洁分区，则判断所述清洁机器人能够到达的二次清洁分区是否唯一；第一导航单元，用于若是，则将所述清洁机器人能够到达的唯一二次清洁分区确定为目标分区，并导航至目标分区；第二导航单元，用于若否，则将距离所述清洁机器人最近的二次清洁分区确定为目标分区，并导航至目标分区。
可选的，在本发明第二方面的第五种实现方式中，所述第二导航单元具体用于：若所述清洁机器人能够到达的二次清洁分区不唯一，则计算所述清洁机器人的当前位置点与每个二次清洁分区的候选点之间的曼哈顿距离，得到至少一个候选曼哈顿距离，所述候选点为二次清洁分区与所述当前位置点之间具有最短直线距离的轮廓点；从所述多个候选曼哈顿距离中筛选出最短的曼哈顿距离；将所述最短的曼哈顿距离对应的二次清洁分区确定为目标分区并导航至目标分区。
可选的，在本发明第二方面的第六种实现方式中，在获取模块之前还包括搭建模块，所述搭建模块具体用于：建立障碍物信息库，所述障碍物信息库用于记录清洁机器人在清洁过程中识别到的实时障碍物信息，所述障碍物信息包括障碍物的位置、障碍物的图片、障碍物的周边环境图片和障碍物的状态；当所述清洁机器人识别到新增的障碍物时，将所述新增的障碍物的信息添加至所述障碍物信息库中；当所述清洁机器人识别到已经记录于所述障碍物信息库中的目标障碍物的位置发生变化时，将所述障碍物信息库中目标障碍物的信息进行更新。
本发明第三方面提供了一种清洁机器人，包括：存储器和至少一个处理器，所述存储器中存储有指令；所述至少一个处理器调用所述存储器中的所述指令，以使得所述清洁机器人执行上述的二次清洁方法。
本发明的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质中 存储有指令，当其在计算机上运行时，使得计算机执行上述的二次清洁方法。
本发明提供的技术方案中，获取目标区域的栅格地图，所述栅格地图包括至少一个初始清洁分区，判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足所述二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，所述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满，根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，所述障碍物信息库用于记录清洁过程中识别到的实时障碍物信息，若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区。本发明实施例中，根据二次清洁条件从初始清洁分区中识别需要进行二次清洁的分区，避免遗漏未清洁完毕的区域，选择清洁机器人能够到达且最近的二次清洁分区，提高清洁效率，清洁机器人到达二次清洁分区后进行区域清洁，提高目标区域的清洁质量。
附图说明
图1为本发明实施例中二次清洁方法的一个实施例示意图；
图2为本发明实施例中栅格地图的简化示意图；
图3为本发明实施例中一种栅格地图分区实施例的示意图；
图4为本发明实施例中二次清洁方法的另一个实施例示意图；
图5为本发明实施例中第一种二次清洁分区生成情境的示意图；
图6为本发明实施例中第二种二次清洁分区生成情境的示意图；
图7为本发明实施例中第三种二次清洁分区生成情境的示意图；
图8为本发明实施例中二次清洁分区候选点的示意图；
图9为本发明实施例中一种辅助说明清洁方式的区域示意图；
图10为本发明实施例中二次清洁装置的一个实施例示意图；
图11为本发明实施例中二次清洁装置的另一个实施例示意图；
图12为本发明实施例中清洁机器人的一个实施例示意图。
具体实施方式
本发明实施例提供了一种二次清洁方法、装置、清洁机器人及存储介质，根据二次清洁条件找出二次清洁分区，将清洁机器人导航至能够达到且距离最近的二次清洁分区进行清洁，提高清洁效率和清洁质量。
本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的实施例能够以除了在这里图示或描述的内容以外的顺序实施。此外，术语“包括”或“具有”及其任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
为便于理解，下面对本发明实施例的具体流程进行描述，本发明的执行主体为清 洁机器人，清洁机器人包括清扫机器人、拖地机器人、吸尘机器人或其他具有地面清洁功能的机器人。
请参阅图1，本发明实施例中二次清洁方法的一个实施例包括：
101、获取目标区域的栅格地图，栅格地图包括至少一个初始清洁分区；
其中，栅格地图是清洁机器人在清洁过程中基于室内真实环境建立的数字化图像，栅格地图中最小的单位为栅格，每个栅格与室内真实环境的一个区域对应。清洁机器人为每个栅格赋予一个灰度值，通过灰度值来判断栅格对应的实际环境是否被障碍物占据，障碍物包括墙体、电器柜体和家具。如图2所示，常用的灰度值为0和255，其中0为黑色，用于表示区域被障碍物占据，255为白色，用于表示区域未被障碍物占据。
清洁机器人将栅格地图中未被占据的区域进行分区，除了根据房间进行分区外，还提供一种分区规则：清洁机器人识别栅格地图中处于最上端、最下端、最左端和最右端的未被占据的栅格，生成穿过最上端栅格的第一水平线和穿过最下端栅格的第二水平线、穿过最左端栅格的第一垂直线以及穿过最右端栅格的第二垂直线，将第一水平线、第二水平线、第一垂直线和第二垂直线围起来的闭合区域进行横向和纵向的切割，切割后得到的最小分区为初始清洁分区，每个初始清洁分区均包含未被占据的栅格。为避免切割次数多造成的初始清洁分区过多的情况，可基于闭合区域的实际长、宽确定横向切割次数和纵向切割次数，并基于横向切割次数和纵向切割次数将闭合区域在长、宽两个维度进行等分切割，切割后得到多个初始清洁分区。在此提供一个计算切割次数的方案：M1和宽M2分别为闭合区域的长和宽，N1和N2分别为预设的长和宽，M1≥N1，M2≥N2，闭合区域的横向切割次数K1取M1/N1的整数部分，闭合区域的纵向切割次数K2取M2/N2的整数部分。这种分区规则通过调整预设的长和宽的数值来得到所需数量的初始清洁分区，当目标区域非常大且目标区域的长远大于宽或者宽远大于长时，能够将目标区域划分成数量、大小合适的分区，数量合适使得清洁机器人二次清洁时能够更加精准定位上次清洁中断时清洁机器人所处的位置，避免出现清洁过的区域再次被清洁或未被清洁的区域因定位不准确被遗漏清洁的现象，大小合适能够避免部分分区狭窄导致的清洁机器人无法进入的情况，提高清洁机器人定位的精度，提高目标区域的清洁质量。当目标区域的长宽相差不大时，可将预设的长宽设置为相同或接近的数值，那么目标区域的横向切割次数和纵向切割次数相等，在长、宽两个维度上将目标区域进行等分。
例如，当闭合区域的长和宽分别为13.2米和10米、预定的长和宽分别为5米和3米时，M1/N1＝2.64，M2/N2＝3.33，则K1＝2，K2＝3。在闭合区域的长度方向切2刀，即将在闭合区域的长度方向上将闭合区域等分为3份；同理，在闭合区域的宽度方向切3刀，即将在闭合区域的宽度方向上将闭合区域等分为4份，最终将闭合区域分为12个初始清洁分区，如图3所示。
102、判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；
清洁机器人的清洁方式包括沿边清洁和区域清洁。其中，沿边清洁是指沿初始清洁分区的边缘进行清洁，当清洁机器人进行沿边清洁时，每隔预设时间或预设行进距离在栅格地图上添加沿墙标记；区域清洁是指在沿墙标记包围的区域内以弓字形路径进行清洁，当清洁机器人进行区域清洁时，每隔预设时间或预设行进距离在栅格地图上添加 清洁标记，沿墙标记和清洁标记可以是两种颜色不同的标记，也可以是两种形状不同的标记。在每个初始清洁分区内，清洁机器人先进行沿边清洁，然后进行区域清洁。
清洁机器人扫描栅格地图上每个初始清洁分区，若初始清洁分区中出现了以下三种情况中的任意一种，则将初始清洁分区确定为二次清洁分区：
1)初始清洁分区的部分边缘有沿墙标记；
2)初始清洁分区的全部边缘有沿墙标记，但初始清洁分区内没有清洁标记；
3)初始清洁分区的全部边缘有沿墙标记，初始清洁分区内有清洁标记，但初始清
洁分区未被清洁标记填满。
例如，目标区域有5个初始清洁分区，其中，初始清洁分区1仅部分边缘有沿墙标记，初始清洁分区2全部边缘有沿墙标记且被清洁标记填满，初始清洁分区3全部边缘有沿墙标记但没有清洁标记，初始清洁分区4部分边缘有沿墙标记且分区内有清洁标记但分区未被清洁标记填满，初始清洁分区5全部边缘有沿墙标记且分区内有清洁标记但分区未被清洁标记填满。因此，将初始清洁分区1、初始清洁分区3、初始清洁分区4和初始清洁分区5确定为二次清洁分区。
103、根据预置的障碍物信息库判断至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；
障碍物信息库中记录清洁机器人在运动过程中通过激光雷达或红外传感器监测到的实时障碍物的信息，障碍物是指在栅格地图中未被占据的位置出现的物体，当清洁机器人感应到障碍物时，判断该障碍物是否已经记录于障碍物信息库中，若未记录，则获取障碍物的位置，开启摄像头对障碍物及周边环境进行拍照，将障碍物的位置、障碍物的照片、障碍物的周边环境图片和障碍物的状态记录到障碍物信息库，并将障碍物的状态设置为“存在”；当清洁机器人识别到障碍物信息库中记录的障碍物不在原本的位置时，将该障碍物的状态设置为“不存在”。
清洁机器人获取自身的位置点和每个二次清洁分区距离清洁机器人最近的候选点，根据位置点和每个二次清洁分区的候选点，得到到达每个二次清洁分区的候选导航路线，调用预置的障碍物信息库，查询每条候选导航路线上是否存在障碍物，若目标候选导航路线上存在障碍物，则清洁机器人不能到目标达候选导航路线对应的二次清洁分区；若目标候选导航路线不存在障碍物，则清洁机器人能够到达目标候选导航路线对应的二次清洁分区。
需要补充说明的是，产生二次清洁分区的原因有三种，第一种，动态障碍在入口；第二种，低矮障碍在入口附近或边缘，由于硬件结构的原因，激光雷达无法识别低矮障碍物，在栅格地图上将低矮障碍物识别为被占据的区域，从而导致入口变小，造成与第三种相同的情况；第三种，入口狭小，入口尺寸与清洁机器人直径相同或者稍大，由于清洁机器人运动控制误差的原因，会导致存在清洁机器人有时不能通过入口的情况。当二次清洁分区的候选点在入口处时，清洁机器人到达入口处；当二次清洁分区的候选点在非入口处时，清洁机器人穿过入口，因此，决定清洁机器人能否到达二次清洁分区的必要条件是二次清洁分区的入口处无障碍物，若目标候选导航路线中存在障碍物但障碍物不在入口处，则再次判断清洁机器人与目标候选导航路线对应的目标二次清洁分区之间是否存在另一条无障碍的导航路线，若有，则将另一条无障碍的导航路线作为目标二 次清洁分区新的候选导航路线。
例如，目标区域有4个二次清洁分区：二次清洁分区1、二次清洁分区2、二次清洁分区3和二次清洁分区4，清洁机器人规划到4个二次清洁分区的导航路线，得到对应的候选导航路线1、候选导航路线2、候选导航路线3和候选导航路线4，调用预置的障碍物信息库，查询4条候选导航路线上是否存在障碍物，经查询，候选导航路线1和候选导航路线2上不存在障碍物、候选导航路线3和候选导航路线4上存在障碍物，但候选导航路线3上的障碍物在二次清洁分区3的入口处、候选导航路线4上的障碍物不在二次清洁分区4的入口处，清洁机器人判断是否存在到二次清洁分区4的无障碍导航路线，经判断，存在无障碍导航路线4，将无障碍导航路线4作为新的候选导航路线4。因此，二次清洁分区1、二次清洁分区2和二次清洁分区4为清洁机器人能够到达的二次清洁分区。
104、若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，目标分区为清洁机器人能够到达且距离最短的二次清洁分区。
将清洁机器人能够到达的二次清洁分区称为可达二次清洁分区，清洁机器人筛选出每个可达二次清洁分区上距离清洁机器人最近的候选点，并计算每个候选点到清洁机器人的距离，距离的计算方式有多种，例如计算欧氏距离、导航距离或曼哈顿距离，优选的，此处计算每个候选点到清洁机器人的曼哈顿距离，得到至少一个曼哈顿距离。当只有一个曼哈顿距离时，将这唯一的曼哈顿距离对应的可达二次清洁分区作为目标分区，清洁机器人导航至目标分区进行清洁；当有两个或两个以上的曼哈顿距离时，选择最短曼哈顿距离对应的可达二次清洁分区作为目标分区，清洁机器人导航至目标分区进行清洁。
提供两种候选点的选取方式，第一种，以清洁机器人为中心，采用宽度优先搜索算法(Breadth First Search，BFS)扫描栅格地图上所有的点，将扫描到的第一个属于目标可达二次清洁分区的点作为目标可达二次清洁分区的候选点。第二种，清洁机器人向目标可达二次清洁分区发出声波信号或者光信号进行探测，将最先接收到的反馈信号对应的点作为目标可达二次清洁分区的候选点。
本发明实施例中，根据二次清洁条件从初始清洁分区中识别需要进行二次清洁的分区，避免遗漏未清洁完毕的区域，选择清洁机器人能够到达且最近的二次清洁分区，提高清洁效率，清洁机器人到达二次清洁分区后进行区域清洁，提高目标区域的清洁质量。
请参阅图4，本发明实施例中二次清洁方法的另一个实施例包括：
401、搭建障碍物信息库；
建立障碍物信息库，障碍物信息库用于记录清洁机器人在清洁过程中识别到的实时障碍物信息，障碍物信息包括障碍物的位置、障碍物的图片、障碍物的周边环境图片和障碍物的状态。当清洁机器人识别到新增的障碍物时，获取障碍物的位置，开启摄像头对障碍物及周边环境进行拍照，将新增的障碍物的状态设置为“存在”，并将障碍物的位置、障碍物的照片、障碍物的周边环境图片和障碍物的状态记录到障碍物信息库中，判断是否有新增障碍物的规则为栅格地图中未被占据的位置检测到障碍物且该障碍物未记录于障碍物信息库中；当清洁机器人识别到记录于障碍物信息库中的目标障碍物的位置发生变化时，将障碍物信息库中目标障碍物的信息进行更新，更新内容可以是目标 障碍物状态的更新也可以是目标障碍物信息的删除，例如，当清洁机器人识别到目标障碍物不在原本的位置时，可以将目标障碍物的状态设置为“不存在”，也可以将目标障碍物的信息从障碍物信息库中删除。
更进一步地，由于障碍物中存在被误判为动态障碍物的固定障碍物，例如，新增的柜子或沙发等移动频率低的物品，因为尺寸原因或空间限制挡住了部分房间入口，为保证障碍物信息库的准确性，需要对清洁机器人识别到的障碍物进行追踪，为每个记录于障碍物信息库的障碍物添加计数器，计数器初始值设置为0，在清洁机器人运动的过程中通过传感器更新障碍物信息库，清洁机器人传感器包括激光雷达、红外传感器或摄像头，若障碍物不在原本的位置，则将障碍物的状态设置为“不存在”或删除该障碍物的信息；若障碍物还在原本的位置时，则保持障碍物的状态为“存在”，并将障碍物的计数器加1，监控每个状态为“存在”的障碍物，当障碍物的计数器上显示的值大于设定值时，将该障碍物确定为固定障碍物，更新栅格地图，并删去障碍物信息库中固定障碍物的信息。
例如，设定值为30，第一天清洁时，清洁机器人识别到新的障碍物A和障碍物B，将两个障碍物的状态设置为“存在”并将障碍物A的信息和障碍物B的信息记录于障碍物信息库中，为障碍物A和障碍物B添加初始值为0的计数器，第二天清洁时，清洁机器人检测到障碍物A不在记录的位置上、障碍物B依旧在记录的位置上，因此，将障碍物A的状态设置为“不存在”、将障碍物B的计数器加1，经过一段时间后，障碍物B计数器上的值为31，大于设定值，将障碍物B确定为固定障碍物，将栅格地图中障碍物B所在位置的栅格更新为被占据状态，并删去障碍物信息库中障碍物B的信息。
402、获取目标区域的栅格地图，栅格地图包括至少一个初始清洁分区；
其中，栅格地图是清洁机器人在清洁过程中基于室内真实环境建立的数字化图像，栅格地图中最小的单位为栅格，每个栅格与室内真实环境的一个区域对应。清洁机器人为每个栅格赋予一个灰度值，通过灰度值来判断栅格对应的实际环境是否被障碍物占据，障碍物包括墙体、电器柜体和家具。例如，常用的灰度值为0和255，其中0为黑色，用于表示区域被障碍物占据，255为白色，用于表示区域未被障碍物占据。
清洁机器人将栅格地图中未被占据的区域进行分区，除了根据房间进行分区外，还提供一种分区规则：清洁机器人识别栅格地图中处于最上端、最下端、最左端和最右端的未被占据的栅格，生成穿过最上端栅格的第一水平线和穿过最下端栅格的第二水平线、穿过最左端栅格的第一垂直线以及穿过最右端栅格的第二垂直线，将第一水平线、第二水平线、第一垂直线和第二垂直线围起来的闭合区域进行横向和纵向的切割，切割后得到的最小分区为初始清洁分区，每个初始清洁分区均包含未被占据的栅格。为避免切割次数多造成的初始清洁分区过多的情况，可基于闭合区域的实际长、宽确定横向切割次数和纵向切割次数，并基于横向切割次数和纵向切割次数将闭合区域在长、宽两个维度进行等分切割，切割后得到多个初始清洁分区。在此提供一个计算切割次数的方案：M1和宽M2分别为闭合区域的长和宽，N1和N2分别为预设的长和宽，M1≥N1，M2≥N2，闭合区域的横向切割次数K1取M1/N1的整数部分，闭合区域的纵向切割次数K2取M2/N2的整数部分。这种分区规则通过调整预设的长和宽的数值来得到所需数量的初始清洁分区，当目标区域非常大且目标区域的长远大于宽或者宽远大于长时，能够将目标区域划分成数量、大小合适的分区，数量合适使得清洁机器人二次清洁时能够更加精准 定位上次清洁中断时清洁机器人所处的位置，避免出现清洁过的区域再次被清洁或未被清洁的区域因定位不准确被遗漏清洁的现象，大小合适能够避免部分分区狭窄导致的清洁机器人无法进入的情况，提高清洁机器人定位的精度，提高目标区域的清洁质量。当目标区域的长宽相差不大时，可将预设的长宽设置为相同或接近的数值，那么目标区域的横向切割次数和纵向切割次数相等，在长、宽两个维度上将目标区域进行等分。
例如，当闭合区域的长和宽分别为13.2米和10米、预定的长和宽分别为5米和3米时，M1/N1＝2.64，M2/N2＝3.33，则K1＝2，K2＝3。在闭合区域的长度方向切2刀，即将在闭合区域的长度方向上将闭合区域等分为3份；同理，在闭合区域的宽度方向切3刀，即将在闭合区域的宽度方向上将闭合区域等分为4份，最终将闭合区域分为12个初始清洁分区。
403、判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；
生成二次清洁分区的情境有以下三种情况：
1)当清洁机器人进行分区时，将一个房间分在不同的初始清洁分区时，其中一个初始清洁分区先沿边清洁，在清洁过程中该房间门口被动态障碍物堵住，导致该房间剩下部分不能进去清洁，但是清洁机器人又曾经去过该房间清洁。如图5所示，房间2被分在两个不同的初始清洁分区，当初始清洁分区A清洁完毕时，房间2的初始清洁分区B部分还没有清洁，但是门口由于障碍物堵住了，导致清洁机器人无法进入，先去其他区域清洁，导致房间2的初始清洁分区B部分被遗漏清洁。
2)当初始清洁分区中包括有像门口等狭小区域时，清洁机器人在当前分区沿墙之后，有动态障碍物把门口堵住了，导致初始清洁分区中部分区域被遗漏清洁，如图6所示，当前初始清洁分区由于沿墙之后门口被障碍物堵住，导致内部斜线区域被遗漏清洁。
3)当清洁机器人进行导航时，清洁机器人曾经沿边经过该区域，但是由于障碍物将门口堵住了，导致被遗漏清洁。如图7所示，清洁机器人在初始清洁分区C，想要导航到初始清洁分区F的某个点，中间经过了初始清洁分区E，待清洁机器人清洁完毕初始清洁分区F后再导航至初始清洁分区E进行清洁时，由于初始清洁分区F和初始清洁分区E的门口被堵住了，导致初始清洁分区E被遗漏清洁。
清洁机器人的清洁方式包括沿边清洁和区域清洁。其中，沿边清洁是指沿初始清洁分区的边缘进行清洁，当清洁机器人进行沿边清洁时，每隔预设时间或预设行进距离在栅格地图上添加沿墙标记；区域清洁是指在沿墙标记包围的区域内以弓字形路径进行清洁，当清洁机器人进行区域清洁时，每隔预设时间或预设行进距离在栅格地图上添加清洁标记，沿墙标记和清洁标记可以是两种颜色不同的标记，也可以是两种形状不同的标记。在每个初始清洁分区内，清洁机器人先进行沿边清洁，然后进行区域清洁。
清洁机器人扫描栅格地图上每个初始清洁分区，若初始清洁分区中出现了以下三种情况中的任意一种，则将初始清洁分区确定为二次清洁分区：
1)初始清洁分区的部分边缘有沿墙标记；
2)初始清洁分区的全部边缘有沿墙标记，但初始清洁分区内没有清洁标记；
3)初始清洁分区的全部边缘有沿墙标记，初始清洁分区内有清洁标记，但初始清
洁分区未被清洁标记填满。
例如，目标区域有5个初始清洁分区，其中，初始清洁分区1仅部分边缘有沿墙标记，初始清洁分区2全部边缘有沿墙标记且被清洁标记填满，初始清洁分区3全部边缘有沿墙标记但没有清洁标记，初始清洁分区4部分边缘有沿墙标记且分区内有清洁标记但分区未被清洁标记填满，初始清洁分区5全部边缘有沿墙标记且分区内有清洁标记但分区未被清洁标记填满。因此，将初始清洁分区1、初始清洁分区3、初始清洁分区4和初始清洁分区5确定为二次清洁分区。
404、根据预置的障碍物信息库判断至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；
根据清洁机器人的当前位置点确定至少一个候选点，候选点为二次清洁分区与当前位置点之间具有最短直线距离的轮廓点，如图8所示，规划当前位置点到每个候选点之间的导航路线，得到至少一条导航路线，调用预置的障碍物信息库查询至少一条候选导航路线上的实时障碍物信息，得到状态为“存在”的障碍物的信息，根据至少一条候选导航路线上存在的障碍物信息判断至少一条候选导航路线中是否存在至少一条无障碍导航路线，若存在，则将至少一条无障碍导航路线对应的二次清洁分区确定为清洁机器人能够到达的二次清洁分区。
需要补充说明的是，产生二次清洁分区的原因有三种，第一种，动态障碍在入口；第二种，低矮障碍在入口附近或边缘，由于硬件结构的原因，激光雷达无法识别低矮障碍物，在栅格地图上将低矮障碍物识别为被占据的区域，从而导致入口变小，造成与第三种相同的情况；第三种，入口狭小，入口尺寸与清洁机器人直径相同或者稍大，由于清洁机器人运动控制误差的原因，会导致存在清洁机器人有时不能通过入口的情况。当二次清洁分区的候选点在入口处时，清洁机器人到达入口处；当二次清洁分区的候选点在非入口处时，清洁机器人穿过入口，因此，决定清洁机器人能否到达二次清洁分区的必要条件是二次清洁分区的入口处无障碍物，若目标候选导航路线中存在障碍物但障碍物不在入口处，则再次判断清洁机器人与目标候选导航路线对应的目标二次清洁分区之间是否存在另一条无障碍的导航路线，若有，则将另一条无障碍的导航路线作为目标二次清洁分区新的候选导航路线。
例如，目标区域有4个二次清洁分区：二次清洁分区1、二次清洁分区2、二次清洁分区3和二次清洁分区4，清洁机器人规划到4个二次清洁分区的导航路线，得到对应的候选导航路线1、候选导航路线2、候选导航路线3和候选导航路线4，调用预置的障碍物信息库，查询4条候选导航路线上是否存在障碍物，经查询，候选导航路线1和候选导航路线2上不存在障碍物、候选导航路线3和候选导航路线4上存在障碍物，但候选导航路线3上的障碍物在二次清洁分区3的入口处、候选导航路线4上的障碍物不在二次清洁分区4的入口处，清洁机器人判断是否存在到二次清洁分区4的无障碍导航路线，经判断，存在无障碍导航路线4，将无障碍导航路线4作为新的候选导航路线4。因此，二次清洁分区1、二次清洁分区2和二次清洁分区4为清洁机器人能够到达的二次清洁分区。
405、若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区，目标分区为清洁机器人能够到达且距离最短的二次清洁分区；
若存在清洁机器人能够到达的二次清洁分区，则判断清洁机器人能够到达的二次清 洁分区是否唯一，若清洁机器人能够到达的二次清洁分区唯一，则将清洁机器人能够到达的唯一二次清洁分区确定为目标分区，并导航至目标分区；若清洁机器人能够到达的二次清洁分区不唯一，则计算清洁机器人的当前位置点与每个二次清洁分区的候选点之间的距离，距离的计算方式有多种，例如计算欧氏距离、导航距离或曼哈顿距离，优选的，此处计算每个候选点到清洁机器人的曼哈顿距离，得到至少一个候选曼哈顿距离，候选点为二次清洁分区与当前位置点之间具有最短直线距离的轮廓点，从多个候选曼哈顿距离中筛选出最短的曼哈顿距离，将最短的曼哈顿距离对应的二次清洁分区确定为目标分区并导航至目标分区。
提供两种候选点的选取方式，第一种，以清洁机器人为中心，采用宽度优先搜索算法(Breadth First Search，BFS)扫描栅格地图上所有的点，将扫描到的第一个属于目标可达二次清洁分区的点作为目标可达二次清洁分区的候选点。第二种，清洁机器人向目标可达二次清洁分区发出声波信号或者光信号进行探测，将最先接收到的反馈信号对应的点作为目标可达二次清洁分区的候选点。
406、当导航结束时，判断是否导航成功，导航成功是指清洁机器人到达规划导航路线的终点；
当清洁机器人按照规划的导航路线到达终点时，导航结束，此时导航成功；当清洁机器人在导航至目标分区的路途中遇到障碍物时，导航结束，此时导航不成功。
407、若导航成功，则控制清洁机器人对目标分区进行清洁；
若导航成功，意味着清洁机器人位于目标分区内，则对目标分区进行清洁，清洁方式有两种：
第一种，查找目标分区中的沿墙标记和清洁标记，当目标分区中有清洁标记时，清洁机器人移动到清洁标记的结束位置，从结束位置开始按照弓字形路径进行区域清洁；当目标分区中有完整的沿墙标记时，清洁机器人移动到角落位置，从角落位置开始按照弓字形路径对已沿墙进行区域清洁；当目标分区中有部分沿墙标记时，清洁机器人移动到沿墙标记的结束位置，从结束位置开始继续进行沿边清洁，待沿边清洁完毕后再按照弓字形路径进行区域清洁。
第二种，查找目标分区中的沿墙标记和清洁标记，带有沿墙标记的部分，根据沿墙标记将已经沿过墙的部分先按照弓字形路径进行区域清洁，若将带沿墙标记的区域清洁完毕之后发现当前分区还有未清洁的部分，则对未清洁的部分先进行沿边清洁，再在沿过墙的部分按照弓字形路径进行区域清洁。如图9所示：被遗漏清洁的区域包括斜线框内已沿边清洁的部分和竖线框内未沿边清洁的部分，如果能从入口进入，则先对已沿边清洁的斜线框区域进行弓字形区域清洁，当斜线框区域清洁完毕之后，再对竖线框区域先沿边清洁，后按照弓字形路径进行区域清洁。
目标区域清洁完毕后，返回步骤204，判断是否还有清洁机器人能够到达的二次清洁分区，若有，则选择下一个目标分区，若没有，则结束清洁。
408、若导航不成功，则判断清洁机器人是否位于目标分区内，若是，则控制清洁机器人对目标分区进行清洁，若否，则再次选择目标分区或结束清洁。
需要补充说明的是，清洁机器人自身具有一定的尺寸，为提高清洁效率，当清洁机器人的一部分进入到目标分区时，等同于清洁机器人位于目标分区内，因此，这里补充一种判断清洁机器人是否位于目标分区内的规则：
将目标分区向外辐射扩充N个栅格，N为预设的正整数值，得到目标扩充分区，若清洁机器人全部位于目标扩充分区内，则确定清洁机器人位于目标分区内，对目标分区进行清洁，清洁方式如步骤207所述，在此不再赘述；若清洁机器人未全部位于目标扩充分区内，则回到步骤204，判断是否还有清洁机器人能够到达的二次清洁分区，若有，则选择下一个目标分区，若没有，则结束清洁。
本发明实施例中，根据二次清洁条件从初始清洁分区中识别需要进行二次清洁的分区，避免遗漏未清洁完毕的区域，通过维护障碍物信息库保证障碍物的最新状态，准确选择清洁机器人能够到达且最近的二次清洁分区，提高清洁效率，对清洁机器人导航至二次清洁分区的结果进行判断，并根据判断结果执行清洁、选择下一个二次清洁分区或结束清洁的操作，最大限度完成二次清洁分区的清洁，提高目标区域的清洁质量。
上面对本发明实施例中二次清洁方法进行了描述，下面对本发明实施例中二次清洁装置进行描述，请参阅图10，本发明实施例中二次清洁装置一个实施例包括：
获取模块1001，用于获取目标区域的栅格地图，栅格地图包括至少一个初始清洁分区；
确定模块1002，用于判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；
判断模块1003，用于根据预置的障碍物信息库判断至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；
导航模块1004，用于若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，目标分区为清洁机器人能够到达且距离最短的二次清洁分区。
本发明实施例中，根据二次清洁条件从初始清洁分区中识别需要进行二次清洁的分区，避免遗漏未清洁完毕的区域，选择清洁机器人能够到达且最近的二次清洁分区，提高清洁效率，清洁机器人到达二次清洁分区后进行区域清洁，提高目标区域的清洁质量。
请参阅图11，本发明实施例中二次清洁装置的另一个实施例包括：
获取模块1001，用于获取目标区域的栅格地图，栅格地图包括至少一个初始清洁分区；
确定模块1002，用于判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；
判断模块1003，用于根据预置的障碍物信息库判断至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；
导航模块1004，用于若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，目标分区为清洁机器人能够到达且距离最短的二次清洁分区。
可选的，判断模块1003包括：
规划单元10031，用于对清洁机器人到至少一个二次清洁分区中每个二次清洁分区的导航路线进行规划，得到至少一条候选导航路线；
调用单元10032，用于调用预置的障碍物信息库查询至少一条候选导航路线上的实时障碍物信息；
第一判断单元10033，用于根据至少一条候选导航路线上的实时障碍物信息判断至少一条候选导航路线中是否存在至少一条无障碍导航路线；
确定单元10034，用于若存在，则将至少一条无障碍导航路线对应的二次清洁分区确定为清洁机器人能够到达的二次清洁分区。
可选的，规划单元10031具体用于：
根据清洁机器人的当前位置点确定至少一个候选点，候选点为二次清洁分区与当前位置点之间具有最短直线距离的轮廓点；规划当前位置点到每个候选点之间的导航路线，得到至少一条导航路线。
可选的，导航模块1004包括：
导航子模块10041，用于若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区；
判断子模块10042，用于当导航结束时，判断是否导航成功，导航成功是指清洁机器人到达规划导航路线的终点；
控制子模块10043，用于若导航成功，则控制清洁机器人对目标分区进行清洁；
判断控制子模块10044，用于若导航不成功，则判断清洁机器人是否位于目标分区内，若是，则控制清洁机器人对目标分区进行清洁，若否，则再次选择目标分区或结束清洁。
可选的，导航子模块10041包括：
第二判断单元100411，用于若存在清洁机器人能够到达的二次清洁分区，则判断清洁机器人能够到达的二次清洁分区是否唯一；
第一导航单元100412，用于若是，则将清洁机器人能够到达的唯一二次清洁分区确定为目标分区，并导航至目标分区；
第二导航单元100413，用于若否，则将距离清洁机器人最近的二次清洁分区确定为目标分区，并导航至目标分区。
可选的，第二导航单元100413具体用于：
若清洁机器人能够到达的二次清洁分区不唯一，则计算清洁机器人的当前位置点与每个二次清洁分区的候选点之间的曼哈顿距离，得到至少一个候选曼哈顿距离，候选点为二次清洁分区与当前位置点之间具有最短直线距离的轮廓点；从多个候选曼哈顿距离中筛选出最短的曼哈顿距离；将最短的曼哈顿距离对应的二次清洁分区确定为目标分区并导航至目标分区。
可选的，二次清洁装置还包括搭建模块1005，搭建模块1005具体用于：
建立障碍物信息库，障碍物信息库用于记录清洁机器人在清洁过程中识别到的实时障碍物信息，障碍物信息包括障碍物的位置、障碍物的图片、障碍物的周边环境图片和障碍物的状态；当清洁机器人识别到新增的障碍物时，将新增的障碍物的信息添加至障碍物信息库中；当清洁机器人识别到已经记录于障碍物信息库中的目标障碍物的位置发生变化时，将障碍物信息库中目标障碍物的信息进行更新。
本发明实施例中，根据二次清洁条件从初始清洁分区中识别需要进行二次清洁的分区，避免遗漏未清洁完毕的区域，通过维护障碍物信息库保证障碍物的最新状态，准确 选择清洁机器人能够到达且最近的二次清洁分区，提高清洁效率，对清洁机器人导航至二次清洁分区的结果进行判断，并根据判断结果执行清洁、选择下一个二次清洁分区或结束清洁的操作，最大限度完成二次清洁分区的清洁，提高目标区域的清洁质量。
上面图10和图11从模块化功能实体的角度对本发明实施例中的二次清洁装置进行详细描述，下面从硬件处理的角度对本发明实施例中清洁机器人进行详细描述。
图12是本发明实施例提供的一种清洁机器人的结构示意图，该清洁机器人1200可因配置或性能不同而产生比较大的差异，可以包括一个或一个以上处理器(central processing units，CPU)1210(例如，一个或一个以上处理器)和存储器1220，一个或一个以上存储应用程序1233或数据1232的存储介质530(例如一个或一个以上海量存储设备)。其中，存储器1220和存储介质1230可以是短暂存储或持久存储。存储在存储介质1230的程序可以包括一个或一个以上模块(图示没标出)，每个模块可以包括对清洁机器人1200中的一系列指令操作。更进一步地，处理器1210可以设置为与存储介质1230通信，在清洁机器人1200上执行存储介质1230中的一系列指令操作。
清洁机器人1200还可以包括一个或一个以上电源1240，一个或一个以上有线或无线网络接口1250，一个或一个以上输入输出接口1260，和/或，一个或一个以上操作系统1231，例如Windows Serve，Mac OS X，Unix，Linux，FreeBSD等等。本领域技术人员可以理解，图12示出的清洁机器人结构并不构成对清洁机器人的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
本发明还提供一种清洁机器人，所述计算机设备包括存储器和处理器，存储器中存储有计算机可读指令，计算机可读指令被处理器执行时，使得处理器执行上述各实施例中的所述二次清洁方法的步骤。
本发明还提供一种计算机可读存储介质，该计算机可读存储介质可以为非易失性计算机可读存储介质，该计算机可读存储介质也可以为易失性计算机可读存储介质，所述计算机可读存储介质中存储有指令，当所述指令在计算机上运行时，使得计算机执行所述二次清洁方法的步骤。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read‐only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (10)

  1. 一种二次清洁方法，其特征在于，所述二次清洁方法包括：
    获取目标区域的栅格地图，所述栅格地图包括至少一个初始清洁分区；
    判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足所述二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，所述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；
    根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，所述障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；
    若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区。
  2. 根据权利要求1所述的二次清洁方法，其特征在于，所述根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，包括：
    对清洁机器人到所述至少一个二次清洁分区中每个二次清洁分区的导航路线进行规划，得到至少一条候选导航路线；
    调用所述预置的障碍物信息库查询所述至少一条候选导航路线上的实时障碍物信息；
    根据所述至少一条候选导航路线上的实时障碍物信息判断所述至少一条候选导航路线中是否存在至少一条无障碍导航路线；
    若存在，则将所述至少一条无障碍导航路线对应的二次清洁分区确定为所述清洁机器人能够到达的二次清洁分区。
  3. 根据权利要求2所述的二次清洁方法，其特征在于，所述对清洁机器人到所述至少一个二次清洁分区中每个二次清洁分区的导航路线进行规划，得到至少一条候选导航路线，包括：
    根据清洁机器人的当前位置点确定至少一个候选点，所述候选点为二次清洁分区与所述当前位置点之间具有最短直线距离的轮廓点；
    规划所述当前位置点到每个候选点之间的导航路线，得到至少一条导航路线。
  4. 根据权利要求1所述的二次清洁方法，其特征在于，所述若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区，包括：
    若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区；
    当导航结束时，判断是否导航成功，所述导航成功是指所述清洁机器人到达规划导航路线的终点；
    若导航成功，则控制所述清洁机器人对所述目标分区进行清洁；
    若导航不成功，则判断所述清洁机器人是否位于所述目标分区内，若是，则控制所述清洁机器人对所述目标分区进行清洁，若否，则再次选择目标分区或结束清洁。
  5. 根据权利要求4所述的二次清洁方法，其特征在于，所述若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区，包括：
    若存在清洁机器人能够到达的二次清洁分区，则判断所述清洁机器人能够到达的二 次清洁分区是否唯一；
    若是，则将所述清洁机器人能够到达的唯一二次清洁分区确定为目标分区，并导航至目标分区；
    若否，则将距离所述清洁机器人最近的二次清洁分区确定为目标分区，并导航至目标分区。
  6. 根据权利要求5所述的二次清洁方法，其特征在于，所述若否，则将距离所述清洁机器人最近的二次清洁分区确定为目标分区，并导航至目标分区，包括：
    若所述清洁机器人能够到达的二次清洁分区不唯一，则计算所述清洁机器人的当前位置点与每个二次清洁分区的候选点之间的曼哈顿距离，得到至少一个候选曼哈顿距离，所述候选点为二次清洁分区与所述当前位置点之间具有最短直线距离的轮廓点；
    从所述多个候选曼哈顿距离中筛选出最短的曼哈顿距离；
    将所述最短的曼哈顿距离对应的二次清洁分区确定为目标分区并导航至目标分区。
  7. 根据权利要求1‐6中任一项所述的二次清洁方法，其特征在于，在获取目标区域的栅格地图之前，还包括：
    建立障碍物信息库，所述障碍物信息库用于记录清洁机器人在清洁过程中识别到的实时障碍物信息，所述障碍物信息包括障碍物的位置、障碍物的图片、障碍物的周边环境图片和障碍物的状态；
    当所述清洁机器人识别到新增的障碍物时，将所述新增的障碍物的信息添加至所述障碍物信息库中；
    当所述清洁机器人识别到已经记录于所述障碍物信息库中的目标障碍物的位置发生变化时，将所述障碍物信息库中目标障碍物的信息进行更新。
  8. 一种二次清洁装置，其特征在于，所述二次清洁装置包括：
    获取模块，用于获取目标区域的栅格地图，所述栅格地图包括至少一个初始清洁分区；
    确定模块，用于判断是否存在满足二次清洁条件的初始清洁分区，若存在，将满足所述二次清洁条件的初始清洁分区确定为二次清洁分区，得到至少一个二次清洁分区，所述二次清洁条件为初始清洁分区内有沿墙标记且未被清洁标记填满；
    判断模块，用于根据预置的障碍物信息库判断所述至少一个二次清洁分区中是否存在清洁机器人能够到达的二次清洁分区，所述障碍物信息库用于记录清洁过程中识别到的实时障碍物信息；
    导航模块，用于若存在清洁机器人能够到达的二次清洁分区，则导航至目标分区进行清洁，所述目标分区为所述清洁机器人能够到达且距离最短的二次清洁分区。
  9. 一种清洁机器人，其特征在于，所述清洁机器人包括：存储器和至少一个处理器，所述存储器中存储有指令；
    所述至少一个处理器调用所述存储器中的所述指令，以使得所述清洁机器人执行如权利要求1‐7中任意一项所述的二次清洁方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，其特征在于，所述指令被处理器执行时实现如权利要求1‐7中任一项所述二次清洁方法。
PCT/CN2023/096950 2022-07-21 2023-05-29 二次清洁方法、装置、清洁机器人及存储介质 WO2024016839A1 (zh)

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