CN113885486B - Autonomous partitioning method of mobile robot and mobile robot - Google Patents
Autonomous partitioning method of mobile robot and mobile robot Download PDFInfo
- Publication number
- CN113885486B CN113885486B CN202010628667.1A CN202010628667A CN113885486B CN 113885486 B CN113885486 B CN 113885486B CN 202010628667 A CN202010628667 A CN 202010628667A CN 113885486 B CN113885486 B CN 113885486B
- Authority
- CN
- China
- Prior art keywords
- boundary
- point
- points
- target
- boundary point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an autonomous partition method of a mobile robot, which is characterized by comprising the following steps of: acquiring map information of a target environment; determining a maximum closed boundary of the target environment according to the map information; numbering all boundary points on the maximum closed boundary in sequence, and marking the ith boundary point as S i; wherein i is a positive integer; establishing a circular ring with the inner diameter of R min and the outer diameter of R max; overlapping the boundary point S i with the circle center of the circular ring, and judging whether the boundary point S k exists in the circular ring; wherein k is a positive integer not equal to i; if a boundary point S k exists in the circle, judging whether the absolute value of the difference value between k and i is larger than a threshold value c, wherein c is a positive integer; if the absolute value of the difference between k and i is greater than the threshold c, determining the boundary points S i and S k as a set of target boundary points; and partitioning the target environment by taking the connecting line between the boundary point S i and the boundary point S k as a partition basis. The autonomous partition method disclosed by the invention has higher accuracy.
Description
Technical Field
The disclosure relates to the technical field of cleaning equipment, in particular to an autonomous partition method of a mobile robot and the mobile robot.
Background
Along with the progress of science and technology and the improvement of living standard of people, intelligent household equipment is gradually going into more and more families. The intelligent floor sweeping robot can replace manual and automatic floor sweeping, and is favored by consumers.
In order for the floor sweeping robot to effectively clean rooms (including living rooms, kitchens and bedrooms), it is generally necessary to partition the rooms when the floor sweeping robot is first used so that the floor sweeping robot can clean area by area.
The traditional method for partitioning the room mainly comprises two modes, namely a manual partitioning mode and an automatic partitioning mode of the sweeping robot. In the manual partitioning mode, a user firstly partitions an indoor space on a map through a mobile phone, a tablet or a computer, and then sends the partitioned map to the sweeping robot. In an automatic partition mode of the sweeping robot, the sweeping robot constructs a home environment through SLAM (synchronous positioning and navigation) technology to obtain a map, acquires an indoor environment in real time in the process, identifies doors, and partitions the house according to the identified doors.
The manual partition mode is accurate and reliable, but requires personnel intervention, so that the user experience is poor. The automatic partition mode of the sweeping robot reduces personnel intervention, and is high in automation degree, but high in misjudgment rate and inaccurate in partition.
Disclosure of Invention
In order to solve the above problems in the prior art, that is, in order to solve the problem that the existing sweeping machine is not accurate enough in automatic partition, the present disclosure provides an autonomous partition method of a mobile robot and a mobile robot. The method comprises the following steps:
in a first aspect, the present disclosure provides an autonomous partition method of a mobile robot, the autonomous partition method including:
Acquiring map information of a target environment;
determining a maximum closed boundary of the target environment according to the map information;
determining the relative target boundary points used for representing the pass gate in the maximum closed boundary;
And carrying out regional division on the target environment by taking the opposite target boundary points as partition basis.
Optionally, the maximum closed boundary includes at least one set of the opposite target boundary points, and a distance between the opposite target boundary points conforms to a preset distance range.
Optionally, "determining the relative target boundary points in the aforementioned maximum closure boundary that characterize the pass gate" further comprises:
Taking any boundary point on the maximum closed boundary as a circle center, taking the lower limit and the upper limit of the preset distance range as inner and outer diameters as a circular ring, and determining other boundary points corresponding to the any boundary point in the circular ring area;
And determining whether the boundary points between the inner and outer circles contain target boundary points opposite to any boundary point according to a preset rule.
Optionally, "taking any boundary point on the maximum closed boundary as a center, taking the lower limit and the upper limit of the preset distance range as the inner diameter and the outer diameter as the ring, and determining other boundary points corresponding to the any boundary point in the ring area" includes:
Numbering all boundary points on the maximum closed boundary in sequence, and marking the currently traversed boundary point i as Si; wherein i is a positive integer;
Establishing a circular ring with the inner diameter Rmin and the outer diameter Rmax;
Overlapping the boundary point Si with the circle center of the circular ring, and judging whether a boundary point Sk exists in the circular ring; wherein k is a positive integer not equal to i;
The "determining whether the boundary point between the inner and outer circles includes the target boundary point opposite to any of the foregoing boundary points according to the preset rule" includes:
If the boundary point Sk exists in the circular ring, judging whether the absolute value of the difference value between k and i is larger than a threshold value c, wherein c is a positive integer;
If the absolute value of the difference between k and i is greater than the threshold c, the boundary points Si and Sk are determined as a set of target boundary points, which are used to characterize the pass gate.
Alternatively, R min =0.8 meters, R max =1.2 meters;
And/or
c＝100；
And/or
"If the absolute value of the difference between k and i is greater than the threshold c, determining the boundary points S i and S k as a set of target boundary points" further includes:
if the absolute value of the difference between k and i is greater than the threshold c, detecting whether an obstacle exists on the line between the boundary point S i and the boundary point S k;
If there is no obstacle on the line between the boundary point S i and the boundary point S k, the boundary point S i and the boundary point S k are determined as a set of target boundary points;
And/or
The "dividing the region of the target environment based on the relative target boundary points" further includes:
and taking a connecting line between the boundary point S i and the boundary point S k as a pass gate to divide the target environment into areas.
Optionally, "the aforesaid target environment is divided into areas based on the opposite aforesaid target boundary points", further comprising the step of "taking the connection line between the boundary points S i and S k as a pass gate" is preceded by: judging whether other connecting lines exist in the vicinity of the connecting line between the boundary point S i and the boundary point S k;
"a line between the boundary point S i and the boundary point S k is taken as a pass gate" includes: if no other connection exists, the connection between the boundary point S i and the boundary point S k is used as a pass gate;
"taking the opposite aforementioned target boundary points as the basis of the partition" further includes: if other connecting lines exist, selecting one connecting line from the area with the most dense connecting lines as a partition basis.
Optionally, the "acquiring map information of the target environment" further includes: determining a first area, a second area and a third area according to the map information; wherein the aforementioned first region characterizes a known and blank region on the map; the aforementioned second region characterizes a region on the map that is known and occupied by an obstacle; the third region characterizes an undetectable region beyond the maximum closed boundary;
Or alternatively
"Determining the maximum closed boundary of the target environment from the map information" further includes:
finding out all closed boundaries in the target environment according to the map information;
one closed boundary with the largest perimeter is found from all the aforementioned closed boundaries as the largest closed boundary.
Optionally, the "acquiring map information of the target environment" further includes: marking the point in the first area as 0; the point in the second area is marked as 1; the point of the third region is designated as-1;
"determining the maximum closed boundary of the target environment from the map information" includes:
Step a, a plane coordinate system is established on the map;
step b, searching a point with a value of 1 according to a preset mode by taking the origin of the plane coordinate system as a starting point;
step c, the first point with the value of 1 is recorded as S1;
step d, searching a point with a second value of 1 in a circle of points adjacent to the S1;
Step e, marking the found second point with the value of 1 as S2;
Step f, repeating the steps d and e until the point adjacent to the S1 is found again and marked as Sn; wherein n is a natural number greater than 2;
and g, marking the set of S1 to Sn as the maximum closed boundary.
Optionally, step a further comprises: selecting an origin of the planar coordinate system to be at an upper left of the map;
And/or step b further comprises: traversing each point along the Y-axis direction by taking the origin of the plane coordinate system as a starting point; if no point with the value of 1 is found, advancing one point along the X-axis direction by taking the origin of the plane coordinate system as a starting point, and traversing each point along the Y-axis direction;
And/or step d further comprises: finding a second point with a value of 1 among points adjacent to the S1 turn in the clockwise direction;
And/or each point in the set is adjacent to at least one point having a value of-1;
And/or "determining a maximum closed boundary of the target environment from the map information" further comprises the step of being located between steps f and g: judging whether n reaches a threshold t or not; if n reaches the threshold t, continuing to execute the step g; if n does not reach the threshold t, repeating the steps b to g, and marking the first grid with the value of 1 outside the set as S1;
and/or the map is a grid map, and the boundary points and the points refer to grids on the grid map.
In a second aspect, the present disclosure provides a mobile robot comprising a processor, a memory and execution instructions stored on the memory, the execution instructions being arranged, when executed by the processor, to enable the mobile robot to perform the autonomous partition method of any of the preceding technical solutions.
Based on the foregoing description, it can be appreciated by those skilled in the art that the autonomous partition method of the present disclosure has at least the following technical effects:
1. And determining the maximum closed boundary of the target environment, and finding out the opposite target boundary point used for representing the pass gate in the maximum closed boundary, so as to divide the target environment into areas by taking the opposite target boundary point as a basis. The method and the device can effectively eliminate the influence of the obstacle on the regional division, thereby effectively improving the accuracy of automatic partitioning of the mobile machine.
2. By sequentially numbering all boundary points on the maximum closed boundary, establishing a circular ring with the inner diameter of R min and the outer diameter of R max, enabling the boundary point S i to coincide with the circle center of the circular ring, judging whether the boundary point S k exists in the circular ring, determining the boundary point S i and the boundary point S k as a group of target boundary points if the boundary point S k exists in the circular ring, and partitioning the target environment by taking the group of target boundary points as partition basis, so that the partition is more accurate. In short, the target boundary point serving as the partition basis is found through the distance factor, so that the partition basis is more reliable, and the target environment can be accurately partitioned.
3. Whether the boundary point S i and the boundary point S k can be used as a group of target boundary points is judged by judging whether the absolute value of the difference value between k and i is larger than the threshold value c, so that the accuracy of the mobile robot partition is effectively improved. Specifically, the boundary point S i and the boundary point S k may be a set of target boundary points only when the absolute value of the difference between k and i is greater than the threshold c. The situation that the number of the partitioned areas is too large or the partitioned areas are improper is avoided.
4. By judging whether an obstacle exists on the connecting line between the boundary point S i and the boundary point S k or not, and taking the connecting line between the boundary point S i and the boundary point S k as a pass gate when the obstacle does not exist on the connecting line between the boundary point S i and the boundary point S k, the target environment is divided into areas, the interference of the obstacle to the subareas is effectively avoided, and the accuracy of the subareas is further improved.
5. When other connecting lines exist near the connecting line between the boundary point S i and the boundary point S k, one connecting line is selected from the area with the denser connecting line to serve as a partition basis, so that the influence on the partition of the mobile robot when a plurality of connecting lines exist simultaneously is overcome, the reliability of the partition basis is effectively ensured, and the partition of the mobile robot to the target environment is more accurate.
6. By finding one closed boundary with the largest perimeter from all the closed boundaries as the largest closed boundary, not only is the algorithm of the mobile robot simplified, but the mobile robot can quickly find the actual boundary of the target environment, namely the largest closed boundary, from a plurality of closed boundaries.
7. By enabling each point in the set forming the maximum closed boundary to be adjacent to at least one point with the value of-1, the situation that the mobile robot mistakes an obstacle inside the maximum closed boundary as a boundary point (such as a table next to a wall) is effectively avoided, and the maximum closed boundary found by the mobile robot is ensured to be an actual wall of the target environment.
8. Only when n reaches a threshold t, the mobile robot marks the acquired set of boundary points as the maximum closed boundary, otherwise, the first grid with the value of 1 outside the set is marked as S1, so that noise outside the maximum closed boundary in the map is effectively removed, and the influence of the noise on the mobile robot to find the maximum closed boundary is eliminated.
Drawings
In order to more clearly illustrate the technical solutions of the present disclosure, the following detailed description will be given for some embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart illustrating the main steps of an autonomic partitioning method in a first embodiment of the present disclosure;
Fig. 2 is a grid map acquired by a mobile robot in a first embodiment of the present disclosure;
FIG. 3 is a marking result of a partial area of the grid map of FIG. 2;
FIG. 4 is a detailed flow chart of steps for obtaining a maximum closed boundary of a target environment in a first embodiment of the present disclosure;
FIG. 5 illustrates the manner in which a mobile robot finds points on a closed boundary in a first embodiment of the present disclosure;
FIG. 6 is a schematic diagram of the grid map of FIG. 2 with noise;
FIG. 7 is a detailed flow chart of steps for partitioning a maximum closed boundary in a first embodiment of the present disclosure;
fig. 8 shows a process in which the mobile robot searches for a target boundary point in the first embodiment;
FIG. 9 shows a mobile robot having an area divided on a map;
FIG. 10 shows that all regions within the maximum closed boundary are known blank regions;
Fig. 11 is a schematic structural view of a functional module of a mobile robot in a second embodiment of the present disclosure.
List of reference numerals:
1. a first region; 2. a second region; 3. and a third region.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present disclosure more apparent, the technical solutions of the present disclosure will be clearly and completely described below with reference to specific embodiments and corresponding drawings. It will be appreciated by those skilled in the art that the embodiments described in this section detailed description are merely some, but not all, embodiments of the present disclosure. All other embodiments, which can be obtained by those skilled in the art without making any inventive effort, based on the embodiments described in the detailed description of the present section, do not deviate from the technical principles of the present disclosure and thus should fall within the scope of the present disclosure.
In the description of the present disclosure, each functional module may be a physical module formed by a plurality of structures, members, or electronic components, or may be a virtual module formed by a plurality of programs; the functional modules may be independent modules or may be functionally divided by a single integral module. It should be understood by those skilled in the art that, on the premise of being able to implement the technical solution described in the present disclosure, the structural manner, implementation manner and positional relationship of each functional module do not deviate from the technical principles of the present disclosure in any way, and therefore, all functional modules shall fall within the protection scope of the present disclosure.
In a first embodiment of the present disclosure:
As shown in fig. 1, the autonomous partition method of the mobile robot of the present embodiment includes:
Step S100, obtaining map information of a target environment;
the target environment is a living environment of a user, including a living room, a kitchen, a bedroom and the like. Or the target environment may be any other feasible environment, such as a mall, a shop, etc.
Alternatively, the user may send map information of the target environment to the mobile robot through a mobile phone, tablet, or computer. Or the mobile robot may construct the target environment through SLAM (synchronous positioning and navigation) technology to obtain map information.
Step S200, determining the maximum closed boundary of the target environment according to the map information;
Alternatively, the mobile robot may find all the closed boundaries from the map of the target environment, and then select one closed boundary with the largest perimeter as the largest closed boundary.
Step S300, determining the relative target boundary points used for characterizing the pass gate in the maximum closed boundary.
The maximum closed boundary comprises at least one group of opposite target boundary points, and the distance between the opposite target boundary points accords with a preset distance range. The pass gate characterizes or refers to a door frame, a gate or a door.
Optionally, the mobile robot finds a bent part from the maximum closed boundary, takes the bent part of the part as a first target boundary point, then finds a second target boundary point meeting the requirements of a preset distance range (such as 0.8-1.2 m, 1-1.2 m, 08-1.1 m and the like) with the first target boundary point, and takes the connecting line between the first target boundary point and the second target boundary point as a pass gate to partition the target environment.
Optionally, the mobile robot searches for all boundary points between which the distance between the two satisfies a preset value range (e.g., 0.8-1.2 meters, 1-1.2 meters, 08-1.1 meters, etc.) from all boundary points on the maximum closed boundary; and grouping all the found boundary points according to the corresponding relation. Wherein each set of boundary points comprises only two of said boundary points. The correspondence refers to a relationship in which the distance between two boundary points is within a preset numerical range. For example, the boundary points a, b, and c are adjacent to each other, the boundary points d, e, and f are adjacent to each other, the distance between a and d, the distance between a and e, the distance between a and f, the distance between b and d, the distance between b and e, the distance between b and f, the distance between c and d, the distance between c and e, and the distance between c and f are all within a preset value range, and the boundary points a, b, c, d, e and f may be divided into 9 groups, i.e., a and d, a and e, a and f, b and d, b and e, b and f, c and d, c and e, c and f.
And step S400, carrying out region division on the target environment by taking the opposite target boundary points as partition basis.
Optionally, each group of boundary points meeting the requirements is marked as the target boundary point, and then the connection line between each group of target boundary points is used as a pass gate to partition the target environment. Preferably, if a plurality of wires intersect or are adjacent to each other, one wire is selected from the area where the wires are most dense as the partition basis. Illustratively, the 9 wires are staggered adjacent to each other as the aforementioned wire between a and d, the wire between a and e, the wire between a and f, the wire between b and d, the wire between b and e, the wire between b and f, the wire between c and d, the wire between c and e, the wire between c and f; one of the connection lines can be selected as a pass gate, or one of the connection lines with the largest number of connection lines on both sides can be found and used as a pass gate (i.e. the connection line with the middle most is used as the pass gate).
Based on the foregoing description, it can be understood by those skilled in the art that the partitioning method of the present embodiment can accurately find the pass gate, and thus can partition the target environment, thereby improving the accuracy of the partitioning.
Preferably, step S100 comprises the steps of:
In step S101, the mobile robot constructs the target environment through SLAM (synchronous localization and navigation) technology and thus obtains a grid map of the target environment (as shown in fig. 2).
Step S102, as shown in fig. 2, determining a first area 1, a second area 2 and a third area 3 according to the grid map; wherein the first region 1 characterizes a region on the map that is known and blank; the second region 2 characterizes a region on the map that is known and occupied by an obstacle; the third region 3 characterizes an undetectable region beyond the maximum closed boundary.
Step S103, as shown in FIG. 3, the point in the first area 1 is marked as 0; the point in the second area 2 is denoted as 1; the point of the third area 3 is denoted-1. Wherein each point in the first region 1, the second region 2 and the third region 3 represents one grid on the grid map, respectively.
Preferably, as shown in fig. 4, step 200 includes the steps of:
step S201, a plane coordinate system is established on the map.
Specifically, as shown in fig. 3, the origin of the plane coordinate system is selected to be at the upper left of the map.
In step S202, a point with a value of 1 is found in a preset manner with the origin of the plane coordinate system as a starting point.
Specifically, traversing each point along the Y-axis direction by taking the origin of the plane coordinate system as a starting point; if no point with the value of 1 is found, the point is advanced along the X-axis direction by taking the origin of the plane coordinate system as a starting point, and each point is traversed along the Y-axis direction. In other words, the preset manner is: traversing each point along the Y-axis direction by taking the origin of the plane coordinate system as a starting point; if no point with the value of 1 is found, the point is advanced along the X-axis direction by taking the origin of the plane coordinate system as a starting point, and each point is traversed along the Y-axis direction.
In step S203, the found first point with the value 1 is denoted as S1.
Specifically, as shown in fig. 3, a point where the first found value is 1 and the point adjacent thereto is-1 is denoted as S1.
In step S204, a point with a second value of 1 is found in a circle of points adjacent to S1.
Specifically, as shown in fig. 3 and 5, a second point having a value of 1 and a point adjacent thereto of-1 is found among points adjacent to S1 in a clockwise direction. In fig. 5, the square having a value of 1 represents S1, and 8 squares around a-h thereof represent 8 grids around S1.
In step S205, the found second point with the value 1 is denoted as S2.
Step S206, repeating steps S204 and S205 until the point adjacent to S1 is found again and marked as Sn; where n is a natural number greater than 2.
Step S207, marking the set of S1 to Sn as the maximum closed boundary.
As shown in fig. 6, in order to eliminate the influence of noise (point with value 1) existing in the third area 3 on the grid map on the result, step S200 further includes a step between step S206 and step S207:
Judging whether n reaches a threshold t or not; where t is a value used to determine whether the closed boundary is too small, the value may be obtained by multiple experiments, and the value may be 100, 150, 200, etc., or one skilled in the art may set t to any other feasible value according to actual needs or practical experience.
If n reaches the threshold t, the step S207 is continued.
If n does not reach the threshold t, steps S204 to S207 are repeatedly performed, and the first grid with value 1 outside the corresponding set is marked as S1. Wherein the set is a set of all boundary points obtained when n does not reach the threshold t.
The noise in the third area 3 is generated when the mobile robot scans a wall body having glass such as a balcony (particularly, a balcony having a floor window), a bathroom, or the like by SLAM technology.
Preferably, as shown in fig. 7, step S300 includes the steps of:
In step S301, as shown in fig. 3, all boundary points on the maximum closed boundary are numbered in sequence, and the i-th boundary point is denoted as S i. Where i is a natural number.
As will be appreciated by those skilled in the art, all the boundary points on the maximum closed boundary may be numbered when one boundary point is found as described in step S204 and step S206; it is also possible to number all boundary points on the maximum closed boundary in sequence after the maximum closed boundary is determined.
In step S302, as shown in FIG. 8, a ring O with an inner diameter R min and an outer diameter R max is established.
Preferably, R min =0.8 meters; r max = 1.2 meters. Wherein 0.8 meters represents the width of the smallest width door in the existing market and 1.2 meters represents the width of the largest width door in the existing market. It should be noted that the present disclosure only measures some of the doors on the market, and not all of the doors on the market, so that the selection of 0.8 meters and 1.2 meters may be mismatching. The accuracy of such data does not preclude an understanding of the disclosed embodiments by those skilled in the art, and therefore any reasonable and appropriate variation of the values of R min and R max by those skilled in the art would not deviate from the technical principles of the present disclosure, and thus, the modified embodiments shall still fall within the scope of the present disclosure.
In step S303, as shown in fig. 8, the boundary point S i is overlapped with the center of the ring O, and it is determined whether or not the boundary point S k exists in the ring O.
Step S304, if the boundary point S k exists in the ring O, judging whether the absolute value of the difference value between k and i is larger than a threshold value c; among them, from the determination as to whether the number difference between the boundary point S i and the boundary point S k is too small, if the number difference is small, a door frame is unlikely to exist between the boundary point S i and the boundary point S k. The value of c may be obtained by a number of experiments, and may be 100, 150, 200, etc., or one skilled in the art may set c to any other feasible value according to actual needs or practical experience.
In step S305, if the absolute value of the difference between k and i is greater than the threshold c, it is detected whether an obstacle exists on the line between the boundary point S i and the boundary point S k.
Specifically, if the absolute value of the difference between k and i is greater than the threshold c, it is detected whether there is a point of-1 on the line between the boundary point S i and the boundary point S k.
A step parallel to step S305: if the absolute value of the difference between k and i is less than or equal to the threshold c, then the decision boundary point S i and the boundary point S k cannot be taken as a set of target boundary points.
In step S306, if there is no obstacle (point having a value of 1) on the line (e.g., the line consisting of points a, b … … n in the right circular ring in fig. 8) between the boundary point S i and the boundary point S k, the boundary point S i and the boundary point S k are determined as a set of target boundary points. Preferably, in order to avoid the case of the left ring in fig. 8, that is, in order to avoid the occurrence of all or part of the line between the boundary point S i and the boundary point S k being located outside the maximum closed boundary, after determining that there is no obstacle (a point having a value of 1) on the line between the boundary point S i and the boundary point S k, it is continued to determine whether the value of the point on the line between the boundary point S i and the boundary point S k is-1, and if the value of the point on the line between the boundary point S i and the boundary point S k is not-1, the boundary point S i and the boundary point S k are determined as a set of target boundary points.
A step parallel to step S306: if there is an obstacle (point of value 1) or an unknown region (point of value-1) as shown in the left-hand circle in fig. 8 on the line between the boundary point S i and the boundary point S k, it is determined that the boundary point S i and the boundary point S k cannot be a set of target boundary points.
Preferably, in order to eliminate the influence of articles such as indoor tables, chairs, sofas, etc. on the division of areas, the present embodiment further includes the steps of, after step S207 and before step S305: all regions within the maximum closed boundary are marked as first region 1 (as shown in fig. 10), i.e. all points within the maximum closed boundary are marked as 0.
It will be appreciated by those skilled in the art that steps S305 and S306 may be replaced as follows:
Step S305 is replaced by whether the values of points other than the boundary points S i and S k on the connection line between the boundary points S i and S k are all 0 if the absolute value of the difference between k and i is less than or equal to the threshold c.
Step S306 is replaced by determining the boundary points S i and S k as a set of target boundary points if the values of points other than the boundary points S i and S k on the connection line between the boundary points S i and S k are all 0.
Preferably, step S400 comprises the steps of:
The target environment is divided into areas (dark areas which have been divided as shown in fig. 9) with the line between the boundary point S i and the boundary point S k as a pass gate.
Based on the foregoing description, it can be understood by those skilled in the art that, in this embodiment, by numbering all the boundary points on the maximum closed boundary in turn, establishing a ring with an inner diameter of R min and an outer diameter of R max, then making the boundary point S i coincide with the center of the ring, determining whether there is a boundary point S k in the ring, if there is a boundary point S k in the ring, determining the boundary point S i and the boundary point S k as a set of target boundary points when the absolute value of the difference between k and i is greater than the threshold c, then determining that there is no obstacle on the connection line between the boundary point S i and the boundary point S k, and partitioning the target environment by using the connection line as a partition basis.
The mobile robot in this embodiment may be a sweeping robot, a mopping robot, or a sweeping and mopping robot.
In a second embodiment of the present disclosure:
As shown in fig. 2, the present disclosure also provides a mobile robot. The mobile robot comprises a processor, optionally a memory and a bus, on a hardware level, and furthermore allows to include the hardware required for other services. The mobile robot may be a sweeping robot, a mopping robot or a sweeping and mopping robot.
The memory is used for storing execution instructions, and the execution instructions are specifically computer programs capable of being executed. Further, the memory may include memory and non-volatile memory (non-volatile memory) and provide the processor with instructions and data for execution. By way of example, the Memory may be a Random-Access Memory (RAM), and the non-volatile Memory may be at least 1 disk Memory.
Wherein the bus is used to interconnect the processor, memory, and network interfaces together. The bus may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, an EISA (Extended Industry Standard Architecture ) bus, etc. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in fig. 3, but this does not represent only one bus or one type of bus.
In one possible implementation manner of the mobile robot, the processor may first read the corresponding execution instruction from the nonvolatile memory to the memory for execution, or may first obtain the corresponding execution instruction from another device for execution. The processor, when executing the execution instructions stored in the memory, can implement the autonomous partition method in the above embodiments of the present disclosure.
Those skilled in the art will appreciate that the autonomous partition method described above may be applied to a processor or may be implemented by a processor. The processor is illustratively an integrated circuit chip having the capability of processing signals. During execution of the autonomous partition method by the processor, each step of the autonomous partition method may be completed by an integrated logic circuit in a hardware form or an instruction in a software form in the processor. Further, the Processor may be a general purpose Processor such as a Central processing unit (Central ProcessingUnit, CPU), a network Processor (Network Processor, NP), a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an Application SPECIFIC INTEGRATED Circuit (ASIC), a Field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, a microprocessor, and any other conventional Processor.
Those skilled in the art will also appreciate that the steps of the above-described autonomous partition method embodiments of the present disclosure may be performed by a hardware decoding processor or by a combination of hardware and software modules in the decoding processor. The software modules may be located in other well-known storage media such as ram, flash memory, rom, eeprom, registers, etc. The storage medium is located in the memory, and the processor performs the steps in the above-described embodiment of the autonomic partitioning method in combination with its hardware after reading the information in the memory.
It should be noted that, in order to highlight the differences between the embodiments of the present disclosure, the embodiments of the present disclosure are laid out and described in a parallel manner and/or a progressive manner, and the following embodiments only focus on the differences between the embodiments and other embodiments, and the same or similar parts between the embodiments may be referred to each other. For example, for the apparatus/product embodiment, since the apparatus/product embodiment is substantially similar to the autonomous partition method embodiment, the description is relatively simple, and reference should be made to the description of the corresponding parts of the autonomous partition method embodiment.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the present disclosure. Various modifications and variations of this disclosure will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the technical principles of the present disclosure should fall within the protection scope of the present disclosure.
Claims (7)
1. An autonomous partition method of a mobile robot, the autonomous partition method comprising:
Acquiring map information of a target environment;
determining a maximum closed boundary of the target environment according to the map information;
determining relative target boundary points in the maximum closed boundary, wherein the relative target boundary points are used for representing the pass gate;
Taking the opposite target boundary points as partition basis, and carrying out region division on the target environment;
The maximum closed boundary comprises at least one group of opposite target boundary points, and the distance between the opposite target boundary points accords with a preset distance range;
"determining the relative target boundary points in the maximum closed boundary that characterize the pass gate" further includes:
Taking any boundary point on the maximum closed boundary as a circle center, taking the lower limit and the upper limit of the preset distance range as inner and outer diameters as a circular ring, and determining other boundary points corresponding to the any boundary point in the circular ring area;
Determining whether a target boundary point opposite to any boundary point is contained in boundary points between the inner circle and the outer circle according to a preset rule;
"taking any boundary point on the maximum closed boundary as a circle center, taking the lower limit and the upper limit of the preset distance range as the inner diameter and the outer diameter as a circle ring, and determining other boundary points corresponding to the any boundary point in the circle ring area" comprises the following steps:
Numbering all boundary points on the maximum closed boundary in sequence, and marking the currently traversed boundary point i as S i; wherein i is a positive integer;
Establishing a circular ring with the inner diameter of R min and the outer diameter of R max;
Overlapping the boundary point S i with the circle center of the circular ring, and judging whether the boundary point S k exists in the circular ring or not; wherein k is a positive integer not equal to i;
The "determining whether the boundary point between the inner and outer circles contains the target boundary point opposite to the arbitrary boundary point" according to the preset rule includes:
if a boundary point S k exists in the circular ring, judging whether the absolute value of the difference value between k and i is larger than a threshold value c, wherein c is a positive integer;
If the absolute value of the difference between k and i is greater than the threshold c, boundary points S i and S k are determined as a set of target boundary points to be used to characterize the pass gate.
2. The autonomous partition method of claim 1, wherein R min =0.8 meters, R max =1.2 meters;
And/or
c＝100；
And/or
"If the absolute value of the difference between k and i is greater than the threshold c, determining the boundary points S i and S k as a set of target boundary points" further includes:
if the absolute value of the difference between k and i is greater than the threshold c, detecting whether an obstacle exists on the line between the boundary point S i and the boundary point S k;
If there is no obstacle on the line between the boundary point S i and the boundary point S k, the boundary point S i and the boundary point S k are determined as a set of target boundary points;
And/or
The "dividing the region of the target environment by using the opposite target boundary points as the partition basis" further includes:
and taking a connecting line between the boundary point S i and the boundary point S k as a pass gate to divide the area of the target environment.
3. The autonomous partition method as claimed in claim 2, wherein the step of dividing the target environment into regions based on the opposite target boundary points further comprises the step of taking a line between the boundary points S i and S k as a pass gate: judging whether other connecting lines exist in the vicinity of the connecting line between the boundary point S i and the boundary point S k;
"a line between the boundary point S i and the boundary point S k is taken as a pass gate" includes: if no other connection exists, the connection between the boundary point S i and the boundary point S k is used as a pass gate;
"taking the opposite target boundary points as the basis of the partition" further includes: if other connecting lines exist, selecting one connecting line from the area with the most dense connecting lines as a partition basis.
4. The autonomous partition method of claim 1, wherein "obtaining map information of a target environment" further comprises: determining a first area, a second area and a third area according to the map information; wherein the first region characterizes a known and blank region on a map; the second region characterizes a region on the map that is known and occupied by an obstacle; the third region characterizes an undetectable region beyond the maximum closed boundary;
Or alternatively
"Determining a maximum closed boundary of the target environment from the map information" further includes:
finding out all closed boundaries in the target environment according to the map information;
Finding one closed boundary with the largest perimeter from all the closed boundaries as the largest closed boundary.
5. The autonomous partition method of claim 4, wherein acquiring map information of a target environment further comprises: marking points within the first region as 0; marking the point in the second area as 1; the point of the third region is denoted as-1;
"determining a maximum closed boundary of the target environment from the map information" includes:
Step a, a plane coordinate system is established on the map;
Step b, searching a point with a value of 1 according to a preset mode by taking the origin of the plane coordinate system as a starting point;
step c, the first point with the value of 1 is recorded as S1;
step d, searching a point with a second value of 1 in a circle of points adjacent to the S1;
Step e, marking the found second point with the value of 1 as S2;
Step f, repeating the steps d and e until the point adjacent to the S1 is found again and marked as Sn; wherein n is a natural number greater than 2;
And step g, marking the set of S1 to Sn as the maximum closed boundary.
6. The autonomous partition method of claim 5, wherein step a further comprises: selecting an origin of the planar coordinate system to be at an upper left of the map;
And/or step b further comprises: traversing each point along the Y-axis direction by taking the origin of the plane coordinate system as a starting point; if no point with the value of 1 is found, advancing one point along the X-axis direction by taking the origin of the plane coordinate system as a starting point, and traversing each point along the Y-axis direction;
And/or step d further comprises: finding a second point with a value of 1 among points adjacent to the S1 turn in the clockwise direction;
and/or each point in the set is adjacent to at least one point having a value of-1;
And/or "determining a maximum closed boundary of the target environment from the map information" further comprises the step of being located between steps f and g: judging whether n reaches a threshold t or not; if n reaches the threshold t, continuing to execute the step g; if n does not reach the threshold t, repeating the steps b to g, and marking the first grid with the value of 1 outside the set as S1;
and/or the map is a grid map, the boundary points and the points referring to grids on the grid map.
7. A mobile robot comprising a processor, a memory and execution instructions stored on the memory, the execution instructions being arranged, when executed by the processor, to cause the mobile robot to perform the autonomous zone method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010628667.1A CN113885486B (en) | 2020-07-02 | 2020-07-02 | Autonomous partitioning method of mobile robot and mobile robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010628667.1A CN113885486B (en) | 2020-07-02 | 2020-07-02 | Autonomous partitioning method of mobile robot and mobile robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113885486A CN113885486A (en) | 2022-01-04 |
| CN113885486B true CN113885486B (en) | 2024-07-12 |
Family
ID=79012935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010628667.1A Active CN113885486B (en) | 2020-07-02 | 2020-07-02 | Autonomous partitioning method of mobile robot and mobile robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113885486B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116067365A (en) * | 2023-04-04 | 2023-05-05 | 科大讯飞股份有限公司 | Map partitioning method, device, equipment and readable storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110888960A (en) * | 2019-11-29 | 2020-03-17 | 深圳市银星智能科技股份有限公司 | Indoor space partitioning method and device and mobile robot |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100791384B1 (en) * | 2006-07-05 | 2008-01-07 | 삼성전자주식회사 | Region Classification Method and Apparatus Using Feature Points and Mobile Cleaning Robot Using It |
| US10653057B2 (en) * | 2016-09-05 | 2020-05-19 | Lg Electronics Inc. | Moving robot and control method thereof |
| CN110141164B (en) * | 2019-06-13 | 2021-08-10 | 深圳市银星智能科技股份有限公司 | Door area identification method, door area identification system and cleaning robot |
| CN110269550B (en) * | 2019-06-13 | 2021-06-08 | 深圳市银星智能科技股份有限公司 | Door position identification method and mobile robot |
| CN110554700A (en) * | 2019-09-03 | 2019-12-10 | 韦云智 | method for identifying room and door of mobile robot |
| CN111127500A (en) * | 2019-12-20 | 2020-05-08 | 深圳市银星智能科技股份有限公司 | Space partitioning method and device and mobile robot |
-
2020
- 2020-07-02 CN CN202010628667.1A patent/CN113885486B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110888960A (en) * | 2019-11-29 | 2020-03-17 | 深圳市银星智能科技股份有限公司 | Indoor space partitioning method and device and mobile robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113885486A (en) | 2022-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109522803A (en) | A kind of room area divides and recognition methods, device and terminal device | |
| US20140039845A1 (en) | Determining a layout and wiring estimation for a heating, ventilation, and air conditioning system of a building | |
| CN107913039A (en) | Block system of selection, device and robot for clean robot | |
| CN113885486B (en) | Autonomous partitioning method of mobile robot and mobile robot | |
| CN111743462B (en) | Sweeping method and device of sweeping robot | |
| TWI630839B (en) | Auto configuration method of wireless access points and device thereof | |
| CN105446757B (en) | A kind of processing method and equipment of data packet | |
| WO2024007807A1 (en) | Error correction method and apparatus, and mobile device | |
| CN109274562B (en) | Voice instruction execution method and device, intelligent household appliance and medium | |
| CN109363578B (en) | Cleaning monitoring method and device, dust collector and mobile terminal | |
| CN111950431A (en) | Object searching method and device | |
| CN113645566B (en) | Robot network switching method, device and storage medium | |
| CN111714029B (en) | Method and device for discriminating cleaning similar area, electronic equipment and readable storage medium | |
| CN115429154B (en) | Cleaning control method of cleaning robot and cleaning robot | |
| CN114557635B (en) | Cleaning robot and partition identification method thereof | |
| CN116185045A (en) | Path planning method, path planning device, electronic equipment and medium | |
| JP2014163846A (en) | Wireless communication station position estimation device, wireless communication station position estimation method, and wireless communication station position estimation program | |
| CN115527157A (en) | Method and device for environmental management, electronic equipment and storage medium | |
| CN115393234A (en) | Map region fusion method and device, autonomous mobile equipment and storage medium | |
| CN115371671A (en) | Equipment position determining method and device based on indoor map | |
| CN115336936B (en) | A control method for a cleaning robot crossing floors and a cleaning robot | |
| US20180018513A1 (en) | Method, System and Computer Program for Automatically Detecting Traffic Circles on Digital Maps | |
| CN115243189B (en) | Home positioning method and device based on indoor cruise and indoor cruise home equipment | |
| CN115996387B (en) | Device binding method, device and storage medium | |
| CN118432966A (en) | Position matching method, storage medium and electronic device of smart device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |