CN113359699B - Full-coverage path planning method, device and storage medium - Google Patents
Full-coverage path planning method, device and storage medium Download PDFInfo
- Publication number
- CN113359699B CN113359699B CN202110498296.4A CN202110498296A CN113359699B CN 113359699 B CN113359699 B CN 113359699B CN 202110498296 A CN202110498296 A CN 202110498296A CN 113359699 B CN113359699 B CN 113359699B
- Authority
- CN
- China
- Prior art keywords
- access
- map
- unit
- column
- access units
- 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
- 238000000034 method Methods 0.000 title claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims abstract description 86
- 238000004364 calculation method Methods 0.000 claims description 18
- 230000009466 transformation Effects 0.000 claims description 18
- 238000000354 decomposition reaction Methods 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 230000002068 genetic effect Effects 0.000 claims description 6
- 230000001131 transforming effect Effects 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 4
- 238000005265 energy consumption Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 241001417527 Pempheridae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
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
- G05D1/0259—Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic 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
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- 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
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Electromagnetism (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a full-coverage path planning method, a full-coverage path planning device and a storage medium, wherein the full-coverage path planning method comprises the steps of decomposing a map into a plurality of access units; acquiring access sequences of a plurality of access units; acquiring a shortest connection path according to the access sequence; acquiring a full-planning path according to the shortest connection path; wherein the step of decomposing the map into a plurality of access units includes acquiring a connected domain of each column of the map; acquiring adjacent matrixes of two adjacent columns; converting the connected domain into an access unit according to the adjacency matrix; the number of the access units is reduced, so that the repeated paths and the turning number in the full-planned path are reduced, and the working time and the energy consumption of the robot are reduced.
Description
Technical Field
The invention relates to the field of path planning, in particular to a full-coverage path planning method, a full-coverage path planning device and a storage medium.
Background
The full coverage path planning refers to the fact that the robot can realize rapid full coverage in a free space in a certain area, is one of the research contents in the application technology of the robot, and is an important technology which is necessary for mobile robots such as a sweeper, a mower, an underwater robot and industrial automatic detection. In the full coverage path planning, a cattle-type unit decomposition method is commonly used for decomposing the units of the map so as to carry out the path planning on the access units. However, the Niu Geng type unit decomposition method generates small-area and large-number access units in a small-size obstacle environment, so that the number of repeated paths and turns is increased, and the working time and energy consumption of the robot are increased.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provide a full-coverage path planning method, a full-coverage path planning device and a storage medium.
The invention solves the problems by adopting the following technical scheme:
in a first aspect of the present invention, a full coverage path planning method includes the steps of:
decomposing the map into a plurality of access units;
acquiring access sequences of a plurality of access units;
acquiring the shortest connection paths among a plurality of access units according to the access sequence;
acquiring a full-planning path according to the shortest connection path;
wherein the step of decomposing the map into a plurality of access units comprises:
acquiring a connected domain of each column of the map;
acquiring an adjacent matrix of two adjacent columns of the map, wherein the adjacent matrix is used for representing the connection condition between two columns of connected domains;
and converting the connected domain into the access unit according to the adjacency matrix.
According to the first aspect of the invention, the step of obtaining the connection matrix of two adjacent columns of the map specifically includes:
setting the size of a map as m x k, wherein the ith column of the map is provided with a connected domain, the (i-1) th column of the map is provided with b connected domains, and establishing an adjacent matrix with the size of a x b, wherein the columns of the adjacent matrix represent the connected domains of the ith column of the map in a one-to-one correspondence manner, and the rows of the adjacent matrix represent the connected domains of the (i-1) th column of the map in a one-to-one correspondence manner, i epsilon {2,3, …, k };
the elements of the adjacency matrix are marked as 0 or 1, wherein 0 represents that two connected domains are connected, and 1 represents that two connected domains are not connected.
According to a first aspect of the present invention, the step of converting the connected domain into the access unit according to the adjacency matrix includes:
when the sum of one row of elements in the adjacency matrix is equal to 0, closing the current access unit to generate a new access unit; when the sum of one row of elements in the adjacency matrix is equal to 1, the connected domain corresponding to the row of elements is attributed to the current access unit; when the sum of the adjacent matrix and a row of elements is greater than 1, closing all current access units, and generating a new access unit by the connected domain corresponding to the row of elements;
closing all current access units when the sum of a column of elements in the adjacency matrix is equal to 0 or 1; and when the sum of the elements in the adjacent matrix and a column is greater than 1, merging the access unit corresponding to the last connected domain in the column with the access unit corresponding to the connected domain.
According to a first aspect of the present invention, the acquiring the access sequences of the plurality of access units includes:
randomly generating a plurality of father individuals and forming a population by the plurality of father individuals, wherein the father individuals are random combination sequences of the access units;
calculating the coverage path length of each parent individual to obtain an optimal individual with the shortest coverage path length;
repeating the following steps until iteration is completed, and outputting the final optimal individual as an access sequence of a plurality of access units:
randomly selecting any two father individuals in the population, and generating a plurality of child individuals by utilizing genetic operators, wherein the number of the child individuals is the same as the number of the father individuals in the population;
randomly transforming the sequence order of any two access units in each child body to obtain a transformed child body;
and replacing the parent individuals of the population with the transformation child individuals, calculating the coverage path length of each transformation child individual, comparing the coverage path length of each transformation child individual with the coverage path length of the optimal individual, and updating the optimal individual.
According to the first aspect of the present invention, the method adopted to obtain the shortest connection path between the plurality of access units according to the access sequence is a visual method, an a-method, a fast random search method, an artificial potential field method or a Dijkstra method.
In a second aspect of the present invention, a full coverage path planning apparatus includes:
a decomposition unit for decomposing the map into a plurality of access units;
a sequence calculation unit, configured to obtain access sequences of a plurality of access units;
a shortest path calculation unit, configured to obtain shortest connection paths among a plurality of access units according to the access sequence;
the full-planning path calculation unit is used for acquiring a full-planning path according to the shortest connection path;
wherein the decomposition unit includes:
a connected domain calculating unit, configured to obtain a connected domain of each column of the map;
the adjacent matrix calculation unit is used for obtaining adjacent matrixes of two adjacent columns of the map, wherein the adjacent matrixes are used for representing the connection condition between two columns of connected domains;
and the conversion unit is used for converting the connected domain into the access unit according to the adjacency matrix.
According to a second aspect of the present invention, the adjacency matrix calculation unit includes:
the device comprises a building unit, a display unit and a display unit, wherein the building unit is used for building an adjacency matrix, the adjacency matrix is a.b, the map is m.k, the ith column of the map is provided with a connected domain, the ith-1 column of the map is provided with b connected domains, the columns of the adjacency matrix are in one-to-one correspondence with the connected domains of the ith column of the map, the rows of the adjacency matrix are in one-to-one correspondence with the connected domains of the ith-1 column of the map, and i epsilon {2,3, …, k };
and a marking unit for marking the element of the adjacent matrix as 0 or 1, wherein 0 represents that the two connected domains are connected, and 1 represents that the two connected domains are not connected.
According to a second aspect of the invention, the conversion unit is adapted to perform the steps of:
when the sum of one row of elements in the adjacency matrix is equal to 0, closing the current access unit to generate a new access unit; when the sum of one row of elements in the adjacency matrix is equal to 1, the connected domain corresponding to the row of elements is attributed to the current access unit; when the sum of the adjacent matrix and a row of elements is greater than 1, closing all current access units, and generating a new access unit by the connected domain corresponding to the row of elements;
closing all current access units when the sum of a column of elements in the adjacency matrix is equal to 0 or 1; and when the sum of the elements in the adjacent matrix and a column is greater than 1, merging the access unit corresponding to the last connected domain in the column with the access unit corresponding to the connected domain.
According to a second aspect of the invention, the sequence calculation unit is adapted to perform the steps of:
randomly generating a plurality of father individuals and forming a population by the plurality of father individuals, wherein the father individuals are random combination sequences of the access units;
calculating the coverage path length of each parent individual to obtain an optimal individual with the shortest coverage path length;
repeating the following steps until iteration is completed, and outputting the final optimal individual as an access sequence of a plurality of access units:
randomly selecting any two father individuals in the population, and generating a plurality of child individuals by utilizing genetic operators, wherein the number of the child individuals is the same as the number of the father individuals in the population;
randomly transforming the sequence order of any two access units in each child body to obtain a transformed child body;
and replacing the parent individuals of the population with the transformation child individuals, calculating the coverage path length of each transformation child individual, comparing the coverage path length of each transformation child individual with the coverage path length of the optimal individual, and updating the optimal individual.
In a third aspect of the present invention, a storage medium stores instructions executable by a processor to implement a full coverage path planning method as in the first aspect of the present invention.
The scheme has at least the following beneficial effects: and converting the connected domains into the access units according to an adjacent matrix used for representing the connection condition between the two rows of connected domains, so that the number of the access units is reduced, the repeated paths and the turning number in the fully planned path are further reduced, and the working time and the energy consumption of the robot are reduced.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further described below with reference to the drawings and examples.
FIG. 1 is a flow chart of a full coverage path planning method according to an embodiment of the present invention;
fig. 2 is a flowchart of step S100 in the full coverage path planning method according to the embodiment of the present invention;
FIG. 3 is a block diagram of a full coverage path planning apparatus according to an embodiment of the present invention;
FIG. 4 is an exemplary diagram of decomposing a map into a plurality of access units;
FIG. 5 is another exemplary diagram of decomposing a map into a plurality of access units;
FIG. 6 is another exemplary diagram of decomposing a map into a plurality of access units;
fig. 7 is another exemplary diagram of decomposing a map into a plurality of access units.
Detailed Description
Reference will now be made in detail to the present embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the accompanying drawings are used to supplement the description of the written description so that one can intuitively and intuitively understand each technical feature and overall technical scheme of the present invention, but not to limit the scope of the present invention.
In the description of the present invention, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description of the present invention and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
Referring to fig. 1 and 2, an embodiment of the first aspect of the present invention provides a full coverage path planning method.
The full coverage path planning method comprises the following steps:
step S100, decomposing the map into a plurality of access units;
step S200, obtaining access sequences of a plurality of access units;
step S300, acquiring the shortest connection path among a plurality of access units according to an access sequence;
step S400, acquiring a full-planning path according to the shortest connection path;
wherein, in step S100, the step of decomposing the map into a plurality of access units includes:
step S110, acquiring a connected domain of each column of the map;
step S120, obtaining an adjacent matrix of two adjacent columns of the map, wherein the adjacent matrix is used for representing the connection condition between two columns of connected domains;
step S130, the connected domain is converted into an access unit according to the adjacency matrix.
For a map of size m×k, the map has k columns of pixels. For a column of pixels, a pixel where an obstacle is present is denoted by 0, a pixel where no obstacle is present is denoted by 1, and a plurality of consecutive pixels denoted by 1 constitute one connected domain.
For the adjacent matrix, one-dimensional array is used for storing the data of the connected domains of two adjacent columns, and one two-dimensional array is used for storing the data of the relation between the connected domains. This two-dimensional array is effectively a contiguous matrix.
In this embodiment, the connected domains are converted into the access units according to the adjacency matrix for representing the connection condition between the two columns of connected domains, so that the number of the access units is reduced, the number of repeated paths and turns in the fully planned path is further reduced, and the working time and energy consumption of the robot are reduced.
The step of decomposing the map into a plurality of access units of the present invention is improved on the basis of a ox cultivation type unit decomposition method. The basic idea of the Niu Geng unit decomposition method is as follows: traversing each column in the map, and calculating connectivity of connected domains of two adjacent columns. Every time connectivity increases, an IN event is triggered, the current access unit is closed, and two new access units are opened. Whenever connectivity is degraded, an OUT event is triggered, the current two access units are closed, and a new access unit is opened. The multiple units in the Middle event are combined into one unit, and the new unit is not started or the current unit is not closed in the Middle event, and the range of the current unit is only updated.
Further, for step S120, the step of obtaining the connection matrix of two adjacent columns of the map specifically includes:
setting the size of a map as m x k, setting the ith column of the map to have a connected domains, setting the ith-1 column of the map to have b connected domains, and establishing an adjacency matrix with the size of a x b, wherein the columns of the adjacency matrix represent the connected domains of the ith column of the map in a one-to-one correspondence manner, and the rows of the adjacency matrix represent the connected domains of the ith-1 column of the map in a one-to-one correspondence manner, wherein i epsilon {2,3, …, k };
the elements of the adjacency matrix are marked as 0 or 1, wherein 0 means that two connected domains are connected and 1 means that two connected domains are not connected.
Further, for step S130, the step of converting the connected domain into the access unit according to the adjacency matrix includes:
judging the connection condition from i-1 columns to i columns: when the sum of one row of elements in the adjacency matrix is equal to 0, closing the current access unit to generate a new access unit; when the sum of one row of elements in the adjacent matrix is equal to 1, the connected domain corresponding to the row of elements is attributed to the current access unit; when the sum of the adjacent matrix and a row of elements is greater than 1, closing all current access units, and generating a new access unit by a connected domain corresponding to the row of elements;
judging the connection condition of the i column to the i-1 column: closing all current access units when the sum of a column of elements in the adjacency matrix is equal to 0 or 1; when the sum of the elements in the adjacent matrix and a column is greater than 1, the access unit corresponding to the last connected domain in the column is merged with the access unit corresponding to the connected domain.
In summary, the basic idea of the present invention of decomposing a map into a plurality of access units is:
judging the connection condition from i-1 columns to i columns: (1) Referring to FIG. 4, if columns i-1 are connected to columns i, columns i and i-1 are combined into one access unit, and their access units are numbered the same; (2) Referring to fig. 5, if an In event occurs, the i column generates two new access units.
Judging the connection condition of the i column to the i-1 column: (1) When the section of column 6,i is connected to the sections of column i-1, column i is connected to the last section of column i-1 and is combined into an access unit, and the access unit label of column i is equal to the access unit label connected to it; (2) All the connection portions of column 7,i are not connected to any portion of column i-1, and the labeling process of the connection portions of column i is the same as the In event. The different access units are marked 1, 2 and 3, respectively; the same access unit is marked with the same number. The column marked i above is denoted as i-th column. The white solid portion is an obstacle.
In certain embodiments of the first aspect of the present invention, obtaining an access sequence for a plurality of access units comprises:
randomly generating a plurality of father individuals, and forming a population by the plurality of father individuals, wherein the father individuals are random combination sequences of access units;
calculating the coverage path length of each parent individual to obtain an optimal individual with the shortest coverage path length;
repeating the following steps until iteration is completed, and outputting the final optimal individual as an access sequence of a plurality of access units:
randomly selecting any two father individuals in the population, and generating a plurality of child individuals by utilizing genetic operators, wherein the number of the child individuals is the same as the number of the father individuals in the population;
randomly transforming the sequence order of any two access units in each sub-unit to obtain a transformed sub-unit;
and replacing parent individuals of the population with the transformation child individuals, calculating the coverage path length of each transformation child individual, comparing the coverage path length of each transformation child individual with the coverage path length of the optimal individual, and updating the optimal individual.
In certain embodiments of the first aspect of the present invention, the method employed to obtain the shortest connection path between the plurality of access units from the access sequence is a visual method.
In certain embodiments of the first aspect of the present invention, the method employed to obtain the full planned path from the shortest connection path is a path planning algorithm based on a biostimulation neural network algorithm.
Referring to fig. 3, an embodiment of the second aspect of the present invention provides a full coverage path planning apparatus.
The full coverage path planning device comprises:
a decomposition unit 100 for decomposing the map into a plurality of access units;
a sequence calculation unit 200, configured to obtain access sequences of a plurality of access units;
a shortest path calculation unit 300, configured to obtain a shortest connection path between a plurality of access units according to an access sequence;
a full-planned path calculation unit 400, configured to obtain a full-planned path according to the shortest connection path;
wherein the decomposition unit 100 includes:
a connected domain calculating unit 110 for acquiring a connected domain of each column of the map;
an adjacency matrix calculation unit 120, configured to obtain adjacency matrices of two adjacent columns of the map, where the adjacency matrices are used to represent a connection situation between two columns of connected domains;
and a conversion unit 130 for converting the connected domain into an access unit according to the adjacency matrix.
In some embodiments of the second aspect of the present invention, the adjacency matrix calculation unit 120 includes:
the device comprises a building unit, a display unit and a display unit, wherein the building unit is used for building an adjacent matrix, the size of the adjacent matrix is a x b, the size of a map is m x k, the ith column of the map is provided with a connected domain, the ith-1 column of the map is provided with b connected domains, the columns of the adjacent matrix are in one-to-one correspondence with the connected domains of the ith column of the map, the rows of the adjacent matrix are in one-to-one correspondence with the connected domains of the ith-1 column of the map, and i epsilon {2,3, …, k };
and a marking unit for marking the elements of the adjacent matrix as 0 or 1, wherein 0 represents that the two connected domains are connected, and 1 represents that the two connected domains are not connected.
In certain embodiments of the second aspect of the present invention, the conversion unit 130 is configured to perform the following steps:
when the sum of one row of elements in the adjacency matrix is equal to 0, closing the current access unit to generate a new access unit; when the sum of one row of elements in the adjacent matrix is equal to 1, the connected domain corresponding to the row of elements is attributed to the current access unit; when the sum of the adjacent matrix and a row of elements is greater than 1, closing all current access units, and generating a new access unit by a connected domain corresponding to the row of elements;
closing all current access units when the sum of a column of elements in the adjacency matrix is equal to 0 or 1; when the sum of the elements in the adjacent matrix and a column is greater than 1, the access unit corresponding to the last connected domain in the column is merged with the access unit corresponding to the connected domain.
In certain embodiments of the second aspect of the present invention, the sequence calculating unit 200 is configured to perform the following steps:
randomly generating a plurality of father individuals, and forming a population by the plurality of father individuals, wherein the father individuals are random combination sequences of access units;
calculating the coverage path length of each parent individual to obtain an optimal individual with the shortest coverage path length;
repeating the following steps until iteration is completed, and outputting the final optimal individual as an access sequence of a plurality of access units:
randomly selecting any two father individuals in the population, and generating a plurality of child individuals by utilizing genetic operators, wherein the number of the child individuals is the same as the number of the father individuals in the population;
randomly transforming the sequence order of any two access units in each sub-unit to obtain a transformed sub-unit;
and replacing parent individuals of the population with the transformation child individuals, calculating the coverage path length of each transformation child individual, comparing the coverage path length of each transformation child individual with the coverage path length of the optimal individual, and updating the optimal individual.
In some embodiments of the second aspect of the present invention, the method employed by the shortest path computing unit 300 is a visual method.
In certain embodiments of the second aspect of the present invention, the method employed by the full-planned path calculation unit 400 is a path planning algorithm based on a biostimulation neural network algorithm.
It should be noted that, the full-coverage path planning device provided in the second aspect of the present invention adopts the full-coverage path planning method provided in the first aspect of the present invention, and each unit of the full-coverage path planning device provided in the second aspect of the present invention corresponds to each step of the full-coverage path planning method provided in the first aspect of the present invention one by one, which has the same technical scheme, solves the same technical problems, has the same technical effects, and is not described in detail herein.
An embodiment of the third aspect of the present invention provides a storage medium. The storage medium stores instructions executable by the processor to implement a full coverage path planning method as an embodiment of the first aspect of the invention.
Examples of storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device.
The present invention is not limited to the above embodiments, but is merely preferred embodiments of the present invention, and the present invention should be construed as being limited to the above embodiments as long as the technical effects of the present invention are achieved by the same means.
Claims (6)
1. The full-coverage path planning method is characterized by comprising the following steps of:
decomposing the map into a plurality of access units;
acquiring access sequences of a plurality of access units;
acquiring the shortest connection paths among a plurality of access units according to the access sequence;
acquiring a full-planning path according to the shortest connection path;
wherein the step of decomposing the map into a plurality of access units comprises:
acquiring a connected domain of each column of the map;
acquiring an adjacent matrix of two adjacent columns of the map, wherein the adjacent matrix is used for representing the connection condition between two columns of connected domains;
converting the connected domain into the access unit according to the adjacency matrix;
the step of obtaining the adjacent matrix of two adjacent columns of the map specifically comprises the following steps:
setting the size of a map as m x k, wherein the ith column of the map is provided with a connected domain, the (i-1) th column of the map is provided with b connected domains, and establishing an adjacent matrix with the size of a x b, wherein the columns of the adjacent matrix represent the connected domains of the ith column of the map in a one-to-one correspondence manner, and the rows of the adjacent matrix represent the connected domains of the (i-1) th column of the map in a one-to-one correspondence manner, i epsilon {2,3, …, k };
marking the elements of the adjacency matrix as 0 or 1, wherein 0 represents that two connected domains are connected, and 1 represents that two connected domains are not connected;
the step of converting the connected domain into the access unit according to the adjacency matrix comprises:
when the sum of one row of elements in the adjacency matrix is equal to 0, closing the current access unit to generate a new access unit; when the sum of one row of elements in the adjacency matrix is equal to 1, the connected domain corresponding to the row of elements is attributed to the current access unit; when the sum of one row of elements in the adjacent matrix is greater than 1, closing all current access units, and generating a new access unit by the connected domain corresponding to the row of elements;
closing all current access units when the sum of a column of elements in the adjacency matrix is equal to 0 or 1; when the sum of elements in a column of the adjacency matrix is larger than 1, the access unit corresponding to the last connected domain of the column is merged with the access unit corresponding to the connected domain.
2. The full coverage path planning method of claim 1, wherein the obtaining the access sequence of the plurality of access units comprises:
randomly generating a plurality of father individuals and forming a population by the plurality of father individuals, wherein the father individuals are random combination sequences of the access units;
calculating the coverage path length of each parent individual to obtain an optimal individual with the shortest coverage path length;
repeating the following steps until iteration is completed, and outputting the final optimal individual as an access sequence of a plurality of access units:
randomly selecting any two father individuals in the population, and generating a plurality of child individuals by utilizing genetic operators, wherein the number of the child individuals is the same as the number of the father individuals in the population;
randomly transforming the sequence order of any two access units in each child body to obtain a transformed child body;
and replacing the parent individuals of the population with the transformation child individuals, calculating the coverage path length of each transformation child individual, comparing the coverage path length of each transformation child individual with the coverage path length of the optimal individual, and updating the optimal individual.
3. The method for planning a full coverage path according to claim 1, wherein the method adopted for obtaining the shortest connection path between the plurality of access units according to the access sequence is a visual method, an a-method, a fast random search method, an artificial potential field method or a Dijkstra method.
4. Full coverage path planning device, characterized by comprising:
a decomposition unit for decomposing the map into a plurality of access units;
a sequence calculation unit, configured to obtain access sequences of a plurality of access units;
a shortest path calculation unit, configured to obtain shortest connection paths among a plurality of access units according to the access sequence;
the full-planning path calculation unit is used for acquiring a full-planning path according to the shortest connection path;
wherein the decomposition unit includes:
a connected domain calculating unit, configured to obtain a connected domain of each column of the map;
the adjacent matrix calculation unit is used for obtaining adjacent matrixes of two adjacent columns of the map, wherein the adjacent matrixes are used for representing the connection condition between two columns of connected domains;
a conversion unit configured to convert the connected domain into the access unit according to the adjacency matrix;
wherein the adjacency matrix calculation unit includes:
the device comprises a building unit, a display unit and a display unit, wherein the building unit is used for building an adjacency matrix, the adjacency matrix is a.b, the map is m.k, the ith column of the map is provided with a connected domain, the ith-1 column of the map is provided with b connected domains, the columns of the adjacency matrix are in one-to-one correspondence with the connected domains of the ith column of the map, the rows of the adjacency matrix are in one-to-one correspondence with the connected domains of the ith-1 column of the map, and i epsilon {2,3, …, k };
a marking unit, configured to mark an element of the adjacency matrix as 0 or 1, where 0 represents that two connected domains are connected, and 1 represents that two connected domains are not connected;
the conversion unit is used for executing the following steps:
when the sum of one row of elements in the adjacency matrix is equal to 0, closing the current access unit to generate a new access unit; when the sum of one row of elements in the adjacency matrix is equal to 1, the connected domain corresponding to the row of elements is attributed to the current access unit; when the sum of one row of elements in the adjacent matrix is greater than 1, closing all current access units, and generating a new access unit by the connected domain corresponding to the row of elements;
closing all current access units when the sum of a column of elements in the adjacency matrix is equal to 0 or 1; when the sum of elements in a column of the adjacency matrix is larger than 1, the access unit corresponding to the last connected domain of the column is merged with the access unit corresponding to the connected domain.
5. The full coverage path planning apparatus of claim 4, wherein the sequence calculation unit is configured to perform the steps of:
randomly generating a plurality of father individuals and forming a population by the plurality of father individuals, wherein the father individuals are random combination sequences of the access units;
calculating the coverage path length of each parent individual to obtain an optimal individual with the shortest coverage path length;
repeating the following steps until iteration is completed, and outputting the final optimal individual as an access sequence of a plurality of access units:
randomly selecting any two father individuals in the population, and generating a plurality of child individuals by utilizing genetic operators, wherein the number of the child individuals is the same as the number of the father individuals in the population;
randomly transforming the sequence order of any two access units in each child body to obtain a transformed child body;
and replacing the parent individuals of the population with the transformation child individuals, calculating the coverage path length of each transformation child individual, comparing the coverage path length of each transformation child individual with the coverage path length of the optimal individual, and updating the optimal individual.
6. A storage medium storing instructions executable by a processor to implement the full coverage path planning method of any one of claims 1 to 3.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110498296.4A CN113359699B (en) | 2021-05-08 | 2021-05-08 | Full-coverage path planning method, device and storage medium |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110498296.4A CN113359699B (en) | 2021-05-08 | 2021-05-08 | Full-coverage path planning method, device and storage medium |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113359699A CN113359699A (en) | 2021-09-07 |
CN113359699B true CN113359699B (en) | 2024-01-12 |
Family
ID=77525900
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110498296.4A Active CN113359699B (en) | 2021-05-08 | 2021-05-08 | Full-coverage path planning method, device and storage medium |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113359699B (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116185045A (en) * | 2023-04-26 | 2023-05-30 | 麦岩智能科技(北京)有限公司 | Path planning method, path planning device, electronic equipment and medium |
CN116820140A (en) * | 2023-08-30 | 2023-09-29 | 兰笺(苏州)科技有限公司 | Path planning method and device for unmanned operation equipment |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107505939A (en) * | 2017-05-13 | 2017-12-22 | 大连理工大学 | A kind of complete coverage path planning method of mobile robot |
CN107843262A (en) * | 2017-10-30 | 2018-03-27 | 洛阳中科龙网创新科技有限公司 | A kind of method of farm machinery all standing trajectory path planning |
CN110502006A (en) * | 2019-07-22 | 2019-11-26 | 中国矿业大学 | A kind of Mine Abandoned Land mobile robot complete coverage path planning method |
CN111724406A (en) * | 2020-07-14 | 2020-09-29 | 苏州精濑光电有限公司 | Region communication merging method, device, equipment and medium |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10488865B2 (en) * | 2014-12-16 | 2019-11-26 | Al Incorporated | Methods and systems for robotic surface coverage |
WO2017092904A1 (en) * | 2015-12-03 | 2017-06-08 | Graf Plessen Mogens Max Sophus Edzard | Path planning for area coverage |
EP3907575B1 (en) * | 2019-01-03 | 2023-09-06 | Ecovacs Robotics Co., Ltd. | Dynamic region division and region channel identification method, and cleaning robot |
-
2021
- 2021-05-08 CN CN202110498296.4A patent/CN113359699B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107505939A (en) * | 2017-05-13 | 2017-12-22 | 大连理工大学 | A kind of complete coverage path planning method of mobile robot |
CN107843262A (en) * | 2017-10-30 | 2018-03-27 | 洛阳中科龙网创新科技有限公司 | A kind of method of farm machinery all standing trajectory path planning |
CN110502006A (en) * | 2019-07-22 | 2019-11-26 | 中国矿业大学 | A kind of Mine Abandoned Land mobile robot complete coverage path planning method |
CN111724406A (en) * | 2020-07-14 | 2020-09-29 | 苏州精濑光电有限公司 | Region communication merging method, device, equipment and medium |
Also Published As
Publication number | Publication date |
---|---|
CN113359699A (en) | 2021-09-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113359699B (en) | Full-coverage path planning method, device and storage medium | |
Sugihara et al. | Genetic algorithms for adaptive motion planning of an autonomous mobile robot | |
Zhou et al. | Genetic algorithm approach on multi-criteria minimum spanning tree problem | |
Davidor | Genetic Algorithms and Robotics: A heuristic strategy for optimization | |
Wang et al. | Fast, accurate gaussian process occupancy maps via test-data octrees and nested bayesian fusion | |
Milford et al. | Solving navigational uncertainty using grid cells on robots | |
CN113031592A (en) | Robot path smoothing method and system based on fifth-order Bezier curve | |
KR20200116831A (en) | Method for managing training data | |
Dziubenko et al. | Algorithm for solving optimal sensor devices placement problem in areas with natural obstacles | |
Rayhan et al. | AIST: An interpretable attention-based deep learning model for crime prediction | |
Patalia et al. | Behavioral analysis of genetic algorithm for function optimization | |
Matthiopoulos | Defining, estimating, and understanding the fundamental niches of complex animals in heterogeneous environments | |
Brooks | Evolution in the information age: Rediscovering the nature of the organism | |
Bhavatarini et al. | Reinforcement Learning-Principles, Concepts and Applications | |
Golda et al. | Algorithmic agent for effective mobile robot navigation in an unknown environment | |
Boğar et al. | A hybrid genetic algorithm for mobile robot shortest path problem | |
CN112036566A (en) | Method and apparatus for feature selection using genetic algorithm | |
Lewis | Hippocampal spatial mapping as fast graph learning | |
Miras | Costs of phenotypic plasticity can undermine its benefits for evolvable digital organisms | |
Lee et al. | Evolvable neural networks based on developmental models for mobile robot navigation | |
Bäck | Binary strings | |
CN113537318B (en) | Robot behavior decision method and device simulating human brain memory mechanism | |
Hao et al. | Masked imitation learning: Discovering environment-invariant modalities in multimodal demonstrations | |
Kinser | Fast analog associative memory | |
Jo et al. | Representation and use of design knowledge in evolutionary design |
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 |