CN113671958B - Determination method and system of obstacle avoidance path of robot, electronic equipment and medium - Google Patents
Determination method and system of obstacle avoidance path of robot, electronic equipment and medium Download PDFInfo
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
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- 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
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- 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
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- G—PHYSICS
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- 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
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Abstract
The invention discloses a method, a system, electronic equipment and a medium for determining an obstacle avoidance path of a robot, wherein the method for determining the obstacle avoidance path of the robot comprises the following steps: acquiring a cell of the current position of the robot on the grid map and a cell of the target position; determining a cell to be analyzed, and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed; judging whether the adjacent cells of the cells to be analyzed have cells positioned in the drivable area according to the grid value, if so, determining the adjacent cells positioned in the drivable area as first cells; acquiring a second cell based on the first cell, and controlling the robot to walk to the second cell; and taking the second cell as a cell to be analyzed, returning to the step of determining the cell to be analyzed and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed until the robot walks to the cell at the target position. The invention reduces the requirement on hardware performance and improves the processing efficiency.
Description
Technical Field
The present invention relates to the field of robots, and in particular, to a method, a system, an electronic device, and a medium for determining an obstacle avoidance path of a robot.
Background
With the development of technology, robots are gradually appearing in people's daily lives, wherein floor washing robots are an emerging application in the current hygiene field, and the traditional cleaning operation capability is replaced by artificial intelligence. The floor cleaning robot is widely applied to large-scale cleaning operation scenes such as markets, parking lots and the like, so that the configuration of cleaning staff is greatly reduced, and the working efficiency is greatly improved.
The navigation obstacle avoidance technology is a key technology for the automatic operation of robots, and the technology affects the stability and the flexibility of the operation of equipment. At present, scholars at home and abroad have completed a great deal of research on global path planning and local path planning. The existing global path planning generally adopts an A-algorithm and an artificial potential field method; the local path planning adopts a DWA algorithm and a neural network algorithm. The local path planning is used for completing the obstacle avoidance function in navigation. The current DWA algorithm and neural network algorithm are adopted to complete the planning of the local path of the robot, and as a result, the searching nodes are more, the requirement on hardware performance is high, if a high-performance processor is needed, a large-space storage medium is needed, and the processing efficiency is lower.
Disclosure of Invention
The invention aims to overcome the defects that an algorithm adopted in the prior art has high requirements on hardware performance and low processing efficiency, and provides a method, a system, electronic equipment and a medium for determining an obstacle avoidance path of a robot.
The invention solves the technical problems by the following technical scheme:
the invention provides a method for determining an obstacle avoidance path of a robot, which comprises the following steps:
acquiring a cell of the current position of the robot on the grid map and a cell of the target position; the cells at the current position and the cells at the target position are positioned on a preset path of the robot and are positioned on two sides of the obstacle respectively;
determining a cell to be analyzed, and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed; the grid value corresponds to the gray value of the cell on the grid map; the initial cell to be analyzed is the cell at the current position or the cell at the target position;
judging whether the adjacent cells of the cells to be analyzed have cells positioned in a drivable area according to the grid value, if so, determining the adjacent cells positioned in the drivable area as first cells;
acquiring a second cell based on the first cell, and controlling the robot to walk to the second cell;
the second cell is used as the cell to be analyzed, the cell to be analyzed is returned to be determined, and the grid value corresponding to the cell adjacent to the cell to be analyzed is obtained until the robot walks to the cell at the target position;
and the paths formed by all the second cells are used for avoiding the obstacle when the robot runs from the current position to the target position.
Preferably, the determining method includes:
and the path distance formed by all the second cells is shortest.
Preferably, the step of acquiring the second cell based on the first cell specifically includes:
obtaining the distance from each first cell to the cell of the target position;
selecting a first cell corresponding to the shortest distance as the second cell;
and/or, the step of acquiring the second cell based on the first cell specifically further includes:
obtaining gradient values of cells from each first cell to the target position;
and selecting a first cell corresponding to the maximum gradient value as the second cell.
Preferably, the method comprises the steps of,
the determining method further comprises the following steps:
and if judging that the cells adjacent to the cells to be analyzed do not exist in the cells located in the drivable area, moving the cells at the target position on a path preset by the robot by a plurality of cells, and returning to the step of acquiring the cells at the current position of the robot on the grid map and the cells at the target position.
The invention also provides a system for determining the obstacle avoidance path of the robot, which comprises:
the first acquisition module is used for acquiring cells of the current position of the robot on the grid map and cells of the target position; the cells at the current position and the cells at the target position are positioned on a preset path of the robot and are positioned on two sides of the obstacle respectively;
the first determining module is used for determining a cell to be analyzed and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed; the grid value corresponds to the gray value of the cell on the grid map; the initial cell to be analyzed is the cell at the current position or the cell at the target position;
the judging module is used for judging whether the adjacent cells of the cells to be analyzed exist in the cells located in the drivable area or not, and if so, the second determining module is called;
the second determining module is used for determining adjacent cells positioned in the drivable area as first cells;
the second acquisition module is used for acquiring a second cell based on the first cell and controlling the robot to walk to the second cell;
the return module is used for taking the second cell as the cell to be analyzed, calling the first determination module and enabling the robot to walk to the cell of the target position;
and the paths formed by all the second cells are used for avoiding the obstacle when the robot runs from the current position to the target position.
Preferably, the determining system comprises:
and the path distance formed by all the second cells is shortest.
Preferably, the second obtaining module specifically includes:
a first obtaining unit, configured to obtain a distance from each first cell to a cell of the target location;
and the first selecting unit is used for selecting the cell with the shortest distance as the second cell.
And/or, the second obtaining module specifically further includes:
a second obtaining unit, configured to obtain a gradient value of each cell from the first cell to the target location;
and the second selecting unit is used for selecting the first cell corresponding to the maximum gradient as the second cell.
Preferably, the determining system further comprises: a mobile module;
the judging module is used for calling the mobile module when judging that the mobile module is not in the process of judging;
the moving module is used for moving the unit cell of the target position by a plurality of unit cells on a path preset by the robot and calling the first obtaining module.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for determining the obstacle avoidance path of the robot when executing the computer program.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of determining an obstacle avoidance path of a robot as described above.
The invention has the positive progress effects that:
the invention provides a method, a system, electronic equipment and a medium for determining an obstacle avoidance path of a robot, wherein the obstacle avoidance method is used for determining a cell in a drivable area, namely a first cell by analyzing a grid value corresponding to a cell adjacent to a cell at the current position of the robot on a grid map, acquiring a second cell according to the first cell, controlling the robot to walk to the second cell, taking the second cell as a cell to be analyzed, and finally acquiring the second cell through repeated circulation to form the obstacle avoidance path of the robot.
Drawings
Fig. 1 is a flowchart of a method for determining an obstacle avoidance path of a robot according to embodiment 1 of the present invention;
fig. 2 is a diagram illustrating the gray value conversion result of the grid map according to embodiment 1 of the present invention;
FIG. 3 is a schematic view of a grid map according to embodiment 1 of the present invention;
fig. 4 is a flowchart of step S105 in the first mode of embodiment 1 of the present invention;
FIG. 5 is a flowchart of step S105 in the second mode of embodiment 1 of the present invention;
fig. 6 is a schematic block diagram of a determination system of an obstacle avoidance path of the robot according to embodiment 2 of the present invention;
fig. 7 is a schematic block diagram of a third obtaining module in a first mode in the determination system of the obstacle avoidance path of the robot according to embodiment 3 of the present invention;
fig. 8 is a schematic block diagram of a third obtaining module in a second mode in the determination system of the obstacle avoidance path of the robot according to embodiment 3 of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, this embodiment discloses a method for determining an obstacle avoidance path of a robot, where the method includes:
step S101, obtaining a cell of the current position of the robot on the grid map and a cell of the target position; the cells at the current position and the cells at the target position are positioned on a preset path of the robot and are positioned on two sides of the obstacle respectively;
step S102, determining a cell to be analyzed, and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed, wherein the cell adjacent to the cell to be analyzed can be eight cells adjacent in azimuth, namely, an upper side, a lower side, a left side, a right side, an upper right side, a lower right side, an upper left side and a lower left side of the cell to be analyzed; the grid value corresponds to the gray value of the cell on the grid map; the initial cell to be analyzed is the cell at the current position or the cell at the target position; in the present embodiment, the grid value is obtained by converting the gray value into a special code format, specifically, the gray value may be converted into a special hexadecimal grid value, as shown in fig. 2, in which the grid values "0X8000", "0XFFFF 0", "0XFFFF 1" are obtained by converting the gray values "255", "200", "0", "50", respectively.
Step S103, judging whether the adjacent cells of the cells to be analyzed exist in the areas which can be driven according to the grid values, if so, executing step S104, and if not, executing step S107;
step S104, determining adjacent cells positioned in the drivable area as first cells;
step 105, based on the first cell, acquiring a second cell, and controlling the robot to walk to the second cell;
step S106, the second cell is used as the cell to be analyzed, the cell to be analyzed is determined, and the step of obtaining the grid value corresponding to the cell adjacent to the cell to be analyzed is returned until the robot walks to the cell at the target position; after the step is executed, ending the flow;
step S107, moving the unit cell of the target position by a plurality of unit cells on a path preset by the robot, and returning to the step S101; specifically, the cell of the target position may be moved by 2 cells on a path preset by the robot.
And the paths formed by all the second cells are used for avoiding the obstacle when the robot runs from the current position to the target position.
The present embodiment requires the construction of a 2D grid map using laser SLAM (simultaneous localization and mapping, positioning and map construction at the same time) under the current environment where the robot is located before executing step S101. The SLAM lays a foundation for autonomous path planning navigation of the unmanned vehicle. The autonomous navigation mobile robot acquires natural environment characteristics according to various built-in sensors, a three-dimensional or two-dimensional map consistent with the real natural environment is constructed, the mobile robot is positioned, a reasonable path is planned according to a specified specific position, and the mobile robot is controlled to safely and accurately reach a specified destination grid map to comprise a feasible region, an infeasible region and an unknown region. As shown in fig. 3, a 2D grid map is created according to the laser sensor, where each grid precision is 0.02m, a white area is a robot feasible area, a gray area is a robot infeasible area, and black is an obstacle area as indicated by an arrow in the figure. A path is preset for the robot in the 2D grid map according to a task received by the robot so that the robot can reach an end point from a start point. After the preset path of the robot is completed, a laser real-time positioning thread is started, automatic operation is performed from an initial point, and meanwhile, a log file thread is started to record related row data.
In one embodiment, when the path distance formed by the second cell is the shortest, the implementation of step S104 includes two ways:
in the first way, as shown in fig. 4, the specific steps of step S105 are as follows:
step S1051, obtaining a distance from each first cell to a cell of the target location;
step S1052, selecting the first cell corresponding to the shortest distance as the second cell.
In the second way, as shown in fig. 5, the specific steps of step S104 are as follows:
step S1071, obtaining gradient values of cells from each first cell to the target position;
step S1072, selecting a first cell corresponding to the maximum gradient value as the second cell.
In this embodiment, when the robot encounters an obstacle, the log file thread starts recording the obstacle residence time, alarms when the obstacle residence exceeds 1s, and resets the obstacle presence flag when the obstacle residence exceeds 2 s. If the obstacle is removed within 2s, the floor scrubbing robot may continue to move normally. When the obstacle exceeds 2s and is not removed, an obstacle mark exists, and new map data are refreshed at the moment.
The embodiment discloses a method for determining obstacle avoidance path of robot, the method is based on grid value in grid map, through analyzing grid value corresponding to adjacent cells of the current position of the robot on grid map, determine the cell in the drivable region, namely first cell, if there is no cell in the drivable region in adjacent cells of the current position of the cell, reset the target position, return to obtain the current position of the cell and the target position of the robot on grid map, obtain second cell according to the first cell, and control the robot to walk to the second cell, and take the second cell as the cell to be analyzed, finally obtain the obstacle avoidance path of the robot through multiple times of circulation, the method reduces the requirement on hardware performance and improves the processing efficiency, can normally operate under the equipment carrying i3 processor, and has faster operation speed under the equipment carrying i7 processor. In addition, in this embodiment, the cell with the shortest distance from the first cell to the cell at the target position may be selected as the second cell, or the cell with the largest gradient value from the distances from the first cell to the cells at the target position may be selected as the second cell, so as to realize the determination of the shortest obstacle avoidance path of the robot.
Example 2
As shown in fig. 6, this embodiment discloses a determination system of an obstacle avoidance path of a robot, the determination system includes:
the first acquisition module 1 is used for acquiring cells of the current position and cells of the target position of the robot on the grid map; the cells at the current position and the cells at the target position are positioned on a preset path of the robot and are positioned on two sides of the obstacle respectively;
the first determining module 2 is configured to determine a cell to be analyzed, and obtain a grid value corresponding to a cell adjacent to the cell to be analyzed, where specifically, the cell adjacent to the cell to be analyzed may be eight cells adjacent in azimuth, that is, an upper, a lower, a left, a right, an upper right, a lower right, an upper left, and a lower left of the cell to be analyzed; the grid value corresponds to the gray value of the cell on the grid map; the initial cell to be analyzed is the cell at the current position or the cell at the target position; in the present embodiment, the grid value is obtained by converting the gray value into a special code format, specifically, the gray value may be converted into a special hexadecimal grid value, as shown in fig. 2, in which the grid values "0X8000", "0XFFFF 0", "0XFFFF 1" are obtained by converting the gray values "255", "200", "0", "50", respectively.
The judging module 3 is configured to judge whether a cell located in a drivable area exists in a neighboring cell of the cell to be analyzed, if so, call the second determining module 4, and if not, call the moving module 7;
the second determining module 4 is configured to determine that an adjacent cell located in the drivable area is a first cell;
the second acquisition module 5 is used for acquiring a second cell based on the first cell and controlling the robot to walk to the second cell;
a return module 6, configured to take the second cell as the cell to be analyzed, and call the first determining module 2 until the robot walks to the cell of the target position;
the moving module 7 is configured to move the unit cell of the target position by a plurality of unit cells on a path preset by the robot and call the first obtaining module 1;
and the paths formed by all the second cells are used for avoiding the obstacle when the robot runs from the current position to the target position.
In this embodiment, before the first acquisition module 1 is invoked, the 2D grid map needs to be constructed by using the laser SLAM in the current environment where the robot is located. The SLAM lays a foundation for autonomous path planning navigation of the unmanned vehicle. The autonomous navigation mobile robot acquires natural environment characteristics according to various built-in sensors, a three-dimensional or two-dimensional map consistent with the real natural environment is constructed, the mobile robot is positioned, a reasonable path is planned according to a specified specific position, and the mobile robot is controlled to safely and accurately reach a specified destination grid map to comprise a feasible region, an infeasible region and an unknown region. As shown in fig. 2, a 2D grid map is created according to the laser sensor, where each grid precision is 0.02m, a white area is a robot feasible area, a gray area is a robot infeasible area, and black is an obstacle area as indicated by an arrow in the figure. A path is preset for the robot in the 2D grid map according to a task received by the robot so that the robot can reach an end point from a start point. After the preset path of the robot is completed, a laser real-time positioning thread is started, automatic operation is performed from an initial point, and meanwhile, a log file thread is started to record related row data.
In an embodiment, when the path distance formed by the second cell is the shortest, the implementation manner of the second obtaining module 5 includes two ways:
in the first way, as shown in fig. 7, the second acquisition module 5 specifically includes:
a first obtaining unit 51, configured to obtain a distance from each of the first cells to a cell of the target position;
and a first selecting unit 52, configured to select a first cell corresponding to the shortest distance as the second cell.
In the second way, as shown in fig. 8, the second acquisition module 5 specifically includes:
a second obtaining unit 53, configured to obtain a gradient value of each of the first cells to a cell of the target position;
the second selecting unit 54 selects the first cell corresponding to the maximum gradient value as the second cell.
In this embodiment, when the robot encounters an obstacle, the log file thread starts recording the obstacle residence time, alarms when the obstacle residence exceeds 1s, and resets the obstacle presence flag when the obstacle residence exceeds 2 s. If the obstacle is removed within 2s, the floor scrubbing robot may continue to move normally. When the obstacle exceeds 2s and is not removed, an obstacle mark exists, and new map data are refreshed at the moment.
The embodiment discloses a system for determining obstacle avoidance path of robot, the determining system is based on grid value in grid map, through analyzing grid value corresponding to adjacent cells of the current position of the robot on grid map, determine the cell in the drivable region, namely first cell, if there is no cell in the drivable region in adjacent cells of the current position of the cell, reset the target position, return to obtain the current position of the cell and the target position of the robot on grid map, obtain second cell according to the first cell, and control the robot to walk to the second cell, and take the second cell as the cell to be analyzed, finally obtain the obstacle avoidance path of the robot through multiple times of circulation, the method reduces the requirement on hardware performance and improves processing efficiency, can normally operate under the condition of carrying i3 processor, and has faster operation speed under the condition of carrying i7 processor. In addition, in this embodiment, the cell with the shortest distance from the first cell to the cell at the target position may be selected as the second cell, or the cell with the largest gradient value from the distances from the first cell to the cells at the target position may be selected as the second cell, so as to realize the determination of the shortest obstacle avoidance path of the robot.
Example 4
Fig. 9 is a schematic structural diagram of an electronic device according to embodiment 4 of the present invention. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the method for determining the obstacle avoidance path of the robot provided in embodiment 1 or embodiment 2 when executing the program. The electronic device 50 shown in fig. 9 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 9, the electronic device 50 may be embodied in the form of a general purpose computing device, which may be a server device, for example. Components of electronic device 50 may include, but are not limited to: the at least one processor 51, the at least one memory 52, a bus 53 connecting the different system components, including the memory 52 and the processor 51.
The bus 53 includes a data bus, an address bus, and a control bus.
Memory 52 may include volatile memory such as Random Access Memory (RAM) 521 and/or cache memory 522, and may further include Read Only Memory (ROM) 523.
Memory 52 may also include a program/utility 525 having a set (at least one) of program modules 424, such program modules 524 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 51 executes a computer program stored in the memory 52 to perform various functional applications and data processing, such as the determination method of the obstacle avoidance path of the robot provided in embodiment 1 or embodiment 2 of the present invention.
The electronic device 50 may also communicate with one or more external devices 54 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 55. Also, model-generating device 50 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet via network adapter 56. As shown, the network adapter 56 communicates with other modules of the model-generating device 50 via the bus 53. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the model-generating device 50, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 5
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of determining an obstacle avoidance path of a robot provided in embodiment 1 or embodiment 2.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the method of determining the obstacle avoidance path of a robot provided by embodiment 1 or embodiment 2, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (8)
1. A method for determining an obstacle avoidance path of a robot, the method comprising:
acquiring a cell of the current position of the robot on the grid map and a cell of the target position; the cells at the current position and the cells at the target position are positioned on a preset path of the robot and are positioned on two sides of the obstacle respectively; the preset path of the robot is a path from the current position to the target position;
determining a cell to be analyzed, and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed; the grid value corresponds to the gray value of the cell on the grid map; the initial cell to be analyzed is the cell at the current position or the cell at the target position;
judging whether the adjacent cells of the cells to be analyzed exist in the cell within the drivable area according to the grid value, if so, determining the adjacent cells in the drivable area as first cells, if not, moving the cells at the target position by a plurality of cells on a path preset by the robot, and returning to the step of acquiring the cells at the current position and the cells at the target position of the robot on a grid map;
acquiring a second cell based on the first cell, and controlling the robot to walk to the second cell;
the second cell is used as the cell to be analyzed, the cell to be analyzed is returned to be determined, and the grid value corresponding to the cell adjacent to the cell to be analyzed is obtained until the robot walks to the cell at the target position;
and the paths formed by all the second cells are used for avoiding the obstacle when the robot runs from the current position to the target position.
2. The method for determining an obstacle avoidance path of a robot as set forth in claim 1, wherein the method comprises:
and the path distance formed by all the second cells is shortest.
3. The method for determining the obstacle avoidance path of a robot according to claim 2, wherein the step of acquiring the second cell based on the first cell specifically comprises:
obtaining the distance from each first cell to the cell of the target position;
selecting a first cell corresponding to the shortest distance as the second cell;
and/or, the step of acquiring the second cell based on the first cell specifically further includes:
obtaining gradient values of cells from each first cell to the target position;
and selecting a first cell corresponding to the maximum gradient value as the second cell.
4. A system for determining an obstacle avoidance path of a robot, the system comprising:
the first acquisition module is used for acquiring cells of the current position of the robot on the grid map and cells of the target position; the cells at the current position and the cells at the target position are positioned on a preset path of the robot and are positioned on two sides of the obstacle respectively; the preset path of the robot is a path from the current position to the target position;
the first determining module is used for determining a cell to be analyzed and acquiring a grid value corresponding to a cell adjacent to the cell to be analyzed; the grid value corresponds to the gray value of the cell on the grid map; the initial cell to be analyzed is the cell at the current position or the cell at the target position;
the judging module is used for judging whether the adjacent cells of the cells to be analyzed exist in the cells located in the drivable area or not, if so, the second determining module is called, and if not, the moving module is called;
the second determining module is used for determining adjacent cells positioned in the drivable area as first cells;
the moving module is used for moving the unit cell of the target position by a plurality of unit cells on a path preset by the robot and calling the first obtaining module;
the second acquisition module is used for acquiring a second cell based on the first cell and controlling the robot to walk to the second cell;
the return module is used for taking the second cell as the cell to be analyzed, and calling the first determination module until the robot walks to the cell of the target position;
and the paths formed by all the second cells are used for avoiding the obstacle when the robot runs from the current position to the target position.
5. The system for determining an obstacle avoidance path of a robot as recited in claim 4, wherein the system comprises:
and the path distance formed by all the second cells is shortest.
6. The system for determining an obstacle avoidance path of a robot of claim 5, wherein the second acquisition module specifically comprises:
a first obtaining unit, configured to obtain a distance from each first cell to a cell of the target location;
a first selecting unit, configured to select a first cell corresponding to the shortest distance as the second cell;
and/or, the second obtaining module specifically further includes:
a second obtaining unit, configured to obtain a gradient value of each cell from the first cell to the target location;
and the second selecting unit is used for selecting the first cell corresponding to the maximum gradient as the second cell.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a method for determining the obstacle avoidance path of a robot according to any one of claims 1 to 3 when executing the computer program.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a method of determining an obstacle avoidance path of a robot according to any one of claims 1 to 3.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102520718A (en) * | 2011-12-02 | 2012-06-27 | 上海大学 | Physical modeling-based robot obstacle avoidance path planning method |
| CN103901887A (en) * | 2014-03-04 | 2014-07-02 | 重庆邮电大学 | Multi-moving-robot formation control method based on improved particle swarm optimization |
| WO2016045615A1 (en) * | 2014-09-25 | 2016-03-31 | 科沃斯机器人有限公司 | Robot static path planning method |
| CN105511485A (en) * | 2014-09-25 | 2016-04-20 | 科沃斯机器人有限公司 | Grid map creating method for self-moving robot |
| WO2017041730A1 (en) * | 2015-09-09 | 2017-03-16 | 北京进化者机器人科技有限公司 | Method and system for navigating mobile robot to bypass obstacle |
| WO2017173990A1 (en) * | 2016-04-07 | 2017-10-12 | 北京进化者机器人科技有限公司 | Method for planning shortest path in robot obstacle avoidance |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107340768B (en) * | 2016-12-29 | 2020-08-28 | 珠海市一微半导体有限公司 | Path planning method of intelligent robot |
| US11530921B2 (en) * | 2018-09-28 | 2022-12-20 | Intel Corporation | Method of generating a collision free path of travel and computing system |
-
2021
- 2021-08-19 CN CN202110952810.7A patent/CN113671958B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102520718A (en) * | 2011-12-02 | 2012-06-27 | 上海大学 | Physical modeling-based robot obstacle avoidance path planning method |
| CN103901887A (en) * | 2014-03-04 | 2014-07-02 | 重庆邮电大学 | Multi-moving-robot formation control method based on improved particle swarm optimization |
| WO2016045615A1 (en) * | 2014-09-25 | 2016-03-31 | 科沃斯机器人有限公司 | Robot static path planning method |
| CN105511485A (en) * | 2014-09-25 | 2016-04-20 | 科沃斯机器人有限公司 | Grid map creating method for self-moving robot |
| WO2017041730A1 (en) * | 2015-09-09 | 2017-03-16 | 北京进化者机器人科技有限公司 | Method and system for navigating mobile robot to bypass obstacle |
| WO2017173990A1 (en) * | 2016-04-07 | 2017-10-12 | 北京进化者机器人科技有限公司 | Method for planning shortest path in robot obstacle avoidance |
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