CN113721628A - Maze robot path planning method fusing image processing - Google Patents
Maze robot path planning method fusing image processing Download PDFInfo
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- CN113721628A CN113721628A CN202111028985.5A CN202111028985A CN113721628A CN 113721628 A CN113721628 A CN 113721628A CN 202111028985 A CN202111028985 A CN 202111028985A CN 113721628 A CN113721628 A CN 113721628A
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- 238000004891 communication Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
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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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
- G05D1/0251—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision
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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
- G05D1/0225—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving docking at a fixed facility, e.g. base station or loading bay
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Abstract
The invention relates to a maze robot path planning method integrating image processing, which comprises the steps of firstly processing a collected maze image by an upper computer, restoring a panoramic map of a maze through a series of steps such as image processing and the like, then planning a shortest path from a starting point to an end point on the identified maze map by using an intelligent algorithm in the upper computer, converting the shortest path into path data which can be executed by a maze robot, and transmitting the data to the maze robot through a Bluetooth module. The invention carries out path planning of the maze robot through the upper computer, and simplifies the path searching process of the maze robot.
Description
Technical Field
The invention relates to the field of robots and image processing, in particular to a maze robot path planning method fusing image processing.
Background
The traditional maze robot is used for searching the maze terrain under an unknown condition to establish a shortest path from a starting point to an end point, and the method gives the maze robot to complete the tasks of searching a whole graph and planning a path, but the traditional maze robot cannot independently execute some advanced intelligent algorithms to plan the path due to the limitation of a main controller. Therefore, the image processing is combined with the path planning of the maze robot, the maze is reproduced through the image processing, the path planning process is carried out by the powerful upper computer, the required intelligent algorithm can be added in the process, and the path searching process of the maze robot is simplified.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a maze robot path planning method fusing image processing.
The technical scheme of the invention is as follows:
a maze robot path planning method fusing image processing comprises the following steps:
step 1: shooting a clearer image of a maze required to be searched by a photographic device, and transmitting the image to an upper computer;
step 2: performing morphological processing on the received image, wherein the morphological processing comprises image graying, image inclination correction, image binarization, image edge detection and image identification;
and step 3: for the maze map processed in the step 2, carrying out path planning by using an intelligent algorithm to find out the shortest path from the starting point to the end point;
and 4, step 4: the wall data and the corresponding path data are converted into information which can be identified by the maze robot and are transmitted to the maze robot through the Bluetooth module.
According to the present invention, preferably, the maze image in step 1 can be a picture taken from any angle.
According to the present invention, preferably, in the step 2, the image is grayed, and the RGB values of each pixel point are unified into the same value, so that the image is changed from three channels to a single channel.
According to the present invention, preferably, in the image tilt correction in step 2, four points on the image, namely, an upper left corner point, an upper right corner point, a lower left corner point and a lower right corner point, are sequentially taken, and the original vertex of the quadrangle and the new vertex of the corrected matrix are combined to solve the transformation coefficient, so as to solve the new vertex after transformation, and store the new image.
According to the present invention, preferably, in the image edge detection in step 2, an edge detection method combining multiple detection operators is adopted, and the specific edge detection method is as follows:
the multi-operator combines the advantages of five operators, namely a sobel operator, a prewitt operator, a canny operator, a roberts operator and a kirsch direction operator, and the operators interact with one another to obtain a union set to finally obtain more accurate image edges;
according to the present invention, preferably, the image recognition in step 2 is implemented by:
dividing the image into small blocks with recognizable sizes through a block function in the matlab, and comparing the small blocks with each standard feature after binary coding to obtain the actual feature of each small block and further obtain the overall view of the maze;
each maze check is composed of four directions, namely four directions of up, down, left and right, and the maze characteristics to be identified comprise fifteen different forms, namely, the maze checks are respectively corresponding to no wall, 0 represents no wall, and 1 represents a wall;
binary 0001 indicates that there is a wall above, 0010 indicates that there is a wall on the right, 0011 indicates that there is a wall above and on the right, 0100 indicates that there is a wall below, 0101 indicates that there is a wall above and below, 0110 indicates that there is a wall on the right and below, 0111 indicates that there is a wall above, right and below, 1000 indicates that there is a wall on the left, 1001 indicates that there is a wall above and on the left, 1010 indicates that there is a wall on the left and on the right, 1011 indicates that there is a wall above, left and right, 1100 indicates that there is a wall on the left and on the below, 1101 indicates that there is a wall above, left and on the below, 1110 indicates that there is a wall on the left, below and on the right, and 1111 indicates that there are walls on all sides.
According to the present invention, preferably, the intelligent algorithm in step 3 adopts a method for making the contour value, and the contour value gradually decreased from the end point to the start point is used to obtain a shortest path, and the specific implementation method is as follows:
each grid is a unit distance, the distance value difference is 1, the numerical value is sequentially increased from the starting point until the distance value is filled in the target position, and the shortest path is found out according to the continuous decrease of the distance value from the end point to the starting point.
According to the present invention, preferably, the wall data in step 4 is converted into a binary number that can be recognized by the maze robot, i.e. the wall data and the corresponding coordinates are transmitted to the maze robot.
According to the invention, preferably, the maze robot mainly comprises a main controller chip (1), a Bluetooth module (2) and an infrared emission receiving part (3), and is characterized in that the main controller chip (1) is used for controlling the operation of the infrared emission receiving part (3), the Bluetooth module (2) is used for receiving path data of an upper computer and transmitting the path data to the main controller chip (1), and the infrared emission receiving part (3) is used for detecting whether a surrounding wall exists or not and correcting actions.
According to the invention, preferably, the bluetooth module in step 4 is an HC05 bluetooth module, four pins of the bluetooth module are connected to the single chip for communication, a power supply terminal is connected to a power supply terminal, a ground terminal is connected to a ground terminal, a TXD terminal of the bluetooth module is connected to an RXD terminal of the single chip, and the RXD terminal of the bluetooth module is connected to a TXD terminal of the single chip.
According to the present invention, it is preferable that the maze robot is placed at the starting point, the power switch is turned on, and the maze robot is operated to directly perform the sprint operation until the ending point.
The invention has the beneficial effects that:
1. the invention adds image processing operation in the existing maze robot search maze, simplifies the path search process of the maze robot, improves the production efficiency and reduces the labor cost.
2. The invention adds the application of the intelligent algorithm in the existing maze robot search algorithm, improves the operation efficiency and reduces the labor cost.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a perspective view of the labyrinth robot structure of the present invention;
FIG. 3 is a connection diagram of the Bluetooth and the single chip port of the invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, which is provided herein for the purpose of illustration only and is not intended to be limiting.
Example 1:
as shown in fig. 1, a maze robot path planning method with image processing fused includes the following steps:
step 1: shooting a clearer image of a maze required to be searched by a photographic device, and transmitting the image to an upper computer;
step 2: performing morphological processing on the received image, wherein the morphological processing comprises image graying, image inclination correction, image binarization, image edge detection and image identification;
step 2-1: for image graying, the RGB values of each pixel point are unified into the same value, so that the image is changed into a single channel from three channels.
Step 2-2: for image inclination correction, four points on an image are sequentially taken, namely an upper left corner point, an upper right corner point, a lower left corner point and a lower right corner point, and a joint equation set of an original quadrilateral vertex and a new vertex of a corrected matrix is solved to obtain a conversion coefficient, so that a new transformed vertex is obtained, and a new image is stored.
Step 2-3: for image edge detection, an edge detection method combining multiple detection operators is adopted, and the operators interact with each other to obtain a union set to finally obtain more accurate image edges.
Step 2-4: for image identification, an image is divided into small blocks with identifiable sizes through a block function in matlab, the small blocks are compared with each standard feature after binary coding, the actual feature of each small block is obtained, and the overall view of the maze is further obtained.
And step 3: for the maze map processed in the step 2, carrying out path planning by using an intelligent algorithm to find out the shortest path from the starting point to the end point;
step 3-1: each grid is a unit distance, the distance value difference is 1, the numerical value is sequentially increased from the starting point until the distance value is filled in the target position, and the shortest path is found out according to the continuous decrease of the distance value from the end point to the starting point.
And 4, step 4: the wall data is converted into a binary array which can be identified by the maze robot, and then the wall data and the corresponding coordinates are transmitted to the maze robot.
Step 4-1: the wall data and the path data are converted into binary data which can be recognized by the maze robot, namely, the data which are represented by 0 and 1 in each lattice of the shortest path are transmitted to the maze robot.
Example 2:
as shown in fig. 2, the maze robot mainly comprises a main controller chip (1), a bluetooth module (2) and an infrared emission receiving part (3), and is characterized in that the main controller chip (1) is used for controlling the operation of the infrared emission receiving part (3), the bluetooth module (2) is used for receiving path data of an upper computer and transmitting the path data to the main controller chip (1), and the infrared emission receiving part (3) is used for detecting whether a wall around exists or not and correcting actions.
Example 3:
as shown in fig. 3, an HC05 bluetooth module is used, four pins of the module are connected with a single chip for communication, a power supply terminal is connected with a power supply terminal, a grounding terminal is connected with a grounding terminal, a TXD terminal of the module is connected with an RXD terminal of the single chip, and the RXD terminal of the module is connected with the TXD terminal of the single chip.
After receiving the path information, the maze robot is placed to the starting point, a power switch is turned on, the maze robot is operated, and the sprint action is directly carried out until the end point.
Claims (7)
1. A maze robot path planning method fusing image processing is characterized by comprising the following steps:
step 1: shooting a clearer image of a maze required to be searched by a photographic device, and transmitting the image to an upper computer;
step 2: performing morphological processing on the received image, wherein the morphological processing comprises image graying, image inclination correction, image binarization, image edge detection and image identification;
and step 3: for the maze map processed in the step 2, carrying out path planning by using an intelligent algorithm to find out the shortest path from the starting point to the end point;
and 4, step 4: the wall data and the corresponding path data are converted into information which can be identified by the maze robot and are transmitted to the maze robot through the Bluetooth module.
2. The fused image processing maze robot path planning method of claim 1,
the maze image in step 1 may be a picture taken from any angle.
3. The fused image processing maze robot path planning method of claim 1,
and in the step 2, the edge detection of the image adopts an edge detection method combining a plurality of detection operators.
4. The fused image processing maze robot path planning method of claim 1,
in the step 2, the image identification, the maze characteristics to be identified include fifteen different forms, the image is divided into small blocks capable of being identified, and the small blocks are compared with the standard characteristics after binary coding, so that the actual characteristics of each small block are obtained.
5. The fused image processing maze robot path planning method of claim 1,
the intelligent algorithm in the step 3 adopts a method for manufacturing the equal-height value, and a shortest path is obtained by the equal-height value gradually decreased from the end point to the starting point.
6. The fused image processing maze robot path planning method of claim 1,
and 4, converting the wall data and the path data in the step 4 into binary arrays which can be identified by the maze robot.
7. The fused image processing maze robot path planning method of claim 1,
the bluetooth module in step 4 is an HC05 bluetooth module, and four pins of the bluetooth module are connected to a single chip for communication.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115402116A (en) * | 2022-09-28 | 2022-11-29 | 天津工业大学 | Labyrinth intelligent vehicle based on speed measurement of external magnetic suspension encoder of motor |
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| CN101231669A (en) * | 2007-12-28 | 2008-07-30 | 北京工业大学 | Method for searching optimum route of maze |
| CN101239466A (en) * | 2007-12-28 | 2008-08-13 | 北京工业大学 | Minisize maze robot |
| CN104142684A (en) * | 2014-07-31 | 2014-11-12 | 哈尔滨工程大学 | Maze searching method for miniature micromouse robot |
| CN106873603A (en) * | 2017-04-17 | 2017-06-20 | 成都信息工程大学 | Computer mouse intelligence vehicle control and control method based on Zynq platforms |
-
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- 2021-09-03 CN CN202111028985.5A patent/CN113721628A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101231669A (en) * | 2007-12-28 | 2008-07-30 | 北京工业大学 | Method for searching optimum route of maze |
| CN101239466A (en) * | 2007-12-28 | 2008-08-13 | 北京工业大学 | Minisize maze robot |
| CN104142684A (en) * | 2014-07-31 | 2014-11-12 | 哈尔滨工程大学 | Maze searching method for miniature micromouse robot |
| CN106873603A (en) * | 2017-04-17 | 2017-06-20 | 成都信息工程大学 | Computer mouse intelligence vehicle control and control method based on Zynq platforms |
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| CN115402116A (en) * | 2022-09-28 | 2022-11-29 | 天津工业大学 | Labyrinth intelligent vehicle based on speed measurement of external magnetic suspension encoder of motor |
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