CN107703937A - Automatic Guided Vehicle system and its conflict evading method based on convolutional neural networks - Google Patents
Automatic Guided Vehicle system and its conflict evading method based on convolutional neural networks Download PDFInfo
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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
- G05D1/0231—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
- G05D1/0253—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 relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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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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- 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
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Abstract
The invention discloses the automatic Guided Vehicle system based on convolutional neural networks and its dolly conflict evading method, automatic Guided Vehicle system includes AGV dollies, leader and remote server.Transmission of video in front of the camera collection AGV dollies of AGV dollies is to control chip.Control chip is connected by wireless communication apparatus and remote server communication.Leader includes the rectangular mesh being arranged on road surface, and rectangular mesh posts road sign on crosspoint.Remote server transmits the coordinate information in crosspoint in the structure of rectangular mesh, rectangular grid and AGV dollies routing information to AGV dollies with wireless communication mode.Conflict evading of the invention for all AGV dollies in automatic Guided Vehicle system is completely by AGV dolly autonomous controls, the unified allocation of resources of central server need not be passed through, conflict of evading for AGV dollies has higher treatment effeciency, while also improves the throughput of whole system.
Description
Technical field
The present invention relates to the automatic Guided Vehicle system based on convolutional neural networks and its conflict evading method.
Background technology
Follow line dolly and more and more extensive utilization has been obtained in industrial practice, because following the high-intelligentization of line dolly
And automation, allow industrial production both to save cost of labor and production cost, improve production efficiency and production cycle again.Follow line
Dolly has extremely prominent status in industrial logistics management.
Due to having a fairly large number of dolly in whole region while travelling, so being needed during automatic Guided Vehicle system design
Dolly is avoided to collide with each other.Traditional solution dispatches all dollies on all time points by server centered
Gait of march, advanced positions determine whether to conflict, then determine which specific dolly parking avoids.The problem of this scheme is
Synchronous all dollies, the efficiency of scheduling are poor in time for server needs.
The content of the invention
It is an object of the invention to provide a kind of automatic Guided Vehicle system and distributed dolly conflict evading method.
Realizing the technical scheme of the object of the invention is：Automatic Guided Vehicle system and its conflict based on convolutional neural networks
Bypassing method, comprise the following steps：
Step 1: structure automatic Guided Vehicle system；The automatic Guided Vehicle system includes AGV dollies and vectoring aircraft
Structure；The AGV dollies are provided with the control chip for including convolutional neural networks that camera is connected with camera；The vectoring aircraft
Structure is included by being arranged on the rectangular mesh formed on road surface by some horizontal lines and ordinate, set all horizontal lines as main road, own
Ordinate is branch road or set using opposite；
Step 2: AGV dollies start running according to routing information；
Step 3: AGV dollies are imaged using the camera for being arranged on AGV dollies front end, front or left is identified
Right both sides appear in other AGV dollies in the visual field, judge other AGV by the control chip identification comprising convolutional neural networks
Whether dolly can collide with this AGV dollies, if so, then the AGV dollies on branch road preferentially pass through by the AGV dollies on main road；
If it is not, then AGV dollies normal pass.
In the step 1, every horizontal line and ordinate in rectangular mesh are one-way traffic line.
In the step 3, when other AGV dollies travel in the same direction with this AGV dollies, pass through the control with convolutional neural networks
The identification of coremaking piece judges that the distance of front AGV dollies and this AGV dollies decides whether that selection parking avoids.
In the step 3, when other AGV dollies come from front both sides, AGV dollies pass through the control with convolutional neural networks
Coremaking piece judges to identify distance, angle, travel direction of other AGV dollies apart from this AGV dollies, passed through with convolutional neural networks
Control chip identification calculate distance and oneself the AGV dolly distance for comparing left and right sides AGV dollies apart from the nearest road sign in front
The nearest road sign distance in front, if hypotelorism, the AGV dollies on branch road preferentially pass through by the AGV dollies on main road；If away from
From normal, then AGV dollies normal pass.
In automatic Guided Vehicle system, the processing procedure of convolutional neural networks model comprises the following steps：
1., sample collection；
Road sign image and dolly image are gathered, classification generation road sign sample and dolly sample are carried out to image；
2., standard specimen sheet of satisfying the need and vehicle sample preprocessing；
According to the sample-size of setting, standard specimen sheet of satisfying the need and dolly sample randomly carry out symmetrical upset change, with machine maintenance
Cut, color jitter, noise disturbance；Road sign will be included in road sign sample, dolly sample manually, the rectangular region frame of dolly selects
Go out, the coordinate value of top left corner pixel point of rectangle frame and the coordinate value of lower right corner pixel recorded, complete dolly or
The mark of road sign；
3., the training of convolutional network：
Step is got the bid in the dolly sample being poured in and road sign sample input depth convolutional network 2., obtains image text
The network output of part, then calculates the difference between network output and the rectangle frame coordinate of mark, then passes through the difference
Back transfer carries out the renewal of convolutional network weight.Step is 3. up to a hundred secondary to all training image iteration, is finally trained
Good convolutional network model；
4., after road sign sample and the input of vehicle sample trained the convolutional neural networks of completion, convolutional neural networks to calculate
Recognition result is obtained, further calculating is done to the position of road sign and dolly, angle according to recognition result.
It is further preferred that 1. middle step includes：A large amount of road sign samples and dolly are intercepted from the video or picture of shooting
Sample forms road sign sample and dolly sample, and road sign sample and dolly sample include each visual angle of road sign image and small respectively
Each visual angle of car image.
It is further preferred that 2. middle step includes：The Pixel Dimensions scope of sample image is changed to 288*288-544*
544。
It is further preferred that 3. middle step includes：After road sign is identified, the road sign center and image bottom line center are connected
The angle of line, center connection and vertical line is the angle of the current direct of travel of this dolly.The length of center connection is that this is small
Distance of the car apart from above road sign.
Above-mentioned technical proposal is employed, the present invention has following beneficial effect：The present invention is for automatic Guided Vehicle system
The conflict evading of all AGV dollies in system by AGV dolly autonomous controls, passes through the control core comprising convolutional neural networks completely
Piece identification judges whether other AGV dollies can collide with this AGV dollies, passes through the main, distribution of branch road and branch in rectangular mesh
Road avoids the regular reasonably avoiding conflict of main road, it is not necessary to by the same allotment of central server, for evading for AGV dollies
Conflict has higher treatment effeciency, improves the overall throughput of system.
Brief description of the drawings
In order that present disclosure is easier to be clearly understood, it is right below according to specific embodiment and with reference to accompanying drawing
The present invention is described in further detail, wherein
Fig. 1 is the dolly conflict evading method flow of the automatic Guided Vehicle system based on convolutional neural networks in the present invention
Figure.
Fig. 2 collides schematic diagram for AGV dollies in embodiment 1 in the present invention.
Fig. 3 collides schematic diagram for AGV dollies in embodiment 2 in the present invention.
Embodiment
(embodiment 1)
As depicted in figs. 1 and 2, the distributed dolly conflict evading method of the present embodiment, comprises the following steps：
Step 1: structure automatic Guided Vehicle system；The automatic Guided Vehicle system includes AGV dollies and vectoring aircraft
Structure；The AGV dollies are provided with the control chip for including convolutional neural networks that camera is connected with camera；The vectoring aircraft
Structure is included by being arranged on the rectangular mesh formed on road surface by some horizontal lines and ordinate, set all horizontal lines as main road, own
Ordinate is branch road or set using opposite；Every horizontal line and ordinate in rectangular mesh are one-way traffic line.
Step 2: AGV dollies start running according to routing information；
Step 3: AGV dollies are imaged using the camera for being arranged on AGV dollies front end, front or left is identified
Right both sides appear in other AGV dollies in the visual field, judge that other AGV are small by the control chip identification with convolutional neural networks
Whether car can collide with this AGV dollies, if so, then the AGV dollies on branch road preferentially pass through by the AGV dollies on main road；If
It is no, then AGV dollies normal pass.
As shown in Fig. 2 when other AGV dollies B and this AGV dollies A is travelled in the same direction, pass through the control with convolutional neural networks
Chip identification judge in front of AGV dolly B and this AGV dollies A distance D1 whether in safe distance so as to determining that selection stops
Car avoids or normally travel.
The step of building convolutional neural networks model is as follows：
(1) convolutional layer Conv1 is inputted using the picture of 352X352 pixel sizes as input layer Input0, every trade is entered to it
Block size is 3X3 pixels, step-length is 1 pixel, the filling distance is 1 pixel convolution operation is obtained, it is necessary to use 16 convolution kernels
The characteristic pattern of 16 352X352 pixels；
(2) 16 characteristic patterns for exporting convolutional layer Conv1 are input to pond layer Pool2, and maximum pond operation is carried out to it,
The size of pond block is 2X2 pixels, and step-length is 2 pixels, obtains the characteristic pattern of 16 176X176 pixels；
(3) 16 characteristic patterns by pond layer Pool2 outputs input convolutional layer Conv3, and it is 3X3 that every trade block size is entered to it
Pixel, step-length are 1 pixel, the filling distance is 1 pixel convolution operation obtains 32 176X176, it is necessary to use 32 convolution kernels
The characteristic pattern of pixel；
(4) 32 characteristic patterns for exporting convolutional layer Conv3 are input to pond layer Pool4, and maximum pond operation is carried out to it,
The size of pond block is 2X2 pixels, and step-length is 2 pixels, obtains the characteristic pattern of 32 88X88 pixels；
(5) 32 characteristic patterns by pond layer Pool4 outputs input convolutional layer Conv5, and it is 3X3 pictures that block size is carried out to it
Element, the convolution operation that step-length is 1 pixel, the filling distance is 1 pixel obtain 64 88X88 pixels, it is necessary to use 64 convolution kernels
Characteristic pattern；
(6) 64 characteristic patterns for exporting convolutional layer Conv5 are input to pond layer Pool6, and maximum pond operation is carried out to it,
The size of pond block is 2X2 pixels, and step-length is 2 pixels, obtains the characteristic pattern of 64 44X44 pixels；
(7) 64 characteristic patterns by pond layer Pool6 outputs input convolutional layer Conv7, and it is 3X3 that every trade block size is entered to it
Pixel, step-length are 1 pixel, the filling distance is 1 pixel convolution operation obtains 128 44X44, it is necessary to use 128 convolution kernels
The characteristic pattern of pixel；
(8) 128 characteristic patterns for exporting convolutional layer Conv7 are input to pond layer Pool8, and maximum pond behaviour is carried out to it
Make, the size of pond block is 2X2 pixels, and step-length is 2 pixels, obtains the characteristic pattern of 128 22X22 pixels；
(9) 128 characteristic patterns by the output of pond layer Pool8 outputs input convolutional layer Conv9, and it is big that every trade block is entered to it
It is small be 3X3 pixels, the convolution operation that step-length is 1 pixel, the filling distance is 1 pixel, it is necessary to use 256 convolution kernels, obtain 256
Open the characteristic pattern of 22X22 pixels；
(10) 256 characteristic patterns for exporting convolutional layer Conv9 are input to pond layer Pool10, and maximum pond behaviour is carried out to it
Make, the size of pond block is 2X2 pixels, and step-length is 2 pixels, obtains the characteristic pattern of 256 11X11 pixels；
(11) 256 characteristic patterns by the output of pond layer Pool10 outputs input convolutional layer Conv11, enter every trade to it
Block size is 3X3 pixels, step-length is 1 pixel, the filling distance is 1 pixel convolution operation is obtained, it is necessary to use 512 convolution kernels
To the characteristic pattern of 512 11X11 pixels；
(12) 512 characteristic patterns of the convolutional layer Conv11 outputs exported are inputted into convolutional layer Conv12, every trade is entered to it
Block size is 3X3 pixels, step-length is 1 pixel, the filling distance is 1 pixel convolution operation is obtained, it is necessary to use 1024 convolution kernels
To the characteristic pattern of 1024 11X11 pixels；
(13) 1024 characteristic patterns of the convolutional layer Conv12 outputs exported are inputted into convolutional layer Conv13, every trade is entered to it
Block size is 1X1 pixels, step-length is 1 pixel, the filling distance is 1 pixel convolution operation is obtained, it is necessary to use 80 convolution kernels
The characteristic pattern of 80 11X11 pixels；
(14) according to the mesh being likely to occur in 80 11*11 finally obtained characteristic pattern judgement figure around each pixel
Mark the apex coordinate of object and its profile rectangle frame.Export judged result.
(embodiment 2)
As shown in figures 1 and 3, it is main road to set in Fig. 3 in rectangular mesh horizontal line as branch road, ordinate, and the present embodiment is substantially
Flow is substantially similar to embodiment 1, and in step 3, other AGV dollies B come difference from any side in front, pass through
Control chip identification with convolutional neural networks, which calculates, compares side AGV dollies apart from the distance D2 of the nearest road sign in front and oneself
AGV dollies A is apart from the nearest road sign distance D1 in front, if hypotelorism, the AGV that the AGV dollies B on branch road allows on main road is small
Car A preferentially passes through；If apart from normal, the equal normal pass of AGV dolly A and B.
The step of convolutional neural networks model is built in the present embodiment is same as Example 1.
Specific embodiment above, has been carried out further specifically to the purpose of the present invention, technical scheme and beneficial effect
It is bright, the specific embodiment that the foregoing is only the present invention is should be understood that, is not intended to limit the invention, it is all at this
Within the spirit and principle of invention, any modification, equivalent substitution and improvements done etc., the protection model of the present invention should be included in
Within enclosing.
Claims (4)
1. the distributed dolly conflict evading method of the automatic Guided Vehicle system based on convolutional neural networks, it is characterised in that：
Comprise the following steps：
Step 1: structure automatic Guided Vehicle system；The automatic Guided Vehicle system includes AGV dollies and guiding mechanism；Institute
State AGV dollies and be provided with the control chip for including convolutional neural networks model that camera is connected with camera；The vectoring aircraft
Structure includes being arranged on the rectangular mesh that some horizontal lines and ordinate are formed on road surface, sets all horizontal lines as main road, all ordinates
For branch road or using opposite setting；
Step 2: AGV dollies start running according to routing information；
Step 3: AGV dollies are imaged using the camera for being arranged on AGV dollies front end, front or left and right two are identified
Side appears in other AGV dollies in the visual field, judges other AGV by the control chip identification comprising convolutional neural networks model
Whether dolly can collide with this AGV dollies, if so, then the AGV dollies on branch road preferentially pass through by the AGV dollies on main road；
If it is not, then AGV dollies normal pass.
2. the distributed dolly conflict rule of the automatic Guided Vehicle system according to claim 1 based on convolutional neural networks
Keep away method, it is characterised in that：
In the step 1, every horizontal line and ordinate in rectangular mesh are one-way traffic line.
3. the distributed dolly conflict rule of the automatic Guided Vehicle system according to claim 2 based on convolutional neural networks
Keep away method, it is characterised in that：
In the step 3, when other AGV dollies travel in the same direction with this AGV dollies, pass through the control core with convolutional neural networks
Piece identification judges that the distance of front AGV dollies and this AGV dollies decides whether that selection parking avoids.
4. the distributed dolly conflict rule of the automatic Guided Vehicle system according to claim 2 based on convolutional neural networks
Keep away method, it is characterised in that：
In the step 3, when other AGV dollies come from front both sides, AGV dollies pass through the control core with convolutional neural networks
Piece judges to identify that other AGV dollies apart from the distance, angle, travel direction of this AGV dollies, pass through the control with convolutional neural networks
The identification of coremaking piece calculates the distance for comparing left and right sides AGV dollies apart from the nearest road sign in front with oneself AGV dolly apart from front
Nearest road sign distance, if hypotelorism, the AGV dollies on branch road preferentially pass through by the AGV dollies on main road；If distance is just
Often, then AGV dollies normal pass.
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| CN109240290A (en) * | 2018-09-04 | 2019-01-18 | 南京理工大学 | A kind of electric inspection process robot makes a return voyage determining method of path |
| CN109284699A (en) * | 2018-09-04 | 2019-01-29 | 广东翼卡车联网服务有限公司 | A kind of deep learning method being applicable in vehicle collision |
| CN109240290B (en) * | 2018-09-04 | 2021-09-03 | 南京理工大学 | Method for determining return route of power inspection robot |
| CN110488835A (en) * | 2019-08-28 | 2019-11-22 | 北京航空航天大学 | A kind of unmanned systems intelligence local paths planning method based on double reverse transmittance nerve networks |
| CN111340880B (en) * | 2020-02-17 | 2023-08-04 | 北京百度网讯科技有限公司 | Method and apparatus for generating predictive model |
| CN111340880A (en) * | 2020-02-17 | 2020-06-26 | 北京百度网讯科技有限公司 | Method and apparatus for generating a predictive model |
| CN112835333A (en) * | 2020-12-31 | 2021-05-25 | 北京工商大学 | Multi-AGV obstacle avoidance and path planning method and system based on deep reinforcement learning |
| CN114415675A (en) * | 2021-12-30 | 2022-04-29 | 西安电子科技大学广州研究院 | Multi-AGV planning method and system based on intelligent prediction and error recognition |
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