CN111966110A - Automatic navigation method and system for port unmanned transport vehicle - Google Patents
Automatic navigation method and system for port unmanned transport vehicle Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000003068 static effect Effects 0.000 claims abstract description 45
- 230000002068 genetic effect Effects 0.000 claims abstract description 9
- 238000007726 management method Methods 0.000 claims description 10
- 230000000694 effects Effects 0.000 abstract description 5
- 230000002411 adverse Effects 0.000 abstract description 2
- 239000003550 marker Substances 0.000 abstract description 2
- 210000000349 chromosome Anatomy 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0261—Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means using magnetic plots
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
Abstract
The invention provides an automatic navigation method and system for a port unmanned transport vehicle, which are characterized in that RFID (radio frequency identification) identifiers are arranged at key nodes of port roads and key nodes of yard roads; generating a three-dimensional map by using a slam algorithm, and marking static feature points where RFID marks are located; collecting and updating the container placement information of the storage yard at any time; designing a first planning scheme for planning the dispatching and the path of the unmanned transport vehicle by adopting a genetic algorithm; combining the first planning scheme with static characteristic points where RFID marks related to the path are located to form a second planning scheme; and navigating the unmanned transport vehicle according to the second planning scheme. The invention can improve the transportation efficiency of the unmanned transport vehicle; and adverse effects on the navigation effect when the marker is wrong or damaged are avoided, and stable navigation is realized.
Description
Technical Field
The invention belongs to the technical field of port transportation, and particularly relates to an automatic navigation method and system for an unmanned port transport vehicle.
Background
Along with the rapid development of industry and economy in China, water transportation also shows a trend of high-speed development, and a port is taken as an important node of water transportation logistics and is handed over between waterway transportation and land transportation, so that the port is particularly important in function.
At present, unmanned transport vehicles are used for port transportation, and navigation technology for the unmanned transport vehicles is also developed and used for guiding the unmanned transport vehicles to run according to a specified path. However, the existing navigation technologies have some defects, and some schemes cannot really realize that a new path can be planned at any time as required to guide the unmanned transport vehicle to run in a port road area (including a container yard and a port area road), so that the high-efficiency transportation efficiency of the unmanned transport vehicle cannot be ensured; some solutions rely on some kind of markers, such as floor lights, RFID tags, etc., for positioning to achieve guidance, but the dependency on the markers is too high, and navigation effect is affected when the markers are wrong or damaged.
Disclosure of Invention
The invention aims to provide an automatic navigation method and system for an unmanned port transport vehicle, which can plan a new path as required to guide the unmanned port transport vehicle to run in a port road area, realize stable navigation and improve the transportation efficiency.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an automatic navigation method for port unmanned transport vehicles comprises the following steps:
s1, setting RFID (radio frequency identification) marks at key nodes of port area roads and key nodes of yard roads;
s2, patrolling all roads in a road area by using the taught unmanned transport vehicle, acquiring a shot video of the roads, generating a three-dimensional map by using a slam algorithm, and marking static feature points where RFID marks are located on the basis of the three-dimensional map and the RFID marks;
s3, collecting and updating the storage yard container placement information at any time;
s4, designing a first planning scheme for planning the dispatching and the path of the unmanned transport vehicle by adopting a genetic algorithm according to the container placement information and the position information of the unmanned transport vehicle;
s5, combining the first planning scheme with static characteristic points where the RFID marks related to the path are located to form a second planning scheme;
and S6, navigating the unmanned transport vehicle according to the second planning scheme, shooting a video of the unmanned transport vehicle in the navigation process, and positioning the road by comparing the static characteristic points.
Further, the key nodes in step S1 include intersections and inflection points of port roads, intersections and inflection points of yard roads, and identification points of container locations.
Further, the method for generating the three-dimensional map in step S2 is: removing dynamic factors in videos shot by the taught unmanned transport vehicle, reserving static environment factors, extracting static feature points according to the static environment factors, and generating a three-dimensional map by using a slam algorithm based on the static feature points and the pose of a camera shooting the videos.
Further, the method for collecting and updating the storage yard container placement information in step S3 includes: and the system is connected with a port logistics management system through a preset interface, and the latest yard container placement information is obtained from the port logistics management system.
Further, in step S6, before comparing the static feature points, relocation is required, where the relocation method is: the current unmanned transport vehicle collects the image frame where the RFID mark of the first key node passes through is located, and the camera pose of the closest frame in the video of the taught unmanned transport vehicle is used as the camera pose of the current frame, so that relocation is realized.
The invention also provides an automatic navigation system of the port unmanned transport vehicle, which comprises the following components:
the teaching module is used for polling all roads in a road area by using a taught unmanned transport vehicle, acquiring a shooting video of the roads, generating a three-dimensional map by using a slam algorithm, and marking static characteristic points where the RFID identifications are located on the basis of the three-dimensional map and the RFID identifications; the RFID identification is arranged on key nodes of port area roads and key nodes of yard roads.
The updating module is used for acquiring and updating the storage yard container placement information at any time;
the first planning module is used for designing a first planning scheme for planning the dispatching and the path of the unmanned transport vehicle by adopting a genetic algorithm according to the container placement information and the position information of the unmanned transport vehicle;
the second planning module is used for combining the first planning scheme with the static characteristic points where the RFID identifications related to the path are located to form a second planning scheme;
and the navigation module is used for navigating the unmanned transport vehicle according to the second planning scheme, shooting videos by the unmanned transport vehicle in the navigation process and positioning roads by comparing static characteristic points.
Further, the teaching module comprises a key node recording unit, and the recorded key nodes comprise intersection points and inflection points of port roads, intersection points and inflection points of yard roads and identification points of container positions.
Further, the teaching module further comprises a three-dimensional map generating unit, wherein the three-dimensional map generating unit is used for removing dynamic factors in videos shot by the taught unmanned transport vehicle, keeping static environment factors, extracting static feature points according to the static environment factors, and generating a three-dimensional map by using a slam algorithm based on the static feature points and the pose of a video camera shooting the videos.
Furthermore, the updating module is provided with a preset interface, is connected with the port logistics management system through the preset interface, and acquires the latest yard container placement information from the port logistics management system.
Further, the navigation module further comprises a relocation unit for relocating before comparing the static feature points, and the relocation method is as follows: the current unmanned transport vehicle collects the image frame where the RFID mark of the first key node passes through is located, and the camera pose of the closest frame in the video of the taught unmanned transport vehicle is used as the camera pose of the current frame, so that relocation is realized.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention can calculate the dispatching and path planning of the unmanned transport vehicle according to the requirement at any time by a genetic algorithm, thereby improving the transport efficiency of the unmanned transport vehicle;
(2) according to the method, the three-dimensional map is generated through the slam algorithm, and the static characteristic points of the positions of the RFID marks on the three-dimensional map are used as comparison objects, so that adverse effects on the navigation effect when the markers are wrong or damaged are avoided, and stable navigation is realized.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, the specific method of the present invention is as follows:
firstly, the intersection and the inflection point of a port road, the intersection and the inflection point of a yard road and the identification point of a container position are set as key nodes, an RFID label is set as a marker at the position of the key node, and the information in the RFID label comprises the ID information of the label. And the unmanned transport vehicle for teaching at the port is provided with RFID reading equipment and communication equipment of the navigation center system, and the RFID information read in real time is sent to the navigation center system.
Secondly, a taught unmanned transport vehicle is used for polling all roads of a port road area (including port roads and container storage yards), the taught unmanned transport vehicle is provided with a camera device in a good sense, a polling video is shot and sent to a navigation center system through communication equipment, the navigation center system constructs a deep learning MaskR-CNN network model, a convolutional neural network structure in the model is used as a feature extractor, feature points of each frame of the video are obtained, dynamic feature points of each frame are identified and removed, and static feature points are reserved; and constructing a three-dimensional map by using a slam algorithm based on the static feature points and the pose of the camera device, and acquiring the static feature points of the frame image where the RFID mark is located in the three-dimensional map.
In the above process, obtaining feature points, identifying dynamic feature points, and constructing a three-dimensional map using slam algorithm are all published conventional technologies in the field of computer image processing, and detailed description is not given in this scheme.
Thirdly, a data connection interface is preset between the navigation center system and the logistics management system of the port, the logistics management system is provided with a trigger, when the placement information (including positions, numbers and the like) of the containers changes, a set program is triggered through the trigger, the changed placement information of the containers is sent to the navigation center system through the data connection interface, and the navigation center system updates the position information of the containers in the system according to the received information.
Fourthly, all the unmanned transport vehicles are provided with communication equipment with a navigation center system, the navigation center system can acquire information such as the positions and the states of the unmanned transport vehicles in real time, and based on the information and the information of the containers and the transport task information (including which goods are transported, which container the unmanned transport vehicles belong to, which position the unmanned transport vehicles are transported to, and the like) which is input to the navigation center system by a worker and needs to be completed by the unmanned transport vehicles, the navigation center system designs a first planning scheme for planning the scheduling and the path of the unmanned transport vehicles by adopting a genetic algorithm;
the specific process is as follows: selecting an unmanned transport vehicle required to be scheduled for a certain transport task according to the position and state information of the unmanned transport vehicle, and obtaining K-1 virtual demand points according to the capacity of the unmanned transport vehicle and the number of containers involved in the transport task; wherein K is the maximum transportation times of the transportation task for the unmanned transportation vehicle; randomly generating a plurality of initial chromosomes according to the virtual demand points and the positions of the containers, performing multiple iterative optimization on the plurality of initial chromosomes through a genetic algorithm by using the shortest path between any two containers, and determining the optimal path according to the optimized plurality of chromosomes.
Fifthly, combining the first planning scheme with static characteristic points at which RFID marks related to the optimal planned path are located, and taking the static characteristic points as positioning reference information of key nodes in the path to form a second planning scheme;
and sixthly, navigating the unmanned transport vehicle according to a second planning scheme, wherein the dispatched unmanned transport vehicle needs to be repositioned in the navigation process, and the repositioning process comprises the following steps: the scheduled unmanned transport vehicle collects the image frame where the RFID identification of the first key node passes through is sent to the navigation center system, and the navigation center system takes the camera pose of the closest frame in the video of the scheduled unmanned transport vehicle as the camera pose of the current frame to realize relocation. If the relocation fails, the relocation operation is repeated at the next key node until the relocation succeeds.
After relocation, the unmanned transport vehicle scheduled in the navigation process shoots videos and sends the videos back to the navigation center system, static feature points are extracted, and road positioning and navigation are carried out by comparing the static feature points of all key nodes.
The navigation center system related in the above method includes:
the teaching module is used for polling all roads in a road area by using a taught unmanned transport vehicle, acquiring a shooting video of the roads, generating a three-dimensional map by using a slam algorithm, and marking static characteristic points where the RFID identifications are located on the basis of the three-dimensional map and the RFID identifications; the RFID identification is arranged on key nodes of port area roads and key nodes of yard roads.
The updating module is used for acquiring and updating the storage yard container placement information at any time;
the first planning module is used for designing a first planning scheme for planning the dispatching and the path of the unmanned transport vehicle by adopting a genetic algorithm according to the container placement information and the position information of the unmanned transport vehicle;
the second planning module is used for combining the first planning scheme with the static characteristic points where the RFID identifications related to the path are located to form a second planning scheme;
and the navigation module is used for navigating the unmanned transport vehicle according to the second planning scheme, shooting videos by the unmanned transport vehicle in the navigation process and positioning roads by comparing static characteristic points.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An automatic navigation method for port unmanned transport vehicles is characterized by comprising the following steps:
s1, setting RFID (radio frequency identification) marks at key nodes of port area roads and key nodes of yard roads;
s2, patrolling all roads in a road area by using the taught unmanned transport vehicle, acquiring a shot video of the roads, generating a three-dimensional map by using a slam algorithm, and marking static feature points where RFID marks are located on the basis of the three-dimensional map and the RFID marks;
s3, collecting and updating the storage yard container placement information at any time;
s4, designing a first planning scheme for planning the dispatching and the path of the unmanned transport vehicle by adopting a genetic algorithm according to the container placement information and the position information of the unmanned transport vehicle;
s5, combining the first planning scheme with static characteristic points where the RFID marks related to the path are located to form a second planning scheme;
and S6, navigating the unmanned transport vehicle according to the second planning scheme, shooting a video of the unmanned transport vehicle in the navigation process, and positioning the road by comparing the static characteristic points.
2. The automatic navigation method for the port unmanned transport vehicle as claimed in claim 1, wherein the key nodes in step S1 include intersection and inflection points of port roads, intersection and inflection points of yard roads, and identification points of container locations.
3. The automatic navigation method for the port unmanned transport vehicle as claimed in claim 1, wherein the step S2 is to generate the three-dimensional map by: removing dynamic factors in videos shot by the taught unmanned transport vehicle, reserving static environment factors, extracting static feature points according to the static environment factors, and generating a three-dimensional map by using a slam algorithm based on the static feature points and the pose of a camera shooting the videos.
4. The automatic navigation method for the port unmanned transport vehicle as claimed in claim 1, wherein the method for collecting and updating the yard container arrangement information in step S3 comprises: and the system is connected with a port logistics management system through a preset interface, and the latest yard container placement information is obtained from the port logistics management system.
5. The automatic navigation method for the port unmanned transport vehicle as claimed in claim 1, wherein in step S6, before comparing the static feature points, a relocation is required, and the relocation method comprises: the current unmanned transport vehicle collects the image frame where the RFID mark of the first key node passes through is located, and the camera pose of the closest frame in the video of the taught unmanned transport vehicle is used as the camera pose of the current frame, so that relocation is realized.
6. An automatic navigation system for port unmanned transport vehicles is characterized by comprising:
the teaching module is used for polling all roads in a road area by using a taught unmanned transport vehicle, acquiring a shooting video of the roads, generating a three-dimensional map by using a slam algorithm, and marking static characteristic points where the RFID identifications are located on the basis of the three-dimensional map and the RFID identifications; the RFID identification is arranged on key nodes of port area roads and key nodes of yard roads.
The updating module is used for acquiring and updating the storage yard container placement information at any time;
the first planning module is used for designing a first planning scheme for planning the dispatching and the path of the unmanned transport vehicle by adopting a genetic algorithm according to the container placement information and the position information of the unmanned transport vehicle;
the second planning module is used for combining the first planning scheme with the static characteristic points where the RFID identifications related to the path are located to form a second planning scheme;
and the navigation module is used for navigating the unmanned transport vehicle according to the second planning scheme, shooting videos by the unmanned transport vehicle in the navigation process and positioning roads by comparing static characteristic points.
7. The automatic navigation system of the port unmanned transport vehicle as claimed in claim 6, wherein the teaching module comprises a key node recording unit, and the recorded key nodes comprise intersection points and inflection points of port area roads, intersection points and inflection points of yard roads, and identification points of container positions.
8. The automatic navigation system of the port unmanned transport vehicle as claimed in claim 6, wherein the teaching module further comprises a three-dimensional map generation unit for removing dynamic factors in a video shot by the taught unmanned transport vehicle, retaining static environment factors, extracting static feature points according to the static environment factors, and generating a three-dimensional map by using a slam algorithm based on the static feature points and the pose of a camera shooting the video.
9. The automatic navigation system of the port unmanned transport vehicle as claimed in claim 6, wherein the update module is provided with a preset interface, the preset interface is connected with the port logistics management system, and the latest container placement information of the storage yard is obtained from the port logistics management system.
10. The automatic navigation system of the port unmanned transport vehicle as claimed in claim 6, wherein the navigation module further comprises a relocation unit for relocation before comparing with static feature points, the relocation method is: the current unmanned transport vehicle collects the image frame where the RFID mark of the first key node passes through is located, and the camera pose of the closest frame in the video of the taught unmanned transport vehicle is used as the camera pose of the current frame, so that relocation is realized.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113091762A (en) * | 2021-04-14 | 2021-07-09 | 欧冶链金再生资源有限公司 | Method and system for planning path of transport vehicle in scrap steel base |
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