CN115586552A - Method for accurately secondarily positioning unmanned truck collection under port tyre crane or bridge crane - Google Patents

Method for accurately secondarily positioning unmanned truck collection under port tyre crane or bridge crane Download PDF

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Publication number
CN115586552A
CN115586552A CN202211051400.6A CN202211051400A CN115586552A CN 115586552 A CN115586552 A CN 115586552A CN 202211051400 A CN202211051400 A CN 202211051400A CN 115586552 A CN115586552 A CN 115586552A
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unmanned
positioning
crane
bridge crane
point cloud
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CN202211051400.6A
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董杰
骆嫚
曹恺
尤敏
汪统
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Dongfeng Yuexiang Technology Co Ltd
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Dongfeng Yuexiang Technology Co Ltd
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Priority to CN202211051400.6A priority Critical patent/CN115586552A/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/18—Control systems or devices
    • B66C13/46—Position indicators for suspended loads or for crane elements
    • 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
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention relates to an accurate secondary positioning method for an unmanned truck collection under a port tyre crane or a bridge crane, which comprises the steps of scanning the tyre crane or the bridge crane through a laser radar arranged on the unmanned truck collection to obtain point cloud data, carrying out noise reduction on the point cloud data through a bilateral filtering algorithm, and then processing the point cloud data through a RANSAC algorithm to obtain a linear equation about the tyre crane or the bridge crane, thereby further obtaining accurate position information and scheduling the unmanned truck collection. The invention has low cost, is not influenced by weather, and further improves the working efficiency of the unmanned card collection.

Description

Method for accurately secondarily positioning unmanned truck collection under port tyre crane or bridge crane
Technical Field
The invention relates to the technical field of container tyre cranes or bridge cranes, in particular to an unmanned truck-collecting accurate secondary positioning method under a port tyre crane or bridge crane.
Background
At present, world trade is developed vigorously, and the globalization trend is overwhelming, and as an important component part of global trade, maritime trade is an important means for each country to participate in international trade. Port terminals are the junction of marine and land transportation, and their importance in trade transportation is self evident. Therefore, how to improve the transfer efficiency of the harbor containers, reduce the labor cost and the operation risk, so as to improve the harbor competitiveness is always the goal pursued by each harbor in the world.
In order to improve the port operation efficiency, reduce the labor cost and reduce the operation risk, the improvement of the automation level and even the intelligence level of port operation is a necessary way for the development of all ports in the world. In the process of port wharf automation, most of the automated wharfs adopt an operation mode of 'bridge crane + tyre crane + IGV', and only newly-built ports often adopt novel operation equipment. However, in the conventional port terminal, a large amount of tyre crane equipment still exists to operate in a port yard, wherein the service life of most tyre cranes still has decades, and in addition, the cost of site construction and the like, the port cannot replace all tyre cranes with other related equipment. Therefore, in the process of automation of the port, the automation of the tire crane can well improve the working efficiency.
With the continuous development of computer technology, detection equipment, intelligent identification and automatic motion control technology, a technical foundation is laid for the automation of the tire crane. Particularly, the development of the identification technology enables the tire crane to automatically enter a fast lane. The container is positioned and tracked by utilizing an image processing technology, such as a container positioning technology based on a camera; there are also methods of identifying and locating containers by laser scanning.
In the prior art, based on a visual image CPS system, a high-definition camera is additionally arranged on a tire crane or a bridge crane, a container is subjected to target identification, feature extraction and positioning by using algorithms such as deep learning, the relative position of the container relative to a tire crane or a bridge crane gripper is obtained, the relative position is sent to an unmanned truck through a cloud control platform, the unmanned truck is controlled to move to a target position, and fine alignment is performed.
In the second prior art, a high-precision three-dimensional laser scanner is additionally arranged on a tire crane and a bridge crane based on a laser point cloud CPS system. The method comprises the steps of utilizing algorithms such as deep learning to carry out target recognition, feature extraction and positioning on a container to obtain the relative position of the container relative to a tire crane and a bridge crane gripper, sending the relative position to an unmanned container truck through a cloud control platform, controlling the unmanned container truck to move to a target position, and carrying out precise alignment.
Disclosure of Invention
In view of the defects of the prior art, the invention provides the accurate secondary positioning method for the unmanned truck under the port tyre crane or the bridge crane, which has low cost, cannot be influenced by weather and further improves the working efficiency of the unmanned truck.
In order to achieve the above objects and other related objects, the present invention provides the following technical solutions: an unmanned truck-collecting accurate secondary positioning method under port tire crane comprises the following steps:
a1: the cloud platform loads a port machine point cloud model and a global positioning initial value according to the task, outputs global path planning information, and the unmanned hub card reaches a global target point based on the global path planning information and acquires GPS information of the global target point;
a2: according to the GPS information of the global target point, the cloud platform dispatches the tire crane to reach the global target point, and primary positioning is completed;
a3: scanning the inner sides of the front and rear side beams of the tire crane based on laser radars symmetrically arranged on the left side and the right side of the unmanned collecting card to obtain point cloud data of the inner sides of the front and rear side beams of the tire crane;
a4: according to the global positioning initial value, based on a bilateral filtering algorithm, carrying out noise reduction on point cloud data on the inner sides of front and rear side beams of the tire crane, and outputting an ordered point cloud data set;
a5: processing the ordered point cloud data set by adopting an RANSAC algorithm according to the ordered point cloud data set, and outputting at least four linear equations about ax + by + c = 0;
a6: and acquiring central point coordinate information between adjacent linear equations based on data of the linear equations, and scheduling the unmanned concentrated card to reach an intersection point or a projection point of the intersection point of the central point coordinate connection line by the cloud platform to complete secondary accurate positioning.
Further, the cloud platform is wirelessly connected with the unmanned truck and the tyre crane through 5G communication respectively.
Further, the first positioning and the second accurate positioning are both established in a unified coordinate system with the global target point as a coordinate origin.
In order to achieve the above objects and other related objects, the invention also provides an unmanned truck-collecting accurate secondary positioning method under the port bridge crane, which comprises the following steps:
b1: the cloud platform loads a port machine point cloud model and a global positioning initial value according to the task, outputs global path planning information and bridge crane midpoint GPS information, and the unmanned card concentrator reaches the bridge crane midpoint based on the global path planning information to complete first positioning;
b2: scanning the inner side of the bridge crane top beam based on a laser radar arranged at the center of the top of the unmanned truck, and acquiring point cloud data of the inner side of the bridge crane top beam;
b3: according to the global positioning initial value, based on a bilateral filtering algorithm, carrying out noise reduction processing on the point cloud data on the inner side of the bridge crane top beam, and outputting an ordered point cloud data set;
b4: performing point cloud plane compression on the ordered point cloud data set according to intervals, extracting an inner edge side line of the ordered point cloud data set by adopting an RANSAC algorithm, and outputting a linear equation of ax '+ by' + c = 0;
b5: and acquiring a midpoint coordinate of the bridge crane top beam based on a linear equation about ax '+ by' + c =0, and scheduling the unmanned truck to reach the midpoint coordinate by the cloud platform to finish secondary accurate positioning.
Further, the cloud platform and the unmanned card concentrator are in wireless connection through 5G communication.
Further, the first positioning and the second accurate positioning are both established in a unified coordinate system with the middle point of the bridge crane as the origin of coordinates.
In order to achieve the above objects and other related objects, the present invention also provides an unmanned secondary harbor tire crane or under-bridge truck positioning system, comprising a computer device programmed or configured to perform any one of the steps of the method for accurately positioning an unmanned secondary harbor tire crane or under-bridge truck.
To achieve the above and other related objects, the present invention also provides a computer readable storage medium having stored thereon a computer program programmed or configured to execute the method for the precise secondary positioning of an unmanned sub-truck for a port tyre crane or bridge crane according to any one of the claims.
The invention has the following positive effects:
1) The invention scans the target by the laser radar, and has low cost and wide applicability.
2) The laser radar adopted by the invention is not interfered and influenced by weather, and the working efficiency of the tire crane and the unmanned truck is improved.
3) The method performs linear fitting and other operations on the acquired point cloud data, so that the unmanned aerial vehicle card is accurately and stably positioned.
Drawings
FIG. 1 is a schematic flow chart of a tire crane and unmanned truck positioning method according to the present invention;
FIG. 2 is a schematic flow chart of the positioning method of the bridge crane and the unmanned truck collection of the present invention;
FIG. 3 is a schematic view of a tire crane lidar scanning of the present invention;
fig. 4 is a schematic view of the laser radar scanning of the bridge crane of the present invention.
Detailed Description
The following embodiments of the present invention are provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
Example 1: as shown in fig. 1, a method for accurately positioning an unmanned truck under a port tire crane for the second time comprises the following steps:
a1: the cloud platform loads a port machine point cloud model and a global positioning initial value according to a task, outputs global path planning information, and the unmanned hub card reaches a global target point based on the global path planning information and acquires GPS information of the global target point;
a2: according to the GPS information of the global target point, the cloud platform schedules the tyre crane to reach the global target point, and primary positioning is completed;
a3: scanning the inner sides of the front and rear side beams of the tire crane based on laser radars symmetrically arranged on the left side and the right side of the unmanned trucks to acquire point cloud data of the inner sides of the front and rear side beams of the tire crane;
a4: according to the global positioning initial value, based on a bilateral filtering algorithm, carrying out noise reduction on point cloud data on the inner sides of the front and rear side beams of the tire crane, and outputting an ordered point cloud data set;
a5: processing the ordered point cloud data set by adopting an RANSAC algorithm according to the ordered point cloud data set, and outputting at least four linear equations about ax + by + c = 0;
a6: and acquiring central point coordinate information between adjacent linear equations based on data of the linear equations, and scheduling the unmanned concentrated card to reach an intersection point or a projection point of the intersection point of the central point coordinate connection line by the cloud platform to complete secondary accurate positioning.
Specifically, the cloud platform is wirelessly connected with the unmanned truck and the tire crane through 5G communication respectively.
Specifically, the first positioning and the second accurate positioning are both established in a unified coordinate system with the global target point as a coordinate origin.
Specifically, as shown in fig. 3, the unmanned aerial vehicle collection card is positioned for the first time, reaches the lower part of the tire crane, scans the inner sides of the front and rear beams of the tire crane through laser radars symmetrically arranged on two sides to obtain a cloud point diagram of the inner sides of the front and rear beams, and passes through different two-point coordinates (x) on the same straight line on the inner sides of the tire crane 1 ,y 1 ) And (x) 2 ,y 2 ) The linear equations can be obtained, at least four linear equations can be obtained, the central point is determined, secondary accurate positioning is further carried out on the unmanned truck, the secondary positioning is more smooth and reliable, and the real-time position of the unmanned truck relative to the tire crane can be dynamically output to be used as control real-time feedback to control vehicle motion.
Example 2: as shown in fig. 2, a method for accurately positioning an unmanned truck collection under a port bridge crane for the second time comprises the following steps:
b1: the cloud platform loads a port machine point cloud model and a global positioning initial value according to the task, outputs global path planning information and bridge crane midpoint GPS information, and the unmanned truck reaches the bridge crane midpoint based on the global path planning information to complete first positioning;
b2: scanning the inner side of the bridge crane top beam based on a laser radar arranged at the center of the top of the unmanned truck, and acquiring point cloud data of the inner side of the bridge crane top beam;
b3: according to the global positioning initial value, based on a bilateral filtering algorithm, carrying out noise reduction processing on the point cloud data on the inner side of the bridge crane top beam, and outputting an ordered point cloud data set;
b4: performing point cloud plane compression on the ordered point cloud data set according to intervals, extracting an inner edge line of the ordered point cloud data set by adopting an RANSAC algorithm, and outputting a linear equation about ax '+ by' + c = 0;
b5: and acquiring a midpoint coordinate of the bridge crane top beam based on a linear equation about ax '+ by' + c =0, and scheduling the unmanned truck to reach the midpoint coordinate by the cloud platform to finish secondary accurate positioning.
Specifically, the cloud platform and the unmanned card concentrator are in wireless connection through 5G communication.
Specifically, the first positioning and the second accurate positioning are both established in a unified coordinate system with the midpoint of the bridge crane as the origin of coordinates.
Specifically, as shown in fig. 4, after the unmanned aerial vehicle is first positioned, the unmanned aerial vehicle reaches the midpoint of the bridge crane, the bridge crane top beam is scanned by a laser radar at the center of the top of the unmanned aerial vehicle, point cloud data on the inner side of the bridge crane top beam is obtained, a linear equation ax '+ by' + c =0 about the bridge crane top beam is further obtained by a straight line fitting method, the coordinates of the center point of the bridge crane top beam are obtained through the points at the two ends, the unmanned aerial vehicle is dispatched by a cloud dispatching platform and reaches the center point of the bridge crane top beam, and thus secondary accurate positioning is completed.
In conclusion, the invention has low cost, is not influenced and interfered by weather, improves the working efficiency of the unmanned truck, improves the working efficiency of the tyre crane and the bridge crane, prevents unnecessary loss and improves the production benefit.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (8)

1. The accurate secondary positioning method for the unmanned collecting card under the port tire crane is characterized in that laser radars are symmetrically arranged on the left side and the right side of the unmanned collecting card, and the secondary positioning method comprises the following steps:
a1: the cloud platform loads a port machine point cloud model and a global positioning initial value according to a task, outputs global path planning information, and the unmanned hub card reaches a global target point based on the global path planning information and acquires GPS information of the global target point;
a2: according to the GPS information of the global target point, the cloud platform dispatches the tire crane to reach the global target point, and primary positioning is completed;
a3: scanning the inner sides of the front and rear side beams of the tire crane based on the laser radar of the unmanned card to acquire point cloud data of the inner sides of the front and rear side beams of the tire crane;
a4: according to the global target point positioning initial value, based on a bilateral filtering algorithm, carrying out noise reduction processing on point cloud data on the inner sides of the front and rear side beams of the tire crane, and outputting an ordered point cloud data set;
a5: processing the ordered point cloud data set by adopting an RANSAC algorithm according to the ordered point cloud data set, and outputting at least four linear equations about ax + by + c = 0;
a6: and acquiring central point coordinate information between adjacent linear equations based on data of the linear equations, and scheduling the unmanned concentrated card to reach an intersection point or a projection point of the intersection point of the central point coordinate connection line by the cloud platform to complete secondary accurate positioning.
2. The method for accurately positioning the unmanned truck under the tire crane in the harbor according to claim 1, which is characterized in that: in step A1, the cloud platform is wirelessly connected with the unmanned truck and the tire crane through 5G communication, respectively.
3. The method for accurately positioning the unmanned truck under the tire crane in the harbor according to claim 1, which is characterized in that: the first positioning in step A1 and the second fine positioning in step A6 are both established in a unified coordinate system with the global target point as the origin of coordinates.
4. An unmanned card collecting accurate secondary positioning method under a port bridge crane is characterized by comprising the following steps:
b1: the cloud platform loads a port machine point cloud model and a global positioning initial value according to a task, outputs global path planning information and bridge crane midpoint GPS information, and the unmanned truck reaches the bridge crane midpoint based on the global path planning information and the bridge crane GPS information to complete first positioning;
b2: scanning the inner side of the top beam of the bridge crane based on a laser radar arranged at the center of the top of the unmanned container truck to acquire point cloud data of the inner side of the top beam of the bridge crane;
b3: according to the global positioning initial value, based on a bilateral filtering algorithm, carrying out noise reduction processing on the point cloud data on the inner side of the bridge crane top beam, and outputting an ordered point cloud data set;
b4: performing point cloud plane compression on the ordered point cloud data set according to intervals, extracting an inner edge side line of the ordered point cloud data set by adopting an RANSAC algorithm, and outputting a linear equation of ax '+ by' + c = 0;
b5: and acquiring a midpoint coordinate of the bridge crane top beam based on a linear equation about ax '+ by' + c =0, and scheduling the unmanned truck to reach the midpoint coordinate by the cloud platform to finish secondary accurate positioning.
5. The method for accurately positioning the unmanned card collection under the port bridge crane according to claim 4, wherein the method comprises the following steps: in step B1, the cloud platform and the unmanned aggregation card are wirelessly connected through 5G communication.
6. The method for accurately secondarily positioning the unmanned trucks under the port bridge crane according to claim 4, wherein the method comprises the following steps: the first positioning in the step B1 and the second accurate positioning in the step B5 are established under a unified coordinate system taking the middle point of the bridge crane as a coordinate origin.
7. An unmanned secondary harbor truck-mounting accurate secondary positioning system for a tire crane or a bridge crane, comprising a computer device, wherein the computer device is programmed or configured to perform the steps of the unmanned secondary harbor truck-mounting accurate secondary positioning method for a tire crane or a bridge crane according to any one of claims 1 to 6.
8. A computer readable storage medium having stored thereon a computer program programmed or configured to perform the method for precise secondary positioning of an unmanned truck for a harbour tyre or bridge according to any one of claims 1 to 6.
CN202211051400.6A 2022-08-31 2022-08-31 Method for accurately secondarily positioning unmanned truck collection under port tyre crane or bridge crane Pending CN115586552A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116902804A (en) * 2023-09-12 2023-10-20 深圳慧拓无限科技有限公司 Tire crane positioning method and system based on single-line laser radar

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116902804A (en) * 2023-09-12 2023-10-20 深圳慧拓无限科技有限公司 Tire crane positioning method and system based on single-line laser radar
CN116902804B (en) * 2023-09-12 2024-02-02 深圳慧拓无限科技有限公司 Tire crane positioning method and system based on single-line laser radar

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