CN112824307A - Crane jib control method based on machine vision - Google Patents
Crane jib control method based on machine vision Download PDFInfo
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- CN112824307A CN112824307A CN201911138377.2A CN201911138377A CN112824307A CN 112824307 A CN112824307 A CN 112824307A CN 201911138377 A CN201911138377 A CN 201911138377A CN 112824307 A CN112824307 A CN 112824307A
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- obstacle
- crane
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- suspension arm
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- 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
- B66C13/16—Applications of indicating, registering, or weighing devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
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- Mechanical Engineering (AREA)
- Control And Safety Of Cranes (AREA)
Abstract
The invention provides a crane jib control method based on machine vision, which comprises the following steps: s1, collecting image data through a camera device on the crane; s2 transmitting the image data collected in the step S1 to the processor on the crane; the S3 processor preprocesses the image data, judges whether there is an obstacle in the image, if yes, determines the position information of the obstacle, and transmits the position information to the suspension arm control system of the crane; s4, analyzing and calculating the position of the obstacle by the boom control system, and planning and updating a moving route to the target position in real time; and S5, the boom control system controls the boom of the crane to move according to the planned moving route, avoids the obstacle and moves to the designated position. The invention has the beneficial effects that: accurately determining the position of the obstacle, controlling the suspension arm to change the path and avoiding the obstacle; and the image around the crane jib can be obtained in real time, the planned path is thinned in real time, and the problem of untimely reminding in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of crane jib control, in particular to a crane jib control method based on machine vision.
Background
The crane is widely used in the building and transportation industry, and in the crane operation process, because a crane operator is not familiar with the hoisting environment, attention is usually focused on the hoisting weight in the hoisting operation process, and the position of the boom is ignored, so that the boom collides with an obstacle, and industrial accidents in the hoisting operation process of the crane are frequent.
In order to prevent the crane from colliding with objects in an operation area in the hoisting process, various sensors are generally installed on obstacles in the crane and the operation area in the hoisting process to detect people and objects in the operation area of the crane so as to establish a position system to avoid potential collision in real time, however, the effect is not good, when collision is dangerous, an operator is reminded through a telephone, an interphone and the like, the reminding is not timely, and the collision danger is still possibly caused.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a crane boom control method based on machine vision.
The embodiment of the invention provides a crane jib control method based on machine vision, which comprises the following steps:
s1, acquiring image data through the camera device on the crane;
s2 transmitting the image data collected in the step S1 to the processor on the crane;
s3, preprocessing the image data by the processor, judging whether an obstacle exists in the image, if so, determining obstacle position information, and transmitting the position information to a suspension arm control system of the crane;
s4, the boom control system analyzes and calculates the position of the obstacle, and plans and updates the moving route reaching the target position in real time;
s5, the suspension arm control system controls the suspension arm of the crane to move according to the planned moving route, avoids the obstacle and moves to the designated position.
Further, the step S1 is specifically: firstly, a camera device is fixed on the crane and positioned at the connecting end of the suspension arm and the crane, the camera device is a rotatable camera, the number of the cameras can be multiple, the cameras are distributed around the suspension arm, and image data at a low position is collected in real time.
Further, the step S3 is specifically:
s3.1, carrying out gray processing on the obtained image data by using an image splicing algorithm to obtain a gray image, when the number of the camera devices is multiple, carrying out splicing fusion algorithm processing on the image data acquired by each camera device respectively to obtain a complete image below the tail end of the crane jib, selecting an SURF (speeded up robust feature) algorithm by using the splicing fusion algorithm, and carrying out gray processing on the complete image to obtain a required gray image;
s3.2, carrying out binarization processing on the gray level image in the step S3.1, firstly calculating a self-adaptive optimal threshold value by using an OTSU algorithm, and then processing the gray level image by using the threshold value as a parameter in a CANNY algorithm to obtain a binary image with obstacle edge information;
s3.3, identifying edge information in each image and determining coordinates of edge pixel points of the obstacle according to the edge information;
and S3.4, performing coordinate transformation according to the relative position of the camera device and the suspension arm, and determining the position coordinate of the obstacle in each image, namely determining the coordinate of the obstacle position relative to the suspension arm.
Further, in step S4, specifically, the method includes:
s4.1, performing coordinate conversion on the position information of the obstacle, and determining the position coordinate of the obstacle relative to the tail end of the crane jib;
and S4.2, judging whether the obstacle is on the existing planned route or not according to the position coordinate of the obstacle relative to the tail end of the crane jib, if so, re-planning the route to bypass the obstacle, and otherwise, continuing to advance according to the original planned route.
The technical scheme provided by the embodiment of the invention has the following beneficial effects: the invention relates to a crane jib control method based on machine vision, which comprises the steps of acquiring images around a crane jib by using a camera device, judging an obstacle, accurately determining the position of the obstacle through coordinate positioning, and further controlling the crane jib to change a path to avoid the obstacle; and the image around the crane jib can be acquired in real time, the planned path is thinned in real time, and the problems that the reminding is not timely and the barrier cannot be avoided in time in the prior art are solved.
Drawings
FIG. 1 is a flow chart of a crane boom control method based on machine vision according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present invention provides a crane boom control method based on machine vision, including the following steps:
s1, acquiring image data through the camera device on the crane; specifically, firstly, the camera device is fixed on the crane and located at the connecting end of the suspension arm and the crane, the camera device is a rotatable camera, and the number of the cameras can be multiple and are distributed around the suspension arm to collect image data of the lower part in real time.
S2, transmitting the image data collected in the step S1 to the processor on the crane, collecting the image data around the crane jib in real time, and transmitting the collected image data to the processor on the crane in real time;
s3, preprocessing the image data by the processor, judging whether an obstacle exists in the image, if so, determining obstacle position information, and transmitting the position information to a suspension arm control system of the crane; specifically, the method comprises the following steps:
s3.1, carrying out gray processing on the obtained image data by using an image splicing algorithm to obtain a gray image, when the number of the camera devices is multiple, carrying out splicing fusion algorithm processing on the image data acquired by each camera device respectively to obtain a complete image below the tail end of the crane jib, selecting an SURF (speeded up robust feature) algorithm by using the splicing fusion algorithm, and carrying out gray processing on the complete image to obtain a required gray image;
s3.2, carrying out binarization processing on the gray level image in the step S3.1, firstly calculating a self-adaptive optimal threshold value by using an OTSU algorithm, and then processing the gray level image by using the threshold value as a parameter in a CANNY algorithm to obtain a binary image with obstacle edge information;
s3.3, identifying edge information in each image and determining coordinates of edge pixel points of the obstacle according to the edge information;
and S3.4, performing coordinate transformation according to the relative position of the camera device and the suspension arm, and determining the position coordinate of the obstacle in each image, namely determining the coordinate of the obstacle position relative to the suspension arm.
Thus, the relative position of the obstacle relative to the crane jib is determined by processing the image data acquired by the camera device.
S4, the boom control system analyzes and calculates the position of the obstacle, and plans and updates the moving route reaching the target position in real time; specifically, the method comprises the following steps:
s4.1, performing coordinate conversion on the position information of the obstacle, and determining the position coordinate of the obstacle relative to the tail end of the crane jib;
and S4.2, judging whether the obstacle is on the existing planned route or not according to the position coordinate of the obstacle relative to the tail end of the crane jib, if so, re-planning the route to bypass the obstacle, and otherwise, continuing to advance according to the original planned route.
S5, the suspension arm control system controls the suspension arm of the crane to move according to the planned moving route, avoids the obstacle and moves to the designated position.
The invention relates to a crane jib control method based on machine vision, which comprises the steps of acquiring images around a crane jib by using a camera device, judging an obstacle, accurately determining the position of the obstacle through coordinate positioning, and further controlling the crane jib to change a path to avoid the obstacle; and the image around the crane jib can be acquired in real time, the planned path is thinned in real time, and the problems that the reminding is not timely and the barrier cannot be avoided in time in the prior art are solved.
In this document, the terms front, back, upper and lower are used to define the components in the drawings and the positions of the components relative to each other, and are used for clarity and convenience of the technical solution. It is to be understood that the use of the directional terms should not be taken to limit the scope of the claims.
The features of the embodiments and embodiments described herein above may be combined with each other without conflict.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A crane boom control method based on machine vision is characterized by comprising the following steps:
s1, acquiring image data through the camera device on the crane;
s2 transmitting the image data collected in the step S1 to the processor on the crane;
s3, preprocessing the image data by the processor, judging whether an obstacle exists in the image, if so, determining obstacle position information, and transmitting the position information to a suspension arm control system of the crane;
s4, the boom control system analyzes and calculates the position of the obstacle, and plans and updates the moving route reaching the target position in real time;
s5, the suspension arm control system controls the suspension arm of the crane to move according to the planned moving route, avoids the obstacle and moves to the designated position.
2. The machine-vision-based crane boom control method as claimed in claim 1, wherein said step S1 is specifically: firstly, a camera device is fixed on the crane and positioned at the connecting end of the suspension arm and the crane, the camera device is a rotatable camera, the number of the cameras can be multiple, the cameras are distributed around the suspension arm, and image data at a low position is collected in real time.
3. The machine-vision-based crane boom control method as claimed in claim 1, wherein said step S3 is specifically:
s3.1, carrying out gray processing on the obtained image data by using an image splicing algorithm to obtain a gray image, when the number of the camera devices is multiple, carrying out splicing fusion algorithm processing on the image data acquired by each camera device respectively to obtain a complete image below the tail end of the crane jib, selecting an SURF (speeded up robust feature) algorithm by using the splicing fusion algorithm, and carrying out gray processing on the complete image to obtain a required gray image;
s3.2, carrying out binarization processing on the gray level image in the step S3.1, firstly calculating a self-adaptive optimal threshold value by using an OTSU algorithm, and then processing the gray level image by using the threshold value as a parameter in a CANNY algorithm to obtain a binary image with obstacle edge information;
s3.3, identifying edge information in each image and determining coordinates of edge pixel points of the obstacle according to the edge information;
and S3.4, performing coordinate transformation according to the relative position of the camera device and the suspension arm, and determining the position coordinate of the obstacle in each image, namely determining the coordinate of the obstacle position relative to the suspension arm.
4. The machine-vision-based crane boom control method as claimed in claim 1, wherein said step S4 specifically comprises:
s4.1, performing coordinate conversion on the position information of the obstacle, and determining the position coordinate of the obstacle relative to the tail end of the crane jib;
and S4.2, judging whether the obstacle is on the existing planned route or not according to the position coordinate of the obstacle relative to the tail end of the crane jib, if so, re-planning the route to bypass the obstacle, and otherwise, continuing to advance according to the original planned route.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911138377.2A CN112824307A (en) | 2019-11-20 | 2019-11-20 | Crane jib control method based on machine vision |
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| CN201911138377.2A CN112824307A (en) | 2019-11-20 | 2019-11-20 | Crane jib control method based on machine vision |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114368693A (en) * | 2021-12-01 | 2022-04-19 | 中联重科股份有限公司 | Anti-collision method and device for boom, processor and crane |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114368693A (en) * | 2021-12-01 | 2022-04-19 | 中联重科股份有限公司 | Anti-collision method and device for boom, processor and crane |
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Application publication date: 20210521 |