CN116160452A - Intelligent factory robot control method - Google Patents
Intelligent factory robot control method Download PDFInfo
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- CN116160452A CN116160452A CN202310288414.8A CN202310288414A CN116160452A CN 116160452 A CN116160452 A CN 116160452A CN 202310288414 A CN202310288414 A CN 202310288414A CN 116160452 A CN116160452 A CN 116160452A
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- intelligent factory
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/02—Sensing devices
- B25J19/021—Optical sensing devices
- B25J19/023—Optical sensing devices including video camera means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses an intelligent factory robot control method, which improves the accuracy and flexibility of robot work, thereby improving the working efficiency of the robot; the method specifically comprises the following steps: acquiring an acquisition image shot by the vision sensing device of each robot in an intelligent factory, and preprocessing the acquisition image; determining the current position and the current state of the robot according to the acquired image obtained after pretreatment; performing recognition analysis on the current state of the robot to obtain a recognition analysis result; according to the identification analysis result, a control command is sent to the robot, and whether the control command contains a mobile position task is judged; if yes, planning a preset moving path for the robot according to the moving position task, so that the robot can complete a target task according to the control instruction.
Description
Technical Field
The invention relates to the technical field related to factory robots, in particular to an intelligent factory robot control method.
Background
Intelligent manufacturing is a core technology of a new round of industry, while industrial robots are the most representative important production equipment in advanced manufacturing systems, and are increasingly applied to modern production and manufacturing processes to replace manual work to efficiently execute industrial production tasks in various complex environments, so that social productivity is improved, and the application of the traditional industrial robots for realizing repeated action tasks according to preset programs cannot adapt to the current requirements of complex and diverse production tasks, so that the robots are required to have higher intelligent degrees to complete specified tasks.
Disclosure of Invention
The invention aims to solve the problems, and designs an intelligent factory robot control method.
The technical scheme of the invention for achieving the purpose is that in the intelligent factory robot control method, the control method comprises the following steps:
acquiring an acquisition image shot by the vision sensing device of each robot in an intelligent factory, and preprocessing the acquisition image;
determining the current position and the current state of the robot according to the acquired image obtained after pretreatment;
performing recognition analysis on the current state of the robot to obtain a recognition analysis result;
according to the identification analysis result, a control command is sent to the robot, and whether the control command contains a mobile position task is judged;
if yes, planning a preset moving path for the robot according to the moving position task, so that the robot can complete a target task according to the control instruction.
Further, in the above intelligent factory robot control method, the preprocessing the collected image includes:
converting the acquired image into a gray image, performing image gray conversion processing to enhance the image quality of the gray image, and changing the overall gray value layout of the gray image to obtain a first processed image;
performing image smoothing on the first processed image to eliminate noise and obtain a second processed image, wherein the image smoothing at least comprises mean filtering, median filtering and Gaussian filtering;
and carrying out image sharpening processing on the second processed image, and highlighting the edge characteristics of the target to obtain a third processed image, wherein the third processed image is an acquired image obtained after preprocessing.
Further, in the above intelligent factory robot control method, the determining the current position and the current state of the robot according to the collected image obtained after the preprocessing includes:
acquiring an acquired image obtained after pretreatment, and determining an area image to be identified;
positioning and acquiring the centroid coordinates of the robot in the region image to be identified through a Blob analysis algorithm;
and obtaining the centroid coordinates of each preset station position in the intelligent factory, comparing the centroid coordinates of the robot with the centroid coordinates of each preset station position to determine the station position closest to the robot, and determining the current position of the robot based on the centroid coordinates of the robot.
Further, in the above-mentioned intelligent factory robot control method, the positioning and obtaining the centroid coordinates of the robot in the image of the area to be identified by using a Blob analysis algorithm includes:
carrying out Gaussian filtering on the region image to be identified through a Canny edge detection algorithm, calculating the amplitude and direction of the gradient, and removing non-extreme points to obtain an edge image;
eliminating noise interference of non-boundary points through Hough transformation curve and least square fitting to complete edge fitting of the image;
and acquiring an image of the region to be identified after edge fitting so as to determine the centroid coordinates of the robot.
Further, in the above intelligent factory robot control method, the identifying and analyzing the current state of the robot to obtain an identifying and analyzing result includes:
acquiring real-time images of all robots in the intelligent factory through a visual sensing device on the robot;
acquiring and processing the real-time image to obtain a state sample data set of the robot, and selecting a training data set from the state sample data set according to a certain proportion;
and constructing an image recognition model through a model of the convolutional neural network, and recognizing and analyzing the current state of the robot through the image recognition model to obtain a recognition and analysis result.
Further, in the above intelligent factory robot control method, the recognition analysis result includes at least a fault state, an idle state, a maintenance state, a busy state, a walking state, and a charging state.
Further, in the above intelligent factory robot control method, the planning a preset moving path for the robot according to the moving position task includes:
analyzing the moving position task and acquiring a target position to which the robot goes in the moving position artifact;
acquiring coordinates of key points according to the current position and the target position of the robot;
and calculating an optimal path of the robot through the key point coordinates to obtain a preset moving path.
Further, in the above-mentioned intelligent factory robot control method, after planning a preset movement path for the robot according to the movement position task, the method further includes:
acquiring a preset moving path of the robot for executing the control command;
when the robot moves, carrying out path tracking by adopting a tracking model to obtain the real-time position of the robot and form an actual running route;
and performing deviation comparison on the preset moving path and the actual running path to obtain course angle deviation.
The visual sensing device has the beneficial effects that the acquired images shot by the visual sensing devices of all robots in the intelligent factory are acquired, and the acquired images are preprocessed; determining the current position and the current state of the robot according to the acquired image obtained after pretreatment; performing recognition analysis on the current state of the robot to obtain a recognition analysis result; according to the identification analysis result, a control command is sent to the robot, and whether the control command contains a mobile position task is judged; if yes, planning a preset moving path for the robot according to the moving position task, so that the robot can complete a target task according to the control instruction; the invention improves the accuracy and the flexibility of the robot work, thereby improving the working efficiency of the robot.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
FIG. 1 is a schematic diagram of a first embodiment of a method for controlling a robot in an intelligent factory according to an embodiment of the present invention;
FIG. 2 is a schematic view of a second embodiment of a method for controlling a robot in an intelligent factory according to an embodiment of the invention
FIG. 3 is a diagram illustrating a third embodiment of a method for controlling a robot in an intelligent factory according to an embodiment of the present invention
FIG. 4 is a diagram illustrating a fourth embodiment of a method for controlling a robot in an intelligent factory according to an embodiment of the invention
FIG. 5 is a schematic view of a fifth embodiment of a method for controlling a robot in an intelligent factory according to an embodiment of the invention
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The invention will be described in detail with reference to the accompanying drawings, as shown in fig. 1, an intelligent factory robot control method, which comprises the following steps:
102, determining the current position and the current state of the robot according to the acquired image obtained after preprocessing;
in this embodiment, the recognition analysis result includes at least a fault state, an idle state, a maintenance state, a busy state, a walking state, and a charging state.
104, sending a control command to the robot according to the identification analysis result, and judging whether the control command contains a mobile position task or not;
In the embodiment of the invention, the acquired images shot by the visual sensing devices of all robots in an intelligent factory are acquired, the acquired images are preprocessed, and the current position and the current state of the robots are determined according to the acquired images obtained after preprocessing; performing recognition analysis on the current state of the robot to obtain a recognition analysis result; according to the identification analysis result, a control command is sent to the robot, and whether the control command contains a mobile position task is judged; if yes, planning a preset moving path for the robot according to the moving position task, so that the robot can complete a target task according to the control instruction; the invention improves the accuracy and the flexibility of the robot work, thereby improving the working efficiency of the robot.
In this embodiment, referring to fig. 2, in a second embodiment of the method for controlling a robot in an intelligent factory according to the present invention, preprocessing an acquired image specifically includes the following steps:
and 203, performing image sharpening processing on the second processed image, and highlighting the edge characteristics of the target to obtain a third processed image, wherein the third processed image is an acquired image obtained after preprocessing.
In this embodiment, referring to fig. 3, in a third embodiment of the method for controlling a robot in an intelligent factory according to the present invention, determining a current position of the robot specifically includes the following steps:
in the embodiment, gaussian filtering is carried out on the area image to be identified through a Canny edge detection algorithm, the amplitude and the direction of the gradient are calculated, and non-extreme points are eliminated, so that an edge image is obtained; eliminating noise interference of non-boundary points through Hough transformation curve and least square fitting to complete edge fitting of the image; and acquiring an image of the region to be identified after edge fitting so as to determine the centroid coordinates of the robot.
In this embodiment, referring to fig. 4, in a fourth embodiment of the method for controlling a robot in an intelligent factory according to the present invention, the identification and analysis process specifically includes the following steps:
and 403, constructing an image recognition model through a model of the convolutional neural network, and recognizing and analyzing the current state of the robot through the image recognition model to obtain a recognition and analysis result.
In this embodiment, referring to fig. 5, in a fifth embodiment of a method for controlling a robot in an intelligent factory according to the present invention, the method for planning a preset moving path for the robot specifically includes the following steps:
In this embodiment, after the robot completes the task of moving the position, a preset movement path of the robot for executing the control command is obtained; when the robot moves, a tracking model is adopted to track a path, so that the real-time position of the robot is obtained, and an actual running route is formed; and carrying out deviation comparison on the preset moving path and the actual running path to obtain course angle deviation.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. An intelligent factory robot control method, wherein a vision sensing device is configured on the robot, the control method comprises the following steps:
acquiring an acquisition image shot by the vision sensing device of each robot in an intelligent factory, and preprocessing the acquisition image;
determining the current position and the current state of the robot according to the acquired image obtained after pretreatment;
performing recognition analysis on the current state of the robot to obtain a recognition analysis result;
according to the identification analysis result, a control command is sent to the robot, and whether the control command contains a mobile position task is judged;
if yes, planning a preset moving path for the robot according to the moving position task, so that the robot can complete a target task according to the control instruction.
2. The intelligent plant robot control method according to claim 1, wherein the preprocessing the acquired image includes:
converting the acquired image into a gray image, performing image gray conversion processing to enhance the image quality of the gray image, and changing the overall gray value layout of the gray image to obtain a first processed image;
performing image smoothing on the first processed image to eliminate noise and obtain a second processed image, wherein the image smoothing at least comprises mean filtering, median filtering and Gaussian filtering;
and carrying out image sharpening processing on the second processed image, and highlighting the edge characteristics of the target to obtain a third processed image, wherein the third processed image is an acquired image obtained after preprocessing.
3. The method according to claim 1, wherein determining the current position and the current state of the robot from the acquired image obtained after the preprocessing comprises:
acquiring an acquired image obtained after pretreatment, and determining an area image to be identified;
positioning and acquiring the centroid coordinates of the robot in the region image to be identified through a Blob analysis algorithm;
and obtaining the centroid coordinates of each preset station position in the intelligent factory, comparing the centroid coordinates of the robot with the centroid coordinates of each preset station position to determine the station position closest to the robot, and determining the current position of the robot based on the centroid coordinates of the robot.
4. The method for controlling an intelligent factory robot according to claim 3, wherein the positioning and obtaining the centroid coordinates of the robot in the image of the area to be identified by using a Blob analysis algorithm comprises:
carrying out Gaussian filtering on the region image to be identified through a Canny edge detection algorithm, calculating the amplitude and direction of the gradient, and removing non-extreme points to obtain an edge image;
eliminating noise interference of non-boundary points through Hough transformation curve and least square fitting to complete edge fitting of the image;
and acquiring an image of the region to be identified after edge fitting so as to determine the centroid coordinates of the robot.
5. The method according to claim 1, wherein the step of performing recognition analysis on the current state of the robot to obtain a recognition analysis result comprises:
acquiring real-time images of all robots in the intelligent factory through a visual sensing device on the robot;
acquiring and processing the real-time image to obtain a state sample data set of the robot, and selecting a training data set from the state sample data set according to a certain proportion;
and constructing an image recognition model through a model of the convolutional neural network, and recognizing and analyzing the current state of the robot through the image recognition model to obtain a recognition and analysis result.
6. The intelligent factory robot control method according to claim 5, wherein the recognition analysis result includes at least a fault state, an idle state, a maintenance state, a busy state, a walking state, and a charging state.
7. The method according to claim 1, wherein the planning a preset moving path for the robot according to the moving position task comprises:
analyzing the moving position task and acquiring a target position to which the robot goes in the moving position artifact;
acquiring coordinates of key points according to the current position and the target position of the robot;
and calculating an optimal path of the robot through the key point coordinates to obtain a preset moving path.
8. The method according to claim 1, further comprising, after planning a preset movement path for the robot according to the movement position task:
acquiring a preset moving path of the robot for executing the control command;
when the robot moves, carrying out path tracking by adopting a tracking model to obtain the real-time position of the robot and form an actual running route;
and performing deviation comparison on the preset moving path and the actual running path to obtain course angle deviation.
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| CN202310288414.8A CN116160452A (en) | 2023-03-23 | 2023-03-23 | Intelligent factory robot control method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116859788A (en) * | 2023-08-04 | 2023-10-10 | 北京三维天地科技股份有限公司 | Multi-equipment task scheduling central control management platform |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116859788A (en) * | 2023-08-04 | 2023-10-10 | 北京三维天地科技股份有限公司 | Multi-equipment task scheduling central control management platform |
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