CN110253581A - A kind of auxiliary grip method of view-based access control model identification - Google Patents
A kind of auxiliary grip method of view-based access control model identification Download PDFInfo
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- CN110253581A CN110253581A CN201910552953.1A CN201910552953A CN110253581A CN 110253581 A CN110253581 A CN 110253581A CN 201910552953 A CN201910552953 A CN 201910552953A CN 110253581 A CN110253581 A CN 110253581A
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000013135 deep learning Methods 0.000 claims abstract description 22
- 230000005540 biological transmission Effects 0.000 claims description 8
- 230000033001 locomotion Effects 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000002604 ultrasonography Methods 0.000 claims 1
- 230000004888 barrier function Effects 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
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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
-
- 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
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
-
- 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
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64C—AEROPLANES; HELICOPTERS
- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D47/00—Equipment not otherwise provided for
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
- H02G1/00—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines
- H02G1/02—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines for overhead lines or cables
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
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- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The present invention relates to a kind of auxiliary grip methods that mechanical arm field more particularly to view-based access control model identify, include the following steps；Build deep learning frame and the network architecture；Acquire route image information；Identification is trained according to collected information；Target road section image information is captured using camera；The image of acquisition is analyzed；Obtain the image and location information of route to be processed；Utilize ultrasonic distance measuring module measuring and calculating and target object distance；Fetching instruction is sent to mechanical arm；The motor drive machinery arm of mechanical arm moves；Grab target object.The present invention is removed by the method for unmanned plane auxiliary mechanical arm, and clearance time is very short, and by deep learning frame, after training, is reached so that the identification of barrier grabs more accurately beneficial effect.
Description
Technical field
The present invention relates to a kind of auxiliary grip methods that mechanical arm field more particularly to view-based access control model identify.
Background technique
More transmission overhead lines are located in the jungle of field mountain area, and the sundries and too long branch being suspended on electric wire cause
Transmission line of electricity, which discharges to plant, to trip, and influences operation of power networks.Using unmanned plane auxiliary mechanical arm method remove route float hang object,
Branch etc. is interfered, the defect elimination time had both been shortened, has been greatly reduced the labor intensity of work.Moreover, clearance time is very short, to line
Road ontology does not cause any damage.Present invention generally provides a kind of methods using unmanned plane auxiliary mechanical arm removing obstacles object.
Summary of the invention
It is an object of the present invention to provide a kind of auxiliary grip method of view-based access control model identification, the present invention passes through unmanned plane auxiliary machine
The method of tool arm is removed, and clearance time is very short, and is reached after training so that the identification of barrier is grabbed by deep learning frame
Take more accurately beneficial effect.
A kind of auxiliary grip method of view-based access control model identification, includes the following steps；
1) deep learning frame and the network architecture are built；
2) route image information is acquired；
3) identification is trained according to collected information；
4) target road section image information is captured using camera；
5) image of acquisition is analyzed；
6) image and location information of route to be processed are obtained；
7) ultrasonic distance measuring module measuring and calculating and target object distance are utilized；
8) fetching instruction is sent to mechanical arm；
9) the motor drive machinery arm movement of mechanical arm；
10) target object is grabbed.
Further, route image information, including acquisition normal picture information, such as acquisition overhead transmission line straight trip are acquired
Place, corner, hand over more place, terminal point image；It further include acquisition abnormity line information, such as the abnormal object above route.
It further, further include obtaining identification model and crawl mould after being trained identification according to collected information
Type.
Further, before capturing target road section image information using camera, further include carrying out GPS positioning, carry out
Heading control.
Further, after the motor drive machinery arm movement of mechanical arm, further include, the crawl joint of mechanical arm is micro-
It is adjusted to the best gripping position.
Further, after grabbing target object, further include record this image recognition information, and using the information into
Row identification deep learning training.
It further, further include record mechanical arm crawl information, and grabbed using this after recording this image recognition information
Breath of winning the confidence carries out crawl deep learning training.
Further, the crawl joint of mechanical arm is finely tuned to before the best gripping position, further include mechanical arm track with
Track.
It further, further include assigning wireless biography before using ultrasonic distance measuring module measuring and calculating and target object distance
Defeated instruction and processing assignment instructions.
It is an object of the present invention to provide a kind of auxiliary grip method and system of view-based access control model identification, compared with prior art,
It has the advantage that
1, using deep learning frame, and after training, so that the identification crawl of barrier is more accurate；
2, information is recorded, inquiry foundation is can be used as, also can be used as trained foundation after crawl target object every time；
Detailed description of the invention
Fig. 1 is a kind of auxiliary grip method flow schematic diagram 1 of view-based access control model identification of the present invention；
Fig. 2 is a kind of auxiliary grip method flow schematic diagram 2 of view-based access control model identification of the present invention；
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which purpose of the present invention technical solution and advantage is more clearly understood
The present invention is described in further detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention,
It is not intended to limit the present invention.
Implement use-case one
As shown in Figure 1, Fig. 1 is a kind of auxiliary grip method flow schematic diagram 1 of view-based access control model identification of the present invention.
A kind of auxiliary grip method of view-based access control model identification, includes the following steps；
Step S101 builds deep learning frame and the network architecture；
Step S102 acquires route image information；
Step S103 is trained identification according to collected information；
Step S104 captures target road section image information using camera；
Step S105 analyzes the image of acquisition；
Step S106 obtains the image and location information of route to be processed；
Step S107 utilizes ultrasonic distance measuring module measuring and calculating and target object distance；
Step S108 sends fetching instruction to mechanical arm；
Step S109, the motor drive machinery arm movement of mechanical arm；
Step S110 grabs target object.
Implement use-case two
As shown in Fig. 2, Fig. 2 is a kind of auxiliary grip method flow schematic diagram 2 of view-based access control model identification of the present invention.
Step S201 builds deep learning frame and the network architecture；
Using caffe deep learning frame and the Faster-RCNN network architecture, but it is not limited to use this deep learning method
And this this network architecture；As the Caffe of California Berkeley, the Theano of the Montreal Institute of Technology, Switzerland's artificial intelligence
It the Brainstorm of laboratory IDSIA, is any frame that all can serve as this deep learning such as Princeton University Marvin；
As SSD, Faster-RCNN etc. can be used as a network architecture of this modeling.
Step S202 is based on deep learning frame and the network architecture, is trained identification according to collected information, training
Obtain identification model；
Acquire route image information under multiple groups different scenes, including acquisition normal picture information, such as acquisition overhead transmission line
At straight trip, corner, hand over more place, terminal point normal picture；It further include acquisition abnormity line information, such as different above route
The image of normal object etc.；
Deep learning is carried out according to collected information, training identification exports each characteristic point；Various features point is set, is put down
Line levels, shape, thickness characteristic point under the varying environments such as original, hills；It turns round, friendship is got over, the height under terminal different shape, shape
The characteristic points such as shape, thickness；Identification model is obtained after training.
Step S203 is based on deep learning frame and the network architecture, carries out crawl training, obtains crawl model；
Deep learning is carried out according to collected information, training identification exports each characteristic point；Various features point is set, respectively
The horizontal distance of joint of mechanical arm and purpose object, vertical range, articulation level moving distance, joint vertical travel distance, crawl
Intensity etc.；Crawl model is obtained after training.
Step S204 carries out GPS positioning, carries out Heading control；
After unmanned plane obtains instruction, according to goal task, GPS positioning is carried out, control course reaches destination locations.
Step S205 captures target road section image information using camera；
After reaching destination locations, pass through the camera photographic subjects section image information on mechanical arm.
Step S206 obtains the image and location information of route to be processed.
Step S207 analyzes the image of acquisition；
Image feature information is obtained by identification module；Identification model analysis after identify route be it is normal or abnormal,
If it is normal, continue next goal task, if it is abnormal, into next process flow.
Step S208 assigns wireless transmission instruction and processing assignment instructions；
If it is exception information, wireless transmission instruction and processing assignment instructions are assigned.
Step S209 utilizes ultrasonic distance measuring module measuring and calculating and target object distance；
After receiving processing assignment instructions, ultrasonic distance measuring module measuring and calculating and target object distance are utilized.
Step S210 sends fetching instruction to mechanical arm；
Unmanned plane sends fetching instruction to mechanical arm.
Step S211, the motor drive machinery arm movement of mechanical arm；
It is carried out according to collected range information, according to crawl model, the motor drive machinery arm movement of mechanical arm.
Step S212, mechanical arm track following；
Record joint of mechanical arm motion track.
Step S213 finely tunes in the crawl joint of mechanical arm to the best gripping position；
The crawl joint of mechanical arm is finely tuned to the best gripping position, it is available in which position by training to grab model
Setting crawl is optimum position.
Step S214 grabs target object；
According to crawl model as a result, implementation grabs, and the object that will be grabbed, such as branch foreign matter are moved up from route
It removes.
Step S215 records this crawl result；
It grabs successfully, typing grabs successful information library；Crawl failure, typing grab failure information library；It takes pictures as evidence simultaneously.
Step S216 records this image recognition information, and carries out identification deep learning training using the information.
Step S217, record mechanical arm grabs information, and carries out crawl deep learning training using the crawl information.
Step S218 feeds back this crawl task performance and gives task publisher；
After the completion of crawl task, route cleaning performance is fed back into task publisher, completes this subtask.
It is an object of the present invention to provide a kind of auxiliary grip method and system of view-based access control model identification, the present invention passes through unmanned plane
The method of auxiliary mechanical arm is removed, and clearance time is very short, and by deep learning frame, after training, is reached so that barrier
Identification grabs more accurately beneficial effect.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modification equivalent replacement and improvement etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (9)
1. a kind of auxiliary grip method of view-based access control model identification, which is characterized in that include the following steps；
1) deep learning frame and the network architecture are built；
2) route image information is acquired；
3) identification is trained according to collected information；
4) target road section image information is captured using camera；
5) image and location information of route to be processed are obtained；
6) image of acquisition is analyzed；
7) ultrasonic distance measuring module measuring and calculating and target object distance are utilized；
8) fetching instruction is sent to mechanical arm；
9) the motor drive machinery arm movement of mechanical arm；
10) target object is grabbed.
2. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that acquisition line map
As information, including at acquisition normal picture information, such as acquisition overhead transmission line straight trip, corner, the figure for handing over more place, terminal point
Picture；It further include acquisition abnormity line information, such as the abnormal object above route.
3. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that according to collecting
Information be trained after identification, further include obtain identification model and crawl model.
4. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that utilizing camera shooting
Before head captures target road section image information, further includes carrying out GPS positioning, carry out Heading control.
5. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that in mechanical arm
It further include finely tuning in the crawl joint of mechanical arm to the best gripping position after the movement of motor drive machinery arm.
6. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that in crawl target
It further include recording this image recognition information, and carry out identification deep learning training using the information after object.
7. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that record this figure
As after identification information, further including record mechanical arm crawl information, and crawl deep learning training is carried out using the crawl information.
8. a kind of auxiliary grip method of view-based access control model identification according to claim 5, which is characterized in that by mechanical arm
Crawl joint is finely tuned to before the best gripping position, further includes mechanical arm track following.
9. a kind of auxiliary grip method of view-based access control model identification according to claim 1, which is characterized in that utilizing ultrasound
It further include assigning wireless transmission instruction and processing assignment instructions before the measuring and calculating of wave range finder module and target object distance.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114145850A (en) * | 2021-12-03 | 2022-03-08 | 张继军 | Intelligent ERCP auxiliary device and automatic control method |
| CN114782827A (en) * | 2022-06-22 | 2022-07-22 | 中国科学院微电子研究所 | Object grabbing point obtaining method and device based on image |
| WO2023108578A1 (en) * | 2021-12-17 | 2023-06-22 | 赛真达国际有限公司 | Expert-type aerial robot system for autonomous field work |
| CN116330322A (en) * | 2023-05-24 | 2023-06-27 | 深圳市大族机器人有限公司 | High-precision industrial cooperative robot system based on machine vision and control method |
| CN116619420A (en) * | 2023-07-10 | 2023-08-22 | 国网江苏省电力有限公司南通供电分公司 | Line auxiliary construction robot |
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