CN109085851A - Unmanned plane pinpoint landing method - Google Patents
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract
The present invention relates to a kind of unmanned plane pinpoint landing methods, the technical issues of can not achieve high-precision, high reliability pinpoint landing which solve existing UAV system, its multiple infrared beacon module first against drop zone arrangement, image recognition is carried out to acquire the image of multiple infrared beacon modules on drop zone using deep learning algorithm by infrared photography head module, model is generated by neural metwork training；Fly in control secondly, trained model is downloaded to unmanned plane, the image with camera input is input, by neural network stress model, carries out processing to image and calculates airplane parking area relative position information；Then, location information is sent to unmanned plane as input and flies control, so that unmanned plane adjustment self-position is controlled, so that unmanned plane drops on airplane parking area.The present invention is widely used in the vehicle technologies such as unmanned plane field.
Description
Technical field
The present invention relates to a kind of vehicle technologies such as unmanned plane fields, in particular to a kind of unmanned plane pinpoint landing
Method.
Background technique
In recent years, the application field of unmanned plane is more and more extensive, and the control technology of unmanned plane reaches its maturity, to further
Realize the automation of unmanned plane, key is the automatic lifting stick of unmanned plane.Automatic pointing descent is complete using GPS positioning system
At, the reasonable accuracy of GPS positioning is 3 meters or so, and GPS signal is easy to be blocked by barrier and influence positioning accuracy, in order to
Realize that high-precision pinpoint landing must increase auxiliary tool in a small range, this just makes the complexity for increasing system.
The prior art generallys use pattern or the higher fixed point drop of infrared beacon realization precision on camera identification ground
It falls, but has certain defect.Camera image identification is affected by light, and the slightly remote reliability of distance just substantially reduces；
Infrared beacon can be very good that light is overcome to interfere, but not can be carried out specific matching at present, i.e., closer distance can not area
Divide multiple beacons, therefore affects the further genralrlization application of the program.
Summary of the invention
The present invention is exactly to solve existing UAV system and can not achieve high-precision, the technology of high reliability pinpoint landing
Problem, providing one kind can accurately, the unmanned plane pinpoint landing method of high reliability pinpoint landing.
Unmanned plane pinpoint landing method provided by the invention, including following methods:
Step 1, for multiple infrared beacon modules of drop zone arrangement, depth is used by infrared photography head module
It practises algorithm and carries out image recognition to acquire the image of multiple infrared beacon modules on drop zone, pass through neural metwork training
Generate model；
Step 2, trained model is downloaded to unmanned plane to fly in control, the image with camera input is input, is passed through
Neural network stress model carries out processing to image and calculates airplane parking area relative position information；
Step 3, location information is sent to unmanned plane as input and flies control, so that unmanned plane adjustment self-position is controlled,
So that unmanned plane drops on airplane parking area.
The present invention also provides a kind of unmanned plane pinpoint landing methods, including following methods:
Step 1, for multiple infrared beacon modules of drop zone arrangement, depth is used by infrared photography head module
It practises algorithm and carries out image recognition to acquire the image of multiple infrared beacon modules on drop zone, pass through SSD network training
Generate VGGNet model；
Step 2, trained VGGNet model is downloaded to unmanned plane to fly in control, the image with camera input is defeated
Enter, VGGNet model is loaded by SSD network, processing is carried out to image and calculates airplane parking area relative position information；
Step 3, location information is sent to unmanned plane as input and flies control, so that unmanned plane adjustment self-position is controlled,
So that unmanned plane drops on airplane parking area.
Preferably, in step 1, the image of collected multiple infrared beacon modules is established as PASCAL VOC format
Data set marks target area, is divided into training data and test data, generates table listings and is converted into LMDB format, adopts
VGGNet model is generated with SSD network training.
The beneficial effects of the present invention are: the present invention, which provides a kind of high frame per second infrared camera of use, identifies specific infrared light
Scheme.Infrared beacon uses certain coded format to emit infrared light in the form of pulse signal, and high frame per second camera is
It can recognize the presence or absence of infrared signal, and the encoded information of acceptable infrared signal, to realize efficient fixed point identification and landing.
Image recognition passes through nerve using the picture of a variety of beacons of various angles and size as learning object using deep learning algorithm
After the good model of network training, binding model realizes the identification of infrared beacon and the calculating of location information.Image recognition deep learning
Algorithm can be very good to guarantee reliability and accuracy.
Further aspect of the present invention and aspect are able in the description of the specific embodiment below with reference to attached drawing
It is clearly stated that.
Detailed description of the invention
Fig. 1 is the schematic diagram of unmanned plane pinpoint landing control system；
Fig. 2 is the top view of infrared beacon；
Fig. 3 is the main view of infrared beacon；
Fig. 4 is to pass through neural metwork training as learning object using the infrared beacon module of collected various angles and size
Produce the flow chart of module；
Fig. 5 is the flow chart of unmanned plane descent.
Symbol description in figure:
10. infrared beacon module, 20. unmanned planes, 11. pedestals, 12.MCU control unit, 13. infrared-emitting diodes,
21. high frame per second infrared photography head module, 22. unmanned planes fly control.
Specific embodiment
Referring to the drawings, with specific embodiment, invention is further described in detail.
As shown in Figure 1, unmanned plane pinpoint landing control system includes infrared beacon module 10 and unmanned plane 20, unmanned plane 20
Fly control 22 including high frame per second infrared photography head module 21 and unmanned plane, high frame per second infrared photography head module 21 is equipped with image procossing mould
Block and infrared camera.Unmanned plane flies control 22 and high 21 connection communication of frame per second infrared photography head module.Unmanned plane flies control 22 and is equipped with
GPS navigation system.
As shown in Figures 2 and 3, infrared beacon module 10 includes pedestal 11, and pedestal 11 is equipped with MCU control unit 12 and driving
Circuit, driving circuit are connect with MCU control unit 12, are distributed multiple infrared-emitting diodes 13 and driving electricity on the base 11
Road connection.Infrared-emitting diode 13 forms the form of dot matrix as signal source, and MCU control unit 11 controls two pole of infrared emission
Pipe 13 it is bright, go out the time, so as to send certain binary-coded information.It is red for the coverage area for increasing infrared signal
Outer emitting diode 13 uses convex surface diode.
The frame per second of high frame per second infrared photography head module 21 determines that infrared beacon module 10 sends the rate of encoded information, speed
Degree will cause very much unmanned plane response slowly reduces efficiency slowly very much, or even influences whether the integrality of encoded information.
Specific region is arrived in actual work, unmanned plane can fly under GPS navigation.If having within the scope of GPS accuracy more
A infrared beacon, camera can receive the infrared pulse signal that coded format is identical but identification code is different simultaneously.Compared to common
Infrared receiving diode, camera can carry out the identification of specific region in two-dimensional surface, so as to avoid multiple infrared signals
The pulse signal in source generates synergistic effect.In conjunction with the algorithm of image recognition, the pulse signal of each pixel region is calculated separately,
Pinpoint landing is realized so that it is determined that unique infrared beacon, shields other infrared beacons with identification code.
The process for making unmanned plane carry out high-precision pinpoint landing is as follows:
As shown in figure 4, preliminary preparation: arranging multiple infrared beacon modules 10 in drop zone, high frame per second is infrared to be taken the photograph
As head module 21 carries out image recognition using deep learning algorithm to acquire multiple infrared beacon modules on drop zone
Image；Model is generated by neural metwork training.Target identification is realized eventually by neural network calling model.Specific steps are such as
Under:
Step 1, a large amount of pictures for shooting the infrared beacon module 10 of different angle and size manually first, as depth
The target of habit.And it needs to be established as specific data set format, such as the data set of PASCAL VOC format, needs to mark
Remember target area, is divided into training data and test data, ultimately produces table listings and be converted into LMDB (Lightning
Memory-Mapped Database) format.
Step 2, pre-training model is downloaded at the end PC, by taking VGGNet model as an example, using SSD (Single Shot
MultiBox Detector) the network training model, adjusting parameter configuration, until training (the loss of ideal VGGNet model
Functional value is within permissible accuracy, such as.
Step 3, trained model is downloaded to unmanned plane to fly in control 22, the image with camera input is input, is led to
SSD network load VGGNet model is crossed, image is handled, to calculate the relative position letter of target information (airplane parking area)
Breath.
Step 4, location information is sent to unmanned plane as input and flies control, so that unmanned plane adjustment self-position is controlled,
Stablize unmanned plane right above airplane parking area, landing then can be realized.
Image recognition deep learning algorithm can be very good to guarantee reliability and accuracy.Above-mentioned descent can be fine
Guarantee reliability and accuracy.
The above is not intended to restrict the invention, only to the preferred embodiment of the present invention for the skill of this field
For art personnel, the invention may be variously modified and varied.
Claims (3)
1. a kind of unmanned plane pinpoint landing method, which is characterized in that including following methods:
Step 1, it for multiple infrared beacon modules of drop zone arrangement, is calculated by infrared photography head module using deep learning
Method carries out image recognition to acquire the image of multiple infrared beacon modules on drop zone, is generated by neural metwork training
Model；
Step 2, trained model is downloaded to unmanned plane to fly in control, the image with camera input is input, passes through nerve
Network stress model carries out processing to image and calculates airplane parking area relative position information；
Step 3, location information is sent to unmanned plane as input and flies control, so that unmanned plane adjustment self-position is controlled, so that
Unmanned plane drops on airplane parking area.
2. a kind of unmanned plane pinpoint landing method, which is characterized in that including following methods:
Step 1, it for multiple infrared beacon modules of drop zone arrangement, is calculated by infrared photography head module using deep learning
Method carries out image recognition to acquire the image of multiple infrared beacon modules on drop zone, is generated by SSD network training
VGGNet model；
Step 2, trained VGGNet model is downloaded to unmanned plane to fly in control, the image with camera input is input, is led to
SSD network load VGGNet model is crossed, processing is carried out to image and calculates airplane parking area relative position information；
Step 3, location information is sent to unmanned plane as input and flies control, so that unmanned plane adjustment self-position is controlled, so that
Unmanned plane drops on airplane parking area.
3. unmanned plane pinpoint landing method according to claim 2, which is characterized in that, will be collected in the step 1
The image of multiple infrared beacon modules is established as the data set of PASCAL VOC format, marks target area, is divided into trained number
According to and test data, generate table listings and be simultaneously converted into LMDB format, VGGNet model is generated using SSD network training.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109508040A (en) * | 2019-01-11 | 2019-03-22 | 福建江夏学院 | A kind of unmanned plane precise positioning landing system and method |
| CN109989332A (en) * | 2019-05-06 | 2019-07-09 | 中国人民解放军陆军军事交通学院镇江校区 | The control system on beach is supported for loading stage |
| CN110231835A (en) * | 2019-07-04 | 2019-09-13 | 深圳市科卫泰实业发展有限公司 | A kind of accurate landing method of unmanned plane based on machine vision |
| CN110231829A (en) * | 2019-06-20 | 2019-09-13 | 上海大学 | Increase the intensified learning miniature self-service gyroplane independent landing method melted based on data |
| CN110879617A (en) * | 2019-12-30 | 2020-03-13 | 湖南星空机器人技术有限公司 | Infrared-guided unmanned aerial vehicle landing method and device |
| CN111123964A (en) * | 2019-12-24 | 2020-05-08 | 浙江大学 | Unmanned aerial vehicle landing method and device and computer readable medium |
| CN111627062A (en) * | 2020-06-08 | 2020-09-04 | 星逻人工智能技术(上海)有限公司 | Aircraft shutdown state control method, device and device using method |
| CN111746810A (en) * | 2019-03-27 | 2020-10-09 | 顺丰科技有限公司 | All-weather unmanned aerial vehicle landing method, all-weather unmanned aerial vehicle landing system, all-weather unmanned aerial vehicle landing equipment and storage medium |
| WO2020211812A1 (en) * | 2019-04-19 | 2020-10-22 | 深圳市道通智能航空技术有限公司 | Aircraft landing method and apparatus |
| CN112278334A (en) * | 2020-11-06 | 2021-01-29 | 北京登火汇智科技有限公司 | Method for controlling the landing process of a rocket |
| CN113448345A (en) * | 2020-03-27 | 2021-09-28 | 北京三快在线科技有限公司 | Unmanned aerial vehicle landing method and device |
| CN113448351A (en) * | 2021-08-30 | 2021-09-28 | 广州知行机器人科技有限公司 | Method and device for guiding unmanned aerial vehicle to land accurately and unmanned aerial vehicle hangar |
| CN114115345A (en) * | 2021-11-19 | 2022-03-01 | 中国直升机设计研究所 | Visual landing guiding method and system for rotor unmanned aerial vehicle |
| CN114296534A (en) * | 2021-12-28 | 2022-04-08 | 广东电网有限责任公司 | Unmanned aerial vehicle parking apron system with deep learning function and low-temperature availability |
| US11580628B2 (en) * | 2019-06-19 | 2023-02-14 | Deere & Company | Apparatus and methods for augmented reality vehicle condition inspection |
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Cited By (17)
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|---|---|---|---|---|
| CN109508040A (en) * | 2019-01-11 | 2019-03-22 | 福建江夏学院 | A kind of unmanned plane precise positioning landing system and method |
| CN111746810A (en) * | 2019-03-27 | 2020-10-09 | 顺丰科技有限公司 | All-weather unmanned aerial vehicle landing method, all-weather unmanned aerial vehicle landing system, all-weather unmanned aerial vehicle landing equipment and storage medium |
| WO2020211812A1 (en) * | 2019-04-19 | 2020-10-22 | 深圳市道通智能航空技术有限公司 | Aircraft landing method and apparatus |
| CN109989332A (en) * | 2019-05-06 | 2019-07-09 | 中国人民解放军陆军军事交通学院镇江校区 | The control system on beach is supported for loading stage |
| US11580628B2 (en) * | 2019-06-19 | 2023-02-14 | Deere & Company | Apparatus and methods for augmented reality vehicle condition inspection |
| CN110231829A (en) * | 2019-06-20 | 2019-09-13 | 上海大学 | Increase the intensified learning miniature self-service gyroplane independent landing method melted based on data |
| CN110231829B (en) * | 2019-06-20 | 2022-01-07 | 上海大学 | Intensive learning small unmanned gyroplane autonomous landing method based on data fusion |
| CN110231835A (en) * | 2019-07-04 | 2019-09-13 | 深圳市科卫泰实业发展有限公司 | A kind of accurate landing method of unmanned plane based on machine vision |
| CN111123964B (en) * | 2019-12-24 | 2021-07-06 | 浙江大学 | Unmanned aerial vehicle landing method and device and computer readable medium |
| CN111123964A (en) * | 2019-12-24 | 2020-05-08 | 浙江大学 | Unmanned aerial vehicle landing method and device and computer readable medium |
| CN110879617A (en) * | 2019-12-30 | 2020-03-13 | 湖南星空机器人技术有限公司 | Infrared-guided unmanned aerial vehicle landing method and device |
| CN113448345A (en) * | 2020-03-27 | 2021-09-28 | 北京三快在线科技有限公司 | Unmanned aerial vehicle landing method and device |
| CN111627062A (en) * | 2020-06-08 | 2020-09-04 | 星逻人工智能技术(上海)有限公司 | Aircraft shutdown state control method, device and device using method |
| CN112278334A (en) * | 2020-11-06 | 2021-01-29 | 北京登火汇智科技有限公司 | Method for controlling the landing process of a rocket |
| CN113448351A (en) * | 2021-08-30 | 2021-09-28 | 广州知行机器人科技有限公司 | Method and device for guiding unmanned aerial vehicle to land accurately and unmanned aerial vehicle hangar |
| CN114115345A (en) * | 2021-11-19 | 2022-03-01 | 中国直升机设计研究所 | Visual landing guiding method and system for rotor unmanned aerial vehicle |
| CN114296534A (en) * | 2021-12-28 | 2022-04-08 | 广东电网有限责任公司 | Unmanned aerial vehicle parking apron system with deep learning function and low-temperature availability |
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