CN107063276A - One kind is without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method - Google Patents
One kind is without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method Download PDFInfo
- Publication number
- CN107063276A CN107063276A CN201611138614.1A CN201611138614A CN107063276A CN 107063276 A CN107063276 A CN 107063276A CN 201611138614 A CN201611138614 A CN 201611138614A CN 107063276 A CN107063276 A CN 107063276A
- Authority
- CN
- China
- Prior art keywords
- delay
- unmanned vehicle
- vehicle
- vehicle navigation
- laser radar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
-
- 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
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
-
- 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
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
-
- 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
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
Landscapes
- 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)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method of GPS navigation field.The guider includes GPS receiver module, micro controller module, display screen, several sensors, laser radar and several high-speed cameras.Methods described, which includes starting, navigates;Feature extraction;Modeling, carries out pseudo-differential processing;Carry out vehicle dynamic detection tracking;Optimization path program results is obtained with algorithm.It is not in path planning failure that the present invention, which can be achieved in the case of gps signal is weak, improves navigation accuracy, and the function with drive recorder.
Description
Technical field
The present invention relates to vehicle mounted guidance field, it is related specifically to a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay
And method.
Background technology
The walking-replacing tool that automobile is gone on a journey as modern, has higher requirement to the navigator fix of vehicle.As people receive
The increase entered, the sales volume of automobile has also increased, and people are gone on a journey from the mode of self driving, needs to obtain in real time accordingly
Position and planning driving path are managed, facilitates driver faster more accurately to arrive at.Into 21st century, technology of Internet of things it is general
And, information is exchanged between realizing any article, to the track and localization of object, is convenient for people to handle emergency case in real time.With various countries
Satellite navigation system obtained preferable development and application, GPS GPS have it is round-the-clock, high-precision, from
The features such as dynamicization, high benefit, it is widely used in the fields such as road, aviation, agricultural.The system is designed based on GP S technologies can
Continuous, accurate automobile navigation location information is provided for driver,
Existing unmanned vehicle vehicle mounted guidance handles the positioning letter received from GPS module by master controller of STM32F103
Information transmission to host computer, is realized remote monitoring, it is the next that the vehicle mounted guidance has the weak situation of signal by breath by GPRS network
Put information updating not in time, easily cause guidance path delay, user is driven to the place of mistake.Therefore it provides a kind of
No-delay vehicle-mounted, high-precision unmanned vehicle on-vehicle navigation apparatus is just necessary.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of no-delay vehicle-mounted, high-precision on-vehicle navigation apparatus.Its
Can be in the case of no gps signal, by detecting itself dynamic position progress position estimation to environment, so as to enter walking along the street
Footpath is planned, is reached and is eliminated delay, the purpose of high accuracy navigation.
The technical solution adopted for the present invention to solve the technical problems is to include:GPS receiver module, micro controller module,
Display screen, several sensors, laser radar and several high-speed cameras;The GPS module is used to receive gps satellite
Data are sent to micro controller module;Several described sensors are used to collection vehicle peripheral location information and environmental information;Institute
State dynamic change parameter of the multi-layer laser radar to recognize itself and environment;The ARM microcontroller is used to computing determine
The orientation and path planning of vehicle;The display screen is to the program results that shows paths.
For optimization design, further, the laser radar is 4 layers of laser radar.
Further, the micro-control module is ARM micro-control modules.
Further, the sensor includes acceleration sensor, direction sensor and range sensor.
Further, aspect sensor quantity >=4.
The present invention also provides a kind of a kind of without the high-precision unmanned vehicle vehicle mounted guidance of delay according to claim 1-5
Method,
Methods described is comprised the steps of:
(1) start and navigate, the unlatching GPS receiver module, micro controller module, display screen LCD, several sensors,
Laser radar and several high-speed cameras;
(2) feature extraction:Extract the echo impulse width of gps data, sensing data and laser radar;
(3) extract feature according to the step (1) to be modeled, carry out pseudo-differential processing;
(4) the echo impulse width characteristics extracted according to step (2), carry out vehicle dynamic detection tracking;
(5) optimization path program results is obtained with algorithm.
Further, feature extraction includes in the step (2), extract laser radar data in each put angle, away from
From and reflected impulse width information, then use the method based on distance that laser spots are carried out into cluster segmentation.
Further, corresponding kinematics model is set up including the use of frame model and expression barrier in the step (3)
M=(w, l, x, y)
Wherein w and l represent the wide of barrier and long respectively, and (x, y) represents the position of barrier.With the obstacle detected
Thing edge features and corner features determination are represented with frame model.
Further, the tracking of vehicle dynamic detection is wide according to step (2) described echo impulse in the step (4)
Degree is in the range of 0~500, and 50~150 concentrate scope for the echo impulse width of lane line, and 220~405 be back echo arteries and veins
Rush width and concentrate scope, select the echo impulse width in the range of this as virtual value, it is then that the echo impulse of barrier is wide
Degree average is used as one of matching characteristic;The corresponding pulse width values of different barriers have very big difference, can be used for of surrounding
With association, itself track computing is carried out, itself dynamic detection result is obtained;It is special according to the figure of high-speed camera to testing result
Levy and checking is compared.
Further, the step (5) uses best-first search algorithm, generates the optimal path of an arrival target point, protects
Card is minimum to be expended;The evaluation function of the best-first search algorithm is:
F (n)=G (n)+H (n)
Wherein F (n) is node n evaluation function, and G (n) is state space from start node to the actual cost of n nodes, H
(n) it is estimate cost from n to destination node optimal path.
The present invention gathers the mode that ambient parameters are combined by using gps signal and sensor, is aided with multilayer radar
The echo impulse width of progress gathers detects itself dynamic position to estimate, carries out making up without gps signal, can obtain following
Technique effect:
Effect one, raising positioning precision;
Effect two, raising path planning correctness;
Effect three, elimination gps signal are weak or without the influence to path planning and positioning precision;
Effect four, high-speed camera can be used as drive recorder.
Brief description of the drawings
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 is structured flowchart of the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
Embodiment 1
The present embodiment provides a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay, including:GPS receiver module, it is micro-
Controller module, display screen, several sensors, laser radar and several high-speed cameras;
The data that the GPS module is used to receive gps satellite are sent to micro controller module;Several described sensors are used
With collection vehicle peripheral location information and environmental information;Dynamic change of the multi-layer laser radar to recognize itself and environment
Parameter;The ARM microcontroller is used to computing determine the orientation and path planning of vehicle;The display screen is to show paths
Program results.
The laser radar is 4 layers of laser radar.The micro-control module is ARM micro-control modules, with outside serial ports.
The sensor includes acceleration sensor, direction sensor and range sensor;Aspect sensor is 4, respectively positioned at car
4 orientation all around.
One kind is comprised the steps of without high-precision unmanned vehicle navigation method is postponed:
(1) start and navigate, the unlatching GPS receiver module, micro controller module, display screen LCD, several sensors,
Laser radar and several high-speed cameras;
(2) feature extraction:Extract the echo impulse width of gps data, sensing data and laser radar;Extract laser
Angle, distance and the reflected impulse width information each put in radar data, then use the method based on distance by laser
Point carries out cluster segmentation.
(3) extract feature according to the step (1) to be modeled, carry out pseudo-differential processing;Including the use of frame model and
Barrier is represented, corresponding kinematics model is set up, and pseudo-differential processing is carried out to kinematics model;
M=(w, l, x, y)
Wherein w and l represent the wide of barrier and long respectively, and (x, y) represents the position of barrier.With the obstacle detected
Thing edge features and corner features determination are represented with frame model.
(4) the echo impulse width characteristics extracted according to step (2), carry out vehicle dynamic detection tracking;The step
Suddenly in (4) vehicle dynamic detection tracking be according to step (2) the echo impulse width in the range of 0~500,50~
150 concentrate scope for the echo impulse width of lane line, and 220~405 be that back echo pulse width concentrates scope, selects this
In the range of echo impulse width as virtual value, then using the echo impulse width average of barrier as matching characteristic it
One;The corresponding pulse width values of different barriers have very big difference, can be used for the matching association of surrounding, carry out itself track fortune
Calculate, obtain itself dynamic detection result;Checking is compared to testing result contrast high-speed camera Graph Extraction feature.
(5) optimization path program results is obtained with algorithm;The step (5) uses best-first search algorithm, generation one
Bar reaches the optimal path of target point, it is ensured that minimum expends;The evaluation function of the best-first search algorithm is:
F (n)=G (n)+H (n)
Wherein F (n) is node n evaluation function, and G (n) is state space from start node to the actual cost of n nodes, H
(n) it is estimate cost from n to destination node optimal path.
Although illustrative embodiment of the invention is described above, in order to the technology of the art
Personnel are it will be appreciated that the present invention, but the present invention is not limited only to the scope of embodiment, to the common skill of the art
For art personnel, as long as long as various change is in the spirit and scope of the invention that appended claim is limited and is determined, one
The innovation and creation using present inventive concept are cut in the row of protection.
Claims (10)
1. it is a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay, it is characterised in that:The on-vehicle navigation apparatus includes:
GPS receiver module, micro controller module, display screen, several sensors, laser radar and several high-speed cameras;
The data that the GPS module is used to receive gps satellite are sent to micro controller module;Several described sensors are to adopt
Collect vehicle periphery positional information and environmental information;The multi-layer laser radar is to recognize that the dynamic change of itself and environment is joined
Number;The ARM microcontroller is used to computing determine the orientation and path planning of vehicle;The display screen is to the rule that show paths
Check off fruit.
2. it is according to claim 1 a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay, it is characterised in that:It is described
Laser radar is 4 layers of laser radar.
3. it is according to claim 1 a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay, it is characterised in that:It is described
Micro-control module is ARM micro-control modules.
4. it is according to claim 1 a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay, it is characterised in that:It is described
Sensor includes acceleration sensor, direction sensor and range sensor.
5. it is according to claim 1 a kind of without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay, it is characterised in that:It is described
Aspect sensor quantity >=4.
6. it is a kind of without the high-precision unmanned vehicle navigation method of delay according to claim 1-5, it is characterised in that:Institute
The method of stating is comprised the steps of:
(1) start navigation, open the GPS receiver module, micro controller module, display screen, several sensors, laser radar
And several high-speed cameras;
(2) feature extraction:The echo impulse of extraction gps data, sensing data, high-speed camera figure and laser radar is wide
Degree;
(3) extract feature according to the step (2) to be modeled, carry out pseudo-differential processing;
(4) the echo impulse width and high-speed camera graphic feature extracted according to step (2), carries out vehicle and dynamically examines
Survey tracking;
(5) optimization path program results is obtained with algorithm.
7. it is according to claim 6 a kind of without the high-precision unmanned vehicle navigation method of delay, it is characterised in that:It is described
Method is comprised the steps of:Feature extraction includes in the step (2), extract laser radar data in each put angle, away from
From and reflected impulse width information, then use the method based on distance that laser spots are carried out into cluster segmentation.
8. it is according to claim 6 a kind of without the high-precision unmanned vehicle navigation method of delay, it is characterised in that:It is described
Method is comprised the steps of:Including the use of frame model and expression barrier in the step (3), corresponding kinematics model is set up
M=(w, l, x, y)
Wherein w and l represent the wide of barrier and long respectively, and (x, y) represents the position of barrier.With the barrier side detected
Represented along feature and corner features determination with frame model.
9. it is according to claim 6 a kind of without the high-precision unmanned vehicle navigation method of delay, it is characterised in that:It is described
Method is comprised the steps of:The tracking of vehicle dynamic detection is wide according to step (2) described echo impulse in the step (4)
Degree is in the range of 0~500, and 50~150 concentrate scope for the echo impulse width of lane line, and 220~405 be back echo arteries and veins
Rush width and concentrate scope, select the echo impulse width in the range of this as virtual value, it is then that the echo impulse of barrier is wide
Degree average is used as one of matching characteristic;The corresponding pulse width values of different barriers have very big difference, can be used for of surrounding
With association, itself track computing is carried out, itself dynamic detection result is obtained.
10. it is according to claim 6 a kind of without the high-precision unmanned vehicle navigation method of delay, it is characterised in that:Institute
The method of stating is comprised the steps of:The step (5) uses best-first search algorithm, generates the optimal path of an arrival target point,
Ensure minimum expend;The evaluation function of the best-first search algorithm is:
F (n)=G (n)+H (n)
Wherein F (n) is node n evaluation function, and G (n) is state space from start node to the actual cost of n nodes, H (n)
It is the estimate cost from n to destination node optimal path.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201611138614.1A CN107063276A (en) | 2016-12-12 | 2016-12-12 | One kind is without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201611138614.1A CN107063276A (en) | 2016-12-12 | 2016-12-12 | One kind is without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method |
Publications (1)
Publication Number | Publication Date |
---|---|
CN107063276A true CN107063276A (en) | 2017-08-18 |
Family
ID=59618806
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201611138614.1A Pending CN107063276A (en) | 2016-12-12 | 2016-12-12 | One kind is without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107063276A (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109946708A (en) * | 2017-12-21 | 2019-06-28 | 北京万集科技股份有限公司 | A kind of method for detecting lane lines and device based on laser radar scanning |
CN110764110A (en) * | 2019-11-12 | 2020-02-07 | 深圳创维数字技术有限公司 | Path navigation method, device and computer readable storage medium |
CN112034108A (en) * | 2020-09-16 | 2020-12-04 | 上海市环境科学研究院 | Device and method for analyzing regional pollution condition and computer readable storage medium |
CN113615591A (en) * | 2021-07-30 | 2021-11-09 | 北方民族大学 | Full-mixed ration intelligent processing and feeding production line |
TWI824773B (en) * | 2022-10-14 | 2023-12-01 | 財團法人車輛研究測試中心 | Self-driving route planning system and method |
CN117452436A (en) * | 2023-12-26 | 2024-01-26 | 中国科学院国家授时中心 | Time service method and device for L frequency band under GNSS refusing situation |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102158684A (en) * | 2010-02-12 | 2011-08-17 | 王炳立 | Self-adapting scene image auxiliary system with image enhancement function |
CN104932493A (en) * | 2015-04-01 | 2015-09-23 | 上海物景智能科技有限公司 | Autonomous navigation mobile robot and autonomous navigation method thereof |
CN105318884A (en) * | 2014-07-28 | 2016-02-10 | 现代自动车株式会社 | Apparatus and method for generating global path for autonomous vehicle |
US9395727B1 (en) * | 2013-03-22 | 2016-07-19 | Google Inc. | Single layer shared aperture beam forming network |
CN105938365A (en) * | 2015-03-02 | 2016-09-14 | 丰田自动车株式会社 | Vehicle control device |
-
2016
- 2016-12-12 CN CN201611138614.1A patent/CN107063276A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102158684A (en) * | 2010-02-12 | 2011-08-17 | 王炳立 | Self-adapting scene image auxiliary system with image enhancement function |
US9395727B1 (en) * | 2013-03-22 | 2016-07-19 | Google Inc. | Single layer shared aperture beam forming network |
CN105318884A (en) * | 2014-07-28 | 2016-02-10 | 现代自动车株式会社 | Apparatus and method for generating global path for autonomous vehicle |
CN105938365A (en) * | 2015-03-02 | 2016-09-14 | 丰田自动车株式会社 | Vehicle control device |
CN104932493A (en) * | 2015-04-01 | 2015-09-23 | 上海物景智能科技有限公司 | Autonomous navigation mobile robot and autonomous navigation method thereof |
Non-Patent Citations (1)
Title |
---|
HOANG HUU VIET, ETC.: "BA*: an online complete coverage algorithm for cleaning robots", 《APPLIED INTELLIGENCE》 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109946708A (en) * | 2017-12-21 | 2019-06-28 | 北京万集科技股份有限公司 | A kind of method for detecting lane lines and device based on laser radar scanning |
CN110764110A (en) * | 2019-11-12 | 2020-02-07 | 深圳创维数字技术有限公司 | Path navigation method, device and computer readable storage medium |
CN112034108A (en) * | 2020-09-16 | 2020-12-04 | 上海市环境科学研究院 | Device and method for analyzing regional pollution condition and computer readable storage medium |
CN113615591A (en) * | 2021-07-30 | 2021-11-09 | 北方民族大学 | Full-mixed ration intelligent processing and feeding production line |
CN113615591B (en) * | 2021-07-30 | 2022-08-19 | 北方民族大学 | Full-mixed ration intelligent processing and feeding production line |
TWI824773B (en) * | 2022-10-14 | 2023-12-01 | 財團法人車輛研究測試中心 | Self-driving route planning system and method |
CN117452436A (en) * | 2023-12-26 | 2024-01-26 | 中国科学院国家授时中心 | Time service method and device for L frequency band under GNSS refusing situation |
CN117452436B (en) * | 2023-12-26 | 2024-03-19 | 中国科学院国家授时中心 | Time service method and device for L frequency band under GNSS refusing situation |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107063276A (en) | One kind is without the high-precision unmanned vehicle on-vehicle navigation apparatus of delay and method | |
US11983894B2 (en) | Determining road location of a target vehicle based on tracked trajectory | |
US20210311490A1 (en) | Crowdsourcing a sparse map for autonomous vehicle navigation | |
CN108139225B (en) | Determining layout information of a motor vehicle | |
CN109215433A (en) | The Driving Scene generator of view-based access control model for automatic Pilot emulation | |
Laugier et al. | Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety | |
US8949016B1 (en) | Systems and methods for determining whether a driving environment has changed | |
CN108732589A (en) | The training data of Object identifying is used for using 3D LIDAR and positioning automatic collection | |
KR20200042760A (en) | Vehicle localization method and vehicle localization apparatus | |
JP2019086508A (en) | Method and device for displaying virtual path, and display device | |
CN109937343A (en) | Appraisal framework for the prediction locus in automatic driving vehicle traffic forecast | |
WO2019173009A1 (en) | Automatic identification of roadside objects for localization | |
CN108604095A (en) | The method of the steering rate of dynamic adjustment automatic driving vehicle | |
EP3663804A1 (en) | Automatic detection of overhead obstructions | |
CN108628298A (en) | Control type planning for automatic driving vehicle and control system | |
JP2020064056A (en) | Device and method for estimating position | |
CN110119138A (en) | For the method for self-locating of automatic driving vehicle, system and machine readable media | |
CN108334077A (en) | Determine the method and system of the unit gain of the speed control of automatic driving vehicle | |
Shunsuke et al. | GNSS/INS/on-board camera integration for vehicle self-localization in urban canyon | |
JP2023021098A (en) | Map construction method, apparatus, and storage medium | |
JP2022022287A (en) | Map making device, method for control, program, and storage medium | |
JP2016080460A (en) | Moving body | |
CN114127738A (en) | Automatic mapping and positioning | |
WO2022147274A1 (en) | Systems and methods for road segment mapping | |
Jiménez et al. | Improving the lane reference detection for autonomous road vehicle control |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170818 |
|
WD01 | Invention patent application deemed withdrawn after publication |