CN110146085A - Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration - Google Patents
Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration Download PDFInfo
- Publication number
- CN110146085A CN110146085A CN201910460860.6A CN201910460860A CN110146085A CN 110146085 A CN110146085 A CN 110146085A CN 201910460860 A CN201910460860 A CN 201910460860A CN 110146085 A CN110146085 A CN 110146085A
- Authority
- CN
- China
- Prior art keywords
- point
- path
- node
- aerial vehicle
- unmanned aerial
- 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.)
- Granted
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
- G01C21/20—Instruments for performing navigational calculations
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention belongs to the technical field of unmanned aerial vehicle systems, and discloses an unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration. In the flight process of the unmanned aerial vehicle, an octree environment local map taking the unmanned aerial vehicle as a center is constructed by utilizing an airborne depth camera sensor, and the local map is updated in real time along with the motion of the unmanned aerial vehicle. On the basis of a local map, a sampling-based path planning method for rapidly exploring a random tree is adopted to find key position points of an obstacle avoidance path of the unmanned aerial vehicle, and a path formed by path key position point line segments is converted into a smooth and dynamic flyable track by using a uniform Betz curve. The method provided by the invention is simple and can be used for real-time obstacle avoidance in the unmanned aerial vehicle airborne system with limited computing resources.
Description
Technical field
Present invention relates generally to UAV system technical field, refer in particular to a kind of based on building figure and rapid discovery random tree
Unmanned plane evades weight planing method in real time.Method proposed by the present invention is simple, can be used for the limited unmanned aerial vehicle onboard of computing resource
Real Time Obstacle Avoiding in system.
Background technique
In recent years, the high agility of unmanned plane, high maneuverability, it is easy to control the features such as, make its take photo by plane, inspection, logistics and
The application fields such as rescue are widely used.In most cases the task environment of unmanned plane is unknown, or is existed
Uncertain obstacle.In order to meet the needs of entirely autonomous flight under unknown complex environment, real-time path re-planning locally generates flat
Sliding, the feasible track of dynamic is of great significance.Avoiding the method for local conflicts, there are two main classes: the first kind is pure reactivity side
Method, it does not need constructing environment map but is directly planned according to the data of sensor, and this method calculating speed is fast, but
It is the influence for not being suitable for mixed and disorderly environment, and being seriously trapped local minimum；Second class is the office based on map
Portion's barrier-avoiding method, it utilizes various technologies, by the local map by sensing data or the building of known environmental information, calculates
The difficult point in feasible out and local optimum path, local map building is to build the rapidity of figure.This patent is expected that by structure
Octree local map is built, real-time local path weight-normality is carried out and draws to generate collision-free trajectory, there is unmanned plane unknown multiple
The ability of safe autonomous flight in heterocycle border.
Summary of the invention
The technical problem to be solved in the present invention is that: for the demand of unmanned plane automatic obstacle avoiding, propose that one kind is based on building figure
With the unmanned plane local path weight planing method of rapid discovery random tree.
In view of the problems of the existing technology, the content of present invention mainly includes three parts:
1, the local map building based on three-dimensional circular buffer area: point cloud data is obtained by depth distance sensor, benefit
Cloud is carried out with voxel filter it is down-sampled, then by cloud be converted into the form of Octree store grating map, and will
Grating map exists using three-dimensional array as the cyclic buffer of storage format.
(1) Octree map structuring
A cloud is obtained by depth distance sensor, cloud is carried out using voxel filter down-sampled.Then cloud will be put
It is converted into the grating map stored in the form of Octree.Particular content are as follows:
The occupancy of Octree map interior joint is indicated with y ∈ R.When constantly perceiving the node and being occupied, increase
Add the value of y；Conversely, then reducing the value of y.The probability that the node is occupied is indicated with x, the probability the big, a possibility that being occupied
It is higher.Transformational relation between x and y is as follows:
Wherein x=0.5 expression does not determine, by probability Logarithm conversion, realizes map structuring and real-time update, dynamic right
Barrier in environment is modeled.
(2) the local map building based on three-dimensional circular buffer area
Based on the Octree map constructed, office is constructed by a three-dimensional circular buffer area centered on unmanned plane
Portion's map.Cyclic buffer is that size is N (N=(2p)) three-dimensional array.Particular content are as follows:
Assuming that the resolution ratio of Octree map is r, the point p (x, y, z) in three-dimensional space is mapped in buffer area
Pointp'(x',y',z') mapping process are as follows:
Assuming that the central point of buffer area is set as O (o1,o2,o3), it can judge whether point p' is buffering by formula below
In area:
Buffer area is moved together with quadrotor.If the coordinate of quadrotor is Q (q1,q2,q3).Buffer the initial bit of district center
It is set to the starting point of quadrotor.With the movement of quadrotor, work as qi-oiWhen > 0, oi=oi+d.Otherwise, oi=oi- d, d are
Buffer the moving step length of district center.
2, the path key position point based on rapid discovery random tree generates: initializing first node of random tree, so
Afterwards in cyclic process each time, an arbitrary random point is generated in entire state space, after generating random point, traversal
Each of random tree node calculates the distance between each node and random point, finds out nearest apart from this random point
Node extends up in the line side of the two nodes and generates a new node, that is, generates a path key position point；New section
The father node of point updates, and is that adjacent node is found in the prescribed limit near new node, the father node as replacement new node
Alternatively, it and selects so that father node of the smallest point of path cost as new node.Particular content are as follows:
(1) the path key position point based on the random tree algorithm of rapid discovery generates
Initialization tree first generates first node xinit, in cyclic process each time, generate a random point xrand。
The generation of random point is arbitrary, it can in entire state space.After generating random point, traverse each in random tree
A node calculates the distance between each node and random point, finds out the node nearest apart from this random point, is denoted as
xnearest.In xnearestWith xrandLine Directional Extension generates new node xnew, new node is path key position point.Quickly
Random tree algorithm is explored in new node xnewAdjacent node is found in neighbouring prescribed limit r, as replacement xnewFather node it is standby
Choosing.Final choice makes the smallest point x the most of path costnewFather node.Then, the random tree algorithm of rapid discovery to set into
Row rewiring keeps the path cost of all nodes minimum.
3, the smooth trajectory based on uniform Bezier curve batten: the part of the smooth trajectory based on uniform Bezier curve batten
Using five uniform Bezier curve battens come come be fitted rapid discovery random tree generation path key position point, obtain smooth
Track.Particular content are as follows:
It is fitted the path key position point of rapid discovery random tree generation using uniform Bezier curve batten, obtains smooth
Track.K-1 times Bezier curve batten can be indicated by following formula:
pi∈R3It istiThe control point of moment Bezier curve batten, Bi,kIt (t) is basic function.In order to enable track by nobody
Machine follows, and track must continuously arrive the Fourth-Derivative of position.Therefore track is indicated using five uniform Bezier curve battens.?
There is fixed time interval Δ t between even Bezier curve spline control points.For 5 Bezier curve battens, in time t ∈ [ti,
ti+1), the value of p (t) is only dependent upon 6 continuous control point [pi,pi+1,…,pi+5].It enables:
Then track can indicate are as follows:
Wherein MkIt is the matrix that a size is k × k
Enable P=[pi,pi+1,…,pi+5]T, the velocity and acceleration of any time on track can be calculated.
Since smooth rear track can deviate initial path, there is the possibility with barrier crash.By increasing between in the paths
The method at midpoint optimizes track.Assuming that p1, p2, p3For the path key position point of rapid discovery random tree algorithmic rule, m1For
Orbit segment p1p2Midpoint, m2For orbit segment p2p3Midpoint.m1And m2It is collisionless, because it is located at rapid discovery random tree
On the optimal segmentation linear path that searching algorithm returns.Calculate m1And m2Terminal p'2, judge p1p'2And p'2p3Whether with obstacle
It collides.If it is not, using p'2Replace the p in path2.Shellfish is solved hereby by increasing the method for path key position point
The problem of curve batten track and obstacle collide, can carry out multiple interpolation when necessary to ensure the safety of track.
The present invention utilizes method set forth above, can solve the automatic obstacle avoiding and path planning of unmanned plane in circumstances not known
Problem.By the Octree local map of building, quickly the obstacle in environment can be modeled in mixed and disorderly environment,
The required time is Millisecond.Path key position point is generated in real time using rapid discovery random tree in local map, and is made
Position point is fitted with uniform Bezier curve batten, obtains the smooth and feasible collision-free trajectory of dynamics, realizes part
Obstacle is evaded in real time, the ability for making unmanned plane have the safe autonomous flight in unknown complex environment.
Detailed description of the invention
Fig. 1 is method for planning track result schematic diagram.
Fig. 2 is local map construction method result schematic diagram.
Fig. 3 is that real-time local path weight-normality draws methods and results schematic diagram.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and specific embodiments.
(1) the local map building based on three-dimensional circular buffer area
Step 1: obtaining the depth information of each point in image from depth distance camera, be converted into a cloud.
Step 2: it is down-sampled to be that 0.2m × 0.2m × 0.2m voxel filter carries out a cloud with a sizing grid.
Step 3: according to formula (1), will be down-sampled after point cloud insertion resolution ratio be 0.2m3Octree map in.
Step 4: according to formula (2), the point in Octree map being mapped to one and is made of the three-dimensional array that length is 64
Cyclic buffer in.
Step 5: the central point of buffer area being set as to the starting point of unmanned plane, one is obtained and unmanned plane is followed to move, and in real time
The local map of update, as shown in Figure 1.
(2) the path clustering point based on rapid discovery random tree generates and smooth trajectory
Step 1: first determine unmanned plane execute task starting point S and target point T, construct local map, local map with
The flight of unmanned plane update in real time.
Step 2: record current time t calculates the location point p conduct on the corresponding global track of t moment according to formula (1)
The target point of this time sector planning.
Step 3: the local map based on building, according to formula (3), if p point is located in local map and p point is not hindered
Hinder object to occupy, the collisionless path from starting point S to target point p is obtained to get to safety using the random tree algorithm of rapid discovery
Path point.Otherwise, after waiting Δ t seconds, (2) are re-execute the steps.
Step 4: according to formula (6), being inserted into path point as control point in Bezier curve batten, fitting control point obtains
The smooth and feasible track of dynamic, as shown in Figure 2.
Step 5: processing being optimized to track using the method for increasing path point, so that smoothed out track and initial road
Diameter is closer to guaranteeing the safety of track.
Step 6: according to formula (8), obtained track and calculated velocity and acceleration being sent to unmanned plane, make nobody
Machine is according to specified speed follower track.
Step 7: the process of step (2)~step (6) is repeated with a time interval Δ t, until unmanned plane reaches finally
Target point T, as shown in Figure 3.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment,
All technical solutions belonged under thinking of the present invention all belong to the scope of protection of the present invention.It should be pointed out that for the art
For those of ordinary skill, several improvements and modifications without departing from the principles of the present invention should be regarded as protection of the invention
Range.
Claims (5)
1. evading weight planing method in real time based on the unmanned plane for building figure and rapid discovery random tree, which is characterized in that mainly include
Three parts: first is that local Octree map structuring；Second is that path on the basis of map based on rapid discovery random tree is crucial
Location point generates；Third is that smooth using uniform Bezier curve batten progress track fitting based on key position point.
2. part Octree map structuring according to claim 1, it is characterised in that: obtained by depth distance sensor
Point cloud data, it is down-sampled to cloud progress using voxel filter, then convert a cloud to the grid stored in the form of Octree
Lattice map, and grating map is existed using three-dimensional array as the cyclic buffer of storage format.
3. the path key position point according to claim 1 based on rapid discovery random tree generates, it is characterised in that: first
First need to initialize first node of random tree, the location point in node on behalf Octree map state space.Setting is random
The searching times of tree, each time search for during, first in Octree map state space generate one arbitrarily with
Machine point, after generating random point, traversal each of random tree node, calculate between each node and random point away from
From finding out the node nearest apart from this random point, then extended up in the line side of the two nodes and generate new node, i.e.,
Obtain a path key position point.
4. the path key position point according to claim 3 based on rapid discovery random tree generates, it is characterised in that:
After obtaining a new node, needs to carry out tree rewiring and make the cost in path minimum.Firstly, it is necessary near new node
Prescribed limit in find adjacent node, as replacement new node father node it is alternative, then selection so that path cost is minimum
Father node of the point as new node.
5. according to claim 1 weigh planing method based on the unmanned plane local path for building figure and rapid discovery random tree,
It is characterized by: being intended based on the part of the smooth trajectory of uniform Bezier curve batten using five uniform Bezier curve battens
The path clustering point that rapid discovery random tree generates is closed, smooth track is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910460860.6A CN110146085B (en) | 2019-05-30 | 2019-05-30 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910460860.6A CN110146085B (en) | 2019-05-30 | 2019-05-30 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110146085A true CN110146085A (en) | 2019-08-20 |
| CN110146085B CN110146085B (en) | 2022-04-19 |
Family
ID=67592138
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910460860.6A Active CN110146085B (en) | 2019-05-30 | 2019-05-30 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110146085B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111707269A (en) * | 2020-06-23 | 2020-09-25 | 东南大学 | Unmanned aerial vehicle path planning method in three-dimensional environment |
| CN111752306A (en) * | 2020-08-14 | 2020-10-09 | 西北工业大学 | Unmanned aerial vehicle route planning method based on fast-expanding random tree |
| CN112379681A (en) * | 2020-11-02 | 2021-02-19 | 中国兵器工业计算机应用技术研究所 | Unmanned aerial vehicle obstacle avoidance flight method and device and unmanned aerial vehicle |
| CN112525195A (en) * | 2020-11-20 | 2021-03-19 | 中国人民解放军国防科技大学 | Multi-target genetic algorithm-based aircraft track rapid planning method |
| CN112747736A (en) * | 2020-12-22 | 2021-05-04 | 西北工业大学 | Indoor unmanned aerial vehicle path planning method based on vision |
| CN113741490A (en) * | 2020-05-29 | 2021-12-03 | 广州极飞科技股份有限公司 | Inspection method, inspection device, aircraft and storage medium |
| CN114200933A (en) * | 2021-12-06 | 2022-03-18 | 太原科技大学 | Three-dimensional path planning method for bridge crane |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867174A (en) * | 2015-05-08 | 2015-08-26 | 腾讯科技（深圳）有限公司 | Three-dimensional map rendering and display method and system |
| CN104965518A (en) * | 2015-05-21 | 2015-10-07 | 华北电力大学 | Power inspection tour flying robot air route planning method based on three-dimensional digital map |
| CN106737671A (en) * | 2016-12-21 | 2017-05-31 | 西安科技大学 | The bilayer personification motion planning method of seven degrees of freedom copy man mechanical arm |
| CN106949893A (en) * | 2017-03-24 | 2017-07-14 | 华中科技大学 | The Indoor Robot air navigation aid and system of a kind of three-dimensional avoidance |
| CN107631734A (en) * | 2017-07-21 | 2018-01-26 | 南京邮电大学 | A kind of dynamic smoothing paths planning method based on D*_lite algorithms |
| CN108088445A (en) * | 2016-11-22 | 2018-05-29 | 广州映博智能科技有限公司 | 3 d grid map path planning system and method based on octree representation |
| CN108776492A (en) * | 2018-06-27 | 2018-11-09 | 电子科技大学 | A kind of four-axle aircraft automatic obstacle avoiding and air navigation aid based on binocular camera |
| CN108876024A (en) * | 2018-06-04 | 2018-11-23 | 清华大学深圳研究生院 | Path planning, path real-time optimization method and device, storage medium |
| KR20180135695A (en) * | 2017-06-13 | 2018-12-21 | 성균관대학교산학협력단 | A method for checking empty space to install devices, install path, and instal order |
| CN109582032A (en) * | 2018-10-11 | 2019-04-05 | 天津大学 | Quick Real Time Obstacle Avoiding routing resource of the multi-rotor unmanned aerial vehicle under complex environment |
-
2019
- 2019-05-30 CN CN201910460860.6A patent/CN110146085B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867174A (en) * | 2015-05-08 | 2015-08-26 | 腾讯科技（深圳）有限公司 | Three-dimensional map rendering and display method and system |
| CN104965518A (en) * | 2015-05-21 | 2015-10-07 | 华北电力大学 | Power inspection tour flying robot air route planning method based on three-dimensional digital map |
| CN108088445A (en) * | 2016-11-22 | 2018-05-29 | 广州映博智能科技有限公司 | 3 d grid map path planning system and method based on octree representation |
| CN106737671A (en) * | 2016-12-21 | 2017-05-31 | 西安科技大学 | The bilayer personification motion planning method of seven degrees of freedom copy man mechanical arm |
| CN106949893A (en) * | 2017-03-24 | 2017-07-14 | 华中科技大学 | The Indoor Robot air navigation aid and system of a kind of three-dimensional avoidance |
| KR20180135695A (en) * | 2017-06-13 | 2018-12-21 | 성균관대학교산학협력단 | A method for checking empty space to install devices, install path, and instal order |
| CN107631734A (en) * | 2017-07-21 | 2018-01-26 | 南京邮电大学 | A kind of dynamic smoothing paths planning method based on D*_lite algorithms |
| CN108876024A (en) * | 2018-06-04 | 2018-11-23 | 清华大学深圳研究生院 | Path planning, path real-time optimization method and device, storage medium |
| CN108776492A (en) * | 2018-06-27 | 2018-11-09 | 电子科技大学 | A kind of four-axle aircraft automatic obstacle avoiding and air navigation aid based on binocular camera |
| CN109582032A (en) * | 2018-10-11 | 2019-04-05 | 天津大学 | Quick Real Time Obstacle Avoiding routing resource of the multi-rotor unmanned aerial vehicle under complex environment |
Non-Patent Citations (3)
| Title |
|---|
| USENKO,V.等: "Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer", 《2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)》 * |
| 刘阿敏: "基于三维点云图的无人机路径规划", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 苏云鹤: "室内轮式机器人RGB-D SLAM的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741490A (en) * | 2020-05-29 | 2021-12-03 | 广州极飞科技股份有限公司 | Inspection method, inspection device, aircraft and storage medium |
| CN111707269A (en) * | 2020-06-23 | 2020-09-25 | 东南大学 | Unmanned aerial vehicle path planning method in three-dimensional environment |
| CN111752306A (en) * | 2020-08-14 | 2020-10-09 | 西北工业大学 | Unmanned aerial vehicle route planning method based on fast-expanding random tree |
| CN111752306B (en) * | 2020-08-14 | 2022-07-12 | 西北工业大学 | Unmanned aerial vehicle route planning method based on fast-expanding random tree |
| CN112379681A (en) * | 2020-11-02 | 2021-02-19 | 中国兵器工业计算机应用技术研究所 | Unmanned aerial vehicle obstacle avoidance flight method and device and unmanned aerial vehicle |
| CN112525195A (en) * | 2020-11-20 | 2021-03-19 | 中国人民解放军国防科技大学 | Multi-target genetic algorithm-based aircraft track rapid planning method |
| CN112747736A (en) * | 2020-12-22 | 2021-05-04 | 西北工业大学 | Indoor unmanned aerial vehicle path planning method based on vision |
| CN114200933A (en) * | 2021-12-06 | 2022-03-18 | 太原科技大学 | Three-dimensional path planning method for bridge crane |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110146085B (en) | 2022-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110146085A (en) | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration | |
| CN108983781B (en) | Environment detection method in unmanned vehicle target search system | |
| Heng et al. | Autonomous visual mapping and exploration with a micro aerial vehicle | |
| US10768623B2 (en) | Drone path planning | |
| Weng et al. | Pole-based real-time localization for autonomous driving in congested urban scenarios | |
| Sadat et al. | Feature-rich path planning for robust navigation of MAVs with mono-SLAM | |
| CA3093522A1 (en) | Swarm path planner system for vehicles | |
| CA3087250A1 (en) | Enhanced vehicle tracking | |
| González-Sieira et al. | Autonomous navigation for UAVs managing motion and sensing uncertainty | |
| Campos‐Macías et al. | Autonomous navigation of MAVs in unknown cluttered environments | |
| KR20220129218A (en) | Speed control method of unmanned vehicle to awareness the flight situation about an obstacle, and, unmanned vehicle the performed the method | |
| CN116576857A (en) | Multi-obstacle prediction navigation obstacle avoidance method based on single-line laser radar | |
| CN114964212A (en) | Multi-machine collaborative fusion positioning and mapping method oriented to unknown space exploration | |
| Lombaerts et al. | Adaptive multi-sensor fusion based object tracking for autonomous urban air mobility operations | |
| Xing et al. | Autonomous landing of a micro aerial vehicle on a moving platform using a composite landmark | |
| Lian et al. | Improved coding landmark-based visual sensor position measurement and planning strategy for multiwarehouse automated guided vehicle | |
| Guan et al. | Autonomous exploration using UWB and LiDAR | |
| CN113064422B (en) | Autonomous underwater vehicle path planning method based on double neural network reinforcement learning | |
| KR20230083846A (en) | Travel Path Planning Method of Aerial Vehicles in 3-dimensional Environment | |
| Mughal et al. | UAVs path planning by particle swarm optimization based on visual-SLAM algorithm | |
| Jung et al. | Enabling operational autonomy for unmanned aerial vehicles with scalability | |
| Marlow et al. | Local terrain mapping for obstacle avoidance using monocular vision | |
| Youssefi et al. | Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars | |
| Yang et al. | An optimization-based selection approach of landing sites for swarm unmanned aerial vehicles in unknown environments | |
| Li et al. | Object-aware view planning for autonomous 3-D model reconstruction of buildings using a mobile robot |
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 | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Niu Diefeng Inventor after: Ma Zhaowei Inventor after: Hu Jia Inventor after: Wu Lizhen Inventor before: Niu Diefeng Inventor before: Ma Zhaowei Inventor before: Hu Jia Inventor before: Wu Lizhen |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |