CN110207708A - A kind of unmanned aerial vehicle flight path device for planning and method - Google Patents
A kind of unmanned aerial vehicle flight path device for planning and method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/20—Instruments for performing navigational calculations
Abstract
The present invention relates to a kind of unmanned aerial vehicle flight path device for planning, belong to unmanned aerial vehicle flight path planning field.The device includes: geographical environment information collection module, unmanned plane characteristic perception module, information analysis module, flight path programming module, track optimization module；This method are as follows: geographical environment information collection module collects the possible flight range geographical environment information of unmanned plane, and is sent to information analysis module；Information analysis module analyzes zone state between the unmanned plane takeoff point and point of destination, obtain unmanned plane during flying danger zone information, and it is sent to flight path programming module, under conditions of considering global information, realize the planning and optimization to the unmanned aerial vehicle flight path.The invention further relates to a kind of unmanned aerial vehicle flight path planing methods.
Description
Technical field
The invention belongs to unmanned aerial vehicle flight path planning field, it is related to a kind of unmanned aerial vehicle flight path device for planning and method.
Background technique
UAV abbreviation unmanned plane is manipulated using radio robot and the presetting apparatus provided for oneself
Not manned vehicle.The advantages such as small in size, at low cost, the low and mobility strong of loss of unmanned plane make it be widely used in express delivery
The industries such as service, city management, security be explosion-proof, agriculture, meteorological, electric power, rescue and relief work, video capture.Unmanned aerial vehicle flight path planning
Refer to that based on unmanned plane mission requirements and flight range characteristic be unmanned plane optimization design flight path.The technology is to realize nobody
Major issue of the machine in each field efficient application.
Research has been considered that unmanned aerial vehicle flight path planning problem, proposes to use based on Voronoi diagram if any document
Dijkstra's algorithm finds unmanned plane optimal trajectory.Each unmanned plane threatening area is approximately a point by this method, chooses each prestige
The intersection point of line perpendicular bisector is as track points between side of body point.This method can effectively ensure that track avoids each threat point.In another example
There is document to propose to devise the group space to be developed by ant group algorithm based on ant group algorithm trajectory planning strategy and by group space
The optimal belief space being deconstructed into, two spaces develop simultaneously, promote each other, so as to determine unmanned aerial vehicle flight path.However, nothing
Since flight space range is big in man-machine practical application, parameter is more and parameter is directly interrelated, and mentioned algorithm is caused to be easy to fall into
Enter locally optimal solution, optimal path can not be determined by even resulting in.It is examined in addition, the research of existing unmanned aerial vehicle flight path programme is less
The design for considering trajectory planning device, causes mentioned algorithm to be limited in practical applications.
Summary of the invention
In view of this, being based on movement area the purpose of the present invention is to provide a kind of unmanned aerial vehicle flight path device for planning and method
Domain geographical environment information, unmanned plane takeoff point, purpose dot position information plan unmanned plane under conditions of considering global information
Track solves the problems, such as to fall into locally optimal solution during planning track.
In order to achieve the above objectives, the invention provides the following technical scheme:
On the one hand, a kind of unmanned aerial vehicle flight path device for planning is provided, comprising:
Geographical environment information collection module: the possible flight range geographical environment letter of unmanned plane is obtained based on numerical map
Breath, and it is sent to information analysis module；
Unmanned plane characteristic perception module: perception unmanned plane takeoff point and purpose dot position information, unmanned plane current residual electricity
Amount, unmanned plane during flying speed, maximum roll angle and minimum enroute I.F.R. altitude, and it is sent to information analysis module；
Information analysis module: receiving information collected by geographical environment information collection module, unmanned plane characteristic perception module,
Zone state between unmanned plane takeoff point and point of destination is analyzed, obtains unmanned plane during flying danger zone information, and send
To flight path programming module；
Flight path programming module: believed according to the information of information analysis module transmission and unmanned plane takeoff point, point of destination position
Breath plans unmanned aerial vehicle flight path, and is sent to track optimization module；
Track optimization module: the unmanned aerial vehicle flight path planning strategy of flight path programming module output is received, judges whether to need to carry out
Track optimization, if so, optimizing processing to track；Otherwise, without track optimization.
On the other hand, the present invention provides a kind of unmanned aerial vehicle flight path planing method, comprising:
The possible flight range geographical environment information of unmanned plane is obtained based on numerical map；
Perceive unmanned plane takeoff point and purpose dot position information, unmanned plane current residual electricity, unmanned plane minimum step, most
Tight turn radius and minimum enroute I.F.R. altitude；
According to unmanned plane current electric quantity determine current maximum can flying distance, to area between unmanned plane takeoff point and point of destination
Domain is analyzed, and unmanned plane during flying danger zone information is obtained；
According to unmanned plane takeoff point, purpose dot position information, unmanned plane during flying danger zone information planning unmanned aerial vehicle flight path；
According to the unmanned aerial vehicle flight path, judge whether that track optimization need to be carried out, if so, optimizing place to the track
Reason；Conversely, without track optimization.
Further, the planning unmanned aerial vehicle flight path, specifically includes the following steps:
S1: unmanned plane during flying region modeling
To the unmanned plane during flying region carry out two-dimensional discrete, be modeled as two-dimensional grid, using the central point of grid as
Track points enable X0, Y0Unmanned plane during flying zone level direction and vertical direction maximum distance are respectively indicated, Δ x, Δ y is enabled to distinguish table
Show grid level direction and vertical direction discrete interval, thenQuantify space-number amount for grid level direction,For grid vertical direction quantized interval quantity, (m, n) indicates flight range grid, wherein 0≤m≤
Mmax,0≤n≤Nmax；I (m, n) is enabled to indicate the unmanned plane during flying region danger Marking the cell, if (m, n) is dangerous grid,
I (m, n)=1；Otherwise, I (m, n)=0 enables the unmanned plane takeoff point be located at (ms,ns), point of destination is located at (md,nd)；
S2: initialization opens, closes list
Enable the unmanned plane takeoff point (ms,ns) it is initial mesh, by initial mesh (ms,ns) be added to and open list Ls,
That is Ls={ (ms,ns), remember ms=m0, ns=n0, claim (m0,n0) it is " father's grid "；It enables and closes list LcFor empty set, i.e. Lc=
{Φ}；
S3: adjacent mesh cost function is calculated
Find " father's grid " (m0, n0) all accessibility not dangerous grids in adjacent mesh, note (m, n) is (m0, n0)
Adjacent mesh, i.e., | m-m0|=1 or | n-n0|=1, and I (m, n)=0, calculate (m0,n0) with the corresponding cost of track between (m, n)
Function is denoted as fm,n, it is modeled as fm,n=gm,n+hm,n, wherein gm,nIt indicates from initial mesh (ms,ns) arrive (m, n) grid reality
Cost, hm,nIt indicates the estimation cost from (m, n) grid to target gridding, records the f of (m, n)m,n、gm,nAnd hm,nValue；
S4: it updates and opens list and closing list
For accessibility grids all around " father's grid ", according to its fm,nIt is worth and descending sequentially adds unlatching list；
If grid in opening list, is checked from initial mesh (ms,ns) pass through " father's grid " (m0, n0) reach grid (m, n) correspondence
Gm,nValue, and with the current g of the gridm,nValue, is denoted asIt compares, ifThen by grid (m, n)It updates
For gm,n, and recalculate grid fm,n；" father's grid " is deleted from unlatching list, is added to closing list；
S5: " father's grid " updates
It finds to open in list and corresponds to fm,nIt is worth minimum grid, is denoted as (m0, n0), enable it for " father's grid "；
S6: step S3-S5 is repeated
Step S3-S5 is repeated, until target gridding is added to closing list, since target gridding, You Geci
" the father's grid " of iterative process dates back starting mesh, determines unmanned aerial vehicle flight path.
Further, g in step S3m,nIt is defined as (ms,ns) with the corresponding flight cost of track between (m, n), i.e., current " father
Grid " actual cost g 'm,nWith it to the sum of corresponding flight cost of track between (m, n) grid, it is modeled as gm,n=C (| m-m0
|2+|n-n0|2)1/2+g′m,n, wherein C is unit grid cost, hm,nIt is defined as from (m, n) grid to level target gridding
With the product of vertical grid quantity summation and unit grids cost, it is modeled as hm,n=C (| md-m|+|nd-n|)。
Further, three adjacent node angles are right angle to unmanned aerial vehicle flight path if it exists, then carry out track optimization processing, according to
The unmanned plane during flying speed v, maximum roll angleBy formulaCalculate to obtain the unmanned plane minimum
Turning radius, wherein g is acceleration of gravity, carries out track optimization based on gained turning radius.
The beneficial effects of the present invention are: the present invention is based on flight range geographical environment information, unmanned plane takeoff points, purpose
Dot position information plans unmanned aerial vehicle flight path under conditions of considering global information, solves planning track and falls into part in the process
The problem of optimal solution.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and
It obtains.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made below in conjunction with attached drawing excellent
The detailed description of choosing, in which:
Fig. 1 is trajectory planning schematic device of the present invention；
Fig. 2 is the flow diagram of path planning method of the present invention.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that diagram provided in following embodiment is only to show
Meaning mode illustrates basic conception of the invention, and in the absence of conflict, the feature in following embodiment and embodiment can phase
Mutually combination.
Wherein, the drawings are for illustrative purposes only and are merely schematic diagrams, rather than pictorial diagram, should not be understood as to this
The limitation of invention；Embodiment in order to better illustrate the present invention, the certain components of attached drawing have omission, zoom in or out, not
Represent the size of actual product；It will be understood by those skilled in the art that certain known features and its explanation may be omitted and be in attached drawing
It is understood that.
The same or similar label correspond to the same or similar components in the attached drawing of the embodiment of the present invention；It is retouched in of the invention
In stating, it is to be understood that if there is the orientation or positional relationship of the instructions such as term " on ", "lower", "left", "right", "front", "rear"
To be based on the orientation or positional relationship shown in the drawings, be merely for convenience of description of the present invention and simplification of the description, rather than indicate or
It implies that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore is described in attached drawing
The term of positional relationship only for illustration, is not considered as limiting the invention, for the ordinary skill of this field
For personnel, the concrete meaning of above-mentioned term can be understood as the case may be.
A kind of unmanned aerial vehicle flight path device for planning of the present invention and method, specifically: geographical environment information collection module
The possible flight range geographical environment information of unmanned plane is collected, and is sent to information analysis module, information analysis module is to described
Zone state is analyzed between unmanned plane takeoff point and point of destination, obtains unmanned plane during flying danger zone information, and be sent to
Flight path programming module plans the unmanned aerial vehicle flight path under conditions of considering global information, solves planning track and falls into the process
The problem of entering locally optimal solution.
Fig. 1 is trajectory planning schematic device of the present invention, as shown in Figure 1, the trajectory planning device includes: geographical ring
Border information collection module: the possible flight range geographical environment information of unmanned plane is obtained based on numerical map, and is sent to information
Analysis module；
Unmanned plane characteristic perception module: perceiving the unmanned plane takeoff point and purpose dot position information, and the unmanned plane is worked as
Preceding remaining capacity, the unmanned plane during flying speed, maximum roll angle and minimum enroute I.F.R. altitude, and it is sent to information analysis mould
Block；
Information analysis module: receiving information collected by geographical environment information collection module, unmanned plane characteristic perception module,
Zone state between the unmanned plane takeoff point and point of destination is analyzed, unmanned plane during flying danger zone information is obtained, and
It is sent to flight path programming module；
Flight path programming module: according to information analysis module and the unmanned plane takeoff point, purpose dot position information, institute is planned
Unmanned aerial vehicle flight path is stated, and is sent to track optimization module；
Track optimization module: the unmanned aerial vehicle flight path planning strategy of flight path programming module output is received, judges whether to need to carry out
Track optimization, if so, optimizing processing to the track；Otherwise, without track optimization.
Fig. 2 is the flow diagram of path planning method of the present invention, as shown in Fig. 2, trajectory planning of the present invention
Method specifically: the possible flight range geographical environment information of unmanned plane is obtained based on numerical map；The unmanned plane is perceived to rise
Flying spot and purpose dot position information, the unmanned plane current residual electricity, the unmanned plane minimum step, minimum turning radius and
Minimum enroute I.F.R. altitude；Region between the unmanned plane takeoff point and point of destination is analyzed, unmanned plane during flying danger is obtained
Danger zone domain information；According to the unmanned plane takeoff point, purpose dot position information, unmanned plane during flying danger zone information planning
The unmanned aerial vehicle flight path, comprising:
To the unmanned plane during flying region carry out two-dimensional discrete, be modeled as two-dimensional grid, using the central point of grid as
Track points enable X0, Y0Unmanned plane during flying zone level direction and vertical direction maximum distance are respectively indicated, Δ x, Δ y is enabled to distinguish table
Show grid level direction and vertical direction discrete interval, thenQuantify space-number amount for grid level direction,For grid vertical direction quantized interval quantity, (m, n) indicates flight range grid, wherein 0≤m≤
Mmax,0≤n≤Nmax；I (m, n) is enabled to indicate the unmanned plane during flying region danger Marking the cell, if (m, n) is dangerous grid,
I (m, n)=1；Otherwise, I (m, n)=0 enables the unmanned plane takeoff point be located at (ms,ns), point of destination is located at (md,nd)。
Enable the unmanned plane takeoff point (ms,ns) it is initial mesh, by initial mesh (ms,ns) be added to and open list Ls,
That is Ls={ (ms,ns), remember ms=m0, ns=n0, claim (m0,n0) it is " father's grid "；It enables and closes list LcFor empty set, i.e. Lc=
{Φ}。
Find " father's grid " (m0, n0) all accessibility not dangerous grids (m, n) in adjacent mesh, even | m-m0|=1
Or | n-n0|=1, and I (m, n)=0, calculate (m0,n0) with the corresponding cost function of track between (m, n), be denoted as fm,n, it is modeled as
fm,n=gm,n+hm,n, wherein gm,nIt indicates from initial mesh (ms,ns)
To the actual cost of (m, n) grid, hm,nIt indicates the estimation cost from (m, n) grid to target gridding, records each
The f of grid (m, n)m,n、gm,nAnd hm,nValue.
For accessibility grids all around " father's grid ", according to its fm,nIt is worth and descending sequentially adds unlatching list；
If grid in opening list, is checked from initial mesh (ms,ns) pass through " father's grid " (m0, n0) reach grid (m, n)
gm,nValue, and it is current with the gridValue is compared, ifThen grid (m, n)It is updated to gm,n, and again
Calculate grid fm,n；" father's grid " is deleted from unlatching list, is added to closing list.
It finds to open in list and corresponds to fm,nIt is worth minimum grid, is denoted as (m0, n0), enable it for " father's grid ".
The above process is repeated, until target gridding is added to closing list.Since target gridding, repeatedly by each time
Starting mesh is dateed back for " the father's grid " of process, that is, can determine unmanned aerial vehicle flight path.
According to the unmanned aerial vehicle flight path, judge whether that track optimization need to be carried out, if so, optimizing place to the track
Reason；Conversely, without track optimization.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (5)
1. a kind of unmanned aerial vehicle flight path device for planning, it is characterised in that: include:
Geographical environment information collection module: obtaining the possible flight range geographical environment information of unmanned plane based on numerical map, and
It is sent to information analysis module；
Unmanned plane characteristic perception module: perception unmanned plane takeoff point and purpose dot position information, unmanned plane current residual electricity, nothing
Man-machine flying speed, maximum roll angle and minimum enroute I.F.R. altitude, and it is sent to information analysis module；
Information analysis module: information collected by geographical environment information collection module, unmanned plane characteristic perception module is received, to nothing
Zone state is analyzed between man-machine takeoff point and point of destination, obtains unmanned plane during flying danger zone information, and be sent to boat
Mark planning module；
Flight path programming module: the information and unmanned plane takeoff point, purpose dot position information sent according to information analysis module, rule
Unmanned aerial vehicle flight path is drawn, and is sent to track optimization module；
Track optimization module: the unmanned aerial vehicle flight path planning strategy of flight path programming module output is received, judges whether that track need to be carried out
Optimization, if so, optimizing processing to track；Otherwise, without track optimization.
2. a kind of unmanned aerial vehicle flight path planing method, it is characterised in that: include:
The possible flight range geographical environment information of unmanned plane is obtained based on numerical map；
Perceive unmanned plane takeoff point and purpose dot position information, unmanned plane current residual electricity, unmanned plane minimum step, minimum turn
Curved radius and minimum enroute I.F.R. altitude；
According to unmanned plane current electric quantity determine current maximum can flying distance, to region between unmanned plane takeoff point and point of destination into
Row analysis, obtains unmanned plane during flying danger zone information；
According to unmanned plane takeoff point, purpose dot position information, unmanned plane during flying danger zone information planning unmanned aerial vehicle flight path；
According to the unmanned aerial vehicle flight path, judge whether that track optimization need to be carried out, if so, optimizing processing to the track；
Conversely, without track optimization.
3. unmanned aerial vehicle flight path planing method according to claim 2, it is characterised in that: the planning unmanned aerial vehicle flight path, tool
Body the following steps are included:
S1: unmanned plane during flying region modeling
Two-dimensional discrete is carried out to the unmanned plane during flying region, two-dimensional grid is modeled as, using the central point of grid as track
Point, enables X0, Y0Unmanned plane during flying zone level direction and vertical direction maximum distance are respectively indicated, Δ x, Δ y is enabled to respectively indicate net
Lattice horizontal direction and vertical direction discrete interval, thenQuantify space-number amount for grid level direction,For grid vertical direction quantized interval quantity, (m, n) indicates flight range grid, wherein 0≤m≤
Mmax,0≤n≤Nmax；I (m, n) is enabled to indicate the unmanned plane during flying region danger Marking the cell, if (m, n) is dangerous grid,
I (m, n)=1；Otherwise, I (m, n)=0 enables the unmanned plane takeoff point be located at (ms,ns), point of destination is located at (md,nd)；
S2: initialization opens, closes list
Enable the unmanned plane takeoff point (ms,ns) it is initial mesh, by initial mesh (ms,ns) be added to and open list Ls, i.e. Ls
={ (ms,ns), remember ms=m0, ns=n0, claim (m0,n0) it is " father's grid "；It enables and closes list LcFor empty set, i.e. Lc={ Φ }；
S3: adjacent mesh cost function is calculated
Find " father's grid " (m0, n0) all accessibility not dangerous grids in adjacent mesh, note (m, n) is (m0, n0) it is adjacent
Grid, i.e., | m-m0|=1 or | n-n0|=1, and I (m, n)=0, calculate (m0,n0) with the corresponding cost letter of track between (m, n)
Number, is denoted as fm,n, it is modeled as fm,n=gm,n+hm,n, wherein gm,nIt indicates from initial mesh (ms,ns) arrive (m, n) grid practical generation
Valence, hm,nIt indicates the estimation cost from (m, n) grid to target gridding, records the f of (m, n)m,n、gm,nAnd hm,nValue；
S4: it updates and opens list and closing list
For accessibility grids all around " father's grid ", according to its fm,nIt is worth and descending sequentially adds unlatching list；If net
Lattice in opening list, are then checked from initial mesh (ms,ns) pass through " father's grid " (m0, n0) reach grid (m, n) it is corresponding
gm,nValue, and with the current g of the gridm,nValue, is denoted asIt compares, ifThen by grid (m, n)It is updated to
gm,n, and recalculate grid fm,n；" father's grid " is deleted from unlatching list, is added to closing list；
S5: " father's grid " updates
It finds to open in list and corresponds to fm,nIt is worth minimum grid, is denoted as (m0, n0), enable it for " father's grid "；
S6: step S3-S5 is repeated
Step S3-S5 is repeated, until target gridding is added to closing list, since target gridding, You Geci iteration
" the father's grid " of process dates back starting mesh, determines unmanned aerial vehicle flight path.
4. unmanned aerial vehicle flight path planing method according to claim 3, it is characterised in that: g in step S3m,nIt is defined as (ms,
ns) with the corresponding flight cost of track between (m, n), i.e., current " father's grid " actual cost g 'm,nWith its to (m, n) grid it
Between the sum of the corresponding flight cost of track, be modeled as gm,n=C (| m-m0|2+|n-n0|2)1/2+g′m,n, wherein C is unit grid
Cost, hm,nBe defined as from (m, n) grid to number of grid summation horizontal and vertical target gridding with unit grids cost it
Product, is modeled as hm,n=C (| md-m|+|nd-n|)。
5. unmanned aerial vehicle flight path planing method according to claim 2, it is characterised in that: unmanned aerial vehicle flight path three phases if it exists
Neighbors angle is right angle, then carries out track optimization processing, according to the unmanned plane during flying speed v, maximum roll angle
By formulaCalculate to obtain the unmanned plane minimum turning radius, wherein g is acceleration of gravity, is based on gained
Turning radius carries out track optimization.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111006669A (en) * | 2019-12-12 | 2020-04-14 | 重庆邮电大学 | Unmanned aerial vehicle system task cooperation and path planning method |
| CN111578945A (en) * | 2020-05-27 | 2020-08-25 | 福州尚易航农业科技有限公司 | Unmanned aerial vehicle track planning method based on GNSS-machine vision |
| WO2021082396A1 (en) * | 2019-11-01 | 2021-05-06 | 南京智慧航空研究院有限公司 | Unmanned aerial vehicle flight network modeling method based on low-altitude airspace restriction conditions |
| CN115953919A (en) * | 2023-03-15 | 2023-04-11 | 国科星图(深圳)数字技术产业研发中心有限公司 | Flight management system and method based on gridding air route analysis |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880186A (en) * | 2012-08-03 | 2013-01-16 | 北京理工大学 | Flight path planning method based on sparse A* algorithm and genetic algorithm |
| CN103697896A (en) * | 2014-01-13 | 2014-04-02 | 西安电子科技大学 | Unmanned aerial vehicle route planning method |
| US20160202695A1 (en) * | 2014-09-12 | 2016-07-14 | 4D Tech Solutions, Inc. | Unmanned aerial vehicle 3d mapping system |
| CN105953800A (en) * | 2016-06-14 | 2016-09-21 | 北京航空航天大学 | Route planning grid space partitioning method for unmanned aerial vehicle |
| CN106203721A (en) * | 2016-07-18 | 2016-12-07 | 武汉理工大学 | Ice formation, the polar region flight-line design system and method for self-adaptive ship ice-breaking capacity |
| CN106441303A (en) * | 2016-09-30 | 2017-02-22 | 哈尔滨工程大学 | Path programming method based on A* algorithm capable of searching continuous neighborhoods |
| CN106687876A (en) * | 2014-07-14 | 2017-05-17 | A·贾雷尔 约翰 | Unmanned aerial vehicle communication, monitoring, and traffic management |
| CN107345815A (en) * | 2017-07-24 | 2017-11-14 | 东北大学 | A kind of home-services robot paths planning method based on improvement A* algorithms |
| CN107677273A (en) * | 2017-09-11 | 2018-02-09 | 哈尔滨工程大学 | A kind of cluster unmanned plane Multiple routes planning method based on two-dimensional grid division |
| CN108274465A (en) * | 2018-01-10 | 2018-07-13 | 上海理工大学 | Based on optimization A*Artificial Potential Field machinery arm, three-D obstacle-avoiding route planning method |
| CN108592925A (en) * | 2018-07-26 | 2018-09-28 | 中国人民解放军陆军工程大学 | Unmanned plane turning Path Planning based on min. turning radius |
| US20180308371A1 (en) * | 2017-04-19 | 2018-10-25 | Beihang University | Joint search method for uav multiobjective path planning in urban low altitude environment |
| CN108846522A (en) * | 2018-07-11 | 2018-11-20 | 重庆邮电大学 | UAV system combines charging station deployment and route selection method |
| CN108932876A (en) * | 2018-08-14 | 2018-12-04 | 湖北工业大学 | A kind of express delivery unmanned aerial vehicle flight path planing method of the A* introducing black area and ant colony algorithm |
| CN108958285A (en) * | 2018-07-17 | 2018-12-07 | 北京理工大学 | It is a kind of that path planning method is cooperateed with based on the efficient multiple no-manned plane for decomposing thought |
| CN109582035A (en) * | 2018-11-29 | 2019-04-05 | 沈阳无距科技有限公司 | A kind of aircraft's flight track air navigation aid, device and electronic equipment |
| CN109670656A (en) * | 2019-02-27 | 2019-04-23 | 重庆邮电大学 | A kind of unmanned plane optimal communication route planning method based on 4G network |
-
2019
- 2019-06-25 CN CN201910556400.3A patent/CN110207708A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880186A (en) * | 2012-08-03 | 2013-01-16 | 北京理工大学 | Flight path planning method based on sparse A* algorithm and genetic algorithm |
| CN103697896A (en) * | 2014-01-13 | 2014-04-02 | 西安电子科技大学 | Unmanned aerial vehicle route planning method |
| CN106687876A (en) * | 2014-07-14 | 2017-05-17 | A·贾雷尔 约翰 | Unmanned aerial vehicle communication, monitoring, and traffic management |
| US20160202695A1 (en) * | 2014-09-12 | 2016-07-14 | 4D Tech Solutions, Inc. | Unmanned aerial vehicle 3d mapping system |
| CN105953800A (en) * | 2016-06-14 | 2016-09-21 | 北京航空航天大学 | Route planning grid space partitioning method for unmanned aerial vehicle |
| CN106203721A (en) * | 2016-07-18 | 2016-12-07 | 武汉理工大学 | Ice formation, the polar region flight-line design system and method for self-adaptive ship ice-breaking capacity |
| CN106441303A (en) * | 2016-09-30 | 2017-02-22 | 哈尔滨工程大学 | Path programming method based on A* algorithm capable of searching continuous neighborhoods |
| US20180308371A1 (en) * | 2017-04-19 | 2018-10-25 | Beihang University | Joint search method for uav multiobjective path planning in urban low altitude environment |
| CN107345815A (en) * | 2017-07-24 | 2017-11-14 | 东北大学 | A kind of home-services robot paths planning method based on improvement A* algorithms |
| CN107677273A (en) * | 2017-09-11 | 2018-02-09 | 哈尔滨工程大学 | A kind of cluster unmanned plane Multiple routes planning method based on two-dimensional grid division |
| CN108274465A (en) * | 2018-01-10 | 2018-07-13 | 上海理工大学 | Based on optimization A*Artificial Potential Field machinery arm, three-D obstacle-avoiding route planning method |
| CN108846522A (en) * | 2018-07-11 | 2018-11-20 | 重庆邮电大学 | UAV system combines charging station deployment and route selection method |
| CN108958285A (en) * | 2018-07-17 | 2018-12-07 | 北京理工大学 | It is a kind of that path planning method is cooperateed with based on the efficient multiple no-manned plane for decomposing thought |
| CN108592925A (en) * | 2018-07-26 | 2018-09-28 | 中国人民解放军陆军工程大学 | Unmanned plane turning Path Planning based on min. turning radius |
| CN108932876A (en) * | 2018-08-14 | 2018-12-04 | 湖北工业大学 | A kind of express delivery unmanned aerial vehicle flight path planing method of the A* introducing black area and ant colony algorithm |
| CN109582035A (en) * | 2018-11-29 | 2019-04-05 | 沈阳无距科技有限公司 | A kind of aircraft's flight track air navigation aid, device and electronic equipment |
| CN109670656A (en) * | 2019-02-27 | 2019-04-23 | 重庆邮电大学 | A kind of unmanned plane optimal communication route planning method based on 4G network |
Non-Patent Citations (3)
| Title |
|---|
| 周璐: "具有方向约束的无人机动态航迹规划研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 姜文: "电力巡线系统四轴飞行器自动避障研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 胡云舒: "无人机航路规划系统研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021082396A1 (en) * | 2019-11-01 | 2021-05-06 | 南京智慧航空研究院有限公司 | Unmanned aerial vehicle flight network modeling method based on low-altitude airspace restriction conditions |
| CN111006669A (en) * | 2019-12-12 | 2020-04-14 | 重庆邮电大学 | Unmanned aerial vehicle system task cooperation and path planning method |
| CN111578945A (en) * | 2020-05-27 | 2020-08-25 | 福州尚易航农业科技有限公司 | Unmanned aerial vehicle track planning method based on GNSS-machine vision |
| CN115953919A (en) * | 2023-03-15 | 2023-04-11 | 国科星图(深圳)数字技术产业研发中心有限公司 | Flight management system and method based on gridding air route analysis |
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