CN115098993A - Unmanned aerial vehicle conflict detection method and device for airspace digital grid and storage medium - Google Patents
Unmanned aerial vehicle conflict detection method and device for airspace digital grid and storage medium Download PDFInfo
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Abstract
The invention provides an unmanned aerial vehicle conflict detection method, device and storage medium of airspace digital grid, comprising the following steps: establishing an airspace discrete subdivision grid model; constructing a grid coding rule and a conversion relation between longitude and latitude coordinates and grid codes; establishing an unmanned aerial vehicle safety protection area to carry out gridding expression on the unmanned aerial vehicle in the airspace; establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates; computing two block minkowski difference sets using the GJK algorithm; and judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set. Through combining airspace grid coding and GJK algorithm, compare with traditional coordinate operation in pairs and can effectively reduce the conflict and survey the complexity, practice thrift a large amount of computing time, effectively improve unmanned aerial vehicle conflict detection efficiency to satisfy the quick real-time conflict detection demand of unmanned aerial vehicle in the airspace.
Description
Technical Field
The invention relates to the field of aviation, in particular to an unmanned aerial vehicle collision detection method and device of an airspace digital grid and a storage medium.
Background
In recent years, with the rapid increase in the number of scales of unmanned aerial vehicles, research in the field of unmanned aerial vehicles has been developed unprecedentedly. Compared with the traditional manned aircraft, the unmanned aerial vehicle has the advantages of low cost, high cost performance, convenience in use, high maneuverability and the like due to man-machine separation. Along with the rapid increase of the number of unmanned aerial vehicles, the low-altitude airspace is gradually blocked, and how to efficiently detect unmanned aerial vehicle conflicts in the low-altitude airspace is a key problem for restricting the safe flight of the unmanned aerial vehicles.
The traditional conflict detection method can be used for conflict detection of the unmanned aerial vehicle running in the low-altitude airspace, and whether conflict exists or not is judged by calculating the distance between the positions of all track points. The number of unmanned aerial vehicle tracks increases along with the increase of the number of unmanned aerial vehicles, and under the condition that the involved airspace range is large, if a traditional collision detection method is adopted, according to the operating characteristics and the volume of the unmanned aerial vehicle, the operation under longitude and latitude coordinates cannot reach high precision, the calculation complexity is high, the collision detection efficiency can be reduced, the algorithm operation time is too long, and even the collision cannot be effectively detected, and the requirement of unmanned aerial vehicle collision detection is difficult to meet.
The spatial domain gridding is a discretization spatial domain method which is used for establishing a spatial domain grid unit subdivision method, constructing spatial domain system data analysis based on network indexes and developing spatial domain performance related researches through a rasterization method. At present, researches prove that the conflict detection efficiency can be greatly improved by a conflict detection algorithm based on grids, but most of data information spaces without integrated grid airspaces are researched, only a grid method is adopted through an application layer, and the proposed grid division method does not carry out modeling aiming at a specific operating environment.
Therefore, how to construct a low-altitude airspace environment-oriented design grid coding to improve the collision detection efficiency of the unmanned aerial vehicle is a problem to be solved urgently at present.
Disclosure of Invention
The invention aims to: the invention aims to solve the technical problem of providing an unmanned aerial vehicle collision detection method of an airspace digital grid aiming at the defects of the prior art, which comprises the following steps:
establishing an airspace discrete subdivision grid model;
constructing a grid coding rule and a conversion relation between longitude and latitude coordinates and codes;
establishing an unmanned aerial vehicle safety protection area to carry out gridding expression on the unmanned aerial vehicle in the airspace;
establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates;
computing the Minkowski difference set for the two volumes using the GJK (Gilbert-Johnson-Keerthi) distance algorithm;
and judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set.
Further, the establishing of the airspace discrete subdivision grid model comprises the following steps:
step 3The altitude is independent of the spherical division, and can be divided into true altitude, scene air pressure altitude, sea level air pressure altitude correction and standard atmospheric pressure altitude according to the difference of altitude reference planes, and the altitude is expressed by X 1 (generally 30m) is expanded upwards as a granularity.
Further, the constructing of the grid coding rule and the transformation relationship between the longitude and latitude coordinates and the codes comprises:
the coding comprises plane coding and height coding, wherein the plane coding and the height coding both adopt Z-shaped coding, and the degree level block coding is denoted by d; "fractional" stage block coding is denoted m; the second level block coding is denoted by s, and the plane coding and the height coding are combined to form the spatial grid system three-dimensional coding.
Further, the establishing the unmanned aerial vehicle safety protection area comprises:
establishing an unmanned aerial vehicle safety protection area according to the operation performance of the unmanned aerial vehicle, wherein the transverse interval and the longitudinal interval of the protection area are D hor At a vertical interval of D ver And selecting a grid with proper granularity according to the size of the protection area. According to unmanned aerial vehicle's classification standard, general civilian consumer grade unmanned aerial vehicle belongs to miniature unmanned aerial vehicle, corresponds 8 th level net granularity, and 6 th, 7 th level net correspond medium-sized unmanned aerial vehicle and unmanned aerial vehicle size respectively, so can represent most unmanned aerial vehicle with 6 th, 7, 8 th level net. If some unmanned aerial vehicle sizes are special, can not directly represent with a grid, can adopt a plurality of meshes to make up the expression.
Further, the method for carrying out gridding expression on the unmanned aerial vehicle in the airspace comprises the following steps:
the gridding expression can express the target object through grid combination or independently. Firstly, the unmanned aerial vehicle is represented by only one cube, the Point represents information of the cube, and the following Point object expression models are established:
in the formula (I), the compound is shown in the specification,
indicating the latitude, longitude and altitude of the location of the point object.
The block with n levels is divided at the longitude and latitude height. In the process of computer storage and operation, use is made of
Representing a point object;
then, representing the path of the unmanned aerial vehicle by using a plurality of continuous cubes, wherein Line represents the flight path of the unmanned aerial vehicle, and establishing the following Line object expression model:
when the unmanned aerial vehicle or the obstacle object cannot be represented by one cube, more than two small grids are stacked to represent an irregular-shaped object, Space represents an object formed by stacking more than two cubes, and the following object expression model is established:
aiming at the current unmanned aerial vehicle detection object, the flight longitude and latitude and altitude information acquired from an airborne ADS-B (ADS-B system is short for a broadcast type automatic correlation monitoring system) device or a ground station is subjected to code conversion, and the formula is as follows:
Code Alt ＝Alt/x 1
wherein, longitude Code, latitude Code and altitude Code are respectively used Lon 、Code Lat And Code Alt Representation, n represents the coding level, gridsize n Represents the n-th level mesh granularity size, Lon d 、Lon m And Lon s Respectively representing degrees, minutes, seconds in longitude coordinates, the altitude level being in x 1 Separate encoding is extended upward for granularity.
Further, establishing the coordinate system to convert the mesh code of the drone into coordinates includes:
after the unmanned aerial vehicle in the airspace is subjected to gridding expression, the unmanned aerial vehicle and a track point thereof are placed in a grid coordinate system, and longitude and latitude coordinates are converted into rectangular coordinate integer arithmetic.
Further, said calculating the two-volume Minkowski difference set using the GJK algorithm includes:
the distance between the two convex bodies is calculated using the GJK algorithm, and the distance between the object a and the object B is represented by d (a, B), defined by the following equation:
d(A,B)＝min{||x-y||:x∈A,y∈B}；
where x and y represent points in object a and object B, respectively; objects a and B are both cubes;
two points a belonging to A and B belonging to B with the shortest distance satisfy d (A, B);
the minkowski difference set is a set of points formed by the difference of all points of object a and all points of object B, as expressed below:
M(A,B)＝{x-y:x∈A,y∈B}；
m (A, B) represents the Minkowski difference set of cubes A and B;
the distance between objects a and B is represented by a minkowski difference set, described as follows:
d(A,B)＝min||M(A,B)||＝min{||x-y||:x∈A,y∈B}。
further, said determining whether the drones collide based on the minkowski difference set comprises: converting the distance between the unmanned aerial vehicles into the Minkowski difference between the unmanned aerial vehicles, determining whether two objects collide by judging whether the difference set contains the original point, wherein the larger the distance between the two unmanned aerial vehicles is, the farther the central position of the difference set is from the original point, and otherwise, the closer the central position of the difference set is to the original point. If the drone block is collided, the difference set polygon will contain the origin.
The invention also provides an unmanned aerial vehicle collision detection device of the airspace digital grid, which comprises the following components:
the airspace discrete subdivision grid model establishing module is used for establishing an airspace discrete subdivision grid model;
the conversion relation building module is used for building a grid coding rule and a conversion relation between the longitude and latitude coordinates and the codes;
the gridding expression module is used for establishing an unmanned aerial vehicle safety protection area and carrying out gridding expression on the unmanned aerial vehicle in the airspace;
the coordinate conversion module is used for establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates;
a Minkowski difference calculation module for calculating Minkowski difference of the two blocks using GJK distance algorithm;
and the unmanned aerial vehicle conflict judging module is used for judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set.
The invention also provides a storage medium which stores a computer program or instructions, and when the computer program or instructions are executed, the unmanned aerial vehicle collision detection method of the airspace digital grid is realized.
The unmanned aerial vehicle collision detection method based on the airspace digital grid has the beneficial effects that the unmanned aerial vehicle collision detection method based on the airspace digital grid is provided. The unmanned aerial vehicle collision detection method based on the airspace digital grid comprises the following steps: establishing an airspace discrete subdivision grid model; constructing a grid coding rule and a conversion relation between longitude and latitude coordinates and grid codes; establishing an unmanned aerial vehicle safety protection area to carry out gridding expression on the unmanned aerial vehicle in the airspace; establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates; computing two block minkowski difference sets using the GJK algorithm; and judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set. Through combining airspace grid coding and GJK algorithm, compare with traditional coordinate operation in pairs and can effectively reduce the conflict and survey the complexity, practice thrift a large amount of computing time, effectively improve unmanned aerial vehicle conflict detection efficiency to satisfy the quick real-time conflict detection demand of unmanned aerial vehicle in the airspace.
Drawings
The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a flowchart of an unmanned aerial vehicle collision detection method based on an airspace digital grid according to the present invention.
FIG. 2 is a schematic diagram of a grid expansion.
Fig. 3 is a schematic diagram of subdivision hierarchy partitioning.
Figure 4 is a schematic of minkowski difference set distance conversion.
Fig. 5 is a schematic diagram of three drones A, B, C of the same type, represented as three 8 th level grid-sized cubes.
Figure 6 is a graph showing the minkowski difference results for a and B.
Figure 7 is a graph showing the minkowski difference results for a and C.
FIG. 8 conflict detection time comparison
Detailed Description
Examples
As shown in fig. 1, the present invention provides a method for detecting unmanned aerial vehicle collision based on an airspace digital grid. The air traffic region division method based on the fuzzy C-means clustering comprises the following steps:
s110: establishing an airspace discrete subdivision grid model;
s120: constructing a grid coding rule and a conversion relation between longitude and latitude coordinates and codes;
s130: establishing an unmanned aerial vehicle safety protection area to carry out gridding expression on the unmanned aerial vehicle in the airspace;
s140: establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates;
s150: computing two block minkowski difference sets using the GJK algorithm;
s160: and judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set.
In this embodiment, step S110 includes: the method comprises the steps of performing spherical subdivision and height subdivision on the earth, performing recursive grid division, analyzing the requirements of a spatial domain gridding method according to the operation characteristics of a low-altitude spatial domain, constructing a discretized gridding spatial domain and establishing grid subdivision of a digital model.
S111, expanding the latitude and longitude space of the earth for three times, namely expanding the geographic space into east-west 512 degrees and south-north 512 degrees, expanding 1 degree into 64 degrees, and expanding 1' into 64 degrees, as shown in FIG. 2;
s112: performing spherical recursive grid division based on the longitude and latitude of the geographic space, performing three-level subdivision of ' degree-minute-second ' on a plane step by step, dividing the spherical surface of the earth into 8-level recursive grids, and dividing the grid into blocks with the minimum side length of 1 ', wherein the blocks are shown in table 1;
s113: the height is independent of spherical division, and can be divided into true height, scene air pressure height, corrected sea level air pressure height and standard atmospheric pressure height according to different height reference surfaces, and upward expansion is carried out by taking 30m as granularity.
In this embodiment, step S120 includes the following steps: the coding is divided into two parts of plane coding and height coding, wherein the plane coding and the height coding both adopt Z-shaped coding, and degree level block coding is represented by d; "fractional" stage block coding is denoted m; the second level block coding is denoted by s, and the plane coding and the height coding are combined to form the three-dimensional coding of a spatial grid system.
TABLE 1
| Subdivision hierarchy | Mesh size | Approximate scale near the equator |
| |
15°×15° | 1669km |
| |
1°×1° | 111km |
| Third stage | 30'×30' | 56km |
| Fourth stage | 10'×10' | 9km |
| Fifth stage | 1'×1' | 1km |
| |
6"×6" | 0.2km |
| |
3"×3" | 0.1km |
| |
1"×1" | 0.03km |
In the present embodiment, step S130 includes the following steps:
s131: establishing a safety protection area of the unmanned aerial vehicle according to the operation performance of the unmanned aerial vehicle, wherein the transverse interval and the longitudinal interval areD hor At a vertical interval of D ver And selecting a grid with proper granularity according to the size of the protection area.
S132: establishing a point object expression model:
in the formula (I), the compound is shown in the specification,
indicating the latitude, longitude and altitude of the location of the point object.
The expression is under this longitude and latitude height, and the subdivision level is the block of n, as shown in fig. 3, according to unmanned aerial vehicle's classification standard, and general civilian consumer grade unmanned aerial vehicle belongs to miniature unmanned aerial vehicle, corresponds 8 th level mesh granularity, and 6 th, 7 th level mesh correspond medium-sized unmanned aerial vehicle and small unmanned aerial vehicle size respectively, so can represent most unmanned aerial vehicle with 6 th, 7, 8 th level mesh. If some unmanned aerial vehicle sizes are special, can not directly represent with a grid, can adopt a plurality of meshes to make up the expression.
In the process of computer storage and operation, use is made of
Representing a point object;
s133: establishing a line object expression model:
s134: building a three-dimensional object expression model:
aiming at the current unmanned aerial vehicle detection object, the flight longitude and latitude and altitude information acquired from airborne ADS-B equipment or a ground station is subjected to code conversion, and the formula is as follows:
Code Alt ＝Alt/x 1
wherein, longitude Code, latitude Code and altitude Code are respectively used Lon 、Code Lat And Code Alt Representation, n represents the coding level, gridsize n Represents the n-th level grid granularity, Lon d 、Lon m And Lon s Respectively representing degrees, minutes, seconds in longitude coordinates, the altitude level being in x 1 Separate encoding is extended upward for granularity.
In this embodiment, step S140 includes: after the airspace is subjected to gridding expression, the unmanned aerial vehicle and the track point thereof are placed in a grid coordinate system, and the longitude and latitude coordinates are converted into rectangular coordinate integer operation.
In this embodiment, step S150 includes:
s151: the GJK algorithm calculates the distance between two convex bodies, the distance between the objects A and B being represented by d (A, B), defined by the following equation:
d(A,B)＝min{||x-y||:x∈A,y∈B}
two points a belonging to A and B belonging to B with the shortest distance satisfy d (A, B);
s152: the minkowski difference set is a set of points formed by the difference of all points of object a and all points of object B, and can be represented as follows:
M(A,B)＝{x-y:x∈A,y∈B}；
s153: the distance between objects a and B can be represented by a minkowski difference set, as shown in figure 4, described as follows:
d(A,B)＝min||M(A,B)||＝min{||x-y||:x∈A,y∈B}。
in this embodiment, step S160 includes: converting the distance between the unmanned aerial vehicles into Minkowski difference between the unmanned aerial vehicles, determining whether two objects collide by judging whether the difference set contains the original point, wherein the larger the distance between the two unmanned aerial vehicles is, the farther the central position of the difference set is from the original point, and conversely, the closer the central position of the difference set is to the original point. If the UAV block is in collision, the difference set polygon will contain the origin. Drones a and B collide if and only if the minkowski difference sets M (a, B) of the two cubes contain the origin. Expressing three drones A, B, C of the same type as three cubes of level 8 grid size, as shown in fig. 5, with a and B touching, C and A, B far away, sets the number of algorithm iterations to 5000, thus resulting in a minkowski difference set containing 5000 points, with these elemental points displayed placed in coordinates, the minkowski difference results for a and B, A and C, as shown in fig. 6 and 7, visually demonstrate the relationship of the minkowski difference set to the origin. As shown in FIG. 6, the location of the origin inside the Minkowski difference set generated by cubes A and B indicates a conflict between the two; and there is no collision between cubes a and C, the origin is outside the minkowski difference set of both, as shown in figure 7. The collision detection time of the method is analyzed, three unmanned aerial vehicles with different sizes are adopted, the cube representations of the three unmanned aerial vehicles with different types are represented by the 6 th, 7 th and 8 th-level grid granularities, 20 unmanned aerial vehicles including 3 medium-sized unmanned aerial vehicles, 3 small-sized unmanned aerial vehicles and 14 micro unmanned aerial vehicles are selected in the experiment, the 8 th-level coding is also used for generating a 1km multiplied by 0.6km gridded airspace environment for the experiment, the average collision detection time of two algorithms in a period of time is recorded, and the result is shown in fig. 8. With the increase of the number of the unmanned aerial vehicles, the conflict detection time of the Euclidean distance conflict detection method for calculating the distance between the unmanned aerial vehicles in pairs under the traditional three-dimensional coordinate system increases approximately exponentially, the conflict detection method based on GJK increases linearly, the increase trend is slow, and the high efficiency of the method is shown.
This embodiment also provides unmanned aerial vehicle collision detection device of airspace digital grid, includes:
the airspace discrete subdivision grid model establishing module is used for establishing an airspace discrete subdivision grid model;
the conversion relation building module is used for building a grid coding rule and a conversion relation between the longitude and latitude coordinates and the codes;
the gridding expression module is used for establishing an unmanned aerial vehicle safety protection area and carrying out gridding expression on the unmanned aerial vehicle in the airspace;
the coordinate conversion module is used for establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates;
a Minkowski difference calculation module for calculating Minkowski difference of the two blocks using GJK distance algorithm;
and the unmanned aerial vehicle conflict judging module is used for judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set.
The embodiment also provides a storage medium, which stores a computer program or instructions, and when the computer program or instructions are executed, the unmanned aerial vehicle collision detection method of the airspace digital grid is realized.
As described above, the apparatus according to the embodiment of the present application may be implemented in various terminal devices, such as a server of a distributed computing system. In one example, the apparatus according to the embodiment of the present application may be integrated into the terminal device as a software module and/or a hardware module. For example, the means may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the apparatus may also be one of many hardware modules of the terminal device.
Alternatively, in another example, the apparatus and the terminal device may be separate terminal devices, and the apparatus may be connected to the terminal device through a wired and/or wireless network and transmit the interaction information according to an agreed data format.
In summary, the present invention provides an unmanned aerial vehicle collision detection method based on an airspace digital grid, including: establishing an airspace discrete subdivision grid model; constructing a grid coding rule and a conversion relation between longitude and latitude coordinates and grid codes; establishing an unmanned aerial vehicle safety protection area to carry out gridding expression on the unmanned aerial vehicle in the airspace; establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates; computing two block minkowski difference sets using the GJK algorithm; and judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set. Through combining airspace grid coding and GJK algorithm, compare with traditional coordinate operation in pairs and can effectively reduce the conflict and survey the complexity, practice thrift a large amount of computing time, effectively improve unmanned aerial vehicle conflict detection efficiency to satisfy the quick real-time conflict detection demand of unmanned aerial vehicle in the airspace.
The present invention provides a method, an apparatus and a storage medium for unmanned aerial vehicle collision detection of airspace digital grid, and a plurality of methods and ways for implementing the technical scheme, and the above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and embellishments can be made, and these should be regarded as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.
Claims (10)
1. An unmanned aerial vehicle collision detection method of an airspace digital grid is characterized by comprising the following steps:
establishing an airspace discrete subdivision grid model;
constructing a grid coding rule and a conversion relation between longitude and latitude coordinates and codes;
establishing an unmanned aerial vehicle safety protection area, and carrying out gridding expression on the unmanned aerial vehicle in the airspace;
establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates;
computing the Minkowski difference set of the two volumes using GJK distance algorithm;
and judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set.
2. The method of claim 1, wherein the establishing the spatial domain discrete subdivision grid model comprises the steps of:
step 1, expanding the longitude and latitude space of the earth for three times, namely expanding the geographic space into east-west 512 degrees and south-north 512 degrees, expanding 1 degree into 64 degrees, and expanding 1 'into 64';
step 2, performing sphere recursive grid division based on longitude and latitude of geographic space, dividing a plane into three levels of degree, minute and second step by step, dividing the earth sphere into 8-level recursive grids, and dividing the recursion grids into blocks with the minimum side length of 1';
step 3, the height is independent of the spherical surface division, and the height is expressed according to the difference of the height reference surfaces and is expressed by X 1 The granularity is expanded upwards.
3. The method of claim 2, wherein constructing the grid coding rule and the transformation relationship of latitude and longitude coordinates and codes comprises:
the coding comprises plane coding and height coding, wherein the plane coding and the height coding both adopt Z-shaped coding, and degree level block coding is represented by d; hierarchical block coding is denoted m; the second-level block coding is denoted by s, and the plane coding and the height coding are combined to form the three-dimensional coding of a spatial grid system.
4. The method of claim 3, wherein the establishing the drone secured area comprises:
establishing an unmanned aerial vehicle safety protection area according to the operation performance of the unmanned aerial vehicle, wherein the transverse interval and the longitudinal interval of the protection area are D hor At a vertical interval of D ver And selecting a grid with proper granularity according to the size of the protection area.
5. The method of claim 4, wherein the gridding the representation of the drones within the airspace comprises:
firstly, the unmanned aerial vehicle is represented by only one cube, the Point represents information of the cube, and the following Point object expression models are established:
in the formula, the ratio of theta,
h represents the latitude, longitude and altitude of the position of the point object respectively;
as indicated at the location of the line in theta,
h, dividing the block with the hierarchy of n under the longitude and latitude height of the place; by using
Representing a point object;
then, representing the path of the unmanned aerial vehicle by using a continuous cube, wherein Line represents the flight path of the unmanned aerial vehicle, and establishing a Line object expression model as follows:
when the unmanned aerial vehicle or the obstacle object cannot be represented by one cube, more than two small grids are used for stacking to represent an irregular-shaped object, Space represents an object formed by stacking more than two cubes, and the following object expression model is established:
aiming at the current unmanned detection object, the flight latitude and longitude and the altitude information acquired from airborne ADS-B equipment or a ground station are subjected to code conversion, and the formula is as follows:
Code Alt ＝Alt/x 1
wherein, longitude Code, latitude Code and altitude Code are respectively used Lon 、Code Lat And Code Alt Representation, n represents the coding level, gridsize n Represents the n-th level mesh granularity size, Lon d 、Lon m And Lon s Respectively representing degrees, minutes, seconds in longitude coordinates, the altitude level being in x 1 Separate encoding is extended upward for granularity.
6. The method of claim 5, wherein establishing the coordinate system to convert the mesh coding of the drone to coordinates comprises: after the unmanned aerial vehicle in the airspace is subjected to gridding expression, the unmanned aerial vehicle and the track point thereof are placed in a grid coordinate system, and the longitude and latitude coordinates are converted into rectangular coordinate integer arithmetic.
7. A method as claimed in claim 6 wherein said calculating Minkowski difference sets for two volumes using the GJK algorithm comprises:
the distance between the two convex bodies is calculated using the GJK algorithm, and the distance between the object a and the object B is represented by d (a, B), defined by the following equation:
d(A,B)＝min{||x-y||:x∈A,y∈B}；
where x and y represent points in object a and object B, respectively;
two points a belonging to A and B belonging to B with the shortest distance satisfy d (A, B);
the minkowski difference set is a set of points formed by the difference of all the points of object a and all the points of object B, as expressed below:
M(A,B)＝{x-y:x∈A,y∈B}；
m (A, B) represents the Minkowski difference set of cubes A and B;
the distance between objects a and B is represented by a minkowski difference set, described as follows:
d(A,B)＝min||M(A,B)||＝min{||x-y||:x∈A,y∈B}。
8. a method as claimed in claim 7, wherein said determining from Minkowski difference set whether a collision occurs for a drone comprises: the distance between the unmanned planes is converted into a Minkowski difference between the unmanned planes, and whether two objects collide is determined by judging whether a difference set contains an original point.
9. Unmanned aerial vehicle collision detection device of digital grid in airspace, its characterized in that includes:
the airspace discrete subdivision grid model establishing module is used for establishing an airspace discrete subdivision grid model;
the conversion relation building module is used for building a grid coding rule and a conversion relation between the latitude and longitude coordinates and the codes;
the gridding expression module is used for establishing an unmanned aerial vehicle safety protection area and carrying out gridding expression on the unmanned aerial vehicle in the airspace;
the coordinate conversion module is used for establishing a coordinate system to convert the grid codes of the unmanned aerial vehicle into coordinates;
a Minkowski difference calculation module for calculating Minkowski difference of the two blocks using GJK distance algorithm;
and the unmanned aerial vehicle conflict judging module is used for judging whether the unmanned aerial vehicle conflicts or not according to the Minkowski difference set.
10. A storage medium, storing a computer program or instructions which, when executed, implement the method of any one of claims 1 to 8.
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Cited By (6)
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| WO2024007256A1 (en) * | 2022-07-07 | 2024-01-11 | 南京航空航天大学 | Unmanned aerial vehicle conflict detection method and apparatus of airspace digital grid and storage medium |
| CN115731747A (en) * | 2022-11-02 | 2023-03-03 | 南京航空航天大学 | Low-altitude multi-unmanned aerial vehicle probability type conflict detection method and storage medium |
| CN115731747B (en) * | 2022-11-02 | 2024-01-09 | 南京航空航天大学 | Low-altitude multi-unmanned aerial vehicle probability type conflict detection method and storage medium |
| CN115830515A (en) * | 2023-01-31 | 2023-03-21 | 中国电子科技集团公司第二十八研究所 | Video target behavior identification method based on spatial grid |
| CN116312072A (en) * | 2023-03-21 | 2023-06-23 | 中国人民解放军93209部队 | Flight path operation conflict decoupling control method based on airspace grids |
| CN116312072B (en) * | 2023-03-21 | 2024-01-26 | 中国人民解放军93209部队 | Flight path operation conflict decoupling control method based on airspace grids |
| CN117351014A (en) * | 2023-12-05 | 2024-01-05 | 青岛市勘察测绘研究院 | Unmanned aerial vehicle flight safety detection method based on live-action three-dimension |
| CN117351014B (en) * | 2023-12-05 | 2024-03-01 | 青岛市勘察测绘研究院 | Unmanned aerial vehicle flight safety detection method based on live-action three-dimension |
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