CN109814598B

CN109814598B - Unmanned aerial vehicle low-altitude public navigation network design method

Info

Publication number: CN109814598B
Application number: CN201910137863.6A
Authority: CN
Inventors: 廖小罕; 徐晨晨; 岳焕印; 鹿明; 缑吉平; 陈西旺
Original assignee: Tianjin Cas Uav Application Research Institute; Institute of Geographic Sciences and Natural Resources of CAS
Current assignee: Tianjin Cas Uav Application Research Institute; Institute of Geographic Sciences and Natural Resources of CAS
Priority date: 2019-02-25
Filing date: 2019-02-25
Publication date: 2021-02-19
Anticipated expiration: 2039-02-25
Also published as: CN109814598A

Abstract

The invention discloses a design method of a low-altitude public navigation network of an unmanned aerial vehicle, and belongs to the technical field of route planning. The method comprises the steps of obtaining low-altitude flight environment data of the unmanned aerial vehicle in a planned area; obtaining unmanned aerial vehicle airport layout information according to the unmanned aerial vehicle low-altitude flight environment data, wherein the unmanned aerial vehicle airport layout information comprises: site selection sites of a plurality of unmanned aerial vehicles airport and service range of each unmanned aerial vehicle airport; obtaining a plurality of three-dimensional air routes according to the unmanned aerial vehicle airport layout information, the unmanned aerial vehicle low-altitude flight environment data and an ant colony algorithm; and forming an unmanned aerial vehicle low-altitude public navigation network according to the plurality of three-dimensional navigation paths. According to the technical scheme, the construction of the unmanned aerial vehicle low-altitude public navigation network is realized, and the route searching efficiency is higher and the time consumption is shorter when the navigation path is constructed.

Description

Unmanned aerial vehicle low-altitude public navigation network design method

Technical Field

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a design method of a low-altitude public navigation network of an unmanned aerial vehicle.

Background

The flight path planning of the unmanned aerial vehicle depends on a space division method and a flight path planning algorithm. At present, the division of a flight path planning space mainly adopts unit decomposition, which is an efficient method for discretizing and expressing the unmanned aerial vehicle flight airspace environment, the method divides the environment into a free unit and an obstacle unit, and a discretization method is adopted to express the environment; the cell representation method includes a Voronoi diagram method and a grid method. The method comprises the steps that an initial selectable path set is built or navigation nodes are set on a Voronoi diagram, then a proper path is selected through an optimization algorithm, and the defect is that repeated deduction is often needed for determining the positions and the number of the navigation nodes, the building precision of the Voronoi diagram determines the optimization precision of track cost, and the adaptability to sudden problem events is poor; the space division based on the grids can effectively solve the problem, and the application range is wide. After a track planning space is constructed, the optimal track is found by utilizing a path search algorithm, wherein the heuristic algorithm is most widely applied and mainly comprises an artificial potential field method, an A-star algorithm, a fast search random tree algorithm, a Dijkstra algorithm, an ant colony algorithm, a particle swarm algorithm, a genetic algorithm and other intelligent bionic algorithms which are established in recent years. The ant colony algorithm successfully and efficiently solves the problem of the travelers, has superiority in solving a complex optimization problem, particularly a discrete optimization problem, and simultaneously has the problems of low convergence speed, easy falling into local optimum and the like.

The unmanned aerial vehicle flight path planning is different from the low-altitude flight path planning of the unmanned aerial vehicle, the flight path planning is mostly used for one time from the aspect of airspace effectiveness, the airspace effectiveness is also terminated after a task is completed, and the airspace related to the public flight path is kept unchanged for a long time, so that the utilization of low-altitude airspace resources can be effectively improved and standardized, and the traffic safety control of the unmanned aerial vehicle is facilitated; from the space object of planning, the route planning is oriented to the line, and the public route planning is oriented to the space body, which is embodied as different representation units in the algorithm; from the composition of planning space environment, the flight path planning only considers low altitude, terrain and electromagnetic interference or missile danger areas, and the public route planning also considers the influence of airspace policy, cellular network and population dense areas on the basis of the flight path planning and is more closely related to human activities; for service objects, the purpose of route planning is strong, most of the route planning is task-oriented, and the route planning is generally used for surveying and mapping, line patrol and the like, and the public route planning needs to consider multiple application purposes and has strong universality. Therefore, a design method of the unmanned aerial vehicle low-altitude public air way network is required to be provided based on the characteristics of the unmanned aerial vehicle low-altitude public air way.

Disclosure of Invention

In order to solve the problems, the invention provides a method for designing a low-altitude public navigation network of unmanned aerial vehicles, which comprises the following steps: acquiring unmanned aerial vehicle low-altitude flight environment data in a planning area, the unmanned aerial vehicle low-altitude flight environment data comprises: the method comprises the following steps of (1) topographic data, low-altitude climate data, airspace policy data and low-altitude mobile communication signal spatial distribution data; obtaining unmanned aerial vehicle airport layout information according to the unmanned aerial vehicle low-altitude flight environment data, wherein the unmanned aerial vehicle airport layout information comprises: site selection sites of a plurality of unmanned aerial vehicles airport and service range of each unmanned aerial vehicle airport; obtaining a plurality of three-dimensional air routes according to the unmanned aerial vehicle airport layout information, the unmanned aerial vehicle low-altitude flight environment data and an ant colony algorithm; and forming an unmanned aerial vehicle low-altitude public navigation network according to the plurality of three-dimensional navigation paths.

In the method, preferably, the obtaining a plurality of three-dimensional air routes according to the unmanned aerial vehicle airport layout information, the unmanned aerial vehicle low-altitude flight environment data, and the ant colony algorithm specifically includes: determining two unmanned aerial vehicle airports in each three-dimensional air route included by each air route level according to the service range of each unmanned aerial vehicle airport and a preset air route level; constructing a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle according to the two unmanned aerial vehicle airports of a three-dimensional route and the corresponding data of the low-altitude flight environment of the unmanned aerial vehicle in the planning area; horizontally slicing the digitalized low-altitude flight environment mathematical model of the unmanned aerial vehicle at a preset flight height to obtain a digitalized two-dimensional low-altitude environment mathematical model at the preset flight height; performing two-dimensional path search in the digitized two-dimensional low-altitude environment mathematical model based on an ant colony algorithm to obtain a two-dimensional airway, wherein in the ant colony algorithm, a search space is formed by buffering by using a connecting line between a starting node and an ending node as a variable distance, and the buffer distance is gradually increased from an initial value by 1 search step until an optimal airway solution exists in the search space; obtaining a three-dimensional airway according to the two-dimensional airway and the reference topographic data; and traversing two unmanned aerial vehicles in each three-dimensional air route of all route levels to obtain a plurality of three-dimensional air routes.

In the method as described above, preferably, the forming of the unmanned aerial vehicle low-altitude public route network according to the plurality of three-dimensional routes specifically includes: and forming an unmanned aerial vehicle low-altitude public navigation network according to different division heights of different route levels, different heights of the routes of the same level when the unmanned aerial vehicles handshake, different priority levels of the unmanned aerial vehicles of the routes of the same level and the plurality of three-dimensional routes.

In the above method, preferably, the obtaining of the unmanned aerial vehicle airport layout information according to the unmanned aerial vehicle low-altitude flight environment data specifically includes: obtaining initial unmanned aerial vehicle airport layout information by utilizing a maximum coverage addressing model according to the unmanned aerial vehicle low-altitude flight environment data, wherein the initial unmanned aerial vehicle airport layout information comprises: the method comprises the following steps that initial site selection sites of a plurality of unmanned aerial vehicles are arranged, and an initial service range of each unmanned aerial vehicle is arranged; and judging whether the initial site selection station has a cross conflict of routes, if so, optimizing the initial unmanned aerial vehicle airport layout information to obtain the unmanned aerial vehicle airport layout information, otherwise, taking the initial unmanned aerial vehicle airport layout information as the unmanned aerial vehicle airport layout information.

In the method as described above, preferably, in the ant colony algorithm, the heuristic function η_ij(t)：

Wherein d is_ijIndicating the distance between the current node i and the next node j,

respectively the normalized distance of the next node from the start node and the end node, d_OiI distance between the starting node and the current node, d_OEC and p are constants, which are the distances between the start node and the end node,

weight representing the distance between the next node and the start node, p representing the path length threshold for the start of introducing direction information, allowed_kRepresenting nodes that ants can reach.

In the method as described above, preferably, the method further includes: in the ant colony algorithm, if the random number rand is generated by a random function<max(P_i) Selecting the next node by adopting a roulette algorithm; if the random number rand generated by the random function is max (P)_i) Then the node with the highest transition probability is used as the next node.

In the method as described above, preferably, the obtaining a three-dimensional route according to the two-dimensional route and the reference terrain data specifically includes: and judging whether the terrain height of the next waypoint in the reference terrain data is greater than the difference value between the interface height under the route and the minimum obstacle clearance, if so, judging that the next waypoint height is equal to the sum of the average value of the grid elevations in the route range and the minimum obstacle clearance, and if not, judging that the next waypoint height is equal to the interface height under the route.

In the method as described above, preferably, after determining no, the method further includes: judging whether the next waypoint is in a mountain range in the reference terrain data or not, whether the current waypoint height is higher than the highest point of the mountain range or not and whether the next waypoint height is smaller than the current waypoint height or not, and if so, judging that the next waypoint height is equal to the current waypoint height; if not, jumping to the step that the height of the next current waypoint is equal to the height of the interface under the route.

In the method as described above, preferably, in the two-dimensional path search, the first resolution is lower than a second resolution, and the second resolution is a resolution adopted by the mountain terrain data; after the obtaining the two-dimensional route and before the obtaining the three-dimensional route according to the two-dimensional route and the reference terrain data, the method further comprises: and judging whether two adjacent waypoints of the two-dimensional navigation path correspond to the mountainous area, and if so, adding a plurality of waypoints between the two adjacent waypoints.

In the method as described above, preferably, the constructing a mathematical model of a low-altitude flight environment of an unmanned aerial vehicle according to two unmanned aerial vehicle airports and corresponding data of the low-altitude flight environment of the unmanned aerial vehicle in a planned area of a three-dimensional air route specifically includes: constructing an unmanned aerial vehicle low-altitude flight space initial model, wherein the lower interface of a flight space in the unmanned aerial vehicle low-altitude flight space initial model is determined by the terrain data corresponding to two unmanned aerial vehicle airports, and the upper interface of the flight space is determined by the spatial distribution of low-altitude mobile communication signals; right the key element that retrains unmanned aerial vehicle safe flight in the unmanned aerial vehicle low-altitude flight space initial model carries out mathematical modeling, accomplishes and founds unmanned aerial vehicle low-altitude flight environment mathematical model, the key element that retrains unmanned aerial vehicle safe flight includes: the method comprises the following steps of (1) peak constraint elements, high-rise building constraint elements, low-altitude climate constraint elements and airspace policy restricted area constraint elements; correspondingly, in the ant colony algorithm, whether the proportion of the obstacles represented by the elements for restraining the safe flight of the unmanned aerial vehicle and the communication blind area restraining elements is lower than a preset proportion threshold value or not is judged in a local search space, and if the proportion is judged to be lower than the preset proportion threshold value, the search step length is adjusted.

The technical scheme provided by the embodiment of the invention has the following beneficial effects:

how to construct the unmanned aerial vehicle low-altitude public navigation network is provided, and the path searching efficiency is higher and the time consumption is shorter when the navigation network is constructed.

Drawings

Fig. 1 is a schematic flow chart of a method for designing a low-altitude public navigation network for unmanned aerial vehicles according to an embodiment of the present invention;

FIG. 2 is a diagram illustrating a search space according to an embodiment of the present invention;

fig. 3 is a schematic flow chart of a method for obtaining a two-dimensional route according to an embodiment of the present invention;

FIG. 4 is a schematic flow chart illustrating a method for obtaining a three-dimensional route according to an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a route model according to an embodiment of the present invention;

FIG. 6 is a schematic view of a route interval according to an embodiment of the present invention;

FIG. 7 is a schematic view of a flight interval provided by an embodiment of the present invention;

FIG. 8 is a schematic diagram illustrating transitions between different levels of airway according to an embodiment of the present invention;

fig. 9 is a schematic view of the operation of the unmanned aerial vehicle in fig. 8 at the airport and the approach and departure routes (at a).

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Referring to fig. 1, an embodiment of the present invention provides a method for designing a low-altitude public navigation network for unmanned aerial vehicles, which includes the following steps:

step 101, acquiring low-altitude flight environment data of the unmanned aerial vehicle in the planned area.

The unmanned aerial vehicle low-altitude flight environment data (or called unmanned aerial vehicle route planning basic database) is composed of elements influencing safe flight of the unmanned aerial vehicle, and flight of the unmanned aerial vehicle is closely related to ground environment and human activities, so that the influencing elements mainly comprise basic geography, low-altitude climate, low-altitude communication environment, airspace policy and the like. The basic geographic data mainly comprises topographic data, water system distribution, ground road network distribution, population distribution data and the like. Low air climate data: unmanned aerial vehicle moves about in troposphere scope, and low weather has important influence to its take off and land, operation and flight. The weather phenomena with large influence mainly include wind shear, thunderstorm, ice accumulation, low visibility and other weather phenomena caused by fog, haze, sand storm and the like. Spatial domain policy data: the unmanned aerial vehicle mainly comprises an unmanned aerial vehicle flight barring area, a limiting area and a danger area which are regulated by the government, and particularly relates to the protection range of a civil aviation airport barrier limiting surface. The low-altitude mobile communication signal spatial distribution data (or called low-altitude communication environment data) is represented by mobile base station data: the unmanned aerial vehicle receives and sends radio signals through the airborne link equipment to communicate with the ground radio station, receives command, control and task instructions of the ground control station, and sends self attitude information, related task information and the like. And along with the high-speed development of the unmanned aerial vehicle industry, the unmanned aerial vehicle communication link presents the development trend of closely combining with the cellular mobile communication technology, and forms the "networking unmanned aerial vehicle". Therefore, ensuring that the unmanned aerial vehicle operates within the coverage area of the cellular network is important for safe flight of the unmanned aerial vehicle. The embodiment of the invention establishes the low-altitude flying cellular network environment of the unmanned aerial vehicle according to the distribution data of the mobile base station and the signal coverage range of the mobile base station. The planning region can be the whole Chinese range, and can also be a region range, such as North China, east China, and the like.

102, obtaining unmanned aerial vehicle airport layout information according to the low-altitude flight environment data of the unmanned aerial vehicle, wherein the unmanned aerial vehicle airport layout information comprises: site selection sites of a plurality of unmanned aerial vehicle airports and service ranges of all unmanned aerial vehicle airports.

Specifically, a drone airport refers to an organic whole formed by a drone airport with a legal airspace and related service facilities, and the drone airport has hardware facilities including, but not limited to: unmanned aerial vehicle runway, unmanned aerial vehicle hangar, unmanned aerial vehicle assembly and debugging area, navigation communication facility and the like; having software facilities including, but not limited to: a flight supervision system, an air traffic control cooperative reporting system and the like. The unmanned aerial vehicle airport is a junction of a low-altitude route and the ground of the unmanned aerial vehicle, and can be used as a take-off and landing point and a transition place of the unmanned aerial vehicle to provide guarantee for the safe flight of the unmanned aerial vehicle. The unmanned aerial vehicle airport location must have a wide vision field, good communication conditions, no high-rise buildings or mountain shelters, and not be in the range of the airspace policy restricted area. According to the low-altitude flight environment data of the unmanned aerial vehicle, based on the distribution of the existing ground traffic hubs, the airport layout information of the unmanned aerial vehicle is obtained by utilizing a maximum coverage site selection model, and the information comprises: the initial site of the unmanned aerial vehicle airport (or called initial site) and the initial service range of the unmanned aerial vehicle airport (initial service range). During application, according to the service range of each level of air routes, the spatial distribution characteristics of population factors, terrain factors and other factors influencing safe flight of the unmanned aerial vehicle are fully considered, and based on the distribution of the existing ground traffic hubs, a primary station is selected as an unmanned aerial vehicle air port supporting an unmanned aerial vehicle air route traffic network by utilizing a maximum coverage addressing model.

The unmanned aerial vehicle airport layout obtained by the maximum coverage addressing model does not consider the influence of cross collision on route safety, has hidden flight safety hazards, and cannot meet the actual flight requirements of the unmanned aerial vehicle. Therefore, the method further comprises the step of site selection optimization: optimizing the unmanned aerial vehicle airport layout, wherein the optimization algorithm comprises the following steps: merging near unmanned aircraft airports, merging routes, collineation adjustment, low-utilization route adjustment, no intersection, non-linear coefficients and the like. That is to say, whether cross conflict exists in the route connecting lines among the unmanned aerial vehicles in the unmanned aerial vehicle airport layout is judged, if yes, the site selection optimization step and the field investigation and low-altitude networking test step are sequentially executed, and if not, the field investigation and low-altitude networking test step is executed.

Field investigation and low-altitude networking test steps: after the layout of the unmanned aerial vehicle airport is obtained preliminarily, field investigation (or called field investigation) needs to be carried out, and a specific site of the unmanned aerial vehicle airport is determined. The conditions required to be met by site selection are as follows:

and (3) airspace condition: when the unmanned aerial vehicle air port is not built in the air restricted area and is built in the area adjacent to the air restricted area, the risk that the unmanned aerial vehicle breaks into the air restricted area is considered.

Geographic conditions: the ground is wide, no obstacles are used for shielding, and enough space is provided for building an unmanned aerial vehicle runway; the influence of factors such as poor geological sections, possibly submerged areas, active fault areas, mining areas, environmental and ecological protection areas, tourist scenic areas, historical relic and ancient trace protection areas and the like is fully considered;

communication conditions are as follows: the unmanned aerial vehicle safety flight communication link index is met;

meteorological conditions: the influence of severe meteorological conditions such as wind fields, thunderstorms, ice accretions, visibility and the like on the flight safety of the unmanned aerial vehicle is fully considered;

noise sensitive area: whether the aviation activity area meets the requirements of the noise control indexes of the peripheral area or not is fully considered;

land utilization: the requirements of relevant land utilization policy and regulation are met;

electromagnetic environment complex, hazardous area: the influence of the electromagnetic environment of the space on airport communication navigation activities and the influence of electromagnetic waves generated by aviation activities on ground sensitive facilities should be fully considered.

In consideration of the importance of the low-altitude communication environment on the control and safe flight of the unmanned aerial vehicle, cellular network low-altitude coverage tests and service tests, such as electronic fence updating, real-time flight data reporting, flight management command receiving and the like, should be performed in the area where the unmanned aerial vehicle is located to ensure that the unmanned aerial vehicle air port meets the safe flight communication link indexes of the unmanned aerial vehicle (civil aviation administration, low-altitude networking unmanned aerial vehicle safe flight test report, 2018.1).

And 103, obtaining a plurality of three-dimensional air routes according to the unmanned aerial vehicle airport layout information, the unmanned aerial vehicle low-altitude flight environment data and the ant colony algorithm.

Specifically, the steps include: determining two unmanned aerial vehicle airports in each three-dimensional route included by each route level according to the service range of each unmanned aerial vehicle airport and a preset route level; constructing a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle according to the two unmanned aerial vehicle airports of a three-dimensional route and the corresponding data of the low-altitude flight environment of the unmanned aerial vehicle in the planning area; horizontally slicing the digitalized unmanned aerial vehicle low-altitude flight environment mathematical model at a preset flight height to obtain a digitalized two-dimensional low-altitude environment mathematical model at the preset flight height; performing two-dimensional path search in a digital two-dimensional low-altitude environment mathematical model based on an ant colony algorithm to obtain a two-dimensional airway, wherein in the ant colony algorithm, a search space is formed by buffering by using a connecting line between a starting node and an ending node as a variable distance, and the buffer distance is gradually increased from an initial value by 1 search step until an optimal airway solution exists in the search space; obtaining a three-dimensional airway according to the two-dimensional airway and the reference topographic data; and traversing two unmanned aerial vehicles in each three-dimensional air route of all the air route levels to obtain a plurality of three-dimensional air routes.

The unmanned aerial vehicle low-altitude route is an air channel which is planned in advance and has a certain width and is specially used for flying of the unmanned aerial vehicle below the lowest flying height of the manned aircraft. The actual flight path of the unmanned aerial vehicle is called the air route, and the air route of the unmanned aerial vehicle flying along the air route is the central line of the air route. The purpose of defining the air route is to maintain and standardize the low-altitude traffic order, improve the utilization rate of low-altitude space resources and ensure aviation and public safety. The invention divides the positioning and service of the low-altitude air route of the unmanned aerial vehicle into four levels: backbone, trunk, branch and terminal routes, i.e. the preset route level is 4.

When the planning area is in the whole Chinese range, the low-altitude backbone airway is an airway connecting the capital with the capital of each province, each autonomous region and each prefecture of the direct prefecture, and an airway connecting each large economic center, each port station hub, each commodity production base and each strategic place; the low-altitude main airway is a provincial low-altitude airway with full-provincial political and economic significance; the low-altitude branch route is a low-altitude route in a connection area and mainly bears the relation between the unmanned aircraft airport and the backbone/trunk route; the low-altitude terminal airway is a low-altitude airway connecting a branch line to an end user or connecting one end user to another end user, and mainly bears the contact of the unmanned aerial vehicle from the branch line to terminal service points/stations such as logistics, catering delivery and the like, or is arranged in complex terrains (such as mountainous areas, sparsely populated areas and the like). The definitions of the low-altitude main navigation path, the low-altitude linear navigation path and the low-altitude tail navigation path can be adaptively adjusted according to the actual application condition and the specific range of the planning area.

When the method is applied, the routes can be hierarchically named by combining initials and numbers, such as: the letters represent all levels of routes, the backbone route is GG, the backbone route is ZG, the branch routes are ZX, and the tail end route is MD; the third digit identifies the course, such as 3 for the north-south course and 2 for the east-west course.

After the route levels are divided, determining which three-dimensional routes each route level comprises according to the service range of each unmanned aerial vehicle airport, thereby determining two unmanned aerial vehicle airports in each three-dimensional route, respectively serving as the starting point and the ending point of a path, and constructing an unmanned aerial vehicle low-altitude flight environment data member unmanned aerial vehicle flight environment book order model in a planning area corresponding to the two unmanned aerial vehicle airports, wherein the book order model specifically comprises the following steps:

firstly, constructing an initial model of a low-altitude flight space of the unmanned aerial vehicle: suppose the ith waypoint coordinate is (x)_i,y_i,z_i) Then, the mathematical expression of the initial model of the low-altitude flight space may be:

wherein x is_iIs longitude, y_iIs latitude, z_iIs height, x_min、x_max、y_minAnd y_maxRepresenting a planned spatial plane range covering the addressed stations of two unmanned aerial vehicles airport, f_1i(x_i,y_i) Reference terrain height for the location of the ith waypoint, f_2i(x_i,y_i) The communication maximum height of the position of the ith waypoint.

In the low-altitude flight space initial model, the elements for determining the flight height range of the unmanned aerial vehicle include: reference terrain and spatial distribution of mobile communication base station signals. Specifically, the lower interface of the flight space is determined by the reference terrain, which reflects the terrain relief of the route planning space and directly affects the minimum safe height of the route planning space. The upper interface of the flight space is determined by the signal space distribution of the mobile communication base station.

The mathematical expression for the reference terrain is as follows:

wherein x and y are point coordinates of the projection of the reference terrain model on the horizontal plane, and z is an elevation value corresponding to the horizontal plane point. a, b, c, d, e, g are constant coefficients for controlling the reference topography in the digital map. Different reference landform characteristics are simulated by determining different constant coefficients and are used as the reference landform of the flight environment of the unmanned aerial vehicle.

Therefore, the reference terrain height expression of the position of the ith waypoint is as follows:

the signal coverage (or spatial distribution) of a mobile communication base station is related to the signal propagation rule, the ground feature shielding and the environmental electromagnetic radiation, and the signal strength shows irregular change along with the change of the horizontal and vertical distances from an antenna. Through practical tests, the mobile cellular network can meet the application requirements of unmanned aerial vehicle industries in most scenes below 120m and the requirements of unmanned aerial vehicle safe flight service link indexes in most areas below 300 m. With the development of mobile communication technology, such as the commercial expansion of 5G technology, the height can be expanded to below 1000 m. Therefore, research can be realized through mobile cellular networking that unmanned aerial vehicle's route real time monitoring. Based on the national cellular mobile communication network, a low-altitude communication environment is constructed, and route planning is carried out on the basis, so that real-time, high-reliability and low-cost unmanned aerial vehicle communication links in the routes are guaranteed. According to the electromagnetic radiation monitoring instrument and method of the environmental protection management guide rule of radiation (HJ-T10.2-1996), the calculation formula of the radiation power density of a single base station antenna is as follows:

according to the relation between the power density and the electric field intensity:

suppose that the jth base station is located at (X)_j,Y_j) Obtaining an electric field intensity expression of the jth base station at the ith waypoint position:

wherein P is the transmitting power (W) of the base station antenna, G is the gain (multiple) of the base station antenna, theta is the included angle between the vertical plane and the axial direction of the base station antenna,

is an included angle between the horizontal plane and the axial direction of the base station antenna,

θ₁is the antenna downward inclination angle, H is the ground clearance (m) of the jth base station antenna, and the above parameters are constants; z is a radical of_iIs the ith waypoint height from the ground (m) and R is the horizontal distance from the ith waypoint to the jth base station antenna (m).

Assuming that n base stations can provide communication service at the position of the ith waypoint, the field strength at the position of the ith waypoint is as follows:

the maximum communication height of the position of the ith waypoint is as follows: e_iWhen being equal to C, z_iWherein C is the minimum value that the base station field strength satisfies the unmanned aerial vehicle safe flight.

Secondly, performing mathematical modeling on elements for restraining safe flight of the unmanned aerial vehicle in the unmanned aerial vehicle low-altitude flight space initial model, and completing construction of the unmanned aerial vehicle low-altitude flight environment mathematical model.

Specifically, after an initial model of the low-altitude flight space of the unmanned aerial vehicle is built, mathematical modeling is carried out on elements for restraining safe flight of the unmanned aerial vehicle in the flight space, and therefore a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle is built. The constraint elements mainly comprise: the system comprises a peak constraint element, a high-rise building constraint element, a low-altitude climate constraint element and an airspace policy restricted area constraint element. The mathematical models of the constraint elements are as follows:

(1) mountain peak constraint element mathematical model: the unmanned aerial vehicle has low flying height, natural elements on the ground surface, particularly mountains, are important to safe flying, the mountains can be simulated and highlighted through the following formula, and a terrain environment model for unmanned aerial vehicle path searching is constructed:

in the formula z₁(x, y) is the elevation function of the peak, (x)_j,y_j) Is the center coordinate of the jth peak, H_jFor the reference terrain height, a and b are the attenuation of the jth peak along the x-axis and y-axis directions, respectively, and the gradient is controlled.

(2) The high-rise building constraint element mathematical model is as follows: the high-rise building can be represented by a cylinder, and the mathematical model thereof can be represented as:

wherein z is₂(x, y) is the elevation function of high-rise buildings, (x)_j,y_j) Is the center coordinate of the jth building, H_jIs the height of the jth high-rise building, and R is the occupied area radius of the high-rise building.

(3) The low-climate constraint element mathematical model is as follows: unmanned aerial vehicle is at troposphere within range activity, and low air climate has important influence to its take off and land, operation and flight, wherein influences great weather phenomenon and mainly has wind shear, thunderstorm, ice accretion to and fog, haze, sand storm etc. can lead to the weather phenomenon of low visibility. When the unmanned aerial vehicle route planning is carried out, the distribution of areas with severe climate, such as high-incidence areas of various climate phenomena in the last two decades, is used as a reference. The study uses a near cylinder to judge the influence range of the atmospheric environment, and the mathematical model can be expressed as:

d_ij＝(x-x_j)²+(y-x_j)²

wherein z is₃(x, y) is a height function of the low climate zone, H is the upper height limit of the routing zone, and (x)_j,y_j) Center coordinates of jth low climate zone of influence, d_ijDistance of the ith waypoint to the jth climate-affected zone, d_minFor the core area of the climate influence area, the damage probability of the unmanned aerial vehicle in the area is 1, d_maxThe maximum radius affected by the atmosphere.

(4) The space domain policy restriction region constraint element mathematical model is as follows: the airspace policy restriction area restriction mainly refers to an unmanned aerial vehicle flight forbidding area, a flight limiting area, a dangerous area and the like issued by government departments in various regions, and a safe and orderly low-altitude flight environment is constructed by introducing a low-altitude airspace policy and setting airspace policy restriction areas such as a population dense area. The airspace policy limiting area can be derived from dynamic geo-fence data, the airport plane range can be determined through civil aviation published 'civil aviation airport barrier limiting surface protection range data', the lower limit is the earth surface, and the upper limit is not set. Other restricted regions can be represented by hemisphere models:

wherein z is₄(x, y) is the influence surface of the jth limiting area, (x)_j,y_j) Is the central coordinate of the jth limiting area, and R is the floor area radius of the limiting area.

After the unmanned aerial vehicle low-altitude flight environment mathematical model is built, the unmanned aerial vehicle low-altitude flight environment mathematical model is digitized, then horizontal slicing is carried out on the digitized unmanned aerial vehicle low-altitude flight environment mathematical model on the preset flight height, and the digitized two-dimensional low-altitude environment mathematical model on the preset flight height is obtained, wherein the step is as follows:

firstly, digitalizing a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle, wherein the digitalization method can adopt a grid method, the grid is in a cube form, and the length of the cube is consistent with the width of a flight path. And then acquiring the preset flying height. In actual application, digitization can be realized based on technologies such as GIS drawing. The preset flying height can be obtained by the following steps: and fitting according to the reference terrain data to obtain the initial height of the air route, and then calculating the sum of the initial height of the air route and the minimum obstacle exceeding margin of the unmanned aerial vehicle, so as to obtain the preset flight height. Then horizontal slicing is carried out on the digitalized unmanned aerial vehicle low-altitude flight environment mathematical model, and a two-dimensional low-altitude environment mathematical model of the unmanned aerial vehicle on the preset flight height is obtained.

After a digitized two-dimensional low-altitude environment mathematical model at a preset flying height is obtained, two-dimensional path search is carried out in the digitized two-dimensional low-altitude environment mathematical model based on an ant colony algorithm to obtain a two-dimensional airway, and the method specifically comprises the following steps:

two unmanned aerial vehicle airports are respectively used as a starting node and a terminating node to conduct two-dimensional path search in a digital two-dimensional low-altitude environment mathematical model based on an ant colony algorithm, and two-dimensional air routes corresponding to the two unmanned aerial vehicle airports are obtained

In the traditional ant colony algorithm, along with the expansion of a search space, the problem of 'combined explosion' is easy to occur, and the search efficiency is reduced. Therefore, in this step, the search space of the ant colony algorithm is formed by buffering the connection line between the start node and the end node with a variable distance, so that the size of the search space can be reasonably controlled, the optimal path solution in the search space is satisfied, the search efficiency is improved as much as possible, and the formed search space is as shown in fig. 2. The buffer distance is gradually increased from the initial value by 1 search step until there is an optimal route solution in the search space.

In order to improve the path search efficiency, a variable search step length search is adopted in the search process, namely, the search step length is determined according to the proportion of the obstacles in the local search space, and when the proportion of the obstacles in the search space is lower than a preset proportion threshold value, the search step length is adjusted, for example, the search step length is longer than that before, for example: when the proportion of the obstacles in the search space is higher than 20%, the search step length is 1, and if the proportion of the obstacles in the search space is lower than 5%, the search step length is 2. The barrier can be characterized by an element for restraining the safe flight of the unmanned aerial vehicle and a communication blind area restraining element, and can also be characterized by an element for restraining the safe flight of the unmanned aerial vehicle, a communication blind area restraining element and an airport element. The elements for restraining the safe flight of the unmanned aerial vehicle can comprise an airspace policy restricted area element, a mountain peak constraint element, a high-rise building constraint element and a low-altitude climate constraint element. The mountain peak constraint element and the high-rise building constraint element may be collectively referred to as a terrain element. That is to say, the element that the unmanned aerial vehicle safety flight is restrained in quantification influences the potential risk that unmanned aerial vehicle flies, and the cost weighted attribute value of every space graticule mesh of being convenient for follow-up calculation receives the potential risk when the unmanned aerial vehicle passes through every graticule mesh for the result that obtains, thereby takes this potential risk factor into account when obtaining the two-dimensional air route. In quantification, in order to simplify calculation, communication blind zone constraint elements, high-rise building constraint elements, airspace policy restriction area elements, peak constraint elements and the like are all converted into 'barriers' in the flight environment, the cost of the grids is 1, and a 'no-fly-in' principle is implemented in path search; the atmospheric environment (namely the low-air climate constraint element) is statistical frequent data, so that the principle of warning flying in is followed during path search, the cost value range of the grid is 0.2-0.8, and the closer to the climate event center, the higher the cost value is; the other regions are optional regions in the path search, and the cost of the grid is 0.

In the ant colony algorithm, the transfer probability from the current node to the next node needs to be calculated, and the calculation formula is the prior art and is as follows:

wherein,

allowed is the transfer probability of an ant from the current node point i to the next node point j_kRepresenting nodes that ants can reach; alpha is pheromone heuristic factor and represents the importance degree of the pheromone accumulated by the path to the path selection, tau_ij(t) the pheromone concentration of track segment ij; beta is an expected heuristic factor and represents the importance degree of the heuristic factor to the path selection, eta_ijAnd (t) is a heuristic function, and represents the reciprocal of the distance between the nodes i and j in the prior art, and the method is easy to cause the current shortest path of the ant greedy graph to fall into local optimization. In the embodiment of the invention, the relation between the next node and the starting node and the ending node is introduced into the heuristic function in the prior art to obtain the improved heuristic function, so that the problem of local optimization can be effectively solved, and the algorithm efficiency is improved. The improved heuristic function has the following calculation formula:

respectively the normalized distance of the next node from the start node and the end node, d_OiI distance between the starting node and the current node, d_OEC and p are constants for the distance between the start node and the end node，

And the weight represents the distance between the next node and the initial node, and the rho represents a path length threshold value for starting to introduce the direction information, so that the ant is prevented from being influenced by the direction information early in the moving process and falling into local optimum, and the ant and the direction information both determine values in practical application. allowed_kRepresenting nodes that ants can reach.

In addition, in order to avoid the problem of premature convergence of the ant colony algorithm, the following algorithm is adopted in the embodiment of the invention to select the next node. The algorithm (or called random roulette algorithm) is improved based on a roulette algorithm (or called traditional roulette algorithm) and a greedy algorithm, so that the convergence rate of the traditional roulette algorithm can be improved, and the defect that the greedy algorithm is easy to fall into a local optimal value can be overcome. Specifically, if the random number rand is generated by a random function (random function in matlab)<max(P_i) Selecting the next node by adopting a roulette method, wherein the algorithm is equivalent to a roulette algorithm (or a traditional roulette algorithm) and can avoid a local optimum trap; if the random number rand generated by the random function equals max (P)_i) And selecting the node with the highest transition probability as the next node, wherein the algorithm is equivalent to a greedy algorithm and has higher convergence speed. When in use, rand epsilon [0, max (P) is generated by adding a random number (generated by matlab) which is subject to uniform distribution_i)]And P_iComparing, screening aggregate allowed_kIs selected probability P_iAnd (3) the set of local target points i which are greater than or equal to rand uses the formula shown as follows, and finally, the optimal local target point is screened out by using the traditional roulette algorithm.

Referring to fig. 3, the specific flow of this step is as follows:

substep 1): initializing a search space, determining a search step length according to the barrier proportion, and constructing an adjacency matrix.

Substep 2): and (3) placing the first generation of ants M (M is 1, 2, …, M) at the initial position, adding the initial position into a taboo list of each ant, wherein M represents the number of the ants.

Substep 3): and searching nodes which can be reached next step to form an optional node set LJD. Whether the distance between the current node and the termination node is smaller than the search step length or whether the optional node set is an empty set; if not, performing substep 4), if yes, performing substep 6).

Substep 4): and calculating the state transition probability of the optional nodes. When the heuristic function is calculated, the valuation function of the A-algorithm is fused, and the connection between the current node and the termination node is introduced to optimize the search.

Substep 5): the next node (to _ visit) is selected using a random roulette method.

Substep 6): and updating the position node and the taboo table of the ant.

Substep 7): the recording path and its length.

Substep 8): repeating the substep (2) to the substep (7) until all ants of the first generation run through;

substep 9): updating the pheromone;

substep 10): repeating the substep (2) to the substep (9) until all algebras are traversed, and K iteration times;

substep 11): and obtaining an optimal path. The path length and the cost attribute value (the following formula) thereof need to be calculated, and the path length and the potential risk of the unmanned aerial vehicle flying on the path are comprehensively considered, so that the optimal path is obtained. The path cost attribute value calculation process is as follows:

w_i＝max(w_{communication},w_{Restricted area},w_Airport,w_Topography,w_Climate)

Where W is the path cost attribute value, W_iIs the cost of the ith node on the pathCost attribute value, N is the number of nodes on the path, w_{Communication},w_{Restricted area},w_Airport,w_Topography,w_ClimateRespectively representing cost attribute values of various grids such as communication blind areas, restricted areas, airports, terrain, climate and the like.

It should be noted that the term "node" in the embodiment of the present invention is a call in the path search process, and the term "waypoint" is a call when the final path search result is obtained.

In practical application, based on the GeoSOT-3D earth mesh subdivision technology, for example, a reference ellipsoid sphere center in a CGC2000 geodetic coordinate system is selected as a subdivision ellipsoid sphere center to respectively extend longitude, latitude and height to-256 degrees, -256 degrees to-256 degrees and 0 degree to 512 degrees, so that a space of 512 degrees by 512 degrees is formed, and octree recursive subdivision is performed in three dimensions, so that a 0-32-level subdivision frame which is as small as a centimeter-level block and is as low as the earth center and as high as 5000km above the ground surface and as large as the whole earth space is formed. Meanwhile, in order to efficiently organize and manage the spatial data, a binary system is adopted to endow a unique code to the spatial position of any integrated block of each level according to the Z-order coding order, so that the convenient index of subsequent route search is facilitated.

On the basis of a two-dimensional airway, obtaining height information (namely, airway point elevation) of each airway point, and further obtaining a three-dimensional airway point coordinate, so as to obtain a three-dimensional airway, and the steps are as follows with reference to fig. 4:

the process of acquiring the elevation of the waypoint is as follows: and judging whether the terrain height of the next waypoint in the reference terrain data is greater than the difference value between the interface height under the route and the minimum obstacle clearance, if so, judging that the next waypoint height is equal to the sum of the average value of the grid elevations in the route range and the minimum obstacle clearance, and if not, judging that the next waypoint height is equal to the interface height under the route. In other words: and the elevation of the waypoint is determined by the initial height of the interface under the route, the height of the terrain where the waypoint is located and the minimum obstacle-exceeding margin of the unmanned aerial vehicle. If the terrain height of the next waypoint is greater than the difference value between the interface height under the route and the minimum obstacle clearance, the unmanned aerial vehicle climbs, and the height of the next waypoint is equal to the sum of the terrain height and the minimum obstacle clearance; otherwise, the pitching angle of the unmanned aerial vehicle is unchanged, and the unmanned aerial vehicle continues flying along the current height, namely the height of the next waypoint is equal to the height of the current waypoint.

In order to safely cross a mountain, the unmanned aerial vehicle needs to climb to fly, and when the unmanned aerial vehicle crosses the highest point, the unmanned aerial vehicle continues to fly at the current height, so that if the unmanned aerial vehicle does not cross the highest point, the method further comprises the following steps: judging whether the next waypoint is in the mountain range in the reference terrain data or not, whether the current waypoint height is higher than the highest point of the mountain range or not and whether the next waypoint height is smaller than the current waypoint height or not, and if so, judging that the next waypoint height is equal to the current waypoint height; that is to say: and when the highest point is crossed, comparing and analyzing the height of the next waypoint with the height of the current waypoint, and if the height of the next waypoint is lower than the height of the current waypoint, the height of the next waypoint is equal to the height of the current waypoint. This step belongs to the step of correcting the elevation of the waypoint. In other embodiments, the next waypoint height may also be compared to the current waypoint height after the waypoint elevations are obtained.

When the unmanned aerial vehicle passes through different terrain environments, the path search of the unmanned aerial vehicle has different requirements on the resolution of a digital environment, for example, terrain factors in plain areas hardly have influence, only other natural or artificial terrain elements are considered, the requirement on the resolution is low, and when the unmanned aerial vehicle passes through a mountain area, the influence on the terrain factors is great, and the requirement on the resolution is high at this moment; however, as the resolution increases, the algorithm efficiency decreases, and therefore, in the two-dimensional path search, the first resolution is lower than the second resolution, the second resolution is the resolution used for the mountain terrain data, and the first resolution is the resolution used for the plain terrain data. After obtaining the two-dimensional airway and before obtaining the three-dimensional airway according to the two-dimensional airway and the reference terrain data, the method further comprises the following steps: and judging whether two adjacent waypoints of the two-dimensional navigation path correspond to the mountainous area, and if so, adding a plurality of waypoints between the two adjacent waypoints. That is to say: the method comprises the steps of firstly carrying out two-dimensional route search by adopting relatively low resolution (namely first resolution), and then carrying out encryption processing on two-dimensional waypoint data aiming at mountainous areas based on high-precision topographic data (namely second resolution), namely adding a plurality of waypoints with equal intervals between two adjacent waypoints and reducing the interval between the adjacent waypoints. In other words, two adjacent waypoints of the two-dimensional route are encrypted according to the terrain resolution. This step belongs to the step of encrypting the two-dimensional waypoints.

According to the method for obtaining the three-dimensional air route, two unmanned aerial vehicles in all three-dimensional air routes of all route levels are traversed to obtain a plurality of three-dimensional air routes.

After obtaining the plurality of three-dimensional routes, the unmanned aerial vehicle routes need to satisfy mobility parameter constraints such as dynamics turning rate, maximum climbing angle and maximum diving angle of the unmanned aerial vehicle in addition to the safety requirement. The dynamic turning requires that the curvature of any point on the airway is smaller than the maximum curvature of the unmanned aerial vehicle, and the flyable path is determined by the curvature and the flexibility in the three-dimensional space, for the unmanned aerial vehicle, the curvature of the path is equivalent to the yaw rate turning, and the flexibility is equivalent to the roll rate rolling. The maximum climbing angle and the maximum diving angle are the maximum angles of the unmanned aerial vehicle which continuously climbs and dives in the vertical direction at a single time, and the unmanned aerial vehicle stalls due to the overlarge angle. Therefore, in order to satisfy the dynamic constraint of the unmanned aerial vehicle, the three-dimensional airway is optimized according to the maximum turning angle, the maximum rolling angle and the maximum climbing angle of the unmanned aerial vehicle.

After the three-dimensional route is optimized, the method further comprises the following steps: a simulation flight step and an actual flight test step.

After the airway is generated, firstly carrying out system simulation flight on the airway to verify the flyability and the safety of the airway, which mainly comprises the following steps:

(1) and acquiring and modeling the live-action three-dimensional data. And carrying out data acquisition on the airway area by using an unmanned aerial vehicle to carry a common optical camera, and performing three-dimensional modeling through point cloud processing to obtain a real-scene three-dimensional environment of the survey area.

(2) And modeling the unmanned aerial vehicle system. The unmanned aerial vehicle system module includes functional modules such as unmanned aerial vehicle body dynamics model, navigation, engine and control, reaches the real-time solution of unmanned aerial vehicle flight parameter.

(3) And (4) modeling atmospheric environment. Including atmospheric data simulation and atmospheric disturbance simulation. The atmospheric data simulation mainly comprises atmospheric temperature, pressure, density and the like, and the atmospheric disturbance simulation mainly comprises a wind field.

And after the simulated flight is qualified, the flight is carried out on the spot in the route generation area, the generated route points are guided into the unmanned aerial vehicle flight control, and the unmanned aerial vehicle flies according to the set route to verify the reasonability and the safety of the route.

It should be noted that the result obtained by the ant colony algorithm is a flight path, and then a space body is formed by taking the flight path as a central line, and the shape of the space body may be a cylinder or other shapes, which is not limited in this embodiment. Due to the influence of ground navigation facilities, air traffic management, flight missions, terrain and other factors, a route is often composed of waypoints such as a start point, a bend point, an end point and the like, and thus, referring to fig. 5, the route is composed of a plurality of segments (legs). The expression and the positioning of the route (route segment) in the three-dimensional space are determined by factors such as a starting point O and an end point E, a route angle a and a height H.

Route { starting point, ending point, route angle, height }

Route { starting point, ending point, route angle, height, route width }

The minimum safe height of the air route is provided for ensuring the safe flight of the unmanned aircraft in the air route and is determined by relevant regulations of the unmanned aircraft, performance constraints of the unmanned aircraft, task constraints, flight environment and the like. The safe flight height of the airplane is equal to the sum of the maximum elevation and the minimum obstacle clearance within the range of the air route, the maximum elevation refers to the highest elevation of the ground object, the minimum obstacle clearance refers to the minimum vertical interval which is guaranteed when the airplane is guaranteed to surmount the obstacle, and the influencing factors comprise meteorological conditions, instrument errors, unmanned aircraft performance and the like which possibly cause height deviation.

Referring to fig. 6-7, the route interval D is the distance between two adjacent routes; the flight interval L is the distance of the aircraft based on time or space, and is divided into a horizontal interval and a vertical interval L_zWherein the horizontal spacing is further divided into lateral spacing L_xAnd a longitudinal spacing L_y. If the flight safety of the aircraft is to be ensured, the interval between the flight paths must be larger than the flight pathThe sum of the lateral safety interval and the airway width W. Thus, the route interval is determined by the flight safety interval of the unmanned aircraft. There are many factors that affect flight intervals, including: 1) the influence of the drone itself. The communication, navigation, monitoring performance and intervention capacity (airborne collision avoidance capacity and control capacity) of the unmanned aerial vehicle system play a very key role in the flight safety interval; 2) the natural environment. Natural environment has very important influence on the flight safety of the unmanned aerial vehicle, for example, accumulated ice can cause the aerodynamic deterioration of wings, wind shear can affect the flight attitude of the unmanned aerial vehicle, rain and snow can affect the visibility in the air, cause the system failure of the unmanned aerial vehicle, and the like; 3) airway structure and traffic flow density. The increase of the complexity of the airway structure and the traffic flow density can cause the increase of the flight collision risk of the unmanned aerial vehicles, so that the number of the unmanned aerial vehicles in a certain airspace at a certain time point should be controlled. The airway interval can be researched by respectively carrying out risk collision modeling in the lateral direction, the longitudinal direction and the vertical direction of an airway system on the basis of an aircraft collision model theory.

Based on the existing low-altitude airspace open policy and the low-altitude airspace type, the communication requirements of the unmanned aerial vehicle and the environmental constraint conditions of the route planning are comprehensively considered, and the space height of the route planning is preliminarily defined by the method.

According to rules in interim regulations on unmanned aircraft flight management (survey papers): the light unmanned aerial vehicle can fly in an airspace below 120 meters at true height without approval; the "low-altitude airspace usage management regulation" defines a low-altitude airspace below 1000m and is divided into three major categories, namely, a control area (including a visual flight route), a report airspace, and a monitoring airspace. In order to ensure the flight safety of a human-computer and an unmanned aerial vehicle, a low-altitude air route is planned in a control area; the safety flight test report of the low-altitude networked unmanned aerial vehicle indicates that: based on a nationwide cellular mobile communication network (4G/5G technology), the current mobile cellular network can meet the application requirements of unmanned aerial vehicle industries in most scenes below 120 meters, the requirements of unmanned aerial vehicle safe flight service link indexes in most areas below 300 meters and the communication full coverage below 1000 meters.

Comprehensively considering the requirements, the actual heights of 120m and 1000m are respectively used as the lower limit height and the upper limit height of the unmanned aerial vehicle low-altitude high-speed route planning, the unmanned aerial vehicle backbone, trunk and branch routes are planned in the height range, the tail-end route is used as the low-speed route, the height range is below 120m to the lowest safe height in the area, the transition function between the route and the take-off and landing point is born, and the problem of the last kilometer of the community route and the like is solved.

And 104, forming an unmanned aerial vehicle low-altitude public navigation network according to the plurality of three-dimensional navigation paths.

After the unmanned aerial vehicle low-altitude public navigation network is obtained, in order to construct a safe and efficient route traffic network, different planning height ranges are set for routes of different levels, such as: according to the main functions and characteristics of all levels of routes, the types of unmanned aerial vehicles suitable for flying are different, so that the minimum safety heights are different, and the height range is planned for all levels of routes according to the minimum safety heights.

When the road network density is big enough, the crossing problem can appear in the same level road, considers unmanned aerial vehicle flight characteristics, and the road crossing easily leads to unmanned aerial vehicle collision incident, brings very big risk for the safe flight and the management of road, consequently, solves the road crossing problem through the priority sequencing that sets up different flight altitude layer and road to the same level road, specifically as follows: 1) priority principle: in terms of time, the unmanned aerial vehicle with higher priority preferentially passes through the intersection point, such as an emergency disaster relief unmanned aerial vehicle. 2) And (3) flight height layering: from a spatial perspective, i.e. when the drones handshake, another altitude layer is temporarily invoked to avoid collisions. The unmanned aerial vehicle low-altitude public navigation network is formed according to the different division heights of different route levels, the different heights of the routes of the same level when the unmanned aerial vehicles handshake, the different priority levels of the unmanned aerial vehicles of the routes of the same level and the plurality of three-dimensional routes.

Referring to fig. 8-9, the transition of the unmanned aerial vehicle to the route between different levels is realized by the unmanned aerial vehicle, the airport, the airspace, the approach route and the departure route, and the specific process is as follows: starting point O → approach route → branch/trunk airport ZG1 → branch/trunk → trunk airport GG1 → trunk airport GG2 → branch/trunk airport ZG2 → departure route → end point E.

In summary, the embodiments of the present invention have the following beneficial effects:

It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.

Claims

1. An unmanned aerial vehicle low-altitude public navigation network design method is characterized by comprising the following steps:

acquiring unmanned aerial vehicle low-altitude flight environment data in a planning area, the unmanned aerial vehicle low-altitude flight environment data comprises: the method comprises the following steps of (1) topographic data, low-altitude climate data, airspace policy data and low-altitude mobile communication signal spatial distribution data;

obtaining unmanned aerial vehicle airport layout information according to the unmanned aerial vehicle low-altitude flight environment data, wherein the unmanned aerial vehicle airport layout information comprises: site selection sites of a plurality of unmanned aerial vehicles airport and service range of each unmanned aerial vehicle airport;

obtaining a plurality of three-dimensional air routes according to the unmanned aerial vehicle airport layout information, the unmanned aerial vehicle low-altitude flight environment data and an ant colony algorithm;

forming an unmanned aerial vehicle low-altitude public navigation network according to the plurality of three-dimensional navigation paths;

the method comprises the following steps of obtaining a plurality of three-dimensional air routes according to the unmanned aerial vehicle airport layout information, the unmanned aerial vehicle low-altitude flight environment data and the ant colony algorithm, and specifically comprises the following steps:

determining two unmanned aerial vehicle airports in each three-dimensional air route included by each air route level according to the service range of each unmanned aerial vehicle airport and a preset air route level;

constructing a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle according to the two unmanned aerial vehicle airports of a three-dimensional route and the corresponding data of the low-altitude flight environment of the unmanned aerial vehicle in the planning area;

horizontally slicing the digitalized low-altitude flight environment mathematical model of the unmanned aerial vehicle at a preset flight height to obtain a digitalized two-dimensional low-altitude environment mathematical model at the preset flight height;

performing two-dimensional path search in the digitized two-dimensional low-altitude environment mathematical model based on an ant colony algorithm to obtain a two-dimensional airway, wherein in the ant colony algorithm, a search space is formed by buffering by using a connecting line between a starting node and an ending node as a variable distance, and the buffer distance is gradually increased from an initial value by 1 search step until an optimal airway solution exists in the search space;

obtaining a three-dimensional airway according to the two-dimensional airway and the reference topographic data;

traversing two unmanned aerial vehicles in each three-dimensional air route of all route levels to obtain a plurality of three-dimensional air routes;

the process of constructing the low-altitude flight environment mathematical model of the unmanned aerial vehicle is as follows:

firstly, constructing an initial model of a low-altitude flight space of the unmanned aerial vehicle: suppose the ith waypoint coordinate is (x)_i，y_i，z_i) Then, the mathematics of the initial model of the low-altitude flight space is as follows:

wherein x is_iIs longitude, y_iIs latitude, z_iIs height, x_min、x_max、y_minAnd y_maxRepresenting a planned spatial plane range covering the addressed stations of two unmanned aerial vehicles airport, f_1i(x_i，y_i) Reference terrain height for the location of the ith waypoint, f_2i(x_i，y_i) The communication maximum height of the position of the ith waypoint;

the reference terrain height of the position of the ith waypoint is as follows:

secondly, performing mathematical modeling on elements for restraining the safe flight of the unmanned aerial vehicle in the initial model of the low-altitude flight space of the unmanned aerial vehicle to complete the construction of a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle;

the elements for restraining the safe flight of the unmanned aerial vehicle mainly comprise: the method comprises the following steps of (1) peak constraint elements, high-rise building constraint elements, low-altitude climate constraint elements and airspace policy restricted area constraint elements;

the peak constraint element mathematical model is as follows:

in the formula z₁(x, y) is the elevation function of the peak, x is the coordinate of the peak in the x-axis direction, y is the coordinate of the peak in the y-axis direction, (x_j，y_j) Is the center coordinate of the jth peak, H_jB and a are attenuation amounts of the jth peak along the directions of an x axis and a y axis respectively for the reference terrain height, and the gradient is controlled;

the high-rise building constraint element mathematical model is expressed as follows:

z₂(x，y)＝H_j，

wherein z is₂(x, y) is the elevation function of the high-rise building, x is the coordinate of the high-rise building in the x-axis direction, y is the coordinate of the high-rise building in the y-axis direction, (x)_j，y_j) Is the center coordinate of the jth high-rise building, H_jThe height of the jth high-rise building is shown, and R is the occupied area radius of the high-rise building;

the low climate constraint element mathematical model is as follows:

d_ij＝(x-x_j)²+(y-x_j)²

wherein z is₃(x, y) is a height function of the low-climate influence area, x is the coordinate of the low-climate influence area in the x-axis direction, y is the coordinate of the low-climate influence area in the y-axis direction, H is the upper limit of the height of the route planning area, (x, y) is the height of the low-climate influence area in the y-axis direction, and_j，y_j) Center coordinates of jth low climate zone of influence, d_ijDistance of the ith waypoint to the jth climate-affected zone, d_minFor the core area in this climate influence district unmanned aerial vehicle's damage probability is 1 in the core area, d_maxIs the maximum radius affected by the atmosphere;

the space domain policy restriction area constraint element mathematical model is as follows:

wherein z is₄(x, y) is the influence surface of the jth limiting area, (x)_j，y_j) Is the central coordinate of the jth limiting area, and R is the floor area radius of the limiting area.

2. The method according to claim 1, wherein the forming of the unmanned aerial vehicle low-altitude public navigation network according to the plurality of three-dimensional navigation paths specifically comprises:

and forming an unmanned aerial vehicle low-altitude public navigation network according to different division heights of different route levels, different heights of the routes of the same level when the unmanned aerial vehicles handshake, different priority levels of the unmanned aerial vehicles of the routes of the same level and the plurality of three-dimensional routes.

3. The method according to claim 1, wherein obtaining the layout information of the unmanned aerial vehicle in the airport according to the data of the low-altitude flight environment of the unmanned aerial vehicle specifically comprises:

obtaining initial unmanned aerial vehicle airport layout information by utilizing a maximum coverage addressing model according to the unmanned aerial vehicle low-altitude flight environment data, wherein the initial unmanned aerial vehicle airport layout information comprises: the method comprises the following steps that initial site selection sites of a plurality of unmanned aerial vehicles are arranged, and an initial service range of each unmanned aerial vehicle is arranged;

and judging whether the initial site selection station has a cross conflict of routes, if so, optimizing the initial unmanned aerial vehicle airport layout information to obtain the unmanned aerial vehicle airport layout information, otherwise, taking the initial unmanned aerial vehicle airport layout information as the unmanned aerial vehicle airport layout information.

4. The method of claim 1,

in the ant colony algorithm, a heuristic function η_ij(t)：

weight representing distance between next node and start node, p representing start indexPath length threshold of incoming direction information, allowed_kRepresenting nodes that ants can reach.

5. The method of claim 4, further comprising:

in the ant colony algorithm, if the random number rand generated by the random function < max (P)_i) Selecting the next node by adopting a roulette algorithm; if the random number rand generated by the random function is max (P)_i) Then the node with the highest transition probability is used as the next node.

6. The method according to claim 1, wherein obtaining a three-dimensional airway from the two-dimensional airway and the reference terrain data specifically comprises:

and judging whether the terrain height of the next waypoint in the reference terrain data is greater than the difference value between the interface height under the route and the minimum obstacle clearance, if so, judging that the next waypoint height is equal to the sum of the average value of the grid elevations in the route range and the minimum obstacle clearance, and if not, judging that the next waypoint height is equal to the interface height under the route.

7. The method of claim 6, wherein, if determined not to be, the method further comprises:

judging whether the next waypoint is in a mountain range in the reference terrain data or not, whether the current waypoint height is higher than the highest point of the mountain range or not and whether the next waypoint height is smaller than the current waypoint height or not, and if so, judging that the next waypoint height is equal to the current waypoint height;

if not, jumping to the step that the height of the next current waypoint is equal to the height of the interface under the route.

8. The method according to claim 1, wherein in the two-dimensional path search, a first resolution is used, the first resolution being lower than a second resolution, the second resolution being a resolution used for mountain terrain data;

after the obtaining the two-dimensional route and before the obtaining the three-dimensional route according to the two-dimensional route and the reference terrain data, the method further comprises:

and judging whether two adjacent waypoints of the two-dimensional navigation path correspond to the mountainous area, and if so, adding a plurality of waypoints between the two adjacent waypoints.

9. The method according to claim 1, wherein the step of constructing a mathematical model of the low-altitude flight environment of the unmanned aerial vehicle based on the two unmanned aerial vehicle airports and the corresponding low-altitude flight environment data of the unmanned aerial vehicle in the planned area of the three-dimensional air route comprises the following steps:

constructing an unmanned aerial vehicle low-altitude flight space initial model, wherein the lower interface of a flight space in the unmanned aerial vehicle low-altitude flight space initial model is determined by the terrain data corresponding to two unmanned aerial vehicle airports, and the upper interface of the flight space is determined by the spatial distribution of low-altitude mobile communication signals;

right the key element that retrains unmanned aerial vehicle safe flight in the unmanned aerial vehicle low-altitude flight space initial model carries out mathematical modeling, accomplishes and founds unmanned aerial vehicle low-altitude flight environment mathematical model, the key element that retrains unmanned aerial vehicle safe flight includes: the method comprises the following steps of (1) peak constraint elements, high-rise building constraint elements, low-altitude climate constraint elements and airspace policy restricted area constraint elements;

correspondingly, in the ant colony algorithm, whether the proportion of the obstacles represented by the elements for restraining the safe flight of the unmanned aerial vehicle and the communication blind area restraining elements is lower than a preset proportion threshold value or not is judged in a local search space, and if the proportion is judged to be lower than the preset proportion threshold value, the search step length is adjusted.