An unmanned aerial vehicle path planning method and device considering urban wind field factors

Abstract

The application discloses a kind of unmanned plane path planning method and device considering urban wind field factor, method includes: obtaining building and unmanned plane airport data and constructs three-dimensional city model and gridding airspace model;Multi-rotor unmanned aerial vehicle energy consumption model is constructed;Obtain dominant wind data and carry out CFD urban wind field simulation;Super wind speed area and turbulence area are drawn as flight risk area;Unmanned plane path planning model considering urban wind field influence is constructed;Unmanned plane path planning under wind field.The present application is based on CFD simulation and carries out risk area division, can capture wind speed and turbulence kinetic energy spatial distribution, adapt to unmanned plane wind resistance and turbulence area flight limit, can reduce energy consumption by using wind field, with the ability of improving energy efficiency, constructs unified, with safety and energy efficiency as target path planning model under wind field.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of unmanned aerial vehicle path planning, and particularly relates to an unmanned aerial vehicle path planning method and device considering urban wind field factors. BACKGROUND

[0002] Unmanned aerial vehicles have strong maneuverability and high flexibility, produce lower noise and are more efficient than traditional helicopters, and have been widely used in urban environments in recent years. To support safe and efficient operation of unmanned aerial vehicles, path planning is one of the key technologies. Unmanned aerial vehicle path planning refers to finding an optimal path between a starting point and a target point using less computational cost and practical search, and ensuring the efficiency and feasibility of the path on the basis of obstacle avoidance.

[0003] For unmanned aerial vehicles operating in cities, wind field is an important consideration factor because it can cause the unmanned aerial vehicle to lose control and stability. Gusts can cause the unmanned aerial vehicle to deviate from its flight path and even collide. To maintain flight stability, the unmanned aerial vehicle needs to output extra power under strong wind, which in turn increases its energy consumption. In addition, urban canyons and single buildings can generate significant turbulence, which can endanger the aircraft. For urban wind fields, traditional unmanned aerial vehicle path planning methods face problems such as safety risks in flight paths and low energy efficiency. Therefore, it is urgent to build a unified path planning model under wind field that targets safety and energy efficiency, effectively avoids flight risk areas and optimizes flight path energy consumption. SUMMARY

[0004] The present application aims to make up for the deficiencies in existing research and provides an unmanned aerial vehicle path planning method and device considering urban wind field factors, which integrates energy consumption model construction, CFD wind field simulation and unmanned aerial vehicle path planning under wind field, can capture wind speed and turbulent kinetic energy spatial distribution, adapt to unmanned aerial vehicle wind resistance and flight restrictions in turbulent areas and avoid flight risk areas, can use wind field to reduce the energy consumption of unmanned aerial vehicles required for flight paths, and has the ability to improve energy efficiency.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, an unmanned aerial vehicle path planning method considering urban wind field factors comprises:

[0007] Step (1): Obtain building and unmanned aerial vehicle airport data to construct a three-dimensional urban model, and rasterize it to construct a rasterized airspace model;

[0008] Step (2): Construct a multi-rotor unmanned aerial vehicle energy consumption model;

[0009] Step (3): Based on the three-dimensional urban model, obtain dominant wind data and perform computational fluid dynamics (CFD) urban wind field simulation to obtain wind speed components and turbulent kinetic energy in each grid in the airspace.

[0010] Step (4): According to the wind resistance of the unmanned aerial vehicle and the flight restriction requirements in the turbulent flow region, the super wind speed region and the turbulent flow region are set as the flight risk region in the gridded airspace model based on the wind speed component and the turbulent kinetic energy in each grid in the airspace, and a gridded airspace model containing wind field information is obtained;

[0011] Step (5): Based on the energy consumption model of the multi-rotor unmanned aerial vehicle, an unmanned aerial vehicle path planning model considering the influence of urban wind field is constructed; wherein the objective function of the unmanned aerial vehicle path planning model is to minimize the energy consumption of the unmanned aerial vehicle, and the constraint conditions include step constraint, energy consumption constraint, turning angle constraint, flight height constraint, grid restriction, airspeed constraint, ground speed constraint and ground speed change constraint;

[0012] Step (6): Based on the unmanned aerial vehicle path planning model and the gridded airspace model containing wind field information, an improved graph search algorithm is used to obtain an unmanned aerial vehicle path planning scheme which minimizes the energy consumption and avoids the flight risk region between the starting point and the target point.

[0013] In a second aspect, the present application provides an unmanned aerial vehicle path planning device considering urban wind field factors, comprising a processor and a storage medium.

[0014] The storage medium is used to store instructions.

[0015] The processor is used to operate according to the instructions to execute the method according to the first aspect.

[0016] In a third aspect, the present application provides a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method according to the first aspect.

[0017] In a fourth aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to the first aspect when executing the computer program.

[0018] Compared with the prior art, the present application has the following beneficial effects:

[0019] Compared with the prior art, the present application has the following advantages: the present application can solve the problem of unmanned aerial vehicle path planning in complex wind field, the method can set the risk region based on CFD simulation, can capture the spatial distribution of wind speed and turbulent kinetic energy in the airspace, adapt to the wind resistance of the unmanned aerial vehicle and the flight restriction in the turbulent flow region, can utilize the wind field to avoid strong headwind navigation and utilize the tailwind to reduce the energy consumption of the unmanned aerial vehicle required for the navigation route, has the ability to improve the energy efficiency of the path, and constructs a unified path planning model under the wind field with the safety and energy efficiency as the target, which provides technical basis and reference for realizing the safe and efficient operation of the urban unmanned aerial vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a schematic diagram of a UAV path planning process in a wind field provided by an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of an over-wind-speed area and a turbulent flow area demarcated using the path planning method described in the embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a UAV path planned using the path planning method described in the embodiment of the present application;

[0023] Figure 4 is a comparative schematic diagram of a UAV path planned using the path planning algorithm described in the embodiment of the present application;

[0024] Figure 5 is a schematic diagram of path energy consumption using the path planning method described in the embodiment of the present application;

[0025] Figure 6 is a schematic diagram of path energy consumption without considering the wind field method in the embodiment of the present application. DETAILED DESCRIPTION

[0026] The present application will be further described below in conjunction with the drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0027] Embodiment 1: The present embodiment provides a UAV path planning method considering urban wind field factors, comprising:

[0028] Step (1): Obtain building and UAV airfield data to construct a three-dimensional urban model, and rasterize to construct a rasterized airspace model;

[0029] Step (2): Construct a multi-rotor UAV energy consumption model;

[0030] Step (3): Based on the three-dimensional urban model, obtain dominant wind data and perform computational fluid dynamics (CFD) urban wind field simulation to obtain wind speed components and turbulent kinetic energy in each grid in the airspace;

[0031] Step (4): From the wind speed components and turbulent kinetic energy in each grid in the airspace, demarcate an over-wind-speed area and a turbulent flow area as a flight risk area in the rasterized airspace model according to the UAV wind resistance capacity limit and the flight limit requirements in the turbulent flow area, to obtain a rasterized airspace model containing wind field information;

[0032] Step (5): based on the multi-rotor unmanned aerial vehicle energy consumption model, the unmanned aerial vehicle path planning model considering the influence of urban wind field is constructed; wherein the objective function of the unmanned aerial vehicle path planning model is to minimize the energy consumption of the unmanned aerial vehicle, and the constraint conditions include step length constraint, energy consumption constraint, turning angle constraint, flight height constraint, grid restriction, airspeed constraint, ground speed constraint and ground speed change constraint;

[0033] Step (6): based on the unmanned aerial vehicle path planning model and the grid airspace model containing wind field information, the improved graph search algorithm is used to obtain the unmanned aerial vehicle path planning scheme minimizing the energy consumption and avoiding the flight risk area between the starting point and the target point.

[0034] In some embodiments, a method for unmanned aerial vehicle path planning considering urban wind field factors, comprising:

[0035] Step (1): obtaining the building coordinates, size and height information in the region from the geographic information database, and adding the positions of the unmanned aerial vehicle hub airport and the sub-airport, constructing a three-dimensional city model and gridding to construct a grid airspace model;

[0036] Step (2): starting from force analysis to construct a multi-rotor unmanned aerial vehicle energy consumption model, deriving the drag D, the required thrust T, the pitch angle a, the induced velocity v i , the flight power P of the unmanned aerial vehicle can be obtained, and the energy consumption E is further calculated; the path planning model will take minimizing the energy consumption as the objective function;

[0037] Step (3): extracting the regional surface dominant wind data from the meteorological database, calculating the dominant wind speed and direction from the u, v wind speed components, and inputting them into the computational fluid dynamics CFD (Computational Fluid Dynamics) solver; the CFD wind field simulation is based on the three-dimensional city model, the grid in the airspace is divided and the boundary conditions are set, the inlet wind speed is set according to the obtained dominant wind data, and the wind speed components and turbulent kinetic energy in each grid in the airspace are obtained by solving according to the turbulent flow model;

[0038] Step (4): based on the wind speed components and turbulent kinetic energy obtained by simulation, according to the unmanned aerial vehicle wind resistance capability limit and flight restriction requirement in turbulent area, the super wind speed area and turbulent area are set as flight risk area in the gridded airspace model, and input into the path planning model;

[0039] Step (5): constructing the unmanned aerial vehicle path planning model considering the influence of urban wind field, the objective function of which is to minimize the energy consumption of the unmanned aerial vehicle, and the constraint conditions include step length constraint, energy consumption constraint, turning angle constraint, flight height constraint, grid restriction, airspeed constraint, ground speed constraint and ground speed change constraint;

[0040] Step (6): Construct and improve the graph search algorithm, in the grid space containing wind field information, the minimum energy consumption and avoiding flight risk area of the UAV path between the starting point and the target point is planned.

[0041] In some embodiments, the step (1) specifically comprises:

[0042] Step (101): Obtain the latitude and longitude coordinates, size and height information of buildings in the region from the open source global geographic information database OSM (Open Street Map), and obtain the latitude and longitude coordinates of the UAV hub airport and sub-airport from the open API (Application Programming Interface) provided by other geographic information databases;

[0043] Step (102): Use Python language to construct a three-dimensional city model containing buildings and UAV airports, which will be used in urban wind field simulation;

[0044] Step (103): For the obtained three-dimensional city model and its airspace OABC-O′A′B′C′, select the grid size l grid , divide the airspace into a plurality of three-dimensional grids; for any grid c i , the information contained therein includes coordinates p i (x i ,y i ,z i ), wind speed v w,i (u i ,v i ,w i ) and turbulent kinetic energy TKE i , that is:

[0045] c i ={p i (x i ,y i ,z i ),v w,i (u i ,v i ,w i ),TKE i};

[0046] Wherein, x i ,y i ,z i are the coordinates of the grid c i in the x, y, z directions, u i ,v i ,w i are the wind speed components of the grid c i in the x, y, z directions; wind speed v w,i and turbulent kinetic energy TKEi To reserve data structure, after the completion of CFD wind field simulation integration.

[0047] In some embodiments, the step (2) of constructing the multi-rotor unmanned aerial vehicle energy consumption model comprises:

[0048] Step (201): force analysis of the unmanned aerial vehicle, when the unmanned aerial vehicle moves forward at airspeed v a and pitch angle α, the wind field influence will be captured through airspeed v a ; the thrust T and the drag D of the unmanned aerial vehicle are expressed as:

[0049]

[0050] Where T is the thrust of the unmanned aerial vehicle, W is the gravity of the unmanned aerial vehicle, D is the drag of the unmanned aerial vehicle, ρ is the air density, C D is the drag coefficient of the unmanned aerial vehicle, and A is the projected area perpendicular to airspeed v a ;

[0051] Step (202): derive the pitch angle α and induced velocity v i :

[0052]

[0053] Where α is the pitch angle, v i is the induced velocity, and v h is the hovering speed, which is solved by: Where T h is the hovering thrust, k is the number of unmanned aerial vehicle rotors, and S is the area occupied by a single rotor when rotating;

[0054] Step (203): calculate the flight power P and energy consumption E of the unmanned aerial vehicle:

[0055]

[0056] Where η is the energy conversion efficiency of the unmanned aerial vehicle, l is the flight distance, v g is the ground speed of the unmanned aerial vehicle;

[0057] Step (204): calculate the optimal airspeed according to the flight power P of the unmanned aerial vehicle, the speed at which the multi-rotor unmanned aerial vehicle depletes its battery power to achieve the maximum range of the flat flight is called the optimal speed, as follows:

[0058]

[0059] Where e is the energy consumption per unit distance, v eco is the optimal airspeed, and P is substituted into the optimal airspeed, which is expressed as:

[0060]

[0061] In some embodiments, the step (3) specifically comprises:

[0062] Step (301): Extract regional surface 10m monthly mean u, v wind speed components, surface 100m monthly mean u, v components, and surface 10m instantaneous gust wind speed from ERA5 (ECMWF Reanalysis v5) meteorological database, and preprocess to obtain surface dominant wind speed, wind direction information; dominant wind data can also be obtained from GFS (Global Forecast System);

[0063] Step (302): Input the three-dimensional city model and the dominant wind data into the CFD solver, divide the grid in the airspace, set the boundary conditions, and set the entrance boundary dominant wind speed. Among them, the grid is divided according to the geometric complexity and the characteristics of the wind field to the calculation domain, high-density grid is used in the key area to capture the details, and the outer area is gradually sparse to optimize the calculation amount; when setting the entrance boundary condition, the wind speed profile is defined according to the dominant wind direction and wind speed distribution, and the turbulence parameter is set, the outlet boundary adopts zero pressure gradient, the building and ground fixed wall adopts no-slip condition, and the top boundary is set as slip condition. The turbulence model selects RANS (Reynolds-Averaged Navier-Stokes) k-ω SST (Shear Stress Transport), the solving method selects SIMPLE (Semi-Implicit Method for Pressure-Linked Equations), and the CFD simulation calculates the urban wind field;

[0064] Step (303): Calculate the wind speed components and turbulent kinetic energy in each CFD grid in the airspace, and post-process to visualize the wind speed and turbulent kinetic energy distribution.

[0065] In some embodiments, in the step (4), the super wind speed area and the turbulent area are divided into flight risk areas, including:

[0066] Step (401): Integrate the wind speed components and turbulent kinetic energy in the CFD grid into the gridded airspace; when 1 airspace grid contains m CFD grids, use the grid c i inner wind speed set {v w,i,1 ,...,v w,i,m} to calculate the average wind speed and maximum wind speed in the grid, and the turbulent kinetic energy set {TKE w,i,1 ,...,TKE w,i,m} to calculate the maximum turbulent kinetic energy in the grid:

[0067]

[0068] where v w,i,mean is the average wind speed within the grid c i , v w,i,max is the maximum wind speed within the grid c i , TKE i,max is the maximum turbulent kinetic energy within the grid c i , j represents the jthCFD grid within the grid c i , v w,i,mean is the flight speed of the UAV under the influence of the wind field, and TKE w,i,max is the turbulent kinetic energy threshold. i,max

[0069] Step (402): judging the wind resistance capability limit of the UAV, using the maximum wind speed v i within the grid c w,i,max to judge whether the airspace grid can be entered. When v w,i,max exceeds the maximum wind speed v w,res that the UAV can withstand, the grid c i cannot be entered, which is represented as follows:

[0070] If v w,i,max > v w,res , c i ∈ C no-fly ;

[0071] where v w,res is the maximum wind speed that the UAV can withstand, and C no-fly represents the set of UAV no-fly zone grids.

[0072] Step (403): judging the turbulence zone flight limit, using the maximum turbulent kinetic energy TKE i within the grid c i,max to judge whether the airspace grid can be entered. When TKE i,max exceeds the turbulent kinetic energy threshold TKE res in the urban wind field, and the minimum distance r i from the building within the airspace c i is less than or equal to the turbulent kinetic energy detection radius r TKE , the grid cannot be entered, which is represented as follows:

[0073] If TKE i,max > TKE res and r i ≤ r TKE , c i ∈ C no-fly ;

[0074] where TKE res is the turbulent kinetic energy threshold, and r TKE ​a detection radius of the turbulent flow energy around the building;

[0075] Step (404): According to the wind resistance of the UAV and the flight restriction in the turbulent flow area, the super wind speed area and the turbulent flow area are respectively demarcated in the airspace grid, and are input into the path planning model.

[0076] In some embodiments, the step (5) constructs the UAV path planning model considering the influence of urban wind field, including:

[0077] Step (501): Constructing the UAV path planning model considering the influence of urban wind field, and the objective function is to minimize the energy consumption of the UAV on the flight route;

[0078]

[0079] wherein E is the energy consumption of the UAV from the starting point c s to the ending point c g , P i (v a,i-1 ) is the required power of the UAV flying from the path node c i-1 to c i at the airspeed v a,i-1 , l i (c i-1 , c i ) is the Euclidean distance from c i-1 to c i , v g,i-1 is the ground speed of the UAV flying from c i-1 to c i ; in order to minimize the objective function, the selection of the path node c i and the airspeed v a,i corresponding to the path node need to be optimized together to obtain the path with the lowest energy consumption and no conflict; n is the total number of path nodes;

[0080] Step (502): The constraint conditions of the UAV path planning model considering the influence of urban wind field include step length constraint, and the distance between adjacent path nodes is greater than or equal to the step length thereof;

[0081] l i ≥ λ min , i = 1, 2,..., n;

[0082] wherein l i is the distance between the path nodes c i-1 and c i , λ min is the minimum step length, which is affected by the moving direction;

[0083] Step (503): Constructing the UAV path planning model considering the influence of urban wind field, and the constraint conditions include energy consumption constraint, that is,

[0084]

[0085] where m batt is the battery mass, s batt is the specific energy, δ is the discharge depth, f is the safety factor, R p is the Euclidean distance from the start c s,p to the end c g,p of the route, e loaded,p , e unloaded,p are the estimated energy consumption per unit distance for the delivery and return of the UAV in the given wind field, respectively, and q is the total number of routes.

[0086] Step (504): Construct the UAV path planning model considering the influence of urban wind field, the constraint conditions of which include the turning angle constraint, and the turning angle of the planned path should satisfy

[0087] |β i |≤β max , i = 1, 2,..., n.

[0088] where β i is the turning angle at the path node c i , and β max is the maximum turning angle of the path.

[0089] Step (505): Construct the UAV path planning model considering the influence of urban wind field, the constraint conditions of which include the flight height constraint, and the height of the horizontal flight route of the UAV should satisfy the airspace restriction, i.e.

[0090] H min ≤z i ≤H max , i = 1, 2,..., n.

[0091] where H min and H max are the minimum and maximum flight heights of the UAV in the cruising phase, respectively, and are related to the airspace policy.

[0092] Step (506): Construct the UAV path planning model considering the influence of urban wind field, the constraint conditions of which include the grid restriction, and any path node c i cannot enter the no-fly zone or the set of building grids, as follows

[0093]

[0094] where p i (x i , y i , z i ) is the coordinate of any node c i of the path, and Cno-fly with C bld respectively are the no-fly zone and building grid set;

[0095] Step (507): Construct the UAV path planning model considering the influence of urban wind field, the constraint condition of which includes airspeed constraint. The flight speed of UAV in the wind field will be affected. The maximum airspeed that UAV can maintain is related to the thrust generated by its rotor, which is limited by its own performance. The airspeed constraint can be expressed as

[0096] v a,i ≤v a,max ;

[0097] wherein v a,i is the airspeed of UAV at path node c i , and v a.max is the maximum airspeed that UAV can maintain under thrust limitation;

[0098] Step (508): Construct the UAV path planning model considering the influence of urban wind field, the constraint condition of which includes ground speed constraint. To ensure the effectiveness of the planned path, UAV should be able to sail forward at a certain ground speed under the given airspeed limitation. The ground speed constraint can be expressed as:

[0099] v g,min ≤v g,i ≤v g,max ;

[0100] wherein v g,min and v g,max are the minimum and maximum ground speed respectively that meet the task requirements of UAV;

[0101] Step (509): Construct the UAV path planning model considering the influence of urban wind field, the constraint condition of which includes ground speed change constraint. The ground speed change value between adjacent path nodes should be limited to ensure that the speed change meets the flight performance limitation of UAV, as follows

[0102] |v g,i -v g,i-1 |≤Δv g ;

[0103] wherein Δv g is the ground speed change limitation value between adjacent path nodes, which depends on factors such as model and UAV size;

[0104] In some embodiments, the step (6) specifically comprises:

[0105] Step (601): Construct and improve the path planning solving algorithm Theta*, and improve its algorithm cost function as follows:

[0106] f(c o,c k )=g(c o ,c k )+h(c k ,c n );

[0107] Among them, f(·) is the planning to c k When the path node c0 to c n The total estimated cost of g(·) is from c0 to c k The actual path cost of c k to c n Specifically, the improvements g(·) and h(·) are:

[0108]

[0109] Among them, P status (·) is the flight power of the UAV in a given state, dist(·) is the Euclidean distance between path nodes, v a,i-1 With v g,i-1 The drone is composed of c i-1 Sail to c i The airspeed and ground speed of the drone are shown in the following table: status indicates whether the drone is loaded or empty. status,est ,v a,est With v g,est For the estimated navigation power, airspeed, and ground speed under a given wind field, the solution algorithm selects the corresponding cost function based on the route information;

[0110] Step (602): Construct and improve the path planning algorithm Theta* and improve its node selection method: To avoid unnecessary nodes, use the line of sight function to check whether there is a connected path between non-adjacent nodes, and set the wind speed change threshold to Δv w,max , the wind direction change threshold is Δθ w,max , if for non-adjacent nodes in the path, there is Δv w ≤Δv w,max ,Δθ w ≤Δθ w,max , and during Then there is a connected path between the two points, and they are connected by a straight line;

[0111] Step (603): Construct and optimize the path planning algorithm Theta* to optimize its flight speed selection: first, the optimal airspeed and ground speed can be calculated based on the heading, wind speed and wind direction. Then, based on the calculated optimal flight speed, it is necessary to determine whether the ground speed change constraint, ground speed constraint and airspeed constraint are met in turn, and finally output the airspeed and ground speed of the explored path node.

[0112] Step (604): constructing and optimizing the path planning solution algorithm Theta*, and preprocessing the optimal speed: in the solution algorithm, the optimal airspeed under different wind fields is first preprocessed, and then the optimal speed preprocessing calculation parameters including gravity, resistance, thrust, pitch angle, induced speed, power and energy consumption per unit distance are calculated, the calculation interval is wind speed 0-15 m / s, wind and route angle 0-180°, after calculation, the optimal airspeed and ground speed of the loaded and unloaded under different wind speed and wind direction intervals can be obtained;

[0113] Step (605): path planning of unmanned aerial vehicle under wind field, taking airspace grid containing wind field information as input, the algorithm plans the path between the starting point and the target point, first initializes the finite queue for storing the nodes to be processed, the list for recording the predecessor nodes, the f function, the g function and the h function are the total estimated cost of the path, the actual path cost and the heuristic cost respectively; starting from the starting point, the algorithm explores and iteratively processes the neighbor nodes, when the target node is explored, the path information is output, otherwise the neighbor nodes are continuously traversed, the exploration logic is as follows: reading the wind speed and direction at the exploration node, if there is a connected path between the current node and the predecessor node, calculating the moving energy consumption and the feasible flight speed between the two points, if the sum of the g function of the predecessor node and the moving energy consumption is less than the g function of the current node, updating the list; if there is no connected path, the current node and the neighbor node are used for calculation;

[0114] Step (606): after completing the path planning, the path nodes are backtracked, and the airspeed, ground speed and wind speed sequence of each path node, the total energy consumption of the path, the route distance and the flight time are output.

[0115] Verification example: in order to verify the effectiveness of the flight risk zoning method provided by the embodiment of the present application, the present application selects Shenzhen Longgang Star River Central Business District in Guangdong Province, analyzes the surface dominant wind data from 2013 to 2023, and performs CFD city wind field simulation and flight risk zoning (the results of zoning under the condition of northeast wind of five levels are shown in FIG. 1), and the distribution of the super wind speed area and the turbulence area at each height layer in the airspace is shown in Table 1. Figure 2

[0116] Table 1: Area low-altitude airspace flight restricted area area statistics

[0117]

[0118] From Table 1, it can be seen that the super wind speed area mainly appears under the condition of high inlet wind speed, the super wind speed area at the height of 80 m is 6,950 m 2 and 11,300 m 2 ​; northeast wind of 5th level, the super wind speed area across the north side of the city canyon, and spread to the hub airport, path planning need to be a substantial detour; in addition, due to the wind speed in urban environment with the increase of the height from the ground, the super wind speed area and height layer is significantly related, northeast wind of 5th level, its area at 60m height layer is only 1,638m 2 , and with the increase of the height layer, doubled to 6,950m 2 , 11,956m 2 and 18,775m 2 . In addition, the increase of the average wind speed will make the building surrounding produce strong shear wind, and then lead to the increase of the turbulence intensity, the CFD calculation results are consistent with this phenomenon, that is, the stronger turbulence is usually associated with higher wind speed, in the case of larger dominant wind speed, the turbulence area range is also expanded, compared with the 80m height layer turbulence zoning results, for example, northeast wind of 5th level, when the wind speed is low, the turbulence area is 21,900m 2 , and with the increase of the wind speed, this area rises to 30,431m 2 and 38,843m 2 , analysis of other scenarios, still can draw similar conclusions.

[0119] In order to show the effect of the unmanned aerial vehicle path planning method provided by the examples of the present application, the examples of the present application plan the path between the hub airport and the sub-airport under different wind fields from 60m to 120m height layer, for example, the path planning results of 80m height layer under northeast wind of 5th level, as shown in Figure 3 , the solid line and the dotted line in the figure are the cargo distribution route and the empty return route respectively, the path planning results under each wind field are shown in table 2.

[0120] Table 2 route data under different dominant wind

[0121]

[0122]

[0123] Table 2 shows that: comparing the path planning results under different wind fields, the wind field has little effect on path planning at Northeast Level 3, and the average route distance, flight time and path energy consumption are the lowest values ​​among the 9 groups of scenarios; in Northeast Level 4, with the increase of the prevailing wind speed, the impact of the wind field on the path increases, and its average distance, time and energy consumption all increase. In particular, although the delivery and return route distances increase, the average time of the return route level flight phase is 37.96s, which is lower than 38.40s in Northeast Level 3; in Northeast Level 5, due to the large-scale super-wind speed area and turbulent area in the low altitude of the city, the UAV logistics delivery has become more difficult at this time, and a detour strategy needs to be adopted, and even the delivery task needs to be suspended. In the delivery route stage, the Northeast Level 5 The average distance, time and energy consumption of the level 5 south wind were 489.50m, 41.54s and 41.32kJ respectively, which were 7.74%, 6.84% and 12.80% higher than those of the northeast level 3 distribution route; when the dominant wind was easterly, due to the low overall wind speed in the low-altitude airspace, the average distance, time and energy consumption indicators were the lowest values ​​under the given dominant wind speed; when the dominant wind was southerly, the local wind near the tallest building in the area accelerated significantly, resulting in the highest route indicators in the simulation scenario for the level 5 south wind, with average distance, time and energy consumption reaching 510.66m, 42.42s and 29.15kJ respectively, which were 12.67%, 9.13% and 13.25% higher than those of the level 3 south wind with the same wind direction.

[0124] In order to verify the effectiveness of the path planning algorithm provided by the embodiment of the present invention, the routes planned by A*, Theta*, JPS (JumpPoint Search) and RBFS (Recursive Best-First Search) are compared. Figure 4 As shown in the figure, compared with the A* planning results, the optimized Theta* is lower than the former in terms of route distance, flight time, route energy consumption and calculation time, which are reduced by 2.28%, 11.19%, 7.89% and 68.61% respectively, mainly due to its ability to expand the path in any direction; the route distance obtained by JPS is reduced, but the flight time and route energy consumption are increased by 3.80% and 1.18% respectively compared with A*, and its calculation time is 47.43% of the baseline; RBFS mainly improves the calculation efficiency, and the running time is reduced by 62.89%, but there are different degrees of increase in route distance, flight time and calculation time.

[0125] In order to verify the effectiveness of the UAV path planning method under wind field provided by the embodiment of the present invention, the method of the present invention ( Figure 5 ) and the method without considering wind field ( Figure 6 ) and calculate the energy consumption of the route between the hub airport and the sub-airport in the altitude layer from 60m to 120m under 9 groups of scenarios using two methods.Figure 5 In the figure, the path energy consumption is relatively stable with its distance, and a few outliers have higher path energy consumption, such as the energy consumption of 300m in the figure can reach 33.94kJ, while Figure 6 In the figure, the path energy consumption is relatively stable with its distance, and a few outliers have higher path energy consumption, such as the energy consumption of 300m in the figure can reach 33.94kJ, while Figure 6 In the figure, the path energy consumption is relatively stable with its distance, and a few outliers have higher path energy consumption, such as the energy consumption of 300m in the figure can reach 33.94kJ, while

[0126] In summary, the unmanned aerial vehicle path planning method in the wind field provided by the present application can make up for the shortcomings of the existing method, and can solve the problem of unmanned aerial vehicle path planning in complex wind field. The method can capture the spatial distribution of wind speed and turbulent kinetic energy in the airspace, adapt to the wind resistance of unmanned aerial vehicle and flight restriction in turbulent area, utilize the wind field, avoid strong headwind navigation and utilize tailwind to reduce the energy consumption of the route required by unmanned aerial vehicle, has the ability to improve the energy efficiency of the route, and constructs a unified path planning model in the wind field, which takes safety and energy efficiency as the target, and provides technical basis and reference for realizing safe and efficient operation of urban unmanned aerial vehicle.

[0127] Embodiment 2: Based on embodiment 1, the embodiment provides an unmanned aerial vehicle path planning device considering urban wind field factors, comprising a processor and a storage medium;

[0128] The storage medium is used for storing instructions;

[0129] The processor is used for operating according to the instructions to execute the method according to embodiment 1.

[0130] Embodiment 3: Based on embodiment 1, the embodiment provides a storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method of embodiment 1.

[0131] Embodiment 4: Based on embodiment 1, the embodiment provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the method of embodiment 1.

[0132] In one embodiment, a computer program product includes a computer readable medium having instructions stored thereon, the instructions being executable by a processor to implement the method of any of the preceding embodiments.

[0133] Those skilled in the art will appreciate that embodiments of the present application can be readily used for providing methods, systems, or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable medium(s) having computer readable program code embodied in the medium.

[0134] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0135] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0137] The above merely describes the preferred embodiments of the present application, and it should be pointed out that, for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A UAV path planning method considering urban wind field factors, characterized by: include: Step (1): Obtain building and drone airport data to construct a 3D city model, and rasterize it to construct a rasterized airspace model; Step (2): Constructing a multi-rotor UAV energy consumption model; Step (3): Based on the three-dimensional urban model, obtain dominant wind data and perform computational fluid dynamics (CFD) urban wind field simulation to obtain wind speed components and turbulent kinetic energy in each grid within the airspace; Step (4): Based on the wind speed components and turbulent kinetic energy in each grid in the airspace, and in accordance with the UAV wind resistance limit and turbulent zone flight restriction requirements, the over-wind speed zone and turbulent zone are demarcated as flight risk zones in the gridded airspace model, and a gridded airspace model containing wind field information is obtained; Step (5): Based on the multi-rotor UAV energy consumption model, a UAV path planning model considering the influence of the urban wind field is constructed; wherein the objective function of the UAV path planning model is to minimize the UAV energy consumption, and the constraints include step size constraint, energy consumption constraint, turning angle constraint, flight altitude constraint, grid constraint, airspeed constraint, ground speed constraint and ground speed change constraint; Step (6): Based on the UAV path planning model and the rasterized airspace model containing wind field information, an improved graph search algorithm is used to obtain a UAV path planning solution that minimizes energy consumption between the starting point and the target point and avoids flight risk areas.

2. The UAV path planning method considering urban wind field factors according to claim 1 is characterized in that: The step (1) specifically includes: Step (101): Acquire building data and drone airport data; wherein the building data includes the longitude and latitude coordinates, dimensions, and height information of buildings in the area, and the drone airport data includes the longitude and latitude coordinates of drone hub airports and sub-airports; Step (102): constructing a three-dimensional city model including buildings and drone airports based on the building data and drone airport data; Step (103): For the three-dimensional city model and the airspace OABC-O′A′B′C′, select the grid size l grid , divide the airspace into several three-dimensional grids; for any grid c i , which contains information including coordinates p i (x i ,y i ,z i ), wind speed v w,i (u i ,v i ,w i ) and turbulent kinetic energy TKE i ,Right now: ci={pi(xi,yi,zi),vw,i(ui,vi,wi),TKEi}; Among them, x i ,y i ,z i is the grid c i Coordinates in the x, y, and z directions, u i ,v i ,w i is the grid c i Wind speed components in the x, y, and z directions; wind speed v w,i and turbulent kinetic energy TKE i It is a reserved data structure obtained from CFD wind field simulation.

3. The UAV path planning method considering urban wind field factors according to claim 1 is characterized in that: In step (2), constructing a multi-rotor UAV energy consumption model includes: Step (201): Perform force analysis on the UAV. When the UAV is in steady-state flight, it moves at an airspeed v a and pitch angle α moving forward, where the wind field influence will be through the airspeed v a Capture; the thrust T and resistance D of the drone are expressed as: Among them, T is the thrust of the UAV, W is the gravity of the UAV, D is the resistance of the UAV, ρ is the air density, C D is the drag coefficient of the UAV, A is perpendicular to the airspeed v a The projected area of Step (202): Derivation of pitch angle α and induced velocity v i : Where α is the pitch angle, v i is the induced velocity; v h is the hovering speed, which can be solved by the following formula: Among them, T h is the hovering thrust, k is the number of UAV rotors, and S is the area occupied by a single rotor when rotating; Step (203): Calculate the UAV flight power P and energy consumption E: Among them, η is the energy conversion efficiency of the UAV, l is the flight distance, v g is the UAV ground speed; Step (204): Calculate the optimal airspeed based on the flight power P of the drone. The speed at which the multi-rotor drone can reach the maximum range of level flight after exhausting its battery power is called the optimal speed, as follows: Among them, e is the energy consumption per unit distance, v eco is the optimal airspeed. Substituting the power P, the optimal airspeed is expressed as:

4. The UAV path planning method considering urban wind field factors according to claim 1 is characterized in that: The step (3) specifically includes: Step (301): Obtaining dominant wind data; wherein the dominant wind data includes dominant wind speed and wind direction information; Step (302): input the three-dimensional city model and the dominant wind data into a computational fluid dynamics (CFD) solver, perform grid division and boundary condition setting in the airspace, and set the dominant wind speed at the inlet boundary; wherein, when setting the inlet boundary condition, define the wind speed profile and set the turbulence parameters according to the dominant wind speed and wind direction distribution, adopt a zero pressure gradient for the outlet boundary, adopt a no-slip condition for the building and the ground solid wall, and set the top boundary to a slip condition; select RANSk-ωSST as the turbulence model, select SIMPLE as the solution method, and perform CFD simulation calculation of the urban wind field; Step (303): After the calculation is completed, the wind speed components and turbulent kinetic energy in each CFD grid in the airspace are obtained.

5. The UAV path planning method considering urban wind field factors according to claim 1 is characterized in that: The step (4) of defining the super-wind speed area and the turbulence area as the flight risk area includes: Step (401): Integrate the wind speed component and turbulent kinetic energy in the CFD grid into the gridded airspace; when one airspace grid contains m CFD grids, use grid c i Internal wind speed set {v w,i,1 ,...,v w,i,m }Calculate the average wind speed and maximum wind speed within the grid, turbulent kinetic energy set {TKE w,i,1 ,...,TKE w,i,m }Calculate the maximum turbulent kinetic energy within the grid: Among them, v w,i,mean is the grid c i The average wind speed in the w,i,max is the grid c i Maximum wind speed within, TKE i,max is the grid c i The maximum turbulent kinetic energy within the grid, j represents the ... i The jth CFD grid in v w,i,mean Used to calculate the flight speed of the UAV under the influence of wind field, v w,i,max With TKE i,max Used to determine whether the airspace grid can be entered; Step (402): Determine the wind resistance limit of the drone, using c i Maximum wind speed v w,i,max Determine whether the airspace grid can be entered. When v w,i,max Exceeds the maximum wind speed v that the drone can withstand w,res When the grid c i Inaccessible, indicated as follows: If v w,i,max >v w,res ,c i ∈C no-fly ; Among them, v w,res is the maximum wind speed that the drone can withstand, C no-fly Represents a set of raster grids for drone no-fly zones; Step (403): Determine the flight restrictions in the turbulent area, using c i Maximum turbulent kinetic energy TKE i,max Determine whether the airspace grid can be entered. When TKE i,max Exceeding the turbulent kinetic energy threshold TKE in urban wind farms res , and c i Minimum distance r from buildings in the airspace i Less than or equal to the turbulent kinetic energy detection radius r TKE , the grid is inaccessible, as shown below: If TKE i,max >TKE res and r i ≤r TKE ,c i ∈C no-fly ; Among them, TKE res is the turbulent kinetic energy threshold, r TKE is the turbulent kinetic energy detection radius around the building; Step (404): Based on the UAV's wind resistance capability limit and turbulent zone flight limit, an overwind zone and a turbulent zone are delineated in the gridded airspace model.

6. The UAV path planning method considering urban wind field factors according to claim 1 is characterized in that: The step (5) specifically includes: The objective function of the UAV path planning model considering the influence of urban wind fields is to minimize the energy consumption of the UAV on the route, which can be expressed as: Among them, E is the distance from the starting point c to the drone. s Fly to the destination c g Energy consumption, P i (v a,i-1 ) is the path node c of the UAV i-1 To c i At airspeed v a,i-1 Power required for navigation, l i (c i-1 ,c i ) is c i-1 to c i The Euclidean distance, v g,i-1 For drones by c i-1 Sail to c i Ground speed; To minimize the objective function, the path node c needs to be optimized together i The choice of path nodes and the corresponding airspeed v a,i To obtain the path with the lowest energy consumption and no conflicts; n is the total number of path nodes; The step size constraint requires that the distance between adjacent path nodes is greater than or equal to the step size; l i ≥λ min ,i=1,2,...,n; Among them, l i is the path node c i-1 to c i The distance between min is the minimum step length, which is affected by the moving direction; The energy consumption constraint is: Among them, m batt is the battery mass, s batt is the specific energy, δ is the discharge depth, f is the safety factor, R p is the starting point c of route p s,p To the end point c g,p The Euclidean distance, e loaded,p ,e unloaded,p are the estimated energy consumption per unit distance for route p to deliver cargo and return empty-handed at a given wind farm; q is the total number of routes; Step (504): The turning angle constraint is expressed as: b i |≤β max ,i=1,2,...,n; Among them, β i is the path node c i Turning angle, β max is the maximum turning angle of the path; Step (505): The flight altitude constraint, the UAV horizontal flight path altitude z i Airspace restrictions should be met; H min ≤z i ≤H max ,i=1,2,...,n; Among them, H min With H max They are the lowest and highest flight altitudes of the UAV during the cruising phase respectively; Step (506): The grid limits any path node c i Do not enter no-fly zones or building grid collections; Among them, p i (x i ,y i ,z i ) is the path node c i The coordinates of x i ,y i ,z i is the path node c i Coordinates in the x, y, and z directions, C no-fly with C bld They are the no-fly zone and building grid sets respectively; Step (507): The airspeed constraint will affect the flight speed of the UAV in the wind field. Due to its own performance limitations, the maximum airspeed that the UAV can maintain is related to the thrust generated by its rotor. The airspeed constraint is expressed as: in a,i ≤in a,max ; Among them, va,i is the UAV at the path node c i Airspeed, v a.max The maximum airspeed that the drone can maintain under thrust limitations; Step (508): The ground speed constraint is expressed as: in g,min ≤in g,i ≤in g,max ; Among them, v g,i For the UAV at path node c i Ground speed, v g,min With v g,max are the minimum and maximum ground speeds required to meet the UAV mission requirements; Step (509): The ground speed change constraint, the ground speed change value of the UAV between adjacent path nodes should be limited to ensure that its speed change meets its flight performance constraints, expressed as: |v g,i -v g,i-1 |≤Δv g ; Where Δv g It is the ground speed change limit between adjacent path nodes.

7. The UAV path planning method considering urban wind field factors according to claim 1 is characterized in that: The step (6) specifically includes: Step (601): Construct and improve the graph search algorithm Theta*. The improved algorithm cost function is as follows: f(c o ,c k )=g(c o ,c k )+h(c k ,c n ); Where f(·) is the planned path to the intermediate node c k When the starting path node c0 to the target path node c n The total estimated cost of g(·) is from c0 to c k The actual path cost, h(·) is c k to c n Specifically, the improvements g(·) and h(·) are: Where k represents the kth path node, n is the total number of path nodes; P status (·) is the flight power of the UAV in a given state, dist(·) is the Euclidean distance between path nodes, v a,i-1 With v g,i-1 They are respectively the nodes c of the previous path of the UAV i-1 Navigate to path node c i The airspeed and ground speed of the drone are shown in the following table: status indicates whether the drone is loaded or empty. status,est ,v a,est With v g,est For the estimated navigation power, airspeed, and ground speed under a given wind field, the solution algorithm selects the corresponding cost function based on the route information; Step (602): Construct and improve the graph search algorithm Theta*, improve the node selection method: to avoid generating unnecessary nodes, use the line of sight function to check whether there is a connected path between non-adjacent nodes, and set the wind speed change threshold to Δv w,max , the wind direction change threshold is Δθ w,max , if there is a wind speed change Δv for non-adjacent nodes in the path w ≤Δv w,max , wind direction change Δθ w ≤Δθ w,max , and during C no-fly with C bld If they are no-fly zones and building grid sets respectively, there is a connected path between the two points and they are connected by a straight line; Step (603): Construct and improve the graph search algorithm Theta* to optimize the flight speed selection: first, calculate the optimal airspeed and ground speed based on the heading, wind speed and wind direction, then determine whether the ground speed change constraint, ground speed constraint and airspeed constraint are met in turn based on the calculated optimal flight speed, and finally output the airspeed and ground speed of the exploration path node; Step (604): Construct and improve the graph search algorithm Theta* and perform optimal speed preprocessing: In the solution algorithm, preprocessing is first performed to obtain the optimal airspeed under different wind fields. The optimal speed preprocessing calculation parameters include gravity, drag, thrust, pitch angle, induced speed, power and energy consumption per unit distance. The calculation range is wind speed 0-15m / s and the angle between wind and route 0-180°. After calculation, the optimal airspeed and ground speed for both loaded and unloaded vehicles in different wind speed and wind direction ranges are obtained; Step (605): UAV path planning under wind field, taking the airspace grid containing wind field information as input, the algorithm plans the path between the starting point and the target point, first initializes a limited queue for storing the nodes to be processed, and a list for recording the predecessor node, and the f function, g function, and h function are the total estimated path cost, the actual path cost, and the heuristic cost respectively; starting from the starting point, the algorithm explores and iteratively processes the neighboring nodes, and when the target node is explored, the path information is output, otherwise the neighboring nodes are continued to be traversed, and the exploration logic is as follows: obtain the wind speed and wind direction at the explored node, if there is a connected path between the current node and the predecessor node, calculate the moving energy consumption and feasible flight speed between the two points, and if the sum of the predecessor node g function and the moving energy consumption is less than the current node g function, update the list; if there is no connected path, use the current node and the neighboring node for calculation; Step (606): After completing the path planning, trace back the path nodes and output the airspeed, ground speed and wind speed sequence of each path node, the total energy consumption of the path, the route distance and the flight time.

8. A UAV path planning device considering urban wind field factors, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the method according to any one of claims 1 to 7.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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