Unmanned aerial vehicle path planning method and device considering urban wind field factor

Abstract

The invention discloses an unmanned aerial vehicle path planning method and device considering urban wind field factors. The method comprises the steps that building and unmanned aerial vehicle airport data are acquired, and a three-dimensional urban model and a rasterized airspace model are constructed; constructing a multi-rotor unmanned aerial vehicle energy consumption model; acquiring dominant wind data and performing CFD urban wind field simulation; dividing a super wind speed area and a turbulent flow area as flight risk areas; constructing an unmanned aerial vehicle path planning model considering the influence of the urban wind field; planning the path of the unmanned aerial vehicle in the wind field. According to the method, risk area division is carried out based on CFD simulation, the wind speed and turbulent kinetic energy spatial distribution can be captured, the method adapts to the wind resistance of the unmanned aerial vehicle and turbulent area flight limitation, the energy consumption can be reduced by utilizing a wind field, the capability of improving the energy efficiency is achieved, and a uniform path planning model with safety and energy efficiency as targets in the wind field is constructed.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) path planning, and in particular relates to a method and device for unmanned aerial vehicle (UAV) path planning that takes urban wind field factors into consideration. Background Art

[0002] Drones are highly maneuverable and flexible, produce less noise and are more efficient than traditional helicopters. Their rapid development in recent years has led to their widespread use in urban environments. To support the safe and efficient operation of drones, path planning is one of the key technologies. Drone path planning refers to searching for the optimal path between the starting point and the target point with less computational cost and practice, ensuring the efficiency and feasibility of the path while avoiding obstacles.

[0003] For drones operating in cities, wind fields are an important consideration because they can cause drones to lose control and stability. Gusts can cause drones to deviate from their routes or even collide. To maintain flight stability, drones need to output additional power in strong winds, which in turn increases their energy consumption. In addition, urban canyons and single buildings can generate significant levels of turbulence that endanger aircraft. For urban wind fields, traditional drone path planning methods face problems such as safety risks and low energy efficiency in routes. Therefore, it is urgent to build a unified path planning model under wind fields that targets safety and energy efficiency to effectively avoid flight risk areas and optimize route energy consumption. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies in existing research and provide a UAV path planning method and device that takes urban wind field factors into consideration. The method and device integrate energy consumption model construction, CFD wind field simulation and UAV path planning under wind field, which can capture the spatial distribution of wind speed and turbulent kinetic energy, adapt to the UAV's wind resistance and flight restrictions in turbulent areas and avoid flight risk areas. The method can utilize wind fields to reduce the energy consumption of UAVs required for the route and has the ability to improve energy efficiency.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In the first aspect, a UAV path planning method considering urban wind field factors includes:

[0007] Step (1): Obtain building and drone airport data to construct a three-dimensional city model, and rasterize to construct a rasterized airspace model;

[0008] Step (2): constructing a multi-rotor UAV energy consumption model;

[0009] Step (3): Based on the three-dimensional city 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): Based on the wind speed components and turbulent kinetic energy in each grid in the airspace, according to the wind resistance limit of the UAV and the flight restriction requirements in the turbulent zone, the overwind speed zone and the turbulent zone are designated as the flight risk zone in the rasterized airspace model, and the rasterized airspace model containing wind field information is obtained;

[0011] Step (5): Based on the multi-rotor UAV energy consumption model, construct a UAV path planning model that takes into account the influence of urban wind field; 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 restriction, airspeed constraint, ground speed constraint and ground speed change constraint;

[0012] 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.

[0013] In a second aspect, the present invention provides a UAV path planning device that takes into account urban wind field factors, including a processor and a storage medium;

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

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

[0016] In a third aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program implements the method described in the first aspect when executed by a processor.

[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 of the first aspect when executing the computer program.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0019] Compared with the prior art, the present invention has significant advantages: the present invention can address the problem of UAV path planning under complex wind fields. The method performs risk zone demarcation based on CFD simulation, can capture the spatial distribution of wind speed and turbulent kinetic energy in the airspace, adapt to the UAV's wind resistance and flight restrictions in turbulent areas, can utilize wind fields, avoid strong headwind sailing and utilize tail winds to reduce the UAV energy consumption required for the route, has the ability to improve path energy efficiency, and constructs a unified path planning model under wind fields with safety and energy efficiency as the goals, providing a technical basis and reference for achieving safe and efficient operation of urban UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 2 It is a schematic diagram of an ultra-wind speed zone and a turbulent zone delineated by using the path planning method described in an embodiment of the present invention;

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

[0023] Figure 4 is a schematic diagram of the comparison of the paths of the UAVs planned by the path planning algorithm described in the embodiment of the present invention;

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

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

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

[0027] Embodiment 1: This embodiment provides a method for UAV path planning taking into account urban wind field factors, including:

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

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

[0030] Step (3): Based on the three-dimensional city 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): Based on the wind speed components and turbulent kinetic energy in each grid in the airspace, according to the wind resistance limit of the UAV and the flight restriction requirements in the turbulent zone, the overwind speed zone and the turbulent zone are designated as the flight risk zone in the rasterized airspace model, and the rasterized airspace model containing wind field information is obtained;

[0032] Step (5): Based on the multi-rotor UAV energy consumption model, construct a UAV path planning model that takes into account the influence of urban wind field; 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 restriction, airspeed constraint, ground speed constraint and ground speed change constraint;

[0033] 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.

[0034] In some specific embodiments, a method for UAV path planning considering urban wind field factors includes:

[0035] Step (1): Obtain the coordinates, size and height information of buildings in the area from the geographic information database, add the locations of drone hub airports and sub-airports, build a three-dimensional city model and rasterize it to build a rasterized airspace model;

[0036] Step (2): Construct the energy consumption model of the multi-rotor UAV by force analysis, and derive the resistance D, required thrust T, pitch angle α, induced speed v, etc. during the flight from the gravity W of the UAV. i , the UAV flight power P can be obtained, and the energy consumption E can be further calculated; the path planning model will take minimizing energy consumption as the objective function;

[0037] Step (3): extract the regional surface dominant wind data from the meteorological database, calculate the dominant wind speed and direction from the u and v wind speed components, and input them into the computational fluid dynamics (CFD) solver; the CFD wind field simulation is based on the three-dimensional city model, divides the grid in the airspace and sets the boundary conditions, and the inlet wind speed is set according to the dominant wind data obtained. The wind speed component and turbulent kinetic energy in each grid in the airspace are obtained by solving the turbulence model;

[0038] Step (4): Based on the wind speed components and turbulent kinetic energy obtained from the simulation, the overwind speed area and turbulent area are designated as flight risk areas in the gridded airspace model according to the UAV wind resistance limit and turbulent area flight limit requirements, and are input into the path planning model;

[0039] Step (5): Construct a UAV path planning model that considers the influence of urban wind field. Its objective function is to minimize the energy consumption of the UAV. 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.

[0040] Step (6): Construct and improve the graph search algorithm to plan the UAV path between the starting point and the target point in the gridded airspace containing wind field information, so as to minimize energy consumption and avoid the flight risk area.

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

[0042] Step (101): Obtain the longitude and latitude coordinates, size and height information of buildings in the area from the open source global geographic information database OSM (Open Street Map), and obtain the longitude and latitude coordinates of the drone 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 including buildings and drone airports, which will be used in urban wind field simulation;

[0044] Step (103): For the obtained 3D city model and its 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:

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

[0046] Among them, x i ,y i ,z i For grid c i Coordinates in the x, y, and z directions, u i ,v i ,w i For grid c i Wind speed components in the x, y, and z directions; wind speed v w,i and turbulent kinetic energy TKEi To reserve the data structure, it is integrated after completing the CFD wind farm simulation.

[0047] In some embodiments, in step (2), constructing a multi-rotor drone energy consumption model includes:

[0048] Step (201): Perform force analysis on the UAV. During steady-state flight, the UAV is moving at an airspeed v a and pitch angle α, the wind field will affect the airspeed v a Capture; the thrust T and resistance D of the drone are expressed as:

[0049]

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

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

[0052]

[0053] 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;

[0054] Step (203): Calculate the UAV flight power P and energy consumption E:

[0055]

[0056] Among them, η is the energy conversion efficiency of the UAV, l is the flight distance, v g is the ground speed of the drone;

[0057] 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:

[0058]

[0059] Where e is the energy consumption per unit distance, v eco is the optimal airspeed. Substituting the power P, the optimal airspeed is expressed as:

[0060]

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

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

[0063] Step (302): Input the three-dimensional city model and the dominant wind data into the CFD solver, perform meshing and boundary condition setting in the airspace, and set the dominant wind speed at the inlet boundary. The meshing is performed on the computational domain according to the geometric complexity and wind field characteristics. High-density meshes are used in key areas to capture details, and the peripheral areas are gradually sparse to optimize the amount of calculation. When setting the inlet boundary conditions, the wind speed profile is defined and the turbulence parameters are set according to the dominant wind direction and wind speed distribution. The outlet boundary uses a zero pressure gradient, the building and the ground solid wall use a no-slip condition, and the top boundary is set to a slip condition. The turbulence model selects RANS (Reynolds-Averaged Navier-Stokes) k-ωSST (Shear Stress Transport), the solution method selects SIMPLE (Semi-Implicit Method for Pressure-Linked Equations), and the CFD simulation calculates the urban wind field.

[0064] Step (303): After the calculation is completed, the wind speed components and turbulent kinetic energy in each CFD grid in the airspace are obtained, and post-processing is performed to visualize the wind speed and turbulent kinetic energy distribution.

[0065] In some embodiments, in step (4), setting the super wind speed area and the turbulence area as the flight risk area includes:

[0066] 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 in the grid, turbulent kinetic energy set {TKE w,i,1 ,...,TKE w,i,m}Calculate the maximum turbulent kinetic energy within the grid:

[0067]

[0068] Among them, v w,i,mean For grid c i The average wind speed in w,i,max For grid c i Maximum wind speed within, TKE i,max For grid c i The maximum turbulent kinetic energy in the grid, j represents the maximum turbulent kinetic energy in the grid, j i The jth CFD grid in v w,i,mean Used to calculate the flight speed of the drone under the influence of wind field, v w,i,max With TKE i,max Used to determine whether the airspace grid can be entered;

[0069] 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 i No access, as indicated below:

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

[0071] 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;

[0072] Step (403): Determine flight restrictions in turbulent areas, 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:

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

[0074] Among them, TKE res is the turbulent kinetic energy threshold, r TKEThe detection radius of turbulent kinetic energy around the building;

[0075] Step (404): Based on the wind resistance limit of the UAV and the flight limit in the turbulent zone, an overwind zone and a turbulent zone are respectively delineated in the airspace grid and input into the path planning model.

[0076] In some embodiments, the step (5) constructs a UAV path planning model that takes into account the impact of urban wind fields, including:

[0077] Step (501): constructing a UAV path planning model that takes into account the impact of urban wind fields, and its objective function is to minimize the energy consumption of the UAV on the route;

[0078]

[0079] 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 drone 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; in order 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;

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

[0081] l i ≥λ min ,i=1,2,...,n;

[0082] 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;

[0083] Step (503): Construct a UAV path planning model that takes into account the impact of urban wind fields, and its constraints include energy consumption constraints, namely

[0084]

[0085] 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 in a given wind farm; q is the total number of routes;

[0086] Step (504): Construct a UAV path planning model that takes into account the impact of urban wind fields. Its constraints include turning angle constraints. The planned path turning angle should satisfy

[0087] |β i |≤β max ,i=1,2,...,n;

[0088] Among them, β i is the path node c i Turning angle, β max is the maximum turning angle of the path;

[0089] Step (505): Construct a UAV path planning model that takes into account the impact of urban wind fields. Its constraints include flight altitude constraints. The altitude of the UAV's horizontal flight path should meet airspace restrictions, that is,

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

[0091] Among them, H min With H max They are the minimum and maximum flight altitudes of drones during the cruising phase, which are related to airspace policies;

[0092] Step (506): Construct a UAV path planning model that takes into account the impact of urban wind fields. Its constraints include grid restrictions, any path node c i Do not enter the no-fly zone or building grid collection, as follows

[0093]

[0094] Among them, p i (x i ,y i ,z i ) is any node c on the path i The coordinates ofno-fly With C bld They are no-fly zone and building grid sets respectively;

[0095] Step (507): Construct a UAV path planning model that takes into account the impact of urban wind fields. Its constraints include airspeed constraints. The flight speed of the UAV in the wind field will be affected. 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 can be expressed as

[0096] v a,i ≤v a,max ;

[0097] Among them, v a,i The UAV is at the path node c i Airspeed, v a.max The maximum airspeed that the drone can maintain under thrust limitations;

[0098] Step (508): Construct a UAV path planning model that takes into account the impact of urban wind fields. Its constraints include ground speed constraints. To ensure the effectiveness of the planned path, the UAV should be able to navigate forward at a certain ground speed under a given airspeed limit. The ground speed constraint can be expressed as:

[0099] v g,min ≤v g,i ≤v g,max ;

[0100] Among them, v g,min With v g,max are the minimum and maximum ground speeds required to meet the UAV mission requirements;

[0101] Step (509): Construct a UAV path planning model that considers the impact of urban wind fields. Its constraints include ground speed change constraints. 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, as follows

[0102] |v g,i -v g,i-1 |≤Δv g ;

[0103] Where Δv g is the limit value of ground speed change between adjacent path nodes, which depends on factors such as aircraft model and size of the drone;

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

[0105] Step (601): Construct and improve the path planning solution 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 c0 to c k The actual path cost of c k to c n Specifically, the improvement of g(·) and h(·) is:

[0108]

[0109] Among them, P status (·) is the flight power of the UAV under a given state, dist(·) is the Euclidean distance between path nodes, v a,i-1 With v g,i-1 The drones are c i-1 Sail to c i The airspeed and ground speed of the drone, status indicates whether the drone is loaded or empty, P 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. Set the wind speed change threshold as 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*, and optimize its flight speed selection: first, the optimal airspeed and ground speed can be calculated from the heading, wind speed and wind direction, and then according to 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 exploration path node;

[0112] Step (604): construct and optimize the path planning solution algorithm Theta*, and perform optimal speed preprocessing: in the solution algorithm, first preprocessing is performed to obtain the optimal airspeed under different wind fields, and then no recalculation is required during the path search process. 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 to 15m / s, and the angle between wind and route is 0 to 180°. After calculation, the optimal airspeed and ground speed for cargo and empty cargo within different wind speed and wind direction ranges can be obtained;

[0113] Step (605): UAV path planning under wind field, with the airspace grid containing wind field information as input, the algorithm plans the path between the starting point and the target point, first initializes the limited queue for storing the nodes to be processed, and the list is used to record the predecessor node, the f function, the g function, and the 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: read 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, 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;

[0114] 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.

[0115] Verification example: In order to verify the effectiveness of the flight risk zone delineation method under wind field provided by the embodiment of the present invention, the present invention selects the Xinghe Central Business District in Longgang, Shenzhen, Guangdong Province, analyzes the surface dominant wind data from 2013 to 2023, and performs CFD urban wind field simulation and flight risk zone delineation (the delineation results when the northeast wind is level 5 are as follows: Figure 2 The distribution of super wind speed area and turbulence area at each altitude layer in the airspace is shown in Table 1.

[0116] Table 1 Statistics of the restricted area of ​​low-altitude airspace in the region

[0117]

[0118] From Table 1, we can see that the super wind speed zone mainly appears when the inlet wind speed is high. The super wind speed zone area at the 80m altitude layer is 6,950m 2 and 11,300m 2; When the northeast wind is level 5, the super-wind zone spans the urban canyon on the north side and spreads to the top of the hub airport, requiring a large detour during route planning; in addition, since the wind speed in the urban environment increases with the increase of altitude, the area of ​​the super-wind zone is significantly related to the altitude layer. When the northeast wind is level 5, the area at the 60m altitude layer is only 1,638m 2 , and it increases exponentially with the increase of altitude, increasing to 6,950m 2 ,11,956m 2 With 18,775m 2 In addition, the increase in average wind speed will cause strong shear wind around the building, which will lead to an increase in turbulence intensity. The CFD calculation results are consistent with this phenomenon, that is, stronger turbulence is usually associated with higher wind speeds. When the dominant wind speed is high, the range of the turbulent zone is also expanded. Compared with the results of the turbulent zone demarcation at the 80m altitude layer, taking the northeast wind level 5 as an example, the area of ​​the turbulent zone is 21,900m when the wind speed is low. 2 , and as the wind speed increases, this area increases to 30,431m 2 With 38,843m 2 ,analyzing other scenarios, similar conclusions can still be drawn.

[0119] In order to demonstrate the effect of the drone path planning method provided by the example of the present invention, the example of the present invention performs round-trip path planning between the hub airport and the sub-airport at altitudes of 60m to 120m under different wind fields, and takes the path planning result at an altitude of 80m under a northeast wind of level 5 as an example. Figure 3 As shown in the figure, the solid line and dotted line are the cargo delivery route and the empty return route respectively. The path planning results under each wind farm are shown in Table 2.

[0120] Table 2 Route data under different dominant winds

[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 in Northeast Level 3, and the average route distance, flight time and path energy consumption are the lower values ​​in the 9 groups of scenarios; in Northeast Level 4, with the increase of the dominant 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 are increased, the average time of the return route level flight stage 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, drone 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, Northeast Level 5 The average distance, time and energy consumption of the first level are 489.50m, 41.54s and 41.32kJ, which are 7.74%, 6.84% and 12.80% higher than those of the northeast third level distribution route; when the dominant wind is easterly, due to the low overall wind speed in the low-altitude airspace, the average distance, time and energy consumption indicators are the lowest values ​​under the given dominant wind speed; when the dominant wind is southerly, the local wind near the highest building in the area accelerates significantly, resulting in the highest route indicators in the simulation scenario for the fifth level of south wind, with an average distance, time and energy consumption of 510.66m, 42.42s and 29.15kJ, which are 12.67%, 9.13% and 13.25% higher than those of the third level of south wind in 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 result, 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 benchmark; 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 farm provided by the embodiment of the present invention, the method of the present invention ( Figure 5 ) and the method without considering the wind field ( Figure 6 ) and 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 by using two methods. Figure 5 In the figure, the path energy consumption varies relatively steadily with the distance, and there are a few outliers with high path energy consumption. For example, the energy consumption at 300m can reach 33.94kJ, while Figure 6 In the above, the path energy consumption under the same distance is always higher than the result obtained by considering the wind field information, exceeding by 8.34% on average. In particular, Figure 6 There are a large number of outliers with large deviations, indicating that the corresponding routes face the influence of strong headwinds in order to reduce the path distance, and the energy consumption increases significantly. The highest energy consumption of the route at 200m and 700m is 51.37kJ and 82.86kJ, which are 289.17% and 83.81% higher than the average value respectively. In general, the average ground speed of the path obtained by inputting wind field information is 12.78m / s, and the average energy consumption is 26.63kJ; while the average ground speed of the path obtained without inputting wind field information is 12.03m / s, and the average energy consumption is 28.85kJ. Compared with the latter, the former reduces energy consumption by 7.69% on the basis of 6.23% increase in ground speed. In addition, the energy consumption difference between the two methods will be more significant under high wind speed conditions.

[0126] In summary, the UAV path planning method under wind fields provided by the present invention can make up for the shortcomings of existing methods. For the problem of UAV path planning under complex wind fields, this method performs risk zone demarcation based on CFD simulation, can capture the spatial distribution of wind speed and turbulent kinetic energy in the airspace, adapt to the UAV's wind resistance and flight restrictions in turbulent areas, can utilize wind fields, avoid strong headwind sailing and utilize tail winds to reduce the UAV energy consumption required for the route, has the ability to improve the path energy efficiency, and constructs a unified path planning model under wind fields with safety and energy efficiency as the goals, providing a technical basis and reference for achieving safe and efficient operation of urban UAVs.

[0127] Embodiment 2: Based on Embodiment 1, this embodiment provides a UAV path planning device taking into account urban wind field factors, including a processor and a storage medium;

[0128] The storage medium is used to store instructions;

[0129] The processor is used to operate according to the instruction to execute the method according to embodiment 1.

[0130] Embodiment 3: Based on Embodiment 1, this embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described in Embodiment 1 is implemented.

[0131] Embodiment 4: Based on Embodiment 1, this embodiment provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in Embodiment 1 when executing the computer program.

[0132] Embodiment 5: Based on Embodiment 1, this embodiment provides a computer program product, including a computer program, which implements the method described in Embodiment 1 when executed by a processor.

[0133] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0134] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0137] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A UAV path planning method considering urban wind field factors, characterized in that: include: Step (1): Obtain building and drone airport data to construct a three-dimensional city model, and rasterize to construct a rasterized airspace model; Step (2): constructing a multi-rotor UAV energy consumption model; Step (3): Based on the three-dimensional city 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; Step (4): Based on the wind speed components and turbulent kinetic energy in each grid in the airspace, according to the wind resistance limit of the UAV and the flight restriction requirements in the turbulent zone, the overwind speed zone and the turbulent zone are designated as the flight risk zone in the rasterized airspace model, and the rasterized airspace model containing wind field information is obtained; Step (5): Based on the multi-rotor UAV energy consumption model, construct a UAV path planning model that takes into account the influence of urban wind field; 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 restriction, 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 method for UAV path planning considering urban wind field factors according to claim 1 is characterized in that: The step (1) specifically comprises: Step (101): Acquire building data and drone airport data; wherein the building data includes the longitude and latitude coordinates, size 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 according to the building data and drone airport data; Step (103): For the three-dimensional city model and the airspace OABC-O′A′B′C′, select a 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 For grid c i Coordinates in the x, y, and z directions, u i ,v i ,w i For 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 by CFD wind field simulation.

3. The method for UAV path planning considering urban wind field factors according to claim 1 is characterized in that: In the step (2), constructing a multi-rotor UAV energy consumption model includes: Step (201): Perform force analysis on the UAV. During steady-state flight, the UAV is moving at an airspeed v a and pitch angle α, the wind field will affect the airspeed v a Capture; the thrust T and resistance D of the drone are expressed as: Where T is the thrust of the drone, W is the gravity of the drone, D is the drag of the drone, ρ is the air density, and C D is the drag coefficient of the drone, 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 ground speed of the drone; 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: Where 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 method for UAV path planning considering urban wind field factors according to claim 1 is characterized in that: The step (3) specifically comprises: 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 at 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 method for UAV path planning considering urban wind field factors according to claim 1 is characterized in that: The step (4) of designating 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 in 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 For grid c i The average wind speed in w,i,max For grid c i Maximum wind speed within, TKE i,max For grid c i The maximum turbulent kinetic energy in the grid, j represents the ... i The jth CFD grid in v w,i,mean Used to calculate the flight speed of the drone 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 i No access, as indicated below: 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 flight restrictions in turbulent areas, 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 The detection radius of turbulent kinetic energy around the building; Step (404): Based on the wind resistance limit of the UAV and the flight limit in the turbulent zone, an overwind zone and a turbulent zone are delineated in the gridded airspace model.

6. The method for UAV path planning considering urban wind field factors according to claim 1 is characterized in that: The step (5) specifically comprises: The objective function of the UAV path planning model considering the influence of urban wind field is to minimize the energy consumption of UAV on the route, which is 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 drone 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; in order 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 length constraint is that the distance between adjacent path nodes is greater than or equal to its step length; 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 in 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 drone during the cruising phase; 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, 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 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 The UAV is at the 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 value between adjacent path nodes.

7. The method for UAV path planning considering urban wind field factors according to claim 1 is characterized in that: The step (6) specifically comprises: 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 c0 to c k The actual path cost of c k to c n Specifically, the improvement of g(·) and h(·) is: Where k represents the kth path node, n is the total number of path nodes; P status (·) is the flight power of the UAV under 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 of the drone from the previous path node c i-1 Navigate to path node c i The airspeed and ground speed of the drone, status indicates whether the drone is loaded or empty, P 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*, and 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 are no-fly zones and building grid sets respectively, then 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 to 15m / s, and the angle between wind and route is 0 to 180°. After calculation, the optimal airspeed and ground speed of cargo and empty cargo in different wind speed and wind direction ranges are obtained; Step (605): UAV path planning under wind field, with the airspace grid containing wind field information as input, the algorithm plans the path between the starting point and the target point, first initializes the limited queue for storing the nodes to be processed, and the list is used to record the predecessor node, the f function, the g function, and the 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, 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 drone 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, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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