Unmanned aerial vehicle route planning method, device, equipment and medium

Abstract

The invention discloses an unmanned aerial vehicle route planning method, device and equipment and a medium, and the method comprises the steps: obtaining the real-time transient wind field data of an unmanned aerial vehicle, and generating the flow field information of time-space correlation according to the real-time transient wind field data; a flight energy consumption model is established based on the flow field information, the flight energy consumption model is adopted to calculate energy consumption in different stages, node energy consumption attribute values are obtained, and the different stages comprise a level flight stage, a climbing stage and a descending stage; and the node energy consumption attribute value is input into the heuristic search algorithm model to obtain the current path planning with the lowest energy consumption, so that the accuracy of the route planning is improved, and the route energy consumption is saved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) route planning, and in particular to a method, device, equipment and medium for UAV route planning. Background Art

[0002] At present, with the rise of the low-altitude economy concept and the development of drone technology, more and more new low-altitude business formats have emerged. Among them, industries such as drone logistics and distribution have entered large-scale commercial operations. At the same time, new demands have been put forward for the understanding of urban wind fields where low-altitude drone operations are located. For a long time, the impact of urban wind fields on UAV flights has been difficult to estimate and has often been replaced by theoretical models. There is a lack of a UAV path planning method that considers the influence of urban wind fields in combination with actual flight conditions.

[0003] Current UAV path optimization technology development focuses on dynamic obstacle adaptability, multi-objective optimization, and machine vision navigation. However, current solutions lack the ability to incorporate real-time dynamic wind field effects into UAV path planning. Existing algorithms often represent wind field effects using spectral equations of atmospheric turbulence, most commonly using the Von Karman and Dryden turbulence models. These models construct piecewise functions based on different gust scales to describe wind field effects at different scales.

[0004] During the development of this invention, the inventors realized that the existing technology has at least the following technical problems: Existing approaches primarily consider the impact of average wind fields to account for real-time wind conditions, failing to factor in actual wind conditions, which can differ from actual conditions. Furthermore, existing approaches use actual wind field information to restrict path planning in the form of no-fly zones, ignoring the variability of real-time wind fields. Consequently, they lack a quantitative representation of the impact of real-time wind fields on flight energy consumption, resulting in inaccurate UAV route planning. Summary of the Invention

[0005] Embodiments of the present invention provide a method, apparatus, computer device, and storage medium for UAV route planning to improve the accuracy of UAV route planning and save energy consumption during low-altitude flight.

[0006] In order to solve the above technical problems, the present invention provides a method for planning a route for a UAV, including: Acquire real-time transient wind field data from the UAV, and generate spatiotemporally correlated flow field information based on the real-time transient wind field data; A flight energy consumption model is established based on the flow field information, and energy consumption in different stages is calculated using the flight energy consumption model to obtain node energy consumption attribute values, where the different stages include level flight, climb, and descent. The node energy consumption attribute value is input into the heuristic search algorithm model to obtain the current minimum energy consumption path planning.

[0007] Optionally, the flow field information includes real-time three-dimensional spatial coordinates and wind field vector distribution corresponding to the three-dimensional spatial coordinates, the wind field vector distribution includes wind field velocity components and wind field direction components at different coordinate points, and the energy consumption of different stages is calculated using the flight energy consumption model to obtain the node energy consumption attribute value including: Based on the wind field direction component of the coordinate points on the surface of the UAV, the windward area of ​​the UAV is determined, and the wind resistance energy consumption in the level flight stage is calculated based on the windward area of ​​the UAV and the wind field velocity component on the surface of the UAV; Collecting the change in gravitational potential energy of the drone, and calculating the energy consumption during the climb phase and the energy consumption during the descent phase based on the change in gravitational potential energy and the wind resistance energy consumption during the level flight phase; The wind resistance energy consumption in the level flight phase, the energy consumption in the climbing phase, and the energy consumption in the descending phase are used as the node energy consumption attribute values.

[0008] Optionally, the real-time transient wind field data includes wind direction parameters, and the calculating of wind resistance energy consumption in the level flight phase based on the windward area of ​​the UAV and the wind field velocity component includes: Decomposing the wind field velocity component into a longitudinal component of the horizontal heading of the UAV and a transverse component of the vertical heading according to the wind direction parameter; The basic drag parameter is determined by the product of air density, UAV drag coefficient and UAV frontal area; The longitudinal component is superimposed with the current flight speed vector of the UAV, the result is squared, and the result is multiplied by the basic resistance parameter to obtain a real-time wind resistance energy consumption value, which is used as the wind resistance energy consumption in the level flight stage.

[0009] Optionally, the introducing of the gravitational potential energy variation and combining the wind resistance energy consumption in the level flight phase to calculate the energy consumption in the climbing phase and the energy consumption in the descending phase includes: Calculate the change in gravitational potential energy during the climbing and descending phases based on the mass of the drone and the acceleration of gravity; The wind resistance energy consumption in the level flight stage is dynamically corrected by the change in the gravitational potential energy to obtain the energy consumption in the climbing stage and the energy consumption in the descending stage.

[0010] Optionally, the heuristic search algorithm model dynamically updates the heuristic function by updating the coefficients in the original heuristic function as the number of iterations increases, including: Calculating the Chebyshev distance based on the spatial coordinate difference between a starting point and an end point, wherein the starting point and the end point are the starting point and the end point of an initial path generated after inputting the node energy consumption attribute value into a heuristic search algorithm model; Determining an estimated total iteration period R based on the Chebyshev distance, wherein the estimated total iteration period R is the total number of iterations required for dynamically updating the heuristic function; Linear interpolation is performed based on the ratio of the current number of iterations r to the estimated total iteration period R, and nonlinear correction is performed on the real-time energy consumption difference of adjacent nodes to update the weight coefficient of the heuristic function in real time. The current number of iterations r is the number of iterations from the first update of the heuristic function to this iteration.

[0011] Optionally, generating spatiotemporally correlated flow field information according to the real-time transient wind field data includes: Performing a fluid dynamics simulation on the real-time transient wind field data and aligning the simulation results with a spatial coordinate system of an electronic airspace map, wherein the electronic airspace map is a three-dimensional spatial map of the UAV aircraft; A three-dimensional vector layer is generated in the airspace below 80 meters to obtain the flow field information, wherein the time dimension update frequency of the vector layer is determined by the time interval of the timestamp information, and each timestamp corresponds to an independent wind field vector distribution, and the wind field vector distribution includes wind field velocity components and wind field direction components at different coordinate points.

[0012] In order to solve the above technical problems, the present invention further provides a UAV route planning device, comprising: A flow field information generation module is used to obtain real-time transient wind field data of the UAV and generate spatiotemporally correlated flow field information based on the real-time transient wind field data; an energy consumption calculation module, configured to establish a flight energy consumption model based on the flow field information, and use the flight energy consumption model to calculate energy consumption in different stages to obtain node energy consumption attribute values, where the different stages include level flight, climb, and descent; The path selection module is used to input the node energy consumption attribute value into the heuristic search algorithm model to obtain the current minimum energy consumption path planning.

[0013] Optionally, the energy consumption calculation module includes: A level flight energy consumption calculation unit is used to determine the windward area of ​​the UAV based on the wind field direction component of the coordinate point on the UAV surface, and calculate the wind resistance energy consumption during the level flight stage based on the windward area of ​​the UAV and the wind field velocity component on the UAV surface; A lifting energy consumption calculation unit is used to collect the change in gravitational potential energy of the UAV, and calculate the energy consumption of the climbing stage and the energy consumption of the descending stage based on the change in gravitational potential energy and the wind resistance energy consumption in the level flight stage; The node energy consumption determination unit is used to use the wind resistance energy consumption in the level flight phase, the energy consumption in the climbing phase and the energy consumption in the descending phase as the node energy consumption attribute value.

[0014] Optionally, the level flight energy consumption calculation unit includes: a transverse component determination subunit, configured to decompose the wind field velocity component into a longitudinal component of the horizontal heading of the UAV and a transverse component of the vertical heading according to the wind direction parameter; A basic resistance calculation subunit is used to determine the basic resistance parameters according to the product of air density, UAV drag coefficient and UAV frontal area; The real-time wind resistance energy consumption calculation subunit is used to square the longitudinal component and the current flight speed vector of the UAV, and multiply it by the basic resistance parameter to obtain a real-time wind resistance energy consumption value as the wind resistance energy consumption in the level flight stage.

[0015] Optionally, the lifting energy consumption calculation unit includes: The gravity potential energy change determination subunit is used to calculate the gravity potential energy change during the climbing and descending phases based on the mass of the UAV and the gravitational acceleration; The energy consumption dynamic correction subunit is used to dynamically correct the wind resistance energy consumption in the level flight stage according to the change in gravitational potential energy to obtain the energy consumption in the climbing stage and the energy consumption in the descending stage.

[0016] Optionally, the flow field information generation module includes: a coordinate alignment unit, configured to perform a fluid dynamics simulation on the real-time transient wind field data and align the simulation result with a spatial coordinate system of an electronic airspace map, wherein the electronic airspace map is a three-dimensional spatial map of the UAV aircraft; A flow field information generation unit is used to generate a three-dimensional vector layer in the airspace below 80 meters to obtain the flow field information, wherein the time dimension update frequency of the vector layer is determined by the time interval of the timestamp information, and each timestamp corresponds to an independent wind field vector distribution, and the wind field vector distribution includes wind field velocity components and wind field direction components at different coordinate points.

[0017] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned UAV route planning method are implemented.

[0018] In order to solve the above technical problems, an embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned drone route planning method are implemented.

[0019] The UAV route planning method, device, computer equipment and storage medium provided by the embodiments of the present invention obtain real-time transient wind field data of the target airspace and generate spatiotemporally correlated flow field information based on the real-time transient wind field data; then establish a flight energy consumption model based on the flow field information, and use the flight energy consumption model to calculate energy consumption in different stages to obtain node energy consumption attribute values; then input the node energy consumption attribute values ​​into the heuristic search algorithm model to obtain the current minimum energy consumption path planning as the current path, thereby improving the accuracy of route planning and saving route energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 is an exemplary system architecture diagram to which the present application may be applied; Figure 2 is a flow chart of an embodiment of the UAV route planning method of the present application; Figure 3 This is an example diagram of a specific process of one-time path planning of a UAV route planning method in this application; Figure 4 This is a schematic structural diagram of an embodiment of a UAV route planning device according to the present application; Figure 5 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0023] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] See also Figure 1 ,like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0026] Users can use terminal devices 101 , 102 , 103 to interact with server 105 via network 104 to receive or send messages, etc.

[0027] The terminal devices 101, 102, and 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptop computers, desktop computers, etc.

[0028] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0029] It should be noted that the drone route planning method provided in the embodiment of the present application is executed by a server, and accordingly, the drone route planning device is set in the server.

[0030] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only illustrative. According to the implementation requirements, there can be any number of terminal devices, networks and servers. The terminal devices 101, 102, 103 in the embodiment of the present application can specifically correspond to application systems in actual production.

[0031] See also Figure 2 , Figure 2 A method for planning a UAV route is shown in the embodiment of the present invention. Figure 1 The server in the example is used as an example, and the details are as follows: S201: Acquire real-time transient wind field data of the target airspace, and generate spatiotemporally correlated flow field information based on the real-time transient wind field data.

[0032] Specifically, the real-time transient wind field data in this embodiment is determined by space-time information, that is, the wind field data at this moment determined by the space and time information of the starting point of the route. The real-time transient wind field data includes wind field velocity components, wind direction parameters and timestamp information, and the flow field information includes three-dimensional spatial coordinates and wind field vector distribution corresponding to the timestamp.

[0033] In this embodiment, an airspace map is generated from the geometric information within the airspace, and the flight boundary is set by setting the path start and end points. First, for the initial time, the corresponding transient wind field data set (actually the urban wind field data predicted during the flight time after takeoff) is extracted based on the spatiotemporal information of the UAV. u 、 v 、 w , i.e., the three velocity components in a Cartesian coordinate system with the UAV as the origin. The potential spacetime movement of the UAV at the next moment is then calculated based on the set traversal movement. Thus, the flight cost of the current discrete flight process is calculated using the updated time and spatial position and the UAV's previous moment, and the final total flight value obtained by the dynamic heuristic algorithm is added. The lowest-cost move is then selected and used to update the UAV's coordinates until the path is reached.

[0034] It should be noted that, in this embodiment, the time resolution of the time series transient wind farm data is used to determine the update period of the wind farm information, specifically in the following manner: in Discrete route starting point for calculating flight cost iteration To the end The starting point and end point of the discrete route are the starting point and end point of a movement process. x M 、yM 、z M is the coordinate of point M, x N 、y N 、z N is the coordinate of point N, is the flight speed of the drone, and the flight time required to complete this discrete route is obtained Used to update time information.

[0035] In a specific optional implementation of this embodiment, in step S201, generating spatiotemporally correlated flow field information includes: Perform fluid dynamics simulations on real-time transient wind field data and align the simulation results with the spatial coordinate system of an electronic airspace map, which is a three-dimensional spatial map of the UAV aircraft. A three-dimensional vector layer is generated in the airspace below 80 meters to obtain flow field information. The time dimension update frequency of the vector layer is determined by the time interval of the timestamp information, and each timestamp corresponds to an independent wind field vector distribution. The wind field vector distribution includes the wind field velocity component and wind field direction component at different coordinate points.

[0036] S202: Establish a flight energy consumption model based on the flow field information, and use the flight energy consumption model to calculate energy consumption at different stages to obtain node energy consumption attribute values.

[0037] Specifically, for unmanned aerial vehicles (UAVs) flying in urban airspace, the extent to which urban wind fields affect their flight energy consumption is an issue that urgently needs to be quantified. This example models the UAV flight process based on a series of simplified physical assumptions. The specific assumptions are as follows: 1) Energy conservation assumption: It is assumed that the impact of the wind environment on the UAV energy consumption is not affected by its own flight airflow disturbance, and all the wind resistance generated by the wind field during flight acts completely on the UAV.

[0038] 2) Simplified assumptions about flight mechanics: Assuming the UAV is a rigid body, ignoring its internal structural changes, and only considering its overall motion under the action of aerodynamic forces, that is, gravity, lift, drag, and thrust are balanced at any time.

[0039] 3) Steady-state flight assumption: The UAV is assumed to be flying horizontally at a constant airspeed, meaning that attitude changes do not result in speed changes throughout the flight. At the same time, to maintain steady-state flight, the UAV needs to overcome the effects of the wind environment, which is factored into the energy consumption model.

[0040] Based on the above setup, this embodiment constructs a UAV flight energy consumption model under the influence of wind field: in is the drag coefficient of the UAV, is the frontal area. According to the wind resistance equation, the speed It can be defined as the disturbance of the environmental wind field to the UAV system. At this time, the velocity term in the drag equation is the actual environmental wind field velocity. The influence of the environmental wind field is incorporated into the equation, and the form of the velocity component is considered to obtain the flight drag equation in the level flight stage.

[0041] in is the velocity component of the ambient wind field in the Cartesian coordinate system. Based on the steady-state flight assumption, it can be assumed that the power equation is satisfied at each point-to-point process during the flight: Here Defined as the relative speed of the UAV in the airspace, the energy consumption equation for the level flight phase can be obtained: At the same time, when determining the energy consumption during the climbing and descending phases, the present embodiment will include the change in gravitational potential energy in consideration of the altitude change Δh: Where W is the vertical velocity of the UAV during the descent phase, and Δh is the height change.

[0042] Based on the above assumptions, the flight process is divided into the level flight cruise process and the climb / descent phase, and the energy consumption equation under the influence of the urban wind field is obtained. Since the purpose of this embodiment is to plan the path with the best energy consumption, the flight energy consumption of the UAV needs to be included in the energy consumption model. In this embodiment, the flight energy consumption is divided into the hovering energy consumption, which is the basic energy consumption for maintaining flight. And forward flight energy consumption The entire flight process is then discretized and integrated over time to obtain the cost function used in the improved A* algorithm for path planning. E represents the energy consumption of the entire flight process, M represents the current position, and N represents the next position.

[0043] Where End is the iteration index when the end point of the route is detected, A is the projected area of ​​the UAV propeller, and Mref is the reference load obtained by fitting the energy consumption increase curve of the UAV under different loads, that is, the benchmark load defined when studying the energy consumption of the UAV under different loads.

[0044] In a specific optional implementation of this embodiment, in step S202, the flight energy consumption model is used to calculate energy consumption in different stages, and the node energy consumption attribute values ​​obtained include: Based on the wind field direction component of the coordinate points on the surface of the UAV, the windward area of ​​the UAV is determined, and the wind resistance energy consumption in the level flight stage is calculated based on the windward area of ​​the UAV and the wind field velocity component on the surface of the UAV; Collect the change in gravitational potential energy of the drone, and calculate the energy consumption during the climbing and descending phases based on the change in gravitational potential energy and the wind resistance energy consumption during the level flight phase; The wind resistance energy consumption in the level flight phase, the energy consumption in the climbing phase, and the energy consumption in the descending phase are taken as the node energy consumption attribute values.

[0045] In a specific optional implementation manner of this embodiment, calculating the wind resistance energy consumption during level flight based on the windward area of ​​the drone and the wind field velocity component on the surface of the drone includes: According to the wind direction parameters, the wind field velocity components are decomposed into the longitudinal component of the horizontal heading of the UAV and the lateral component of the vertical heading; The base drag is determined by multiplying the air density, the drag coefficient of the drone, and the frontal area of ​​the drone; The longitudinal component is superimposed with the current flight velocity vector of the UAV, and then squared and multiplied by the basic resistance parameter to obtain the real-time wind resistance energy consumption value, which is used as the wind resistance energy consumption in the level flight stage.

[0046] In a specific optional implementation manner of the first embodiment, collecting the change in gravitational potential energy of the drone and calculating the energy consumption in the climbing phase and the energy consumption in the descending phase using the change in gravitational potential energy and the wind resistance energy consumption in the level flight phase include: Calculate the change in gravitational potential energy during the climbing and descending phases based on the mass of the drone and the acceleration of gravity; The energy consumption dynamic correction subunit is used to dynamically correct the wind resistance energy consumption in the level flight stage through the change in gravitational potential energy to obtain the energy consumption in the climbing stage and the energy consumption in the descending stage.

[0047] S203: Input the node energy consumption attribute value into the heuristic search algorithm model to obtain the current minimum energy consumption path plan as the current path.

[0048] In this embodiment, the heuristic search algorithm model adopts an improved A* search algorithm model, wherein the improved A* search algorithm model dynamically updates the heuristic function by updating the coefficients in the original heuristic function as the number of iterations increases.

[0049] Figure 3 This is an example diagram of the specific process of one-time path planning of a UAV route planning method in this application.

[0050] In a specific optional implementation of this embodiment, in step S203, the improved A-star search algorithm model dynamically updates the heuristic function by updating the coefficients in the original heuristic function in increasing numbers with the number of iterations, including: Calculating the Chebyshev distance based on the spatial coordinate difference between the starting point and the end point, where the starting point and the end point are the starting point and the end point of the initial path generated after inputting the node energy consumption attribute value into the heuristic search algorithm model; Determine an estimated total iteration period R based on the Chebyshev distance, wherein the estimated total iteration period R is the total number of iterations required for dynamically updating the heuristic function; Linear interpolation is performed based on the ratio of the current number of iterations r to the estimated total iteration period R, and nonlinear correction is performed on the real-time energy consumption difference of adjacent nodes to update the weight coefficient of the heuristic function in real time. The current number of iterations r is the number of iterations from the first update of the heuristic function to this iteration.

[0051] Specifically, for the A-star search algorithm model, this embodiment adopts a dynamically updated heuristic function. The principle is to update the coefficients in the original heuristic function as the number of iterations increases, so that the closer the search process is to the target area, the larger the heuristic function value is, and the target point can be found faster.

[0052] Among them, k(M) is a parameter that is updated with the iteration cycle. Its updating principle is to obtain an estimated discrete route iteration number R based on the straight-line distance from the starting point to the end point. When the ratio of the current iteration cycle to R becomes closer to 1, the coefficient of the heuristic function will be larger. The estimated number of iterations for the path is determined by the iteration cycle required to assume that there is no obstacle between the starting point and the end point. For the current iteration cycle, =0.5.

[0053] The overall algorithm logic is as follows: (1) Extract the coordinates of the starting point and the end point; (2) Calculate the Chebyshev distance from the starting point to the end point to obtain the expected iteration period R updated in the heuristic function; (3) Input the starting point as the discrete starting point; (4) Extracting the corresponding spatiotemporal flow field information from the transient urban flow field information of the time series; (5) The path consumption in the A* path algorithm is calculated by constructing an energy consumption model under the influence of the low-altitude flow field and the lowest cost point is defined as the discrete end point. The cost includes the path consumption and the heuristic cost obtained by the heuristic function: ; Among them, f(N) is the total cost obtained from the previous node, g(M) is the path cost of moving to this node, and h(M) is the heuristic cost of this node, which is used to characterize whether the path planning process is moving towards the end point.

[0054] (6) Set the discrete end point as the discrete starting point of a new round of iteration; (7) Find the path planning with the lowest energy consumption from the starting point s to the end point In summary, based on the traditional A* algorithm, this embodiment improves its modules related to cost calculation and heuristic function, so that the currently used method can effectively incorporate the impact of the wind field on UAV flight into path planning under the condition of known urban wind field information.

[0055] In this embodiment, by obtaining real-time transient wind field data of the target airspace, and based on the real-time transient wind field data, a fluid mechanics simulation model is used to generate time-space correlated flow field information; then, a flight energy consumption model is established based on the flow field information, and the flight energy consumption model is used to calculate the energy consumption in different stages to obtain the node energy consumption attribute value; the node energy consumption attribute value is then input into the improved A-star search algorithm model to obtain the current minimum energy consumption path planning as the current path, thereby improving the accuracy of route planning and saving route energy consumption.

[0056] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0057] Figure 4 The principle block diagram of the UAV route planning device is shown in one-to-one correspondence with the UAV route planning method in the above embodiment. Figure 4 As shown, the UAV route planning device includes a flow field information generation module 31, an energy consumption calculation module 32 and a path selection module 33. The functional modules are described in detail as follows: The flow field information generation module 31 is used to obtain the real-time transient wind field data of the UAV and generate spatiotemporally correlated flow field information based on the real-time transient wind field data; The energy consumption calculation module 32 is used to establish a flight energy consumption model based on the flow field information, and use the flight energy consumption model to calculate the energy consumption in different stages to obtain node energy consumption attribute values. The different stages include level flight, climb, and descent. The path selection module 33 is used to input the node energy consumption attribute value into the heuristic search algorithm model to obtain the current minimum energy consumption path planning.

[0058] Optionally, the energy consumption calculation module 32 includes: A level flight energy consumption calculation unit is used to determine the windward area of ​​the UAV based on the wind field direction component of the coordinate point on the UAV surface, and calculate the wind resistance energy consumption during the level flight stage based on the windward area of ​​the UAV and the wind field velocity component on the UAV surface; The lifting energy consumption calculation unit is used to collect the change in the gravitational potential energy of the UAV, and calculate the energy consumption of the climbing stage and the energy consumption of the descending stage through the change in gravitational potential energy and the wind resistance energy consumption during the level flight stage; The node energy consumption determination unit is used to take the wind resistance energy consumption in the level flight phase, the energy consumption in the climbing phase and the energy consumption in the descending phase as the node energy consumption attribute value.

[0059] Optionally, the level flight energy consumption calculation unit includes: The transverse component determination subunit is used to decompose the wind field velocity component into the longitudinal component of the horizontal heading of the UAV and the transverse component of the vertical heading according to the wind direction parameter; The basic drag calculation subunit is used to determine the basic drag parameters according to the product of air density, UAV drag coefficient and UAV frontal area; The real-time wind resistance energy consumption calculation subunit is used to superimpose the longitudinal component with the current flight speed vector of the UAV, square it, and multiply it by the basic resistance parameter to obtain the real-time wind resistance energy consumption value as the wind resistance energy consumption in the level flight stage.

[0060] Optionally, the lifting energy consumption calculation unit includes: The gravity potential energy change determination subunit is used to calculate the gravity potential energy change during the climbing and descending phases based on the mass of the UAV and the gravitational acceleration; The energy consumption dynamic correction subunit is used to dynamically correct the wind resistance energy consumption in the level flight stage through the change in gravitational potential energy to obtain the energy consumption in the climbing stage and the energy consumption in the descending stage.

[0061] Optionally, the flow field information generating module 31 includes: A coordinate alignment unit is used to perform fluid dynamics simulation on real-time transient wind field data and align the simulation results with the spatial coordinate system of the electronic airspace map, where the electronic airspace map is a three-dimensional spatial map of the UAV aircraft; The flow field information generation unit is used to generate a three-dimensional vector layer in the airspace below 80 meters to obtain flow field information. The time dimension update frequency of the vector layer is determined by the time interval of the timestamp information, and each timestamp corresponds to an independent wind field vector distribution. The wind field vector distribution includes the wind field velocity component and wind field direction component at different coordinate points.

[0062] For the specific definition of the UAV route planning device, please refer to the definition of the UAV route planning method above, which will not be repeated here. The various modules in the above-mentioned UAV route planning device can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0063] To solve the above technical problems, the present application also provides a computer device. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0064] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components connected to the memory 41, the processor 42, and the network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0065] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0066] The memory 41 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, card-type memory (e.g., SD or D-interface display memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or internal memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. Of course, the memory 41 may also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as the program code of the drone route planning method. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0067] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute program code or process data stored in the memory 41, such as executing the program code for the drone route planning method.

[0068] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0069] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores an interface display program, and the interface display program can be executed by at least one processor to enable the at least one processor to perform the steps of the drone route planning method as described above.

[0070] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of this application.

[0071] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for planning a UAV route, characterized in that: include: Acquire real-time transient wind field data from the UAV, and generate spatiotemporally correlated flow field information based on the real-time transient wind field data; A flight energy consumption model is established based on the flow field information, and energy consumption in different stages is calculated using the flight energy consumption model to obtain node energy consumption attribute values, where the different stages include level flight, climb, and descent. The node energy consumption attribute value is input into the heuristic search algorithm model to obtain the current minimum energy consumption path planning.

2. The UAV route planning method according to claim 1, wherein: The flow field information includes real-time three-dimensional spatial coordinates and wind field vector distribution corresponding to the three-dimensional spatial coordinates. The wind field vector distribution includes wind field velocity components and wind field direction components at different coordinate points. The energy consumption at different stages is calculated using the flight energy consumption model to obtain node energy consumption attribute values ​​including: Based on the wind field direction component of the coordinate points on the UAV surface, the UAV's frontal area is determined, and the wind resistance energy consumption in the level flight stage is calculated based on the UAV's frontal area and the wind field velocity component on the UAV surface; Collecting the change in gravitational potential energy of the drone, and calculating the energy consumption during the climb phase and the energy consumption during the descent phase based on the change in gravitational potential energy and the wind resistance energy consumption during the level flight phase; The wind resistance energy consumption in the level flight phase, the energy consumption in the climbing phase, and the energy consumption in the descending phase are used as the node energy consumption attribute values.

3. The UAV route planning method according to claim 2, wherein: The real-time transient wind field data includes wind direction parameters, and the calculation of wind resistance energy consumption in the level flight phase based on the windward area of ​​the UAV and the wind field velocity component includes: Decomposing the wind field velocity component into a longitudinal component of the horizontal heading of the UAV and a transverse component of the vertical heading according to the wind direction parameter; The basic drag parameter is determined by the product of air density, UAV drag coefficient and UAV frontal area; The longitudinal component is superimposed with the current flight speed vector of the UAV, the result is squared, and the result is multiplied by the basic resistance parameter to obtain a real-time wind resistance energy consumption value, which is used as the wind resistance energy consumption in the level flight stage.

4. The UAV route planning method according to claim 2, wherein: The introduction of the gravitational potential energy change, combined with the wind resistance energy consumption in the level flight phase, and calculation of the energy consumption in the climbing phase and the energy consumption in the descending phase include: Calculate the change in gravitational potential energy during the climbing and descending phases based on the mass of the drone and the acceleration of gravity; The wind resistance energy consumption in the level flight stage is dynamically corrected by the change in the gravitational potential energy to obtain the energy consumption in the climbing stage and the energy consumption in the descending stage.

5. The UAV route planning method according to any one of claims 1 to 4, characterized in that: The heuristic search algorithm model dynamically updates the heuristic function by updating the coefficients in the original heuristic function as the number of iterations increases, including: Calculating the Chebyshev distance based on the spatial coordinate difference between a starting point and an end point, wherein the starting point and the end point are the starting point and the end point of an initial path generated after inputting the node energy consumption attribute value into a heuristic search algorithm model; Determining an estimated total iteration period R based on the Chebyshev distance, wherein the estimated total iteration period R is the total number of iterations required for dynamically updating the heuristic function; Linear interpolation is performed based on the ratio of the current number of iterations r to the estimated total iteration period R, and nonlinear correction is performed on the real-time energy consumption difference of adjacent nodes to update the weight coefficient of the heuristic function in real time. The current number of iterations r is the number of iterations from the first update of the heuristic function to this iteration.

6. The UAV route planning method according to claim 1, wherein: Generating spatiotemporally correlated flow field information according to the real-time transient wind field data includes: Performing a fluid dynamics simulation on the real-time transient wind field data and aligning the simulation results with a spatial coordinate system of an electronic airspace map, wherein the electronic airspace map is a three-dimensional spatial map of the UAV aircraft; A three-dimensional vector layer is generated in the airspace below 80 meters to obtain the flow field information, wherein the time dimension update frequency of the vector layer is determined by the time interval of the timestamp information, and each timestamp corresponds to an independent wind field vector distribution, and the wind field vector distribution includes wind field velocity components and wind field direction components at different coordinate points.

7. A UAV route planning device, characterized in that: include: A flow field information generation module is used to obtain real-time transient wind field data of the UAV and generate spatiotemporally correlated flow field information based on the real-time transient wind field data; an energy consumption calculation module, configured to establish a flight energy consumption model based on the flow field information, and use the flight energy consumption model to calculate energy consumption in different stages to obtain node energy consumption attribute values, where the different stages include level flight, climb, and descent; The path selection module is used to input the node energy consumption attribute value into the heuristic search algorithm model to obtain the current minimum energy consumption path planning.

8. The UAV route planning device according to claim 7, characterized in that: The energy consumption calculation module includes: A level flight energy consumption calculation unit is used to determine the windward area of ​​the UAV based on the wind field direction component of the coordinate point on the UAV surface, and calculate the wind resistance energy consumption during the level flight stage based on the windward area of ​​the UAV and the wind field velocity component on the UAV surface; A lifting energy consumption calculation unit is used to collect the change in gravitational potential energy of the UAV, and calculate the energy consumption of the climbing stage and the energy consumption of the descending stage based on the change in gravitational potential energy and the wind resistance energy consumption in the level flight stage; The node energy consumption determination unit is used to use the wind resistance energy consumption in the level flight phase, the energy consumption in the climbing phase and the energy consumption in the descending phase as the node energy consumption attribute value.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the UAV route planning method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the UAV route planning method according to any one of claims 1 to 6 is implemented.

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