UAV path planning method, device, equipment and readable storage medium

Abstract

The present application discloses a method, device, equipment and readable storage medium for drone path planning, which meshes the target area, responds to the user's drone flight setting operation, selects the target starting point and the target end point from each grid vertex; determines the vertex set corresponding to the target grid point; deletes the adjacent grid vertices that need to pass through the no-fly grid for the straight path between the target grid point and the vertex set; calculates the flight path distance between the target grid point and each adjacent grid vertex; takes each adjacent grid vertex in the vertex set as a new target grid point in turn, and returns to execute the step of determining the vertex set until the target end point is the adjacent grid vertex of the latest target grid point; generates the drone path based on the target flight path distance required to reach the target end point from the target starting point. It can be seen that the present application reduces the redundancy of the drone flight path through the synergy of meshing, no-fly zone marking, and vertex iterative pathfinding.

Description

Technical Field

[0001] The present application relates to the field of drone flight technology, and more specifically, to a drone path planning method, device, equipment and readable storage medium. Background Art

[0002] In the context of the rapid development of drone technology today, drone application systems have been widely deployed in many regions. In the drone application process, flight path planning constitutes an indispensable key link. Within the current technical framework, the conventional practice is to locate the starting point of the drone flight as the core coordinate point of the starting grid, and correspond the flight end point to the center coordinate of the end grid, and then plan the feasible flight trajectory from the starting grid to the end grid, so as to achieve the drone flight path planning task. However, in this traditional mode, the flight direction of the drone is basically limited to pointing directly from the center of the starting grid to the center of the adjacent grid. The inherent defect of this flight direction setting mechanism is that the drone is forced to move accurately from one grid center to another during the actual flight process. However, considering the flight principle of drones and actual operational requirements, drones do not have to accurately reach the center of the grid to have the ability to turn or endurance. This limitation directly leads to the inevitable embedding of a large number of redundant flight sections in the planned drone flight path, which ultimately leads to unnecessary extension of the overall flight path mileage. In view of this, in the field of drone technology, how to innovatively design a drone path planning strategy that can more effectively reduce flight path redundancy and optimize flight path length has become a core technical point and research hotspot focused on by R&D personnel in the industry. Summary of the invention

[0003] In view of this, the present application provides a drone path planning method, device, equipment and readable storage medium to address the shortcomings of the prior art that a large number of redundant flight segments are embedded in the drone flight path.

[0004] In order to achieve the above objectives, the proposed solution is as follows:

[0005] A UAV path planning method, comprising:

[0006] Meshing the target area;

[0007] In response to the user's drone flight setting operation, a target start point and a target end point are selected from each grid vertex;

[0008] Mark the no-fly grids based on the flight properties of the drone and the environmental data of each grid;

[0009] Taking the target starting point as the target grid point;

[0010] Determine a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point;

[0011] Deleting, from the vertex set, adjacent grid vertices whose straight line path to the target grid point needs to pass through the no-fly grid;

[0012] Calculating a flight path distance between the target grid point and each adjacent grid vertex in the vertex set;

[0013] Taking each adjacent mesh vertex in the vertex set as a new target grid point in turn, and returning to the step of determining a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point, until the target end point is an adjacent mesh vertex of the latest target grid point;

[0014] In combination with a path-finding algorithm, a drone path is generated based on the target flight path distance required to reach the target end point from the target starting point.

[0015] Optionally, marking a no-fly grid based on the flight attributes of the drone and the environmental data of each grid includes:

[0016] Determine the wind speed, precipitation and temperature of each grid in real time, and determine the maximum wind resistance, heat resistance and rain resistance of the drone;

[0017] The grids corresponding to wind speed exceeding the maximum wind resistance, corresponding precipitation exceeding the rain resistance and / or corresponding temperature exceeding the heat resistance are marked as no-fly grids.

[0018] Optionally, determining a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point includes:

[0019] A vertex set including at least five adjacent mesh vertices corresponding to the target grid point is determined.

[0020] Optionally, determining a vertex set including at least five adjacent mesh vertices corresponding to the target grid point includes:

[0021] Determine all first grids with the target grid point as grid vertices;

[0022] Determine all second meshes that share a common edge with any first mesh;

[0023] All grid vertices except the target grid point in each first grid are taken as a plurality of adjacent grid vertices;

[0024] Taking the grid vertices farthest from the target grid point in each second grid as a plurality of adjacent grid vertices;

[0025] All adjacent mesh vertices constitute the vertex set corresponding to the target grid point.

[0026] Optionally, calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set includes:

[0027] Based on the haversine formula, the flight path distance between the target grid point and each adjacent grid vertex in the vertex set is calculated.

[0028] Optionally, after calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set, the method further includes:

[0029] Determine the flight speed of the drone;

[0030] For each adjacent grid vertex in the vertex set, based on the grid wind speed of the target grid point, determine the wind speed projection value of the flight from the target grid point to the adjacent grid vertex; superimpose the wind speed projection value on the flight speed to obtain the moving speed after superposition; and calculate the flight time of the UAV from the target grid point to the adjacent grid vertex based on the moving speed and the flight path distance corresponding to the adjacent grid vertex;

[0031] In combination with the path-finding algorithm, based on the target flight path distance required to reach the target end point from the target starting point, the drone path is generated, including:

[0032] In combination with a path-finding algorithm, the target flight path distance required to fly from the target starting point to the target end point is accumulated to generate multiple flight paths;

[0033] The flight time corresponding to the flight path distance of each target in each flight path is accumulated, and the path with the shortest time is selected from each flight path as the UAV path.

[0034] Optionally, determining a wind speed projection value from the target grid point to the adjacent grid vertex based on the grid wind speed of the target grid point includes:

[0035] Combined with the vector product formula, the wind speed projection value of the grid wind speed from the target grid point to the adjacent grid vertex is determined.

[0036] A UAV path planning device, comprising:

[0037] A target area mesh generation module is used to perform mesh generation on the target area;

[0038] The target starting point determination module is used to respond to the user's UAV flight setting operation and select the target starting point and target end point from each grid vertex;

[0039] The no-fly grid marking module is used to mark the no-fly grids based on the flight properties of the drone and the environmental data of each grid;

[0040] A target grid point determination module, used to take the target starting point as the target grid point;

[0041] A vertex set determination module, used to determine a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point;

[0042] An adjacent mesh vertex deletion module is used to delete, from the vertex set, adjacent mesh vertices whose straight line paths to the target grid point need to pass through the no-fly grid;

[0043] A flight path distance calculation module is used to calculate the flight path distance between the target grid point and each adjacent grid vertex in the vertex set; each adjacent grid vertex in the vertex set is used as a new target grid point in turn, and the vertex set determination module and its subsequent modules are called until the target end point is the adjacent grid vertex of the latest target grid point;

[0044] The drone path generation module is used to combine the path-finding algorithm to accumulate the flight path distance required to reach the target end point from the target starting point to generate the drone path.

[0045] A UAV path planning device, comprising a memory and a processor;

[0046] The memory is used to store programs;

[0047] The processor is used to execute the program to implement each step of the above-mentioned drone path planning method.

[0048] A readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, each step of the above-mentioned drone path planning method is implemented.

[0049] It can be seen from the above technical solutions that the drone path planning method provided by the present application can mesh the target area; in response to the user's drone flight setting operation, the target starting point and the target end point are selected from each grid vertex; based on this, the target starting point and the target end point can be deployed in the grid vertices of different grids to facilitate accurate analysis of the path. Subsequently, the no-fly grid can be marked based on the flight properties of the drone and the environmental data of each grid; the target starting point is used as the target grid point; the vertex set corresponding to the target grid point containing multiple adjacent grid vertices is determined; the adjacent grid vertices of the no-fly grid that the straight path between the target grid point needs to pass through are deleted from the vertex set; based on this, the present application can eliminate the invalid path exploration direction caused by the no-fly area from the root by deleting the adjacent grid vertices of the no-fly grid that the straight path between the target grid points needs to pass through. This prevents the drone from falling into a no-fly zone or being forced to take a detour due to being close to a no-fly zone when planning a path, thereby reducing the detours and unnecessary turns of the path and effectively reducing path redundancy. Then, the flight path distance between the target grid point and each adjacent grid vertex in the vertex set can be calculated. Each adjacent grid vertex in the vertex set is taken as a new target grid point in turn, and the step of determining the vertex set containing multiple adjacent grid vertices corresponding to the target grid point is returned to execute until the target end point is the adjacent grid vertex of the latest target grid point. Combined with the path-finding algorithm, the drone path is generated based on the target flight path distance required to reach the target end point from the target starting point. Based on this, the present application can sequentially take the adjacent grid vertices in the vertex set as new target grid points and iterate continuously until the target end point becomes an adjacent grid vertex, so that the drone can move directly from one grid vertex to another, which is convenient for the drone to flexibly select a path. Through continuous optimization movement between multiple vertices, redundant branch paths are eliminated, and the generation of redundant paths is further reduced. It can be seen that the present application can significantly reduce the redundancy of UAV flight paths and improve the efficiency and quality of UAV flight path planning through the synergistic effect of a series of technical means such as grid division, no-fly zone marking, vertex iterative pathfinding, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0051] Figure 1 A flow chart of a drone path planning method disclosed in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of a vertex set disclosed in an embodiment of the present application;

[0053] Figure 3 This is a structural block diagram of a drone path planning device disclosed in an embodiment of the present application;

[0054] Figure 4 This is a hardware structure block diagram of a drone path planning device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] The present application embodiment provides a method for drone path planning. The drone path planning method can be applied to various drone systems or flight systems, and can also be applied to various computer terminals or smart terminals. The execution subject can be a processor or server of the computer terminal or smart terminal. The method flow chart of the drone path planning method is as follows: Figure 1 As shown, specifically including:

[0057] Next, combine Figure 1 The UAV path planning method of this application is introduced in detail, including the following steps:

[0058] Step S1, meshing the target area.

[0059] Specifically, with a resolution of 0.05°×0.05° and combined with the networkx library, the target area can be meshed to obtain multiple grids.

[0060] Step S2: In response to the user's drone flight setting operation, a target starting point and a target end point are selected from each grid vertex.

[0061] Specifically, in response to the user's operation of setting the UAV flight starting point, a target starting point can be selected from each grid vertex.

[0062] Similarly, the target endpoint can be selected from each grid vertex in response to the user's operation of setting the drone's flight endpoint.

[0063] Step S3: Mark the no-fly grid based on the flight attributes of the UAV and the environmental data of each grid.

[0064] Specifically, the flight properties of the drone, such as wind resistance, flight speed, working environment temperature, whether it can fly in a rainy environment, and environmental data such as wind speed, precipitation, and temperature field of each grid can be comprehensively referred to to determine whether the drone can fly normally in each grid. If the drone cannot fly normally, the corresponding grid will be marked as a no-fly grid to avoid drone flight failures.

[0065] Step S4: taking the target starting point as the target grid point.

[0066] Specifically, the target starting point may be taken as the first target grid point.

[0067] Step S5: Determine a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point.

[0068] Step S6: delete, from the vertex set, adjacent grid vertices whose straight line path to the target grid point needs to pass through the no-fly grid.

[0069] Specifically, the target grid point and each adjacent grid vertex in the vertex set may be connected in sequence to determine whether the UAV will pass through the no-fly grid when flying from the target grid point to the adjacent grid vertex;

[0070] If so, delete the corresponding adjacent mesh vertex from the vertex set;

[0071] If not, keep the corresponding adjacent mesh vertex in the vertex set.

[0072] When different mesh vertices are used as target grid points, the vertex sets corresponding to the target grid points change accordingly.

[0073] Step S7: Calculate the flight path distance between the target grid point and each adjacent grid vertex in the vertex set.

[0074] Specifically, the distance calculation algorithm may be combined to calculate the straight-line distance between the target grid point and each adjacent grid vertex in the vertex set as the flight path distance between the target grid point and each adjacent grid vertex in the vertex set.

[0075] Step S8, taking each adjacent mesh vertex in the vertex set as a new target grid point in turn, and returning to execute step S5 until the target end point is the adjacent mesh vertex of the latest target grid point.

[0076] Specifically, after obtaining the flight path distance between the target grid point and each adjacent grid vertex in the vertex set, it can be determined whether the vertex set contains the target end point. If not, the target grid point can be iterated, each adjacent grid vertex in the vertex set is used as a new target grid point, and the process returns to step S5.

[0077] If yes, execute step S9.

[0078] Step S9: In combination with the path-finding algorithm, a drone path is generated based on the target flight path distance required to reach the target end point from the target starting point.

[0079] Specifically, a pathfinding algorithm such as the dijkstra algorithm, Bellman-Ford algorithm, Johnson algorithm, Floyd-Warshall algorithm or improved A* algorithm can be combined to determine the grid vertices that need to be passed from the target starting point to the target end point, generate multiple paths, and accumulate the flight path distances between the grid vertices that each path needs to pass through. Based on the accumulated results corresponding to each path, the path with the shortest distance is selected from each path as the UAV path;

[0080] It is also possible to combine the path-finding algorithm to determine the grid vertices that need to be passed from the target starting point to the target end point, generate multiple paths, and accumulate the flight path distance and flight time between the grid vertices that each path needs to pass. Based on the distance accumulation results and flight time accumulation results corresponding to each path, the path with the shortest flight time accumulation result is selected from each path as the drone path.

[0081] It can be seen from the above technical solutions that the drone path planning method provided by the present application can mesh the target area; in response to the user's drone flight setting operation, the target starting point and the target end point are selected from each grid vertex; based on this, the target starting point and the target end point can be deployed in the grid vertices of different grids to facilitate accurate analysis of the path. Subsequently, the no-fly grid can be marked based on the flight properties of the drone and the environmental data of each grid; the target starting point is used as the target grid point; the vertex set corresponding to the target grid point containing multiple adjacent grid vertices is determined; the adjacent grid vertices of the no-fly grid that the straight path between the target grid point needs to pass through are deleted from the vertex set; based on this, the present application can eliminate the invalid path exploration direction caused by the no-fly area from the root by deleting the adjacent grid vertices of the no-fly grid that the straight path between the target grid points needs to pass through. This prevents the drone from falling into a no-fly zone or being forced to take a detour due to being close to a no-fly zone when planning a path, thereby reducing the detours and unnecessary turns of the path and effectively reducing path redundancy. Then, the flight path distance between the target grid point and each adjacent grid vertex in the vertex set can be calculated. Each adjacent grid vertex in the vertex set is taken as a new target grid point in turn, and the step of determining the vertex set containing multiple adjacent grid vertices corresponding to the target grid point is returned to execute until the target end point is the adjacent grid vertex of the latest target grid point. Combined with the path-finding algorithm, the drone path is generated based on the target flight path distance required to reach the target end point from the target starting point. Based on this, the present application can sequentially take the adjacent grid vertices in the vertex set as new target grid points and iterate continuously until the target end point becomes an adjacent grid vertex, so that the drone can move directly from one grid vertex to another, which is convenient for the drone to flexibly select a path. Through continuous optimization movement between multiple vertices, redundant branch paths are eliminated, and the generation of redundant paths is further reduced. It can be seen that the present application can significantly reduce the redundancy of UAV flight paths and improve the efficiency and quality of UAV flight path planning through the synergistic effect of a series of technical means such as grid division, no-fly zone marking, vertex iterative pathfinding, etc.

[0082] In some embodiments of the present application, step S3, the process of marking a no-fly grid based on the flight attributes of the drone and the environmental data of each grid, is described in detail, and the steps are as follows:

[0083] S30, determining the wind speed, precipitation and temperature of each grid in real time, and determining the maximum wind resistance, heat resistance and rain resistance of the drone.

[0084] Specifically, the wind speed, precipitation and temperature of each grid can be determined in real time based on the weather forecast.

[0085] The no-fly altitude, no-fly zone, maximum wind resistance, heat resistance and rain resistance of the drone can be determined according to the model of the drone.

[0086] S31. Mark the grids whose corresponding wind speed exceeds the maximum wind resistance, whose corresponding precipitation exceeds the rain resistance, and / or whose corresponding temperature exceeds the heat resistance as no-fly grids.

[0087] Specifically, when any grid belongs to a no-fly zone, a no-fly altitude, the corresponding wind speed exceeds the maximum wind resistance, the corresponding precipitation exceeds the rain resistance and / or the corresponding temperature exceeds the heat resistance, the corresponding grid will be marked as a no-fly grid.

[0088] For example, the maximum wind speed of a drone is 10.7m / s, and the working environment temperature is -10-40°C. Therefore, grids with 900hPa wind speed greater than 10.7m / s, 2m temperature lower than -10°C, 2m temperature higher than 40°C, and / or precipitation greater than 0 are set as no-fly grids.

[0089] It can be seen from the above technical solution that this embodiment provides an optional method of marking no-fly grids based on the flight properties of the drone and the environmental data of each grid. Through the above method, the no-fly judgment can be made by comprehensively referring to the grid wind speed, grid rainfall and grid temperature, further ensuring the flight safety of the drone of this application.

[0090] In some embodiments of the present application, the process of step S5, determining a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point, is described in detail, and the steps are as follows:

[0091] S50: Determine a vertex set including at least five adjacent mesh vertices corresponding to the target grid point.

[0092] Specifically, the mesh vertices corresponding to the mesh containing the target mesh point and the mesh vertices of other meshes sharing edges with the mesh containing the target mesh point can be used as adjacent mesh vertices of the target mesh point, and the adjacent mesh vertices of the target mesh point are combined into a vertex set.

[0093] If the target grid point is located at the boundary corner of the target area, the number of adjacent grid vertices of the target grid point may be five; if the target grid point is located at the area edge of the target area, the number of adjacent grid vertices of the target grid point may be nine. Generally, the number of adjacent grid vertices of the target grid point may be sixteen.

[0094] It can be seen from the above technical solution that this embodiment provides an optional method for determining the vertex set corresponding to the target grid point, which includes multiple adjacent grid vertices. Through the above method, more than five adjacent grid vertices can be collected for the target grid point, and the directed edge from the target grid point to each adjacent grid vertex is the flight direction of the UAV. Therefore, by setting more than five adjacent grid vertices, more than five flight directions can be provided for the UAV, further reducing the detours and unnecessary turns of the UAV path, thereby reducing path redundancy.

[0095] In some embodiments of the present application, the process of step S50, determining a vertex set including at least five adjacent mesh vertices corresponding to the target grid point, is described in detail, and the steps are as follows:

[0096] S500: Determine all first grids that use the target grid point as grid vertices.

[0097] Specifically, there is a grid vertex in each first grid that belongs to the target grid point.

[0098] See also Figure 2 It can be found that the third row and the third column is the target grid point. Generally, the target grid point is surrounded by four first grids.

[0099] S501: Determine all second meshes that share a common edge with any first mesh.

[0100] Specifically, for each first grid, other grids that share a grid edge with the first grid and do not belong to the first grid may be determined as second grids.

[0101] See also Figure 2 It can be found that, generally, each first grid may correspond to two second grids.

[0102] S502: All grid vertices except the target grid point in each first grid are taken as a plurality of adjacent grid vertices.

[0103] Specifically, the remaining three mesh vertices except the target grid point among the four mesh vertices of each first mesh may be used as three adjacent mesh vertices.

[0104] S503: Taking the grid vertices farthest from the target grid point in each second grid as a plurality of adjacent grid vertices.

[0105] Specifically, a grid vertex in each second grid that is farthest from the target grid point may be used as an adjacent grid vertex.

[0106] The directed edges from the target grid point to the adjacent grid vertices can be the flight directions that the drone can choose when it starts from the target grid point, such as Figure 2At this point, the UAV can have sixteen flight directions at the target grid point.

[0107] S504: All adjacent mesh vertices form the vertex set corresponding to the target grid point.

[0108] Specifically, all adjacent mesh vertices may be combined to form a vertex set corresponding to the target grid point.

[0109] See also Figure 2 It can be found that, in general, the number of adjacent mesh vertices can be sixteen.

[0110] It can be seen from the above technical solution that this embodiment provides an optional method for determining the vertex set corresponding to the target grid point, which contains at least five adjacent grid vertices. Generally, the above method can expand the flight direction of the UAV to sixteen, further improving the selectivity of the UAV flight direction of the present application and further reducing the generation of redundant paths.

[0111] In some embodiments of the present application, the process of calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set in step S7 is described in detail, and the steps are as follows:

[0112] S70. Based on the haversine formula, calculate the flight path distance between the target grid point and each adjacent grid vertex in the vertex set.

[0113] Specifically, when a drone flies in a three-dimensional space or an approximately spherical environment such as the earth's surface, each grid vertex has corresponding longitude and latitude coordinates.

[0114] The path-finding algorithm can be combined with the haversine formula. By substituting the longitude and latitude information of the target grid point and each adjacent grid vertex into the haversine formula, the distance between the two vertices can be calculated. The flight path distance between the target grid point and each adjacent grid vertex in the vertex set can be calculated.

[0115] It can be seen from the above technical solution that this embodiment provides an optional method for calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set. Through the above method, the actual situation of the geographical space in which the drone is located can be further considered, thereby further improving the reliability of the flight path distance of the present application.

[0116] In some embodiments of the present application, in consideration of actual flight conditions, during the process of path planning, the focus is not only on the shortest path, but also on the shortest flight time. Therefore, the present application may also add a process of calculating the flight time and planning the path with the shortest flight time. Next, this process will be described in detail.

[0117] After calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set in step S7, the following steps may be added to calculate the flight time:

[0118] S70: Determine the flight speed of the UAV.

[0119] Specifically, the flight speed corresponding to the UAV can be determined.

[0120] S71. For each adjacent grid vertex in the vertex set, based on the grid wind speed of the target grid point, determine the wind speed projection value from the target grid point to the adjacent grid vertex; superimpose the wind speed projection value on the flight speed to obtain the moving speed; based on the moving speed and the flight path distance corresponding to the adjacent grid vertex, calculate the flight time of the UAV from the target grid point to the adjacent grid vertex.

[0121] Specifically, the wind speed corresponding to the target grid point can be determined, and the grid wind speed can be projected onto the directed edge between the target grid point and each adjacent grid vertex to obtain the wind speed projection value of each adjacent grid vertex;

[0122] Each wind speed projection value is superimposed on the UAV's flight speed to obtain the UAV's moving speed to the corresponding adjacent grid vertex;

[0123] Based on the flight path distance corresponding to each adjacent mesh vertex and its corresponding moving speed, the flight time of the UAV to the corresponding adjacent mesh vertex is calculated.

[0124] In this case, step S9, in combination with the path finding algorithm, based on the target flight path distance required to reach the target end point from the target starting point, the process of generating the drone path may include:

[0125] S90. In combination with a path-finding algorithm, a drone path is generated based on the target flight path distance required to reach the target end point from the target starting point and the flight time corresponding to each target flight path distance.

[0126] Specifically, the path-finding algorithm can be combined to generate multiple paths from the target starting point to the target end point, accumulate the flight time required for each path, and select the path with the shortest time from each path as the drone path.

[0127] It can be seen from the above technical solution that compared with the previous embodiment, this embodiment provides an optional method for calculating flight time and selecting a flight path. Through the above method, the present application can not only generate a drone path with the shortest distance, but also generate a drone path with the shortest flight time, thereby further improving the wide application of the present application.

[0128] In some embodiments of the present application, the process of determining the wind speed projection value from the target grid point to the adjacent grid vertex based on the grid wind speed of the target grid point in step S71 is described in detail, and the steps are as follows:

[0129] S710. Determine the wind speed projection value of the grid wind speed from the target grid point to the adjacent grid vertex in combination with the vector product formula.

[0130] Specifically, the wind speed corresponding to the target grid point can be determined, and based on the direction of the vector between the target grid point and each adjacent grid vertex, the vector product formula of each adjacent grid vertex can be determined;

[0131] Based on the vector product formula, the grid wind speed is projected onto the directed edge between the target grid point and each adjacent grid vertex to obtain the wind speed projection value of each adjacent grid vertex.

[0132] It can be seen from the above technical solution that this embodiment provides an optional method for calculating the wind speed projection value. The above method can be combined with the vector formula to calculate the wind speed projection value, further improving the standardization of the wind speed projection value calculated in this application.

[0133] Next, we will combine Figure 3 The UAV path planning device provided in the present application is introduced in detail. The UAV path planning device provided below can be compared with the UAV path planning method provided above.

[0134] See also Figure 3 It can be found that the UAV path planning device may include:

[0135] A target area mesh generation module 10 is used to perform mesh generation on the target area;

[0136] The target starting point determination module 20 is used to select a target starting point and a target end point from each grid vertex in response to the user's UAV flight setting operation;

[0137] A no-fly grid marking module 30 is used to mark a no-fly grid based on the flight properties of the UAV and the environmental data of each grid;

[0138] A target grid point determination module 40, configured to use the target starting point as a target grid point;

[0139] A vertex set determination module 50, used to determine a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point;

[0140] An adjacent mesh vertex deletion module 60 is used to delete, from the vertex set, adjacent mesh vertices whose straight line paths to the target grid point need to pass through a no-fly grid;

[0141] The flight path distance calculation module 70 is used to calculate the flight path distance between the target grid point and each adjacent grid vertex in the vertex set; each adjacent grid vertex in the vertex set is used as a new target grid point in turn, and the vertex set determination module 50 and its subsequent modules are called until the target end point is the adjacent grid vertex of the latest target grid point;

[0142] The drone path generation module 80 is used to generate a drone path by combining a path-finding algorithm to accumulate the flight path distance required to reach the target end point from the target starting point.

[0143] Further, the no-fly grid marking module may include:

[0144] A wind speed determination unit, for determining the wind speed, precipitation and temperature of each grid in real time, and determining the maximum wind resistance, heat resistance and rain resistance of the UAV;

[0145] The wind speed comparison unit is used to mark the grids whose corresponding wind speed exceeds the maximum wind resistance, whose corresponding precipitation exceeds the rain resistance and / or whose corresponding temperature exceeds the heat resistance as no-fly grids.

[0146] Further, the vertex set determination module may include:

[0147] The adjacent mesh vertex determination unit is used to determine a vertex set including at least five adjacent mesh vertices corresponding to the target grid point.

[0148] Further, the adjacent mesh vertex determination unit may include:

[0149] A first adjacent mesh vertex determination component, used to determine all first meshes with the target grid point as mesh vertices;

[0150] A second adjacent mesh vertex determination component, used to determine all second meshes that share a common edge with any first mesh;

[0151] A third adjacent mesh vertex determination component, used for taking all mesh vertices except the target grid point in each first mesh as a plurality of adjacent mesh vertices;

[0152] A fourth adjacent mesh vertex determination component, used to use the mesh vertices farthest from the target grid point in each second mesh as a plurality of adjacent mesh vertices;

[0153] The fifth adjacent mesh vertex determination component is used for all adjacent mesh vertices to form the vertex set corresponding to the target grid point.

[0154] Further, the flight path distance calculation module may include:

[0155] The haversine formula utilizing unit is used to calculate the flight path distance between the target grid point and each adjacent grid vertex in the vertex set based on the haversine formula.

[0156] Furthermore, the UAV path planning device may also include:

[0157] A flight speed determination unit, used to determine the flight speed of the UAV;

[0158] A flight time calculation unit is used to determine, for each adjacent grid vertex in the vertex set, a wind speed projection value of flying from the target grid point to the adjacent grid vertex based on the grid wind speed of the target grid point; superimpose the wind speed projection value on the flight speed to obtain a moving speed after superposition; and calculate the flight time of the UAV flying from the target grid point to the adjacent grid vertex based on the moving speed and the flight path distance corresponding to the adjacent grid vertex;

[0159] At this point, the drone path generation module may include:

[0160] A flight path generation unit, used to generate multiple flight paths by accumulating the target flight path distance required to fly from the target starting point to the target end point in combination with a path finding algorithm;

[0161] The UAV path selection unit is used to accumulate the flight time corresponding to the distance of each target flight path in each flight path, and select the path with the shortest time from each flight path as the UAV path.

[0162] Further, the flight time calculation unit may include:

[0163] The wind speed projection value calculation component is used to determine the wind speed projection value of the grid wind speed flying from the target grid point to the adjacent grid vertex in combination with the vector product formula.

[0164] The drone path planning device provided in the embodiment of the present application can be applied to drone path planning devices, such as PC terminals, cloud platforms, servers and server clusters. Figure 4 The hardware structure diagram of the UAV path planning device is shown in FIG. Figure 4 ,The hardware structure of the UAV path planning device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0165] In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 communicate with each other through the communication bus 4;

[0166] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0167] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), etc., such as at least one disk memory;

[0168] The memory stores a program, and the processor can call the program stored in the memory, wherein the program is used to:

[0169] Meshing the target area;

[0170] In response to the user's drone flight setting operation, a target start point and a target end point are selected from each grid vertex;

[0171] Mark the no-fly grids based on the flight properties of the drone and the environmental data of each grid;

[0172] Taking the target starting point as the target grid point;

[0173] Determine a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point;

[0174] Deleting, from the vertex set, adjacent grid vertices whose straight line path to the target grid point needs to pass through the no-fly grid;

[0175] Calculating a flight path distance between the target grid point and each adjacent grid vertex in the vertex set;

[0176] Taking each adjacent mesh vertex in the vertex set as a new target grid point in turn, and returning to the step of determining a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point, until the target end point is an adjacent mesh vertex of the latest target grid point;

[0177] In combination with a path-finding algorithm, a drone path is generated based on the target flight path distance required to reach the target end point from the target starting point.

[0178] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0179] The embodiment of the present application further provides a readable storage medium, which may store a program suitable for execution by a processor, wherein the program is used to:

[0180] Meshing the target area;

[0181] In response to the user's drone flight setting operation, a target start point and a target end point are selected from each grid vertex;

[0182] Mark the no-fly grids based on the flight properties of the drone and the environmental data of each grid;

[0183] Taking the target starting point as the target grid point;

[0184] Determine a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point;

[0185] Deleting, from the vertex set, adjacent grid vertices whose straight line path to the target grid point needs to pass through the no-fly grid;

[0186] Calculating a flight path distance between the target grid point and each adjacent grid vertex in the vertex set;

[0187] Taking each adjacent mesh vertex in the vertex set as a new target grid point in turn, and returning to the step of determining a vertex set including a plurality of adjacent mesh vertices corresponding to the target grid point, until the target end point is an adjacent mesh vertex of the latest target grid point;

[0188] In combination with a path-finding algorithm, a drone path is generated based on the target flight path distance required to reach the target end point from the target starting point.

[0189] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0190] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0191] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0192] The above description of the disclosed embodiments enables professionals and technicians in the field to implement or use the present application. Various modifications to these embodiments will be apparent to professionals and technicians in the field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments of the present application can be combined with each other. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for UAV path planning, characterized in that: include: Meshing the target area; In response to the user's drone flight setting operation, a target start point and a target end point are selected from each grid vertex; Mark the no-fly grids based on the flight properties of the drone and the environmental data of each grid; Taking the target starting point as the target grid point; Determine all first grids with the target grid point as grid vertices; Determine all second meshes that share a common edge with any first mesh; All grid vertices except the target grid point in each first grid are taken as a plurality of adjacent grid vertices; Taking the grid vertices farthest from the target grid point in each second grid as a plurality of adjacent grid vertices; All adjacent mesh vertices constitute a vertex set corresponding to the target grid point; in the vertex set, adjacent mesh vertices whose straight line path to the target grid point needs to pass through the no-fly grid are deleted; Calculating a flight path distance between the target grid point and each adjacent grid vertex in the vertex set; Taking each adjacent grid vertex in the vertex set as a new target grid point in turn, and returning to execute the step of determining all first grids with the target grid point as a grid vertex, until the target end point is an adjacent grid vertex of the latest target grid point; In combination with a path-finding algorithm, a drone path is generated based on the target flight path distance required to reach the target end point from the target starting point.

2. The UAV path planning method according to claim 1, characterized in that: The marking of the no-fly grid based on the flight attributes of the drone and the environmental data of each grid includes: Determine the wind speed, precipitation and temperature of each grid in real time, and determine the maximum wind resistance, heat resistance and rain resistance of the drone; The grids corresponding to wind speed exceeding the maximum wind resistance, corresponding precipitation exceeding the rain resistance and / or corresponding temperature exceeding the heat resistance are marked as no-fly grids.

3. The UAV path planning method according to claim 1, characterized in that: Calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set, comprising: Based on the haversine formula, the flight path distance between the target grid point and each adjacent grid vertex in the vertex set is calculated.

4. The UAV path planning method according to claim 1, characterized in that: After calculating the flight path distance between the target grid point and each adjacent grid vertex in the vertex set, the method further includes: Determine the flight speed of the drone; For each adjacent grid vertex in the vertex set, based on the grid wind speed of the target grid point, determine the wind speed projection value of the flight from the target grid point to the adjacent grid vertex; superimpose the wind speed projection value on the flight speed to obtain the moving speed after superposition; and calculate the flight time of the UAV from the target grid point to the adjacent grid vertex based on the moving speed and the flight path distance corresponding to the adjacent grid vertex; In combination with the path-finding algorithm, based on the target flight path distance required to reach the target end point from the target starting point, the drone path is generated, including: In combination with a path-finding algorithm, the target flight path distance required to fly from the target starting point to the target end point is accumulated to generate multiple flight paths; The flight time corresponding to the distance of each target flight path in each flight path is accumulated, and the path with the shortest time is selected from each flight path as the UAV path.

5. The UAV path planning method according to claim 4, characterized in that: Determining a wind speed projection value from the target grid point to the adjacent grid vertex based on the grid wind speed of the target grid point includes: Combined with the vector product formula, the wind speed projection value of the grid wind speed from the target grid point to the adjacent grid vertex is determined.

6. A drone path planning device, characterized in that: include: A target area mesh generation module is used to perform mesh generation on the target area; The target starting point determination module is used to respond to the user's UAV flight setting operation and select the target starting point and target end point from each grid vertex; The no-fly grid marking module is used to mark the no-fly grids based on the flight properties of the drone and the environmental data of each grid; A target grid point determination module, used to take the target starting point as the target grid point; The vertex set determination module is used to determine all first meshes with the target grid point as mesh vertices; determine all second meshes that share a common edge with any first mesh; use all mesh vertices in each first mesh except the target grid point as multiple adjacent mesh vertices; use the mesh vertex farthest from the target grid point in each second mesh as multiple adjacent mesh vertices; all adjacent mesh vertices constitute a vertex set corresponding to the target grid point; An adjacent mesh vertex deletion module is used to delete, from the vertex set, adjacent mesh vertices whose straight line paths to the target grid point need to pass through the no-fly grid; A flight path distance calculation module is used to calculate the flight path distance between the target grid point and each adjacent grid vertex in the vertex set; each adjacent grid vertex in the vertex set is used as a new target grid point in turn, and the vertex set determination module and its subsequent modules are called until the target end point is the adjacent grid vertex of the latest target grid point; The drone path generation module is used to generate a drone path based on the target flight path distance required to reach the target end point from the target starting point in combination with a path-finding algorithm.

7. A drone path planning device, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the drone path planning method as described in any one of claims 1-5.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the UAV path planning method as described in any one of claims 1 to 5 is implemented.

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