A method and device for planning a drone route, electronic equipment, and storage medium
Through the spatial gridization of airspace map files and the calculation of route score data, the problems of low efficiency and poor route planning of traditional drone routes are solved, and more efficient route planning is achieved.
Patent Information
- Application Number
- CN202411706803.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-04-04
AI Technical Summary
The traditional drone route planning method has low allocation efficiency, the selected route is not necessarily the optimal route, and it relies on manual planning and lacks flexibility.
By obtaining the airspace map file of the drone's flight area, performing spatial grids, determining multiple candidate routes between the route starting point and the route destination point, computing the route score data of each candidate route, and selecting the optimal target route based on the scoring data.
The overall evaluation of multiple candidate routes was achieved, and the optimal target route was selected based on the route score data to improve the efficiency of drone route planning.
Smart Images
Figure CN119756358A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) route planning, and in particular to a method and device for planning an UAV route, an electronic device, and a storage medium. Background Art
[0002] The traditional scheme pre-determines the allocation of routes in the preset routes according to the time division method. For example, there is a static list of available routes ListALL_a_b from point A to point B and a list of currently allocated routes from point A to point B ListAllocated_a_b. The difference between ListALL_a_b and ListAllocated_a_b is recorded as List_a_b_1, and then a route is randomly selected from List_a_b_1 as the route to be allocated.
[0003] However, traditional solutions have the problem of low allocation efficiency, and the selected routes are often not the optimal ones. In addition, the pre-planned routes often rely on manual planning and are not flexible enough. Summary of the invention
[0004] In view of the above problems, a method and device for planning a route for a drone, an electronic device, and a storage medium are proposed to overcome the above problems or at least partially solve the above problems, including:
[0005] A method for planning a route for an unmanned aerial vehicle, the method comprising:
[0006] Get the airspace map file of the drone’s flight area;
[0007] Spatially gridding the airspace map file to obtain a plurality of grid units;
[0008] determining a plurality of candidate routes between a route starting point and a route destination point based on the grid attributes of the plurality of grid cells;
[0009] Determine the route scoring data corresponding to each candidate route;
[0010] A target route is determined from a plurality of candidate routes according to the route scoring data.
[0011] Optionally, determining the route scoring data corresponding to each candidate route includes:
[0012] Determine the target grid cell on each candidate route;
[0013] Determining route length data for each candidate route based on the target grid cell;
[0014] determining route intersection data between a plurality of candidate routes based on the target grid unit;
[0015] The route scoring data corresponding to each candidate route is determined according to the route length data and the route intersection data.
[0016] Optionally, determining the route length data of each candidate route based on the target grid unit includes:
[0017] The number of target grid cells on each candidate route is used as the route length data of each candidate route.
[0018] Optionally, determining route intersection data between a plurality of candidate routes based on the target grid unit includes:
[0019] Determine the number of overlapping target grid cells of the first candidate route and the second candidate route other than the first candidate route;
[0020] Route intersection data between the first candidate route and the second candidate route is determined based on the number of overlaps.
[0021] Optionally, determining route scoring data corresponding to each candidate route according to the route length data and the route intersection data includes:
[0022] Determine a first scoring coefficient corresponding to the route length data and a second scoring coefficient corresponding to the route intersection data;
[0023] The route scoring data corresponding to each candidate route is determined based on the first scoring system, the second scoring coefficient, the route length data, and the route intersection data.
[0024] Optionally, determining route scoring data corresponding to each candidate route based on the first scoring system, the second scoring coefficient, the route length data, and the route intersection data includes:
[0025] Multiplying the first scoring coefficient by the route length data to obtain a first product;
[0026] multiplying the second scoring coefficient by the route intersection data to obtain a second product;
[0027] The first product and the second product are added together to obtain route rating data.
[0028] Optionally, it also includes:
[0029] Determining the flight time data corresponding to each candidate route based on the route length data;
[0030] The determining of route scoring data corresponding to each candidate route according to the route length data and the route intersection data includes:
[0031] Route score data corresponding to each candidate route is calculated according to the route length data, the route intersection data and the flight time data.
[0032] A device for planning a route of an unmanned aerial vehicle, the device comprising:
[0033] The airspace map file acquisition module is used to obtain the airspace map file of the UAV flight area;
[0034] A spatial gridding module, used for spatially gridding the airspace map file to obtain a plurality of grid units;
[0035] a candidate route determination module, configured to determine a plurality of candidate routes between a route starting point and a route destination point based on the grid attributes of the plurality of grid cells;
[0036] A route scoring data determination module, used to determine the route scoring data corresponding to each candidate route;
[0037] The target route determination module is used to determine the target route from multiple candidate routes according to the route scoring data.
[0038] An electronic device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the method for planning a route for a drone as described above is implemented.
[0039] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for planning a drone route as described above is implemented.
[0040] The embodiments of the present invention have the following advantages:
[0041] In the embodiment of the present invention, an airspace map file of the drone flight area can be obtained, and then the airspace map file can be spatially gridded to obtain multiple grid units; multiple candidate routes between the route starting point and the route destination are determined based on the grid attributes of the multiple grid units; the route scoring data corresponding to each candidate route is determined; and the target route is determined from the multiple candidate routes according to the route scoring data. In this way, an overall evaluation of the multiple automatically planned candidate routes is achieved, so as to select the most optimized target route according to the route scoring data, and improve the planning efficiency of the drone route. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.
[0043] Figure 1a It is a flowchart of the steps of a method for planning a drone route provided by one embodiment of the present invention;
[0044] Figure 1b is a schematic diagram of a grid unit in a current airspace provided by an embodiment of the present invention;
[0045] Figure 2 is a flowchart of another method for planning a drone route provided by an embodiment of the present invention;
[0046] Figure 3a It is a schematic diagram of a two-dimensional UAV route planning provided by an embodiment of the present invention;
[0047] Figure 3b It is another schematic diagram of planning a two-dimensional UAV route provided by an embodiment of the present invention;
[0048] Figure 3c It is another schematic diagram of planning a two-dimensional UAV route provided by an embodiment of the present invention;
[0049] Figure 3d It is another schematic diagram of planning a two-dimensional UAV route provided by an embodiment of the present invention;
[0050] Figure 4 It is a structural schematic diagram of a UAV route planning device provided by one embodiment of the present invention. DETAILED DESCRIPTION
[0051] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0052] Reference Figure 1a , shows a flowchart of a method for planning a drone route provided by an embodiment of the present invention, which may specifically include the following steps:
[0053] Step S101, obtaining an airspace map file of the drone flight area;
[0054] In actual applications, when a drone plans its route, it is necessary to obtain an airspace map file of the drone's flight area. The airspace map file can be used to describe a map of the airspace (i.e., airspace) above the earth's surface that is available for aircraft flight, and provide information such as routes, terrain, landmarks, and navigation equipment locations required for flight, to help pilots plan routes, locate positions, understand the flight environment, and avoid potential dangers.
[0055] The airspace map file can be a file stored locally on the drone. When the drone needs to plan a route, the airspace map file can be directly retrieved from the local computer for automatic planning. The airspace map file can also be a file stored in the cloud connected to the drone. When the drone needs to plan a route, a request message can be sent to the cloud so that the cloud responds to the request message and sends the airspace map file to the drone.
[0056] Step S102, spatially gridding the airspace map file to obtain a plurality of grid units;
[0057] After the drone obtains the airspace map file, it can perform spatial gridding to divide the three-dimensional airspace map into cubes of preset sizes, each cube being a grid unit. In practical applications, an n*n*n cube can be used as a basic grid unit, where n is an integer greater than 1. n can be set according to actual needs, and there are no excessive restrictions on this in the embodiments of the present invention.
[0058] In an embodiment of the present invention, after the drone reads the airspace map file, it divides the grid units, and then a directed graph can be established inside the drone to describe the positional relationship between the unit grids. The grid attributes of the unit grid include the following attributes: grid id, left grid id, right grid id, front grid id, back grid id, upper grid id, lower grid id, occupancy status, center point coordinates. From the directed graph, each unit grid can be traversed and the occupancy status and center point coordinates can be obtained.
[0059] like Figure 1b As shown, it is a schematic diagram of a grid unit in a current airspace in an embodiment of the present invention, wherein the occupancy status of the grid unit can also be indicated by the color of the grid unit.
[0060] Step S103, determining a plurality of candidate routes between the route starting point and the route destination point based on the grid attributes of the plurality of grid units.
[0061] During the UAV route planning process, the starting point and destination point of the route to be planned can be selected, and then the route can be automatically planned based on the starting point and destination point, where the starting point and destination point are points in the airspace map file.
[0062] In one embodiment of the present invention, after determining the route starting point and the route destination point, it can be determined whether the route starting point and the route destination point are in the airspace map file. When the route starting point and the route destination point are not points in the airspace map file, a prompt message is generated. The prompt message can be used to prompt the user to reselect the route starting point and the route destination point or to re-acquire the airspace map file corresponding to the route starting point and the route destination point.
[0063] After determining the route starting point and the route destination point, route planning can be performed between the route starting point and the route destination point based on the grid attributes of multiple grid cells to obtain multiple candidate routes.
[0064] The grid attributes may include the positional relationship and occupancy status of the grid units, and then the route planning may be performed between the route starting point and the route destination point according to the positional relationship and the occupancy relationship to obtain multiple candidate routes.
[0065] Step S104, determining route scoring data corresponding to each candidate route;
[0066] After the candidate routes are automatically planned, each candidate route can be scored to determine the route scoring data of each candidate route. Specifically, several scoring indicators that affect the quality of the route can be pre-selected, and then, each candidate route can be scored based on the selected scoring indicators to obtain the route scoring data corresponding to each candidate route.
[0067] Among them, the scoring indicators may include but are not limited to route distance, navigation time, and the intersection status of the route with other routes.
[0068] Step S105 , determining a target route from a plurality of candidate routes according to the route scoring data.
[0069] After the route scoring data is determined, multiple candidate routes may be sorted from high to low according to the route scoring data, so that the candidate route with the highest route score is determined as the target route.
[0070] In the embodiment of the present invention, an airspace map file of the drone flight area can be obtained, and then the airspace map file can be spatially gridded to obtain multiple grid units; multiple candidate routes between the route starting point and the route destination are determined based on the grid attributes of the multiple grid units; the route scoring data corresponding to each candidate route is determined; and the target route is determined from the multiple candidate routes according to the route scoring data. In this way, an overall evaluation of the multiple candidate routes automatically planned is achieved, so as to select the most optimized target route according to the route scoring data, and improve the planning efficiency of the drone route.
[0071] Reference Figure 2, shows a flowchart of another method for planning a drone route provided by an embodiment of the present invention, which may specifically include the following steps:
[0072] Step S201, obtaining an airspace map file of the drone flight area;
[0073] In actual applications, when a drone plans its route, it is necessary to obtain an airspace map file of the drone's flight area. The airspace map file can be used to describe a map of the airspace (i.e., airspace) above the earth's surface that is available for aircraft flight, and provide information such as routes, terrain, landmarks, and navigation equipment locations required for flight, to help pilots plan routes, locate positions, understand the flight environment, and avoid potential dangers.
[0074] The airspace map file can be a file stored locally on the drone. When the drone needs to plan a route, the airspace map file can be directly retrieved from the local computer for automatic planning. The airspace map file can also be a file stored in the cloud connected to the drone. When the drone needs to plan a route, a request message can be sent to the cloud so that the cloud responds to the request message and sends the airspace map file to the drone.
[0075] Step S202, spatially gridding the airspace map file to obtain a plurality of grid units;
[0076] After the drone obtains the airspace map file, it can perform spatial gridding to divide the three-dimensional airspace map into cubes of preset sizes, each cube being a grid unit. In practical applications, an n*n*n cube can be used as a basic grid unit, where n is an integer greater than 1. n can be set according to actual needs, and there are no excessive restrictions on this in the embodiments of the present invention.
[0077] In an embodiment of the present invention, after the drone reads the airspace map file, it divides the grid units, and then a directed graph can be established inside the drone to describe the positional relationship between the unit grids. The grid attributes of the unit grid include the following attributes: grid id, left grid id, right grid id, front grid id, back grid id, upper grid id, lower grid id, occupancy status, center point coordinates. From the directed graph, each unit grid can be traversed and the occupancy status and center point coordinates can be obtained.
[0078] Step S203: determining a plurality of candidate routes between the route starting point and the route destination point based on the grid attributes of the plurality of grid units.
[0079] During the UAV route planning process, the starting point and destination point of the route to be planned can be selected, and then the route can be automatically planned based on the starting point and destination point, where the starting point and destination point are points in the airspace map file.
[0080] After determining the route starting point and the route destination point, route planning can be performed between the route starting point and the route destination point based on the grid attributes of multiple grid cells to obtain multiple candidate routes.
[0081] The grid attributes may include the positional relationship and occupancy status of the grid units, and then the route planning may be performed between the route starting point and the route destination point according to the positional relationship and the occupancy relationship to obtain multiple candidate routes.
[0082] Step S204, determining the target grid cell on each candidate route;
[0083] In practical applications, after dividing the grid units, each position in the UAV flight space corresponds to a grid unit. After determining the candidate routes, each candidate route can be regarded as a combination of multiple target grid units, and multiple target grid units can cover each point on the candidate route.
[0084] Step S205, determining the route length data of each candidate route based on the target grid unit;
[0085] After determining the target grid unit of each candidate route, the target grid unit is a grid of fixed length, and the candidate route can be represented by multiple target grid units, so that the route length data of each candidate route can be estimated according to the target grid unit.
[0086] Specifically, the number of target grid cells on each candidate route may be used as route length data of each candidate route.
[0087] Step S206, determining route intersection data between multiple candidate routes based on the target grid unit;
[0088] In practical applications, accidents are likely to occur when the routes within the flight area intersect with the surrounding routes. In the route area, the target grid cell can be used to represent the flight position. The time for the drone to fly over each grid cell is equal, so the position of each candidate route at any time can be determined. When the position difference of the drone in two routes at the same time is less than a known threshold, the two routes are considered to intersect.
[0089] Each route can be represented by the grid coordinates of all the routes and the time of the flight mission. At any time t, the position of the drone at any time on the route is represented by Because the route is the relationship between time t and position, and the target grid unit can be used to represent the position, and the time it takes for the drone to fly over each target grid unit is the same, it is possible to determine the route intersection data between multiple candidate routes based on the target grid unit.
[0090] Specifically, step S206 may include: determining the number of overlapping target grid cells of the first candidate route and the second candidate route other than the first candidate route; and determining route intersection data between the first candidate route and the second candidate route based on the number of overlapping target grid cells.
[0091] In practical applications, the candidate routes may include a first candidate route and a second candidate route other than the first candidate route. When the first candidate route and the second candidate route have the same target grid unit (i.e., overlapping target grid units), the overlapping target grid units are the closest positions of the two routes. Therefore, there may be a probability of intersection. Therefore, the route intersection data between the first candidate route and the second candidate route can be determined based on the number of overlaps.
[0092] For example, the time it takes for a drone to reach the overlapping target grid cells along two candidate routes can be determined, and then it can be determined whether the overlapping target grid cells intersect. Since the flight time of each grid cell is fixed, the flight time can be determined by the number of grids from the starting point to each overlapping target grid cell. When the number of grids is the same, that is, the two candidate routes are at the same position at the same time, it can be determined that the two candidate routes intersect.
[0093] In one embodiment of the present invention, the position of each grid unit can be represented by the center point coordinates of the grid unit, and the distance between any two grid units in the two routes can be calculated in turn. Therefore, the grid unit data corresponding to the two route data at the same time can be determined first, and then the center coordinates of the grid unit data can be determined, the distance between the center coordinates can be calculated, and it can be determined whether the distance between the center coordinates is greater than a preset distance threshold. When the distance between the center coordinates is greater than the preset distance threshold, it is determined that the two candidate routes do not intersect, and when the distance between the center coordinates is less than or equal to the preset distance threshold, it is determined that the two candidate routes intersect.
[0094] Step S207: determining the route scoring data corresponding to each candidate route according to the route length data and the route intersection data.
[0095] After the route length data and the route intersection data are determined, the route scoring data corresponding to each candidate route may be determined according to the route length data and the route intersection data.
[0096] In one embodiment of the present invention, determining route scoring data corresponding to each candidate route according to route length data and route intersection data includes the following sub-steps:
[0097] Sub-step S11, determining a first scoring coefficient corresponding to the route length data and a second scoring coefficient corresponding to the route intersection data;
[0098] In practical applications, scoring coefficients can be set according to the degree of influence of different scoring indicators on the quality of the route. Specifically, a first scoring coefficient can be set for route length data, and a second scoring coefficient can be set for route intersection data.
[0099] Sub-step S12, determining the route scoring data corresponding to each candidate route based on the first scoring coefficient, the second scoring coefficient, the route length data and the route intersection data.
[0100] Specifically, the first scoring coefficient may be multiplied by the route length data to obtain a first product; the second scoring coefficient may be multiplied by the route intersection data to obtain a second product; and the first product and the second product may be added to obtain the route scoring data.
[0101] In practical applications, the first scoring coefficient and the second scoring coefficient may be used as weights of the route length data and the route intersection data for weighted calculation.
[0102] Among them, the first product = first scoring coefficient * route length data.
[0103] Second product + second rating coefficient * route rating data.
[0104] The route score above is = the first product + the second product.
[0105] Therefore, the route scoring data = first scoring coefficient * route length data + second scoring coefficient * route intersection data.
[0106] Step S208, determining a target route from a plurality of candidate routes according to the route scoring data.
[0107] After the route scoring data is determined, multiple candidate routes may be sorted from high to low according to the route scoring data, so that the candidate route with the highest route score is determined as the target route.
[0108] In one embodiment of the present invention, the flight time data may also be used as one of the scoring indicators for evaluating the route, that is, the drone may determine the flight time data corresponding to each candidate route based on the route length data before scoring the route.
[0109] In the drone flight area, the length of the grid unit is fixed, the drone flight speed is fixed, and the flight time of each grid unit is fixed. Therefore, the flight time of each candidate route can be determined according to the target grid unit.
[0110] After the introduction of the navigation time, the route scoring data corresponding to each candidate route is determined according to the route length data and the route intersection data. Specifically, the route scoring data corresponding to each candidate route is determined according to the route length data, the route intersection data and the navigation time data.
[0111] In practical applications, the route scoring data corresponding to each candidate route is determined by integrating route length data, route intersection data, and navigation time data. This allows the quality of the route to be evaluated by combining multiple evaluation indicators, thereby improving the utilization rate of the route.
[0112] Specifically, the third scoring data corresponding to the flight time data may be determined, and then the route scoring data = first scoring coefficient*route length data+second scoring coefficient*route intersection data+third scoring coefficient*flight time data.
[0113] In an embodiment of the present invention, an airspace map file of the drone flight area can be obtained, and then the airspace map file can be spatially gridded to obtain multiple grid units; multiple candidate routes between the route starting point and the route destination are determined based on the grid attributes of the multiple grid units; the target grid unit on each candidate route is determined; the route length data of each candidate route is determined based on the target grid unit; the route intersection data between the multiple candidate routes is determined based on the target grid unit; the route scoring data corresponding to each candidate route is determined according to the route length data and the route intersection data. The target route is determined from the multiple candidate routes according to the route scoring data. In this way, multiple candidate routes can be automatically planned, and the candidate routes can be evaluated as a whole based on the route length data and the route intersection data, so as to select the most optimized target route according to the route scoring data, thereby improving the planning efficiency of the drone route.
[0114] Describe the process of route allocation in two dimensions. If you want to use point A as the starting point of the drone route to navigate to point D, there are a total of 4 routes in the static available route list from point A to point D, among which: Figure 3a For the first route, Figure 3b For the second route, Figure 3c For the third route, Figure 3d This is the fourth route.
[0115] Assume that the list of assigned routes from A to D is empty, and the time it takes for the drone to fly over each square is equal. Traverse the four routes separately. The length of route 1 is 7 squares, which does not intersect with route 2. The number of grid codes intersecting with route 3 is 3, and the number of grid codes intersecting with route 4 is 3. Therefore, the number of times route 1 intersects with other routes in the static available route list is 6. In the route scoring formula, the first scoring coefficient is -0.5, the second scoring coefficient is -0.5, and the third scoring coefficient is -1.5.
[0116] According to the route score calculation formula, the Score can be calculated 航线1 =7*-0.5+7*-0.5+6*-1.5=-16.
[0117] The Score can be obtained in the same way. 航线2 =15*-0.5+15*-0.5+15*-1.5=-37.5, Score 航线3 =15*-0.5+15*-0.5+12*-1.5=-33, Score 航线4 =15*-0.5+15*-0.5+15*-1.5=-37.5.
[0118] Based on the above calculation results, it can be known that route 1 has the highest score, so route 1 can be selected as the route to be allocated.
[0119] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.
[0120] Reference Figure 4 , shows a schematic diagram of the structure of a UAV route planning device provided by an embodiment of the present invention, which may specifically include the following modules:
[0121] The airspace map file acquisition module 401 is used to acquire the airspace map file of the UAV flight area;
[0122] A spatial gridding module 402 is used to spatially grid the airspace map file to obtain a plurality of grid units;
[0123] A candidate route determination module 403, configured to determine a plurality of candidate routes between a route starting point and a route destination point based on grid attributes of the plurality of grid units, wherein the grid attributes are used to describe a positional relationship between the plurality of grid units;
[0124] A route scoring data determination module 404 is used to determine the route scoring data corresponding to each candidate route;
[0125] The target route determination module 405 is used to determine a target route from a plurality of candidate routes according to the route scoring data.
[0126] In one embodiment of the present invention, the route rating data determination module 404 may include:
[0127] A target grid unit determination submodule is used to determine the target grid unit on each candidate route;
[0128] A route length data determination submodule, used to determine the route length data of each candidate route based on the target grid unit;
[0129] A route intersection data determination submodule, used to determine route intersection data between a plurality of candidate routes based on the target grid unit;
[0130] The route scoring data determining submodule is used to determine the route scoring data corresponding to each candidate route according to the route length data and the route intersection data.
[0131] In one embodiment of the present invention, when the route length data determination submodule is used to determine the route length data of each candidate route based on the target grid unit, it specifically includes: using the number of target grid units on each candidate route as the route length data of each candidate route.
[0132] In one embodiment of the present invention, the route intersection data determination submodule may include:
[0133] an overlap data determination unit, used to determine the number of overlapped target grid cells of the first candidate route and the second candidate route other than the first candidate route;
[0134] A route intersection data determining unit is used to determine the route intersection data between the first candidate route and the second candidate route based on the overlap quantity.
[0135] In one embodiment of the present invention, the route rating data determination submodule may include:
[0136] A scoring coefficient determination unit, used to determine a first scoring coefficient corresponding to the route length data and a second scoring coefficient corresponding to the route intersection data;
[0137] A route scoring data determining unit is used to determine the route scoring data corresponding to each candidate route based on the first scoring system, the second scoring coefficient, the route length data and the route intersection data.
[0138] In one embodiment of the present invention, the route rating data determination unit may include:
[0139] A first product determination subunit, configured to multiply the first scoring coefficient by the route length data to obtain a first product;
[0140] A second product determination subunit, configured to multiply the second scoring coefficient by the route intersection data to obtain a second product;
[0141] The route scoring data determining subunit is used to add the first product and the second product to obtain the route scoring data.
[0142] In one embodiment of the present invention, the device may further include:
[0143] A flight time data determination submodule, used to determine the flight time data corresponding to each candidate route based on the route length data;
[0144] The route scoring data determination submodule, when used to determine the route scoring data corresponding to each candidate route according to the route length data and the route intersection data, is specifically used to determine the route scoring data corresponding to each candidate route according to the route length data, the route intersection data and the flight time data.
[0145] In the embodiment of the present invention, an airspace map file of the drone flight area can be obtained, and then the airspace map file is spatially gridded to obtain multiple grid units; multiple candidate routes between the route starting point and the route destination are determined based on the grid attributes of the multiple grid units; the route scoring data corresponding to each candidate route is determined; and the target route is determined from the multiple candidate routes according to the route scoring data. In this way, an overall evaluation of the multiple automatically planned candidate routes is achieved, so as to select the most optimized target route according to the route scoring data, and improve the planning efficiency of the drone route.
[0146] An embodiment of the present invention further provides an electronic device, which may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the above-mentioned drone route planning method is implemented.
[0147] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for planning a UAV route is implemented.
[0148] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0149] 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.
[0150] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0151] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate a device for implementing the functions specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.
[0152] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.
[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce computer-implemented processing, so that the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.
[0154] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.
[0155] 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", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal 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 terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.
[0156] The above is a detailed introduction to a method and device for planning a drone route, an electronic device, and a storage medium. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A method for planning a drone route, characterized in that: The method comprises: Get the airspace map file of the drone’s flight area; Spatially gridding the airspace map file to obtain a plurality of grid units; determining a plurality of candidate routes between a route starting point and a route destination point based on the grid attributes of the plurality of grid cells; Determine the route scoring data corresponding to each candidate route; A target route is determined from a plurality of candidate routes according to the route scoring data.
2. The method according to claim 1, characterized in that: The determining of route scoring data corresponding to each candidate route includes: Determine the target grid cell on each candidate route; Determining route length data for each candidate route based on the target grid cell; determining route intersection data between a plurality of candidate routes based on the target grid unit; The route scoring data corresponding to each candidate route is determined according to the route length data and the route intersection data.
3. The method according to claim 2, characterized in that The determining of the route length data of each candidate route based on the target grid unit includes: The number of target grid cells on each candidate route is used as the route length data of each candidate route.
4. The method according to claim 2, characterized in that: The determining, based on the target grid unit, route intersection data between a plurality of candidate routes comprises: Determine the number of overlapping target grid cells of the first candidate route and the second candidate route other than the first candidate route; Route intersection data between the first candidate route and the second candidate route is determined based on the number of overlaps.
5. The method according to claim 2, characterized in that: Determining route scoring data corresponding to each candidate route according to the route length data and the route intersection data includes: Determine a first scoring coefficient corresponding to the route length data and a second scoring coefficient corresponding to the route intersection data; The route scoring data corresponding to each candidate route is determined based on the first scoring system, the second scoring coefficient, the route length data, and the route intersection data.
6. The method according to claim 5, characterized in that Determining route scoring data corresponding to each candidate route based on the first scoring system, the second scoring coefficient, the route length data, and the route intersection data includes: Multiplying the first scoring coefficient by the route length data to obtain a first product; multiplying the second scoring coefficient by the route intersection data to obtain a second product; The first product and the second product are added together to obtain route rating data.
7. The method according to claim 2, characterized in that: Also includes: Determining the flight time data corresponding to each candidate route based on the route length data; The determining of route scoring data corresponding to each candidate route according to the route length data and the route intersection data includes: The route scoring data corresponding to each candidate route is determined according to the route length data, the route intersection data and the flight time data.
8. A device for planning a route for a drone, characterized in that: The device comprises: The airspace map file acquisition module is used to obtain the airspace map file of the UAV flight area; A spatial gridding module, used for spatially gridding the airspace map file to obtain a plurality of grid units; a candidate route determination module, configured to determine a plurality of candidate routes between a route starting point and a route destination point based on the grid attributes of the plurality of grid cells; A route scoring data determination module, used to determine the route scoring data corresponding to each candidate route; The target route determination module is used to determine the target route from multiple candidate routes according to the route scoring data.
9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the method for planning a route for a drone as claimed in any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for planning a drone route as described in any one of claims 1 to 7 is implemented.