UAV flight path planning method, electronic device and storage medium

Abstract

The present invention provides a method for planning a flight path of a drone, an electronic device and a storage medium, and relates to the technical field of drone flight path planning. The method comprises: obtaining each airspace grid adjacent to the airspace grid DQ where the target drone is currently flying, so as to obtain an adjacent airspace grid list C; obtaining an estimated cost of each adjacent airspace grid in DQ and C, so as to obtain an estimated cost list F; according to F, determining a target estimated cost F min ; F min The corresponding airspace grid is determined as the next airspace grid for path planning of the target UAV; the calculation of the target estimated cost in the present invention is based on the real-time environmental state, so that the calculation of the estimated cost corresponding to the airspace grid is more accurate, thereby avoiding unpredictable flight risks of the UAV.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle flight path planning, and in particular to a method for unmanned aerial vehicle flight path planning, electronic equipment and storage medium. Background Art

[0002] Drones are widely used in various application scenarios, including agricultural applications, environmental and wildlife monitoring, surveillance, public safety, three-dimensional (3D) modeling photogrammetry, and logistics. Drones have many advantages, including high maneuverability, low energy consumption, and real-time monitoring capabilities; however, with the increase in the number of unmanned aerial vehicles (UAVs), the difficulty of drone management has become more challenging, especially for low-altitude airspace, due to complex issues of safety, privacy, and flexibility; when a drone flies, the flight path is usually planned before the flight, and then it flies along the planned flight path; however, when the drone flies along the planned flight path, the environmental state on the flight path may have changed, resulting in unpredictable flight risks for the drone. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present application, a method for planning a flight path of an unmanned aerial vehicle is provided, the method comprising the following steps:

[0005] Q100, obtain each airspace grid adjacent to the airspace grid DQ where the target UAV is currently flying, to obtain an adjacent airspace grid list C = (C1, C2, ..., C a , …, C b ), a=1, 2, …, b; where C a is the ath spatial grid adjacent to DQ, and b is the number of spatial grids adjacent to DQ.

[0006] Q200, obtain the estimated cost of each adjacent spatial grid in DQ and C to obtain an estimated cost list F = (F1, F2, ..., F a , …, F b ), where F a DQ and C a The estimated cost of a =g a +h a +W a ; g a is the path cost from the airspace grid where the take-off point is located to DQ, h a DQ to C a The heuristic estimate of the estimated cost, W a C a The corresponding increased weight value; ga and h a According to DQ and C a The latitude and longitude difference, altitude difference and time difference between them are obtained; W a According to C a Risk level, risk level weight and occupation C a The type of drone you get.

[0007] Q300, based on F, determine the target estimated cost F min =MIN(F); wherein MIN() is a preset minimum value function.

[0008] Q400, F min The corresponding airspace grid is determined as the next airspace grid for path planning of the target UAV.

[0009] According to another aspect of the present application, a non-transitory computer-readable storage medium is also provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned UAV flight path planning method.

[0010] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0011] The present invention has at least the following beneficial effects:

[0012] The method for planning the flight path of a UAV of the present invention obtains each airspace grid adjacent to the airspace grid DQ where the target UAV is currently flying, so as to obtain an adjacent airspace grid list C; obtains the estimated cost of each adjacent airspace grid in DQ and C, so as to obtain an estimated cost list F; and determines the target estimated cost F according to F. min ; F min The corresponding airspace grid is determined as the next airspace grid for path planning of the target UAV; when determining the next airspace grid for path planning, the risk level, risk level weight and the type of UAV occupying the next airspace grid are considered at the same time, that is, the calculation of the target estimated cost is based on the real-time environmental status, so that the calculation of the estimated cost corresponding to the airspace grid is more accurate, thereby avoiding unpredictable flight risks of UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A flowchart of a method for planning a flight path for a drone provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0016] It should be noted that, based on the present disclosure, those skilled in the art should understand that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0017] The following will refer to Figure 1 The flowchart of the UAV flight path planning method shown in the figure introduces a UAV flight path planning method.

[0018] In this embodiment, firstly, the airspace grid corresponding to the flight path of the target UAV is encoded in the time dimension, which may specifically include the following steps:

[0019] S100, obtaining a flight path RE of the target UAV; wherein RE includes a take-off point, a landing point, and several passing points of the target UAV.

[0020] In this embodiment, the target UAV can be any UAV; before executing the corresponding flight mission, the target UAV will plan a corresponding flight path; it can be understood that the flight path includes the take-off point, landing point and several waypoints of the target UAV during flight.

[0021] S200, determining the level L of the airspace grid corresponding to the RE according to the take-off point and landing point corresponding to the RE.

[0022] In this embodiment, the airspace grid can be used to divide the airspace outside the earth into airspace grids through a three-dimensional geographic spatial reference system (GeoSOT-3D); specifically, the earth can be subdivided through iteration; first, the longitude and latitude of the earth 180°×360° is expanded to a 512°×512° grid; secondly, each 1° is expanded to 64'; finally, each 1' is expanded to 64″ to obtain a two-dimensional quadtree subdivision at the degree, minute and second levels. In addition, the two-dimensional global subdivision framework is extended to the third spatial dimension: elevation. The division of the elevation dimension is directly performed using a binary search method. The entire range of the elevation dimension is divided from the center of the earth to an altitude of 50,000 kilometers.

[0023] The maximum subdivision grid of the highest level Level0 of GeoSOT-3D can represent the entire earth space, while the minimum subdivision grid of the lowest level Level32 can represent the centimeter scale. In this way, the GeoSOT-3D grid system not only has the global multi-dimensional octree hierarchy characteristics, but also is consistent with different types of applications. GeoSOT-3D encoding has an octree-like structure and can display 3D components. It should be noted that those skilled in the art can use GeoSOT-3D to divide the airspace corresponding to RE into airspace grids according to actual needs, which will not be elaborated here.

[0024] Further, step S200 may include the following steps:

[0025] S210, obtaining the straight-line distance S between the take-off point and the landing point corresponding to RE RE .

[0026] In this embodiment, the take-off point and landing point corresponding to the RE have corresponding longitude and latitude information, so that the S can be determined according to the corresponding longitude and latitude information. RE .

[0027] S220, according to S RE And the preset straight-line distance route type mapping table QR, determine S RE Corresponding route type; wherein, QR includes several rows, each row corresponds to a route type, and each route type corresponds to a straight-line distance range and an airspace grid level.

[0028] In this embodiment, it can be understood that S RE The size of will determine the corresponding route type; for example, S RE It is at the township level and does not cross the county, so the corresponding route type is township level; the relative airspace grid level is also larger; QR can be shown in Table 1:

[0029] Table 1

[0030]

[0031]

[0032] FW1, FW2, FW3 and FW4 in the above QR are preset straight-line distance ranges, which can be determined by S RE The corresponding route type is determined by the straight-line distance range. For example, S RE In FW4, determine S RE The corresponding route type is township level.

[0033] S230, according to S RE The corresponding route type determines the level L of the airspace grid corresponding to RE.

[0034] In this embodiment, after the route type is determined, the level of the corresponding airspace grid can be further determined; for example, if the route type is township level, then L=21.

[0035] S300, obtaining each L-level target airspace grid corresponding to RE, to obtain an airspace grid list A=(A1, A2, ..., A i , …, A n ), i=1, 2,...,n; where, A i is the i-th L-level target airspace grid corresponding to RE, n is the number of L-level target airspace grids corresponding to RE; the target airspace grid is the airspace grid that RE passes through.

[0036] In this embodiment, GeoSOT-3D can be used to encode the airspace grids at the level of 0-32 for the airspace outside the earth; after determining L, it is only necessary to obtain the target airspace grid at the L level, and there is no need to obtain airspace grids at other levels; thereby reducing the amount of calculation and improving calculation efficiency; whether the airspace grid to be judged is the target airspace grid can be determined by judging whether the airspace grid contains the take-off point, landing point or waypoint on RE.

[0037] S400, according to A, determine the L-level code of each target airspace grid to obtain the target airspace grid code list G L =(G L 1, G L 2, …, G L i , …, G L n ), where G L i A i The corresponding target spatial grid code; G L i =(G L i，P , G Li，H );G L i，P is the latitude and longitude code of the target airspace grid of the i-th L level corresponding to RE, G L i，H It is the height code of the target spatial grid of the i-th level L corresponding to RE.

[0038] In this embodiment, each L-level target airspace grid corresponds to a target airspace grid code, and the target airspace grid code corresponding to each target airspace grid can be obtained, thereby obtaining G L ; Understandably, G L i It only contains latitude, longitude and altitude information, but does not contain time information.

[0039] S500, according to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid, so as to obtain a time list to be encoded t=(t1, t2, ..., t i ,…,t n ), where t i Fly the target drone to A i The time of time.

[0040] In this embodiment, when planning the flight path of the target UAV, the take-off point, landing point and waypoint contain corresponding time information, that is, the time to fly to the corresponding waypoint; thus, the time for the target UAV to fly to each target airspace grid can be determined, thereby obtaining the time list t to be encoded; for example, the target UAV flies to A i The time is 10:10:20, then t i =10:10:20.

[0041] S600, encode each time to be encoded in t to obtain an L-level time encoding list T L =(T L 1. T L 2, …, T L i , …, T L n ), where T L i t i The corresponding time code.

[0042] Further, step S600 may include the following steps:

[0043] S610, obtain t i The corresponding hour i 、Minutesmin i and secondsi .

[0044] S620, for hour i 、min i and second i Convert binary integer to get hour i 、min i and second i The corresponding binary integer hour' i 、min' i and second' i ; Among them, hour' i =hour i ×32 / 24;min' i =min i ×64 / 60; second' i =second i ×64 / 60.

[0045] S630, will hour' i 、min' i and second' i Convert to binary code to get T L i .

[0046] In this embodiment, the time dimension is added, the time segment dichotomy and multi-scale coding are used, and coding clustering is relied on to quickly retrieve data containing time information. The information mainly takes the form of expanding 1 year to 16 months, 1 month to 32 days, 1 hour to 32 hours, 1 hour to 64 minutes, 1 hour to 64 minutes, and 1 second to 1024 microseconds through the common master time binary expansion within a limited time domain.

[0047] Due to the particularity of the data transmitted by the drone, a three-fold expansion method is used to fit the binary integer profile, that is, the daily time span of 0-24h is extended to 0-32h, 0-60 minutes is extended to 0-64 minutes, and 0-60 seconds is extended to 0-64 seconds. The three-fold expansion method is shown in Table 2.

[0048] Table 2

[0049]

[0050] The advantage of integer bifurcation is that data in the same time period can be given the same prefix, and data requests within a time period can be based on the prefix index of the database, rather than sequentially retrieving the entire timestamp information across domains. This process ultimately simplifies the time period query in database retrieval into a simple binary code matching query, greatly shortening the retrieval cycle.

[0051] Furthermore, before step S610, the method may further include the following steps:

[0052] S601, if the hour corresponding to t1 is the same as t n If the corresponding hours are different, then go to S610; otherwise, go to S602.

[0053] S602, obtain t i Corresponding minutes min i and seconds i .

[0054] S603, for min i and second i Convert binary integer to get min i and second i The corresponding binary integers min' i and second' i ; Among them, min' i =min i ×64 / 60; second' i =second i ×64 / 60.

[0055] S604, min' i and second' i Convert to binary code to get T L i .

[0056] In this embodiment, the above method can be used to determine whether it is necessary to perform binary conversion on the hours, that is, if the hours do not change from the start of the flight to the end of the flight, there is no need to perform binary conversion on the hours, thereby reducing the amount of calculation and improving conversion efficiency.

[0057] S700, T L i Add to G L i In order to get A i The corresponding target airspace grid encoding GA including flight time L i =(G L i，P , GL i，H , T L i ).

[0058] In this embodiment, the time information corresponding to RE is encoded and added to G L i So we get A i The corresponding target airspace grid encoding GA including flight time L i .

[0059] Furthermore, after step S700, the method may further include the following steps:

[0060] S800, at a distance of 100 km, the target drone flies into A i When the preset time is before, get A i The corresponding occupation time code is used to obtain the occupation time code list ZT i =(ZT i，1 , ZT i，2 , …, ZT i，j , …, ZT i，f(i) ), j = 1, 2, ..., f(i); where ZT i，j A i The corresponding j-th occupied time code, f(i) is A i The number of corresponding occupied time codes; the time corresponding to the occupied time code is A i The time occupied.

[0061] In this embodiment, if other drones plan their flight paths, they will send the occupied airspace grids and corresponding time codes; the preset duration may be 2 seconds.

[0062] S810, traverse ZT i , if ZT i There exists any L i The occupation time code that meets the preset occupation judgment condition is determined by A. i is the airspace grid to be avoided; otherwise, determine A i It is the non-obstacle avoidance airspace grid.

[0063] In this embodiment, it is understandable that it takes a certain amount of time for a drone to fly from entering a target airspace grid to flying out of the target airspace grid, and each drone will not fly into the corresponding target airspace grid at the same time; therefore, step S810 may include the following steps:

[0064] S811, get the target drone to fly out of A i The time code corresponding to the time THi .

[0065] S812, if ZT i There exists any one in T L i and TH i The occupied time code between i is the airspace grid to be avoided; otherwise, determine A i It is the non-obstacle avoidance airspace grid.

[0066] In this embodiment, determine A i After the airspace grid is formed, the RE needs to be replanned to avoid collision with the target UAV.

[0067] The airspace gridding method based on flight time of this embodiment obtains the flight path RE of the target UAV; determines the level L of the airspace grid corresponding to RE according to the take-off point and landing point corresponding to RE; obtains the target airspace grid of each level L corresponding to RE to obtain the airspace grid list A; determines the L-level code of each target airspace grid according to A to obtain the target airspace grid code list G L ; According to the waypoint information corresponding to RE, determine the time when the target UAV flies to each target airspace grid to obtain the time list t to be encoded; encode each time to be encoded in t to obtain the L-level time encoding list T L ; T L i Add to G L i In order to get A i The corresponding target airspace grid code including the flight time; through the above method, the time information corresponding to the flight path of the target UAV is encoded and added to the corresponding target airspace grid code, so that the airspace grid code contains the time information of the corresponding flight path, so that the target UAV can determine whether the airspace grid is occupied according to the corresponding time code information before flying into the target airspace grid, thereby avoiding the occurrence of collision.

[0068] In an exemplary embodiment, determining A i After the airspace grid to be avoided is obtained, the RE needs to be replanned; a method for planning a flight path for a UAV is provided, which may include the following steps:

[0069] Q100, obtain each airspace grid adjacent to the airspace grid DQ where the target UAV is currently flying, to obtain an adjacent airspace grid list C = (C1, C2, ..., C a , …, C b ), a=1, 2, …, b; where C ais the ath spatial grid adjacent to DQ, and b is the number of spatial grids adjacent to DQ.

[0070] In this embodiment, the airspace grid exists in three dimensions, and there are multiple grids adjacent to the airspace grid DQ where the target UAV is currently flying. Each airspace grid adjacent to DQ can be acquired to obtain C.

[0071] Q200, obtain the estimated cost of each adjacent spatial grid in DQ and C to obtain an estimated cost list F = (F1, F2, ..., F a , …, F b ), where F a DQ and C a The estimated cost of a =g a +h a +W a ; g a is the path cost from the airspace grid where the take-off point is located to DQ, h a DQ to C a The heuristic estimate of the estimated cost, W a C a The corresponding increased weight value; g a and h a According to DQ and C a The latitude and longitude difference, altitude difference and time difference between them are obtained; W a According to C a Risk level, risk level weight and occupation C a The type of drone you get.

[0072] In this embodiment, based on the A* algorithm, C a The risk level, risk level weight and target drone type are combined to obtain DQ and C a The estimated cost F a .

[0073] The degree of area occupancy is the first factor for drone avoidance; when the selected airspace grid is occupied or marked as a no-fly zone, the weight value is maximized to achieve circumvention of the area.

[0074] Due to the particularity of drone airspace, the traffic of drones in the grid is combined with the risk level of the current grid. The risk level of the current area is proportional to the drone traffic, and different traffic thresholds are set to divide the risk level.

[0075] Taking into account the passability of different types of drones in the airspace grid, when the risk level is too high, that is, when the drone traffic in the current grid is large, the pass rate of micro drones is higher than that of larger drones. Conventional drone types usually include large, medium, small and micro drones, which can be further subdivided by their weight.

[0076] Furthermore, g a This can be determined by the following steps:

[0077] Q210, Get DQ and C a The height difference Δh a =|h DQ -hC a |, DQ and C a The distance difference Δd a =((Δlat a ) 2 +(Δlon a ) 2 ) 1 / 2 ; Among them, h DQ is the height of DQ, hC a C a Height; Δlat a DQ and C a The latitude difference between DQ and C a The longitude difference between .

[0078] Q211, get the target drone into DQ and C a The time interval ΔT a =|T DQ -T a |; Among them, T DQ is the time when the target UAV enters the DQ, T a Enter C for the target drone a Time; T DQ and T a It is obtained through the time code in the grid code of the corresponding spatial grid.

[0079] Q212, based on Δh, Δd and ΔTa, determine g a =g'+α×Δh a +β×Δd a +δΔT a ; Wherein, α is the first weight factor of the preset height difference, β is the second weight factor of the preset distance difference, and δ is the third weight factor of the preset time interval; g' is the cumulative cost of the target UAV from the corresponding airspace grid of the take-off point to the previous airspace grid of the DQ.

[0080] In this embodiment, through the above steps, the longitude and latitude changes, altitude changes and time changes between the airspace grids can be comprehensively considered, so that the calculated F a More reasonable and accurate; α, β and δ can be obtained through a large number of experiments and fine-tuned according to the results of each experiment.

[0081] Furthermore, h a This can be determined by the following steps:

[0082] Q220, based on Δh, Δd and ΔTa, determine h a =ε×Δh a +γ×Δd a +θΔT a ; Wherein, ε is the fourth weight factor of the preset height difference, γ is the fifth weight factor of the preset distance difference, and θ is the sixth weight factor of the preset time interval.

[0083] In this embodiment, ε, γ and θ can also be obtained through a large number of experiments and fine-tuning according to the results of each experiment.

[0084] Furthermore, W a This can be determined by the following steps:

[0085] Q230, if C a is occupied, then determine W a =MQ; where MQ is the preset value.

[0086] In this embodiment, if C a is occupied, then C a The weight value of C is set to a very high value. In this formula, the value of MQ can be set to 3000; this value can ensure that C a The weight value is relatively high.

[0087] Q231, if C a If not occupied, get C a The corresponding risk level r, the corresponding weight ω r The risk value de corresponding to the type of target drone.

[0088] Q232, according to r, ω r and de, determine W a =(ω r ×r)×((NUM+de) / NUM); where NUM is the number of preset drone types.

[0089] In this embodiment, if C a Not occupied, consider C a The risk level r and risk level weight ω rThe risk value de corresponding to the type of target drone; ω r It is a preset value and can be obtained through a large number of experiments.

[0090] Furthermore, de can be determined by the following steps:

[0091] Q21, obtain the weight ZT of the target UAV.

[0092] Q22, based on ZT, determine the level JB of the drone type corresponding to the target drone.

[0093] Q23, JB is determined as de.

[0094] Specifically, the drone types are divided into NUM types according to the drone weight, and the drones are ranked from light to heavy. For example, the level of micro drones is 1, the level of small drones is 2, and the level of medium drones is 3, increasing in sequence; if the target drone is a small drone, JB is 2; so that W a The value of increases as the level of the target drone increases.

[0095] Furthermore, NUM can be 3 or 4. When NUM is 4, a large drone can be added, and the corresponding level is 4.

[0096] Furthermore, r can be determined by the following steps:

[0097] Q24, obtain the current UAV traffic λ.

[0098] In this embodiment, the current drone traffic may be the traffic of drones in a certain area around the target drone.

[0099] Q25, determine r according to λ, where r satisfies the following relationship:

[0100]

[0101] Among them, λ1 is the preset first drone flow threshold, λ2 is the preset second drone flow threshold, and λ3 is the preset third drone flow threshold.

[0102] Q300, based on F, determine the target estimated cost F min =MIN(F); wherein MIN() is a preset minimum value function.

[0103] In summary, considering the three factors, the total cost is updated in real time according to the provided calculation formula. The path of the target UAV is updated in real time so that the UAV can achieve the optimal shortest path under the constraints during the flight.

[0104] Q400, Fmin The corresponding airspace grid is determined as the next airspace grid for path planning of the target UAV.

[0105] In this embodiment, GeoSOT-4D grid is used to divide the airspace information and encode it to realize the gridding of the airspace. The obtained encoding value is used to divide the airspace grid cube, and an airspace information database is constructed. The airspace encoding data is used to calculate the airspace grid range of the drone during flight, and the four-dimensional data is analyzed in real time to generate the flight path of the drone.

[0106] Furthermore, this embodiment makes the airspace multi-dimensional data and has a unique code at each independent position. According to the results of the airspace space-time division, the track search phase can be based on the improved airspace grid trajectory planning algorithm, taking the reachable neighborhood grid of the drone as a unit, searching for the optimal neighborhood grid during the flight process, and forming the most feasible flight path.

[0107] In this embodiment, experiments are conducted to verify the improvement of drone management by using GeoSOT-4D subdivision grids. The implementation of the grid changes the data storage architecture. Previously, the spatial information of routes and other objects was recorded by ObjectID, but in our design, a database based on GeoSOT-4D coding was constructed, and both air routes and object data were recorded in one database. The airspace environment at latitude 44° and longitude 29.8° was simulated, and several actual track data were added to the airspace. After completing the airspace modeling, the number and data size of stored tracking records were compared using longitude and latitude, time dimensions, and different grid levels. The amount of data and information recorded by the subdivided grid increases with the increase in level, because the higher the level of the partitioned grid, the more detailed the description of the route and spatial objects, and the larger the storage space. If the data is not converted to a grid representation, the data size and the number of tracking records are greater than the converted data size and the number of tracking records. The experimental results show that the amount of data storage can be significantly reduced by subdivision. The data size using the traditional latitude and longitude expression is about 1.5 times that using the 21-level grid, which belongs to the high grid level.

[0108] Due to the huge amount of real-time environmental data transmitted by drones, the traditional latitude and longitude method leads to large planning time and storage requirements. However, GeoSOT-4D meets the requirements of fast drone path design and can quickly complete drone path construction at a high grid level.

[0109] In the airspace grid database, GeoSOT-4D can establish grid code databases of different levels and sizes according to actual needs, and store airspace, UAV, time and other information at the grid level to adapt to the current UAV flight. In the same airspace, the storage size is half of that in the coordinate method. In terms of the time consumed by the neighborhood position, the GeoSOT-4D time is about 30% of the database index coordinate method, while the GeoSOT-4D time based on the total coordinates of the path planning time is one-seventh of the calculation time of the conventional algorithm path planning. It is not difficult to understand that the GeoSOT-4D space-time division grid as a storage method for airspace expression during UAV flight has great advantages over the traditional latitude and longitude method.

[0110] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0111] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0112] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0115] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0116] An embodiment of the present invention further provides an electronic device, comprising a processor and the aforementioned non-transitory computer-readable storage medium.

[0117] The electronic device is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0118] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one memory mentioned above, and a bus connecting different system components (including the memory and the processor).

[0119] The memory stores program codes, which can be executed by the processor, so that the processor executes the steps in various embodiments described in this specification.

[0120] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0121] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0122] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0123] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0124] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0125] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0126] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for planning a flight path of an unmanned aerial vehicle, characterized in that: The method comprises the following steps: Q100, obtain each airspace grid adjacent to the airspace grid DQ where the target UAV is currently flying, to obtain an adjacent airspace grid list C = (C1, C2, ..., C a , …, C b ), a=1, 2, …, b; where C a is the ath spatial grid adjacent to DQ, b is the number of spatial grids adjacent to DQ; Q200, obtain the estimated cost of each adjacent spatial grid in DQ and C to obtain an estimated cost list F = (F1, F2, ..., F a , …, F b ), where F a DQ and C a The estimated cost of a =g a +h a +W a ; g a is the path cost from the airspace grid where the take-off point is located to DQ, h a DQ to C a The heuristic estimate of the estimated cost, W a C a The corresponding increased weight value; g a and h a According to DQ and C a The latitude and longitude difference, altitude difference and time difference between them are obtained; W a According to C a Risk level, risk level weight and occupation C a The type of drone obtained; Q300, based on F, determine the target estimated cost F min =MIN(F); where MIN() is a preset minimum value function; Q400, F min The corresponding airspace grid is determined as the next airspace grid for path planning of the target UAV.

2. The method for planning a flight path of an unmanned aerial vehicle according to claim 1, characterized in that: g a Determine by following these steps: Q210, Get DQ and C a The height difference Δh a =|h DQ -hC a |, DQ and C a The distance difference Δd a =((Δlat a ) 2 +(Δlon a ) 2 ) 1 / 2 ; Among them, h DQ is the height of DQ, hC a C a Height; Δlat a DQ and C a The latitude difference between DQ and C a The longitude difference between Q211, get the target drone into DQ and C a The time interval ΔT a =|T DQ -T a |; Among them, T DQ is the time when the target UAV enters the DQ, T a Enter C for the target drone a Time; T DQ and T a It is obtained by the time code in the grid code of the corresponding spatial grid; Q212, based on Δh, Δd and ΔTa, determine g a =g'+α×Δh a +β×Δd a +δΔT a ; Wherein, α is the first weight factor of the preset height difference, β is the second weight factor of the preset distance difference, and δ is the third weight factor of the preset time interval; g' is the cumulative cost of the target UAV from the corresponding airspace grid of the take-off point to the previous airspace grid of the DQ.

3. The method for planning a flight path of an unmanned aerial vehicle according to claim 2, characterized in that: h a Determine by following these steps: Q220, based on Δh, Δd and ΔTa, determine h a =ε×Δh a +γ×Δd a +θΔT a ; Wherein, ε is the fourth weight factor of the preset height difference, γ is the fifth weight factor of the preset distance difference, and θ is the sixth weight factor of the preset time interval.

4. The method for planning a flight path of an unmanned aerial vehicle according to claim 1, characterized in that: W a Determine by following these steps: Q230, if C a is occupied, then determine W a =MQ; where MQ is the preset value; Q231, if C a If not occupied, get C a The corresponding risk level r, the corresponding weight ω r The risk value de corresponding to the type of target drone; Q232, according to r, ω r and de, determine W a =(ω r ×r)×((NUM+de) / NUM); where NUM is the number of preset drone types.

5. The method for planning a flight path of an unmanned aerial vehicle according to claim 4, characterized in that: de is determined by the following steps: Q21, obtain the weight ZT of the target UAV; Q22, according to ZT, determine the level JB of the drone type corresponding to the target drone; Q23, JB is determined as de.

6. The method for planning a flight path of an unmanned aerial vehicle according to claim 4, characterized in that: r is determined by the following steps: Q24, obtain the current UAV traffic λ; Q25, determine r according to λ, where r satisfies the following relationship: Among them, λ1 is the preset first drone flow threshold, λ2 is the preset second drone flow threshold, and λ3 is the preset third drone flow threshold.

7. The method for planning a flight path of an unmanned aerial vehicle according to claim 4, characterized in that: NUM is equal to 3 or 4.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the UAV flight path planning method as described in any one of claims 1-7.

9. An electronic device, characterized in that: Includes a processor and the non-transitory computer-readable storage medium of claim 8.

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