A method for UAV flight path planning based on ground population density

Abstract

This invention provides a method for planning unmanned aerial vehicle (UAV) flight paths based on ground population density. The method includes: determining a target critical flight path corresponding to each target grid based on the historical average population density corresponding to several target grids on each initial flight path of the target UAV; determining the density cost value corresponding to each target grid based on the number of target critical flight paths corresponding to each target grid; and determining the target flight path from several initial flight paths based on the density cost value corresponding to each target grid on each initial flight path. By analyzing the population density within the area along the initial flight path of the target UAV, the safety of the determined target flight path is improved, reducing the risk of the UAV crashing and colliding with ground populations due to communication failures or other reasons during flight, thus ensuring both the flight safety of the UAV and the safety of populations in high-density areas on the ground.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicles (UAVs), and in particular to a method for UAV flight path planning based on ground population density. Background Technology

[0002] With the booming development of the drone industry, the number of drone flights is constantly increasing. According to statistics from the Civil Aviation Administration of China, in 2023, the cumulative flight hours of civilian drones nationwide reached 23.11 million, an increase of 11.8% year-on-year. The vast majority of these drones operate at low altitudes, leading to an increasing density of low-altitude flights and consequently highlighting low-altitude safety issues. Therefore, it is necessary to plan drone flight routes more scientifically and rationally to improve the safety level of low-altitude operations.

[0003] Traditional flight path planning methods, such as the A* algorithm, are based on heuristic search algorithms and are suitable for quickly finding the shortest flight path. However, since drones mostly fly in low-altitude airspace, when planning drone flight paths, it is also necessary to fully consider the relationship between the drone and the relative position of people on the ground in densely populated areas. The drone should be kept away from high-density areas on the ground to reduce the risk of the drone crashing and colliding with people on the ground in the event of power outages or communication failures. However, traditional flight path planning methods do not focus on analyzing the safety risks in high-density areas, making it difficult to fully guarantee the flight safety of drones. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0005] According to one aspect of this application, a method for planning unmanned aerial vehicle (UAV) flight paths based on ground population density is provided and applied to a flight path planning system. The flight path planning system includes a target grid map corresponding to the target space. The target grid map includes several target grids and a grid coordinate system. Each target grid has grid coordinates in the grid coordinate system and a grid safety level. The grid safety level of the target grid is determined based on the safety distance between the target grid and the obstacle.

[0006] The UAV flight path planning method based on ground population density includes the following steps:

[0007] Step S100: In response to receiving the flight path planning request from the target UAV, determine several initial flight paths from the starting grid to the destination grid based on the starting grid coordinates of the starting grid and the destination grid coordinates of the destination grid included in the flight path planning request.

[0008] Step S200: Based on the historical average population density of several target grids located between the starting grid and the destination grid on each initial flight path within a historical time period, determine the target critical flight path corresponding to each target grid located between the starting grid and the destination grid on each initial flight path from several critical flight paths; wherein, the critical flight path is the flight path planned by the key UAV within the planned flight time period corresponding to the key UAV; the key UAV is the UAV whose planned flight time period coincides with the planned flight time period of the target UAV included in the flight path planning request;

[0009] Step S300: Determine the density cost value corresponding to each target grid based on the number of target critical flight paths corresponding to each target grid located between the starting grid and the destination grid on each initial flight path.

[0010] Step S400: Determine the target flight path from several initial flight paths based on the density value corresponding to each target grid on each initial flight path.

[0011] In one exemplary embodiment of this application, step S100 includes:

[0012] Step S110: The grid coordinates are (x...) a ,y a ), (x a ,y b ), (x b ,y a ) and (x b ,y b The area composed of the target grid is defined as the target flight area; where x a The x-coordinate of the starting raster coordinates; y a The ordinate of the starting raster coordinates; x b y is the x-coordinate of the target raster coordinates; b The ordinate of the target grid coordinates; the starting grid coordinates correspond to the starting grid, which is the target grid in the target grid map where the starting position of the target UAV is located; the target grid coordinates correspond to the target grid, which is the target grid in the target grid map where the target position of the target UAV is located.

[0013] Step S120: According to the preset flight path planning algorithm, starting from the starting grid, traverse each target grid in the target flight area until the destination grid is reached, so as to determine several initial flight paths.

[0014] In one exemplary embodiment of this application, step S200 includes:

[0015] Step S210: Obtain the historical average crowd density of several target grids located between the starting grid and the destination grid on each initial flight path within a historical time period, and obtain the historical average crowd density list set Q = (Q1, Q2, ..., Q...). i ,...,Q n );Q i =(Q i1 Q i2 ,...,Q ig ,...,Q if(i) ); where i = 1, 2, ..., n; n is the number of initial flight paths; Q i Let g be the list of historical average population densities corresponding to the i-th initial flight path; g = 1, 2, ..., f(i); f(i) is the number of target grids located between the starting grid and the destination grid on the i-th initial flight path; Q ig The historical average crowd density is the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path within a historical time period. The historical average crowd density is obtained by averaging the historical crowd densities of the target grid at several historical moments within the historical time period.

[0016] Step S220: Obtain the planned flight time period F = (F1, F2) of the target UAV included in the flight route planning request; where F1 is the start time of F and F2 is the end time of F.

[0017] Step S230: Obtain the key flight path identifiers for several key flight paths of each key UAV, and obtain a list set of key flight path identifiers J = (J1, J2, ..., J...). q ,...,J s ); J q =(J q1 J q2 ,...,J qp ,...,J qr(q) ); where q = 1, 2, ..., s; s is the number of key UAVs; J q Here is a list of critical flight path identifiers for the q-th critical UAV; p = 1, 2, ..., r(q); r(q) is the number of critical flight paths for the q-th critical UAV; J qp The key flight path identifier is the key flight path of the p-th key flight path of the q-th key UAV; the key UAV is the UAV whose planned flight time period coincides with F; the key flight path is the flight path that the key UAV plans to fly within the planned flight time period corresponding to the key UAV.

[0018] Step S240: Traverse the list set J of key flight route identifiers. If J qpThe corresponding critical flight path includes Q ig The corresponding target raster will then be J qp The corresponding critical flight path is determined to be Q. ig The target critical flight path of the corresponding target grid.

[0019] In one exemplary embodiment of this application, step S300 includes:

[0020] Step S310: Determine the density cost Y corresponding to the g-th target grid located between the starting grid and the target grid on the i-th initial flight path. ig =Q ig +N ig ; where N ig For Q ig The number of target critical flight paths for the corresponding target grid.

[0021] In one exemplary embodiment of this application, step S400 includes:

[0022] Step S410: Based on the grid security level of each target grid on each initial flight path and the grid security level of the key grid corresponding to the target grid, determine the security level cost value and security level cost coefficient of the target grid; the key grid corresponding to the target grid is the next target grid of the target grid on its corresponding initial flight path;

[0023] Step S420: Determine the estimated cost of each target grid on each initial flight path based on the starting grid coordinates, the target grid coordinates, and the corresponding grid coordinates on each initial flight path.

[0024] Step S430: Determine the total cost value corresponding to each initial flight path based on the density cost value, estimated cost value, safety level cost value, and safety level cost coefficient of several target grids on each initial flight path.

[0025] Step S440: Determine the initial flight route with the lowest total value as the target flight route.

[0026] In one exemplary embodiment of this application, step S410 includes:

[0027] Step S411: Obtain the grid safety level corresponding to several target grids located between the starting grid and the destination grid on each initial flight path, and obtain the grid safety level list set V = (V1, V2, ..., V i ,...,V n );V i =(V i1 V i2 ,...,V ig,...,V if(i) ); where V i V is a list of grid safety levels corresponding to the i-th initial flight path; ig The grid safety level is the grid corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path;

[0028] Step S412, according to V ig and V i(g+1) Determine the change in safety level O corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig ;

[0029] Step S413: Determine the safety level cost corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. Where e is the natural constant;

[0030] Step S414, if V i(g+1) If c1, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c3×O ig ;

[0031] If V i(g+1) If c2, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c2×O ig ;

[0032] If V i(g+1) If c3 is true, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c1×O ig ;

[0033] Where t is the preset security level cost weighting coefficient.

[0034] In one exemplary embodiment of this application, step S412 includes:

[0035] Step S4121, if V ig It is c3, and V i(g+1) If it is c3, then

[0036] If V ig c2, and V i(g+1) If it is c3, then

[0037] If V ig Let c1 be the value of V. i(g+1) If it is c3, then

[0038] If V ig Let c1 be the value of V. i(g+1) If it is c2, then

[0039] If V ig c2, and V i(g+1) If it is c2, then

[0040] If V ig It is c3, and V i(g+1) If it is c2, then

[0041] If V ig Let c1 be the value of V. i(g+1) If it is c1, then

[0042] If V ig c2, and V i(g+1) If it is c1, then

[0043] If V ig It is c3, and V i(g+1) If c1, then O ig =c3.

[0044] In one exemplary embodiment of this application, step S420 includes:

[0045] Step S421: Obtain the grid coordinates of several target grids located between the starting grid and the destination grid on each initial flight path, and obtain the initial flight path coordinate list set E = (E1, E2, ..., E i ,...,E n ); E i =(E i1 E i2 ,...,E ig ,...,E if(i) ); E ig =(E ig1 E ig2 ); where E i E is a list of initial flight path coordinates corresponding to the i-th initial flight path. ig E represents the grid coordinates of the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path; ig1 For E ig The x-coordinate; E ig2 For Eig The ordinate;

[0046] Step S422: Determine the path from the starting grid to E along the i-th initial flight path. ig The cumulative cost H at the corresponding target grid ig =|E ig1 -x a |+|E ig2 -y a |;

[0047] Step S423: Determine the initial flight path from E along the i-th initial flight path. ig The corresponding critical value M from the target grid to the destination grid ig =((E) ig1 -x b ) 2 +(E ig2 -y b ) 2 ) 1 / 2 ;

[0048] Step S424, if H ig ≤M ig Then determine E ig The position coefficient T at the corresponding target grid ig =M ig / H ig Otherwise, determine E. ig The position coefficient T at the corresponding target grid ig =H ig / M ig ;

[0049] Step S425, Determine E ig The cumulative cost coefficient of the corresponding target raster Where e is the natural constant;

[0050] Step S426, Determine E ig The corresponding key cost coefficient W of the target grid ig =1-U ig ;

[0051] Step S427, according to U ig and W ig Determine E ig The estimated cost Z of the corresponding target raster ig =U ig ×H ig +W ig ×M ig .

[0052] In one exemplary embodiment of this application, step S430 includes:

[0053] Step S431, according to Y ig Z ig P ig and G ig Determine the total agency value B corresponding to the i-th initial flight route. i =∑ f(i) g＝1 (Y ig +Z ig +P ig ×G ig ).

[0054] In one exemplary embodiment of this application, the grid security level corresponding to the target grid is determined through the following steps:

[0055] Step S010: Obtain the distance between each target grid and each obstacle in the target space, resulting in a distance list set A = (A1, A2, ..., A...). m ,...,A h A m =(A m1 A m2 ,...,A mj ,...,A mk ); where m = 1, 2, ..., h; h is the number of target grid cells; A m This is a list of distances corresponding to the m-th target grid; j = 1, 2, ..., k; k is the number of obstacles in the target space; A mj Let be the distance between the m-th target grid and the j-th obstacle in the target space;

[0056] Step S020: MIN(A) m The corresponding distance is determined as the safety interval D corresponding to the m-th target grid. m ; where MIN() is the preset minimum value determination function;

[0057] Step S030, if 0 < D m If ≤d1, then the grid security level corresponding to the m-th target grid is determined to be c1;

[0058] If d1 < D m If ≤d2, then the grid security level corresponding to the m-th target grid is determined to be c2;

[0059] If D m If d2 > d2, then the grid security level corresponding to the m-th target grid is determined to be c3;

[0060] Where d1 and d2 are preset safety interval thresholds; c3 > c2 > c1 > 0.

[0061] According to one aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned UAV route planning method based on ground population density.

[0062] According to one aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0063] The present invention has at least the following beneficial effects:

[0064] This invention presents a UAV flight path planning method based on ground population density. It determines several initial flight paths for the target UAV based on its initial and destination grid coordinates. Then, based on the historical average population density of several target grids along each initial flight path within a historical time period, it determines the critical flight path for each target grid. Next, based on the number of critical flight paths corresponding to each target grid, it determines the density cost of each target grid. Finally, based on the density cost of each target grid along each initial flight path, it determines the target flight path from the several initial flight paths. By analyzing the population density within the area along the initial flight path of the target UAV, the safety of the determined target flight path is improved. This reduces the risk of the UAV crashing and colliding with ground populations due to communication failures or other issues during flight, ensuring both the safety of the UAV's flight and the safety of people in high-density areas on the ground. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 A flowchart of a drone flight path planning method based on ground population density provided in an embodiment of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] With the increasing number of drones in flight, flight management is necessary for flight safety, which involves flight path planning to ensure each drone flies according to its designated route. The low-altitude airspace environment in which drones operate is becoming increasingly complex and variable with the development of the drone industry. Therefore, to ensure the safety of drones during flight, reasonable and safe flight path planning is required. This invention proposes a drone flight path planning method based on ground population density.

[0069] A method for UAV flight path planning based on ground population density is applied to a flight path planning system. The flight path planning system includes a target grid map corresponding to the target space. The target grid map includes several target grids and corresponds to a grid coordinate system. Each target grid has grid coordinates in the grid coordinate system and a grid safety level. The grid safety level of a target grid is determined based on the safety distance between the target grid and obstacles.

[0070] The grid security level corresponding to the target grid is determined through steps S010-S030:

[0071] Step S010: Obtain the distance between each target grid and each obstacle in the target space, resulting in a distance list set A = (A1, A2, ..., A...). m ,...,A h A m =(A m1 A m2 ,...,A mj ,...,A mk ); where m = 1, 2, ..., h; h is the number of target grid cells; A m This is a list of distances corresponding to the m-th target grid; j = 1, 2, ..., k; k is the number of obstacles in the target space; A mj Let be the distance between the m-th target grid and the j-th obstacle in the target space;

[0072] Step S020: MIN(A) m The corresponding distance is determined as the safety interval D corresponding to the m-th target grid. m ; where MIN() is the preset minimum value determination function;

[0073] Step S030, if 0 < D m If ≤d1, then the grid security level corresponding to the m-th target grid is determined to be c1;

[0074] If d1 < D mIf ≤d2, then the grid security level corresponding to the m-th target grid is determined to be c2;

[0075] If D m If d2 > d2, then the grid security level corresponding to the m-th target grid is determined to be c3;

[0076] Where d1 and d2 are preset safety interval thresholds; c3 > c2 > c1 > 0.

[0077] C1, C2, and C3 can be determined by staff based on existing grid security level determination methods, or they can be set by staff themselves according to civil aviation drone flight standards or rules.

[0078] like Figure 1 As shown, the UAV flight path planning method based on ground population density described in this application includes the following steps:

[0079] Step S100: In response to receiving the flight path planning request from the target UAV, determine several initial flight paths from the starting grid to the destination grid based on the starting grid coordinates of the starting grid and the destination grid coordinates of the destination grid included in the flight path planning request.

[0080] Furthermore, step S100 includes steps S110-S120:

[0081] Step S110: The grid coordinates are (x...) a ,y a ), (x a ,y b ), (x b ,y a ) and (x b ,y b The area composed of the target grid is defined as the target flight area; where x a The x-coordinate of the starting raster coordinates; y a The ordinate of the starting raster coordinates; x b y is the x-coordinate of the target raster coordinates; b The ordinate of the target raster coordinates;

[0082] The starting grid coordinates correspond to the starting grid, which is the target grid in the target grid map where the starting position of the target UAV is located; the destination grid coordinates correspond to the destination grid, which is the target grid in the target grid map where the destination position of the target UAV is located.

[0083] Step S120: According to the preset flight path planning algorithm, starting from the starting grid, traverse each target grid in the target flight area until the destination grid is reached, so as to determine several initial flight paths.

[0084] The preset flight route planning algorithm can adopt existing path planning algorithms, such as the traditional A* algorithm.

[0085] Step S200: Based on the historical average population density of several target grids located between the starting grid and the destination grid on each initial flight path within a historical period, determine the target critical flight path corresponding to each target grid located between the starting grid and the destination grid on each initial flight path from several critical flight paths.

[0086] Among them, the key flight route is the flight route that the key UAV plans to fly within the planned flight time period corresponding to the key UAV.

[0087] The key drone is the drone whose planned flight time period overlaps with the planned flight time period of the target drone included in the flight route planning request.

[0088] Furthermore, step S200 includes steps S210-S240:

[0089] Step S210: Obtain the historical average crowd density of several target grids located between the starting grid and the destination grid on each initial flight path within a historical time period, and obtain the historical average crowd density list set Q = (Q1, Q2, ..., Q...). i ,...,Q n );Q i =(Q i1 Q i2 ,...,Q ig ,...,Q if(i) ); where i = 1, 2, ..., n; n is the number of initial flight paths; Q i Let g be the list of historical average population densities corresponding to the i-th initial flight path; g = 1, 2, ..., f(i); f(i) is the number of target grids located between the starting grid and the destination grid on the i-th initial flight path; Q ig The historical average population density of the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path during the historical time period;

[0090] The historical average population density is obtained by averaging the historical population density of the target raster at several historical moments in a historical period.

[0091] Step S220: Obtain the planned flight time period F = (F1, F2) of the target UAV included in the flight route planning request; where F1 is the start time of F and F2 is the end time of F.

[0092] Step S230: Obtain the key flight path identifiers for several key flight paths of each key UAV, and obtain a list set of key flight path identifiers J = (J1, J2, ..., J...). q ,...,J s ); J q =(J q1 J q2 ,...,J qp ,...,J qr(q) ); where q = 1, 2, ..., s; s is the number of key UAVs; J q Here is a list of critical flight path identifiers for the q-th critical UAV; p = 1, 2, ..., r(q); r(q) is the number of critical flight paths for the q-th critical UAV; J qp The critical flight path identifier for the p-th critical flight path of the q-th critical UAV;

[0093] The key drones are those whose planned flight time overlaps with F.

[0094] Step S240: Traverse the list set J of key flight route identifiers. If J qp The corresponding critical flight path includes Q ig The corresponding target raster will then be J qp The corresponding critical flight path is determined to be Q. ig The target critical flight path of the corresponding target grid.

[0095] Step S300: Determine the density cost value corresponding to each target grid based on the number of target critical flight paths corresponding to each target grid located between the starting grid and the destination grid on each initial flight path.

[0096] Furthermore, step S300 includes step S310:

[0097] Step S310: Determine the density cost Y corresponding to the g-th target grid located between the starting grid and the target grid on the i-th initial flight path. ig =Q ig +N ig ; where N ig For Q ig The number of target critical flight paths for the corresponding target grid.

[0098] Step S400: Determine the target flight path from several initial flight paths based on the density value corresponding to each target grid on each initial flight path.

[0099] Furthermore, step S400 includes steps S410-S440:

[0100] Step S410: Based on the grid security level of each target grid on each initial flight path and the grid security level of the key grid corresponding to the target grid, determine the security level cost value and security level cost coefficient of the target grid.

[0101] The key grid corresponding to the target grid is the next target grid on its corresponding initial flight path.

[0102] Step S410 includes steps S411-S414:

[0103] Step S411: Obtain the grid safety level corresponding to several target grids located between the starting grid and the destination grid on each initial flight path, and obtain the grid safety level list set V = (V1, V2, ..., V i ,...,V n );V i =(V i1 V i2 ,...,V ig ,...,V if(i) ); where V i V is a list of grid safety levels corresponding to the i-th initial flight path; ig The grid safety level is the grid corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path;

[0104] Step S412, according to V ig and V i(g+1) Determine the change in safety level O corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig ;

[0105] In step S412, the change in safety level is determined through step S4121:

[0106] Step S4121, if V ig It is c3, and V i(g+1) If it is c3, then

[0107] If V ig c2, and V i(g+1) If it is c3, then

[0108] If V ig Let c1 be the value of V. i(g+1) If it is c3, then

[0109] If V ig Let c1 be the value of V. i(g+1) If it is c2, then

[0110] If V ig c2, and V i(g+1) If it is c2, then

[0111] If V ig It is c3, and V i(g+1) If it is c2, then

[0112] If V ig Let c1 be the value of V. i(g+1) If it is c1, then

[0113] If V ig c2, and V i(g+1) If it is c1, then

[0114] If V ig It is c3, and V i(g+1) If c1, then O ig =c3.

[0115] Step S413: Determine the safety level cost corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. Where e is the natural constant;

[0116] Step S414, if V i(g+1) If c1, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c3×O ig ;

[0117] If V i(g+1) If c2, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c2×O ig ;

[0118] If V i(g+1) If c3 is true, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c1×O ig ;

[0119] Where t is the preset security level cost weighting coefficient.

[0120] Step S420: Determine the estimated cost of each target grid on each initial flight path based on the starting grid coordinates, the target grid coordinates, and the corresponding grid coordinates on each initial flight path.

[0121] Step S420 includes steps S421-S427:

[0122] Step S421: Obtain the grid coordinates of several target grids located between the starting grid and the destination grid on each initial flight path, and obtain the initial flight path coordinate list set E = (E1, E2, ..., E i ,...,E n ); E i =(E i1 E i2 ,...,E ig ,...,E if(i) ); E ig =(E ig1 E ig2 ); where E i E is a list of initial flight path coordinates corresponding to the i-th initial flight path. ig E represents the grid coordinates of the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path; ig1 For E ig The x-coordinate; E ig2 For E ig The ordinate;

[0123] Step S422: Determine the path from the starting grid to E along the i-th initial flight path. ig The cumulative cost H at the corresponding target grid ig =|E ig1 -x a |+|E ig2 -y a |;

[0124] Step S423: Determine the initial flight path from E along the i-th initial flight path. ig The corresponding critical value M from the target grid to the destination grid ig =((E) ig1 -x b ) 2 +(E ig2 -y b ) 2 ) 1 / 2 ;

[0125] Step S424, if H ig ≤M ig Then determine E igThe position coefficient T at the corresponding target grid ig =M ig / H ig Otherwise, determine E. ig The position coefficient T at the corresponding target grid ig =H ig / M ig ;

[0126] Step S425, Determine E ig The cumulative cost coefficient of the corresponding target raster Where e is the natural constant;

[0127] Step S426, Determine E ig The corresponding key cost coefficient W of the target raster ig =1-U ig ;

[0128] Step S427, according to U ig and W ig Determine E ig The estimated cost Z of the corresponding target raster ig =U ig ×H ig +W ig ×M ig .

[0129] Step S430: Determine the total cost value corresponding to each initial flight path based on the density cost value, estimated cost value, safety level cost value, and safety level cost coefficient of several target grids on each initial flight path.

[0130] Furthermore, step S430 includes step S431:

[0131] Step S431, according to Y ig Z ig P ig and G ig Determine the total agency value B corresponding to the i-th initial flight route. i =∑ f(i) g＝1 (Y ig +Z ig +P ig ×G ig ).

[0132] Step S440: Determine the initial flight route with the lowest total value as the target flight route.

[0133] This invention presents a UAV flight path planning method based on ground population density. Based on the starting and destination grid coordinates of the target UAV, several initial flight paths are determined. The density cost of each target grid within each initial flight path is determined. Then, based on the grid security level of each target grid and the grid security level of the corresponding critical grid, the security level cost, security level cost coefficient, and estimated cost of each target grid are determined. This results in the total cost of each initial flight path, and the initial flight path with the lowest total cost is selected as the target UAV's flight path. By analyzing the population density within the area along the initial flight path of the target UAV, the safety of the determined target flight path is improved, avoiding serious consequences caused by sudden accidents during UAV flight and ensuring both UAV flight safety and the safety of people in high-density areas on the ground.

[0134] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0135] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0136] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0137] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0138] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0139] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0140] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0141] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0142] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0143] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0144] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0145] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.

[0146] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0147] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0148] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0150] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0151] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0152] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0153] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0154] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for unmanned aerial vehicle (UAV) flight path planning based on ground population density, characterized in that, Applied to flight route planning systems; The method includes the following steps: Step S100: In response to receiving a flight path planning request from the target UAV, determine several initial flight paths from the starting grid to the destination grid based on the starting grid coordinates of the starting grid and the destination grid coordinates of the destination grid included in the flight path planning request. Step S200: Based on the historical average population density of several target grids located between the starting grid and the destination grid on each initial flight path within a historical time period, determine the target key flight path corresponding to each target grid located between the starting grid and the destination grid on each initial flight path from several key flight paths; wherein, the key flight path is the flight path planned by the key UAV within the planned flight time period corresponding to the key UAV; the key UAV is the UAV whose planned flight time period coincides with the planned flight time period of the target UAV included in the flight path planning request; Step S300: Determine the density cost value corresponding to each target grid based on the number of target critical flight paths corresponding to each target grid located between the starting grid and the target grid on each initial flight path. Step S400: Determine the target flight path from a plurality of initial flight paths based on the density value corresponding to each target grid on each initial flight path. Step S200 includes: Step S210: Obtain the historical average crowd density of several target grids located between the starting grid and the destination grid on each initial flight path within a historical time period, and obtain a list set of historical average crowd densities Q=(Q1,Q2,...,Q i ,...,Q n );Q i =(Q i1 Q i2 ,...,Q ig ,...,Q if(i) ); where i = 1, 2, ..., n; n is the number of the initial flight paths; Q i This is a list of historical average population densities corresponding to the i-th initial flight path; g = 1, 2, ..., f(i); f(i) is the number of target grids located between the starting grid and the destination grid on the i-th initial flight path; Q ig The historical average crowd density is the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path within a historical time period; the historical average crowd density is obtained by averaging the historical crowd densities of the target grid at several historical moments in the historical time period. Step S220: Obtain the planned flight time period F=(F1,F2) of the target UAV included in the flight route planning request; where F1 is the start time of F and F2 is the end time of F. Step S230: Obtain the key flight path identifiers for several key flight paths of each key UAV, and obtain a list set of key flight path identifiers J=(J1,J2,...,J...). q ,...,J s ); J q =(J q1 J q2 ,...,J qp ,...,J qr(q) ); where q=1,2,...,s; s is the number of key UAVs; J q Here is a list of critical flight path identifiers for the q-th critical UAV; p = 1, 2, ..., r(q); r(q) is the number of critical flight paths for the q-th critical UAV; J qp The key flight route identifier is the key flight route of the p-th key flight route of the q-th key UAV; the key UAV is the UAV whose planned flight time period coincides with F; the key flight route is the flight route that the key UAV plans to fly within the planned flight time period corresponding to the key UAV. Step S240: Traverse the list set J of key flight route identifiers. If J qp The corresponding critical flight path includes Q ig The corresponding target raster will then be J qp The corresponding critical flight path is determined to be Q. ig The target critical flight path of the corresponding target grid.

2. The method according to claim 1, characterized in that, The flight path planning system includes a target grid map corresponding to the target space. The target grid map includes several target grids. The target grid map corresponds to a grid coordinate system. Each target grid corresponds to a grid coordinate in the grid coordinate system. Each target grid corresponds to a grid safety level. The grid safety level corresponding to the target grid is determined according to the safety distance between the target grid and the obstacle. Step S100 includes: Step S110: The grid coordinates are (x... a ,y a ), (x a ,y b ), (x b ,y a ) and (x b ,y b The area composed of the target grid is defined as the target flight area; where x a y is the x-coordinate of the starting grid coordinates; a x is the ordinate of the starting grid coordinates; b y is the x-coordinate of the target raster coordinates; b The ordinate of the target grid coordinates; the starting grid corresponding to the starting grid coordinates is the target grid in the target grid map where the starting position of the target UAV is located; the target grid corresponding to the target grid coordinates is the target grid in the target grid map where the target position of the target UAV is located. Step S120: According to the preset flight path planning algorithm, starting from the starting grid, traverse each target grid in the target flight area until the destination grid is reached, so as to determine several initial flight paths.

3. The method according to claim 2, characterized in that, Step S300 includes: Step S310: Determine the density cost Y corresponding to the g-th target grid located between the starting grid and the target grid on the i-th initial flight path. ig =Q ig +N ig ; where N ig For Q ig The number of target critical flight paths for the corresponding target grid.

4. The method according to claim 3, characterized in that, Step S400 includes: Step S410: Based on the grid security level of each target grid on each initial flight path and the grid security level of the key grid corresponding to the target grid, determine the security level cost value and security level cost coefficient of the target grid; the key grid corresponding to the target grid is the next target grid of the target grid on its corresponding initial flight path; Step S420: Determine the estimated cost of each target grid on each initial flight path based on the starting grid coordinates, the target grid coordinates, and the corresponding grid coordinates on each initial flight path. Step S430: Determine the total cost value corresponding to each initial flight path based on the density cost value, estimated cost value, safety level cost value, and safety level cost coefficient of several target grids on each initial flight path. Step S440: Determine the initial flight route with the lowest total value as the target flight route.

5. The method according to claim 4, characterized in that, Step S410 includes: Step S411: Obtain the grid security level corresponding to several target grids located between the starting grid and the destination grid on each initial flight path, and obtain a grid security level list set V=(V1,V2,...,V...). i ,...,V n );V i =(V i1 V i2 ,...,V ig ,...,V if(i) ); where V i V is a list of grid safety levels corresponding to the i-th initial flight path; ig The grid security level is the grid security level corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path; Step S412, according to V ig and V i(g+1) Determine the safety level change O corresponding to the g-th target grid located between the starting grid and the target grid on the i-th initial flight path. ig ; Step S413: Determine the safety level cost corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. Where e is the natural constant; Step S414, if V i(g+1) If c1, then determine the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path. ig =t×c3×O ig ; If V i(g+1) If c2 is true, then the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path is determined. ig =t×c2×O ig ; If V i(g+1) If c3 is true, then the safety level cost coefficient G corresponding to the g-th target grid located between the starting grid and the destination grid on the i-th initial flight path is determined. ig =t×c1×O ig ; Where t is the preset security level cost weighting coefficient.

6. The method according to claim 5, characterized in that, Step S412 includes: Step S4121, if V ig It is c3, and V i(g+1) If it is c3, then ; If V ig c2, and V i(g+1) If it is c3, then ; If V ig Let c1 be the value of V. i(g+1) If it is c3, then ; If V ig Let c1 be the value of V. i(g+1) If it is c2, then ; If V ig c2, and V i(g+1) If it is c2, then ; If V ig It is c3, and V i(g+1) If it is c2, then ; If V ig Let c1 be the value of V. i(g+1) If it is c1, then ; If V ig c2, and V i(g+1) If it is c1, then ; If V ig It is c3, and V i(g+1) If it is c1, then .

7. The method according to claim 6, characterized in that, Step S420 includes: Step S421: Obtain the grid coordinates corresponding to several target grids located between the starting grid and the destination grid on each initial flight path, to obtain the initial flight path coordinate list set E=(E1,E2,...,E...). i ,...,E n ); E i =(E i1 E i2 ,...,E ig ,...,E if(i) ); E ig =(E ig1 E ig2 ); where E i E is a list of initial flight path coordinates corresponding to the i-th initial flight path; ig E represents the grid coordinates of the g-th target grid located between the starting grid and the target grid on the i-th initial flight path; ig1 For E ig The x-coordinate; E ig2 For E ig The ordinate; Step S422: Determine the distance from the starting grid to E along the i-th initial flight path. ig The cumulative cost H at the corresponding target grid ig =|E ig1 -x a |+|E ig2 -y a |; Step S423: Determine the initial flight path along the i-th route from E ig The critical cost M from the corresponding target grid to the destination grid ig =((E ig1 -x b ) 2 +(E ig2 -y b ) 2 ) 1 / 2 ; Step S424, if H ig ≤M ig Then determine E ig The position coefficient T at the corresponding target grid ig =M ig / H ig Otherwise, determine E. ig The position coefficient T at the corresponding target grid ig =H ig / M ig ; Step S425, Determine E ig The cumulative cost coefficient of the corresponding target raster Where e is the natural constant; Step S426, Determine E ig The corresponding key cost coefficient W of the target raster ig =1-U ig ; Step S427, according to U ig and W ig Determine E ig The estimated cost Z of the corresponding target raster ig =U ig ×H ig +W ig ×M ig .

8. The method according to claim 7, characterized in that, Step S430 includes: Step S431, according to Y ig Z ig P ig and G ig Determine the total agency value B corresponding to the i-th initial flight route. i =∑ f(i) g=1 (Y ig +Z ig +P ig ×G ig ).

9. The method according to claim 8, characterized in that, The grid security level corresponding to the target grid is determined through the following steps: Step S010: Obtain the distance between each target grid and each obstacle in the target space, and obtain a distance list set A=(A1,A2,...,A1). m ,...,A h A m =(A m1 A m2 ,...,A mj ,...,A mk ); where m = 1, 2, ..., h; h is the number of target grid cells; A m This is a list of distances corresponding to the m-th target grid; j = 1, 2, ..., k; k is the number of obstacles in the target space; A mj Let m be the distance between the m-th target grid and the j-th obstacle in the target space; Step S020: MIN(A) m The corresponding distance is determined as the safety interval D corresponding to the m-th target grid. m ; where MIN() is the preset minimum value determination function; Step S030, if 0 < D m If ≤d1, then the grid security level corresponding to the m-th target grid is determined to be c1; If d1 < D m If ≤d2, then the grid security level corresponding to the m-th target grid is determined to be c2; If D m If d2 > d2, then the grid security level corresponding to the m-th target grid is determined to be c3; Where d1 and d2 are preset safety interval thresholds; c3 > c2 > c1 > 0.

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