Unmanned aerial vehicle route planning method based on ground crowd density

Abstract

The invention provides an unmanned aerial vehicle route planning method based on ground crowd density, and the method comprises the steps: determining a target key flight route corresponding to each target grid according to the historical average crowd density corresponding to a plurality of target grids on each initial flight route of a target unmanned aerial vehicle; determining a density cost value corresponding to each target grid according to the number of the target key flight routes corresponding to each target grid; and determining a target flight route from the plurality of initial flight routes according to the density cost value corresponding to each target grid on each initial flight route. The crowd density in the area on the initial flight route of the target unmanned aerial vehicle is analyzed, so that the safety of the determined target flight route is improved, and the risk that the unmanned aerial vehicle crashes to the crowd on the ground due to the fact that communication faults occur in the flight process of the unmanned aerial vehicle is reduced; and the flight safety of the unmanned aerial vehicle and the safety of people in a high-density area on the ground are ensured.

Description

Background Art

[0002] With the booming development of the drone industry, the number of drone flights has continued to increase. According to statistics from the Civil Aviation Administration of China, the total flight hours of civil drones in China in 2023 reached 23.11 million, an increase of 11.8% year-on-year. Among them, most drones operate at low altitudes, and the density of low-altitude flights is increasing. As a result, low-altitude safety issues continue to emerge. 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 route planning methods, such as the A* algorithm, are based on heuristic search algorithms and are suitable for quickly searching for the shortest flight route. However, since the flight environment of drones is mostly low-altitude airspace, when planning drone flight routes, it is also necessary to fully consider the correlation between the relative positions of drones and the ground population in densely populated areas, and stay away from areas with high population density on the ground to reduce the risk of the drone falling and colliding with the ground population in the event of power outages or communication failures. Traditional flight route planning methods do not focus on analyzing the safety risks in areas with high population density, and it is difficult to fully ensure the flight safety of drones. Summary of the invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] According to one aspect of the present application, a method for UAV route planning based on ground crowd density is provided, which is applied to a flight route planning system, wherein the flight route planning system includes a target grid map corresponding to the target space, the target grid map includes a plurality of 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, and the grid safety level corresponding to the target grid is determined according to the safety interval between the target grid and the obstacle;

[0006] The method for planning a UAV route based on ground crowd density comprises the following steps:

[0007] Step S100, in response to receiving a flight route planning request from a target UAV, determining a plurality of initial flight routes from the starting grid to the destination grid according to the starting grid coordinates of the starting grid and the destination grid coordinates of the destination grid included in the flight route planning request;

[0008] Step S200: According to the historical average population density corresponding to the several target grids between the starting grid and the destination grid on each initial flight route in the historical period, a target key flight route corresponding to each target grid between the starting grid and the destination grid on each initial flight route is determined from the several key flight routes; wherein the key flight route is a flight route planned to be flown by the key UAV within the planned flight time period corresponding to the key UAV; the key UAV is a UAV whose planned flight time period overlaps with the planned flight time period of the target UAV included in the flight route planning request;

[0009] Step S300, determining a density cost value corresponding to each target grid according to the number of target key flight routes corresponding to each target grid located between the starting grid and the destination grid on each initial flight route;

[0010] Step S400: determining a target flight route from a plurality of initial flight routes according to a density cost value corresponding to each target grid on each initial flight route.

[0011] In an exemplary embodiment of the present 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 ) is determined as the target flight area; where x a is the horizontal coordinate of the starting grid coordinate; y a is the ordinate of the starting grid coordinate; x b is the horizontal coordinate of the target grid coordinate; y b is the ordinate of the destination grid coordinate; the starting grid corresponding to the starting grid coordinate is the target grid of the starting position point of the target UAV in the target grid map; the destination grid corresponding to the destination grid coordinate is the target grid of the destination position point of the target UAV in the target grid map;

[0013] Step S120 , according to a preset flight route planning algorithm, starting from the starting grid, traversing each target grid in the target flight area, and ending at the destination grid, to determine a number of initial flight routes.

[0014] In an exemplary embodiment of the present application, step S200 includes:

[0015] Step S210: Obtain the historical average population density corresponding to a number of target grids between the starting grid and the destination grid on each initial flight route during the historical period, and obtain a historical average population 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, and n is the number of initial flight routes; Q i is the list of historical average population density corresponding to the i-th initial flight route; g = 1, 2, ..., f(i); f(i) is the number of target grids between the starting grid and the destination grid on the i-th initial flight route; Q ig is the historical average population density corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route during the historical period; the historical average population density is obtained by averaging the historical population densities corresponding to several historical moments of the target grid during the historical period;

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

[0017] Step S230: Obtain key flight route identifiers of several key flight routes of each key UAV, and obtain a key flight route identifier list set 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 is the key flight route identification list corresponding to the qth key UAV; p = 1, 2, ..., r(q); r(q) is the number of key flight routes of the qth key UAV; J qp is the key flight route identifier of the pth key flight route of the qth 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 during the planned flight time period corresponding to the key UAV;

[0018] Step S240, traverse the key flight route identifier list set J, if J qpThe corresponding key flight routes include Q ig The corresponding target grid is J qp The corresponding key flight route is determined as Q ig The target critical flight path of the corresponding target grid.

[0019] In an exemplary embodiment of the present application, step S300 includes:

[0020] Step S310: Determine the density cost value Y corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route. ig =Q ig +N ig ; Among them, N ig Q ig The number of target critical flight paths of the corresponding target grid.

[0021] In an exemplary embodiment of the present application, step S400 includes:

[0022] Step S410: Determine the security level cost value and security level cost coefficient corresponding to the target grid according to the grid security level of each target grid on each initial flight route and the grid security level of the key grid corresponding to 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 route;

[0023] Step S420, determining an estimated cost value of each target grid on each initial flight route according to the starting grid coordinates, the destination grid coordinates and each corresponding grid coordinate on each initial flight route;

[0024] Step S430, determining a total cost value corresponding to each initial flight route according to the density cost values, estimated cost values, safety level cost values ​​and safety level cost coefficients of a number of target grids on each initial flight route;

[0025] Step S440: determine the initial flight route with the smallest total cost as the target flight route.

[0026] In an exemplary embodiment of the present application, step S410 includes:

[0027] Step S411: Obtain the grid security levels corresponding to a number of target grids between the starting grid and the destination grid on each initial flight route, 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 is the grid safety level list corresponding to the i-th initial flight route; V ig is the grid safety level corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route;

[0028] Step S412: According to V ig and V i(g+1) , determine the safety level change O corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route ig ;

[0029] Step S413: Determine the safety level cost value corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route. Among them, e is a natural constant;

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

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

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

[0033] Among them, t is the preset security level cost weight coefficient.

[0034] In an exemplary embodiment of the present application, step S412 includes:

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

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

[0037] If V ig is c1, and V i(g+1) is c3, then

[0038] If V ig is c1, and V i(g+1) is c2, then

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

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

[0041] If V ig is c1, and V i(g+1) is c1, then

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

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

[0044] In an exemplary embodiment of the present application, step S420 includes:

[0045] Step S421, obtain the grid coordinates corresponding to a number of target grids between the starting grid and the destination grid on each initial flight route, and obtain the initial flight route 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 is the initial flight route coordinate list corresponding to the i-th initial flight route; E ig is the grid coordinates corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route; E ig1 For E ig The horizontal axis of E ig2 For Eig The vertical coordinate of

[0046] Step S422: determine the number of cells along the i-th initial flight route from the starting grid to E ig The corresponding cumulative cost value H at the target grid ig =|E ig1 -x a |+|E ig2 -y a |;

[0047] Step S423: Determine the route along the i-th initial flight path from E ig The corresponding key cost 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 corresponding position coefficient T at the target grid ig =M ig / H ig Otherwise, determine E ig The corresponding position coefficient T at the target grid ig =H ig / M ig ;

[0049] Step S425: Determine E ig The corresponding cumulative cost coefficient of the target grid Among them, e is a 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 value Z of the corresponding target grid ig =U ig ×H ig +W ig ×M ig .

[0052] In an exemplary embodiment of the present application, step S430 includes:

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

[0054] In an exemplary embodiment of the present application, the grid security level corresponding to the target grid is determined by the following steps:

[0055] 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, ..., 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 grids, A m is the distance list corresponding to the mth target grid; j = 1, 2, ..., k; k is the number of obstacles in the target space; A mj is the distance between the mth target grid and the jth obstacle in the target space;

[0056] Step S020: MIN(A m ) is determined as the safety interval D corresponding to the mth target grid. m ; Wherein, MIN() is a preset minimum value determination function;

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

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

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

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

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

[0062] According to one aspect of the present 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] The method for planning the route of a drone based on ground population density of the present invention determines several initial flight routes corresponding to the target drone according to the starting grid coordinates and the destination grid coordinates of the target drone, determines the target key flight route corresponding to each target grid according to the historical average population density corresponding to several target grids on each initial flight route in the historical period, determines the density proxy value corresponding to each target grid according to the number of target key flight routes corresponding to each target grid, and finally determines the target flight route from several initial flight routes according to the density proxy value corresponding to each target grid on each initial flight route. By analyzing the population density in the area on the initial flight route of the target drone, the safety of the determined target flight route is improved, the risk of the drone crashing and causing collision with the ground population due to communication failures and the like during flight is reduced, and the flight safety of the drone and the safety of the population in the high-density area on the ground are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] 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.

[0066] Figure 1 A flowchart of a method for UAV route planning based on ground population density provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] 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.

[0068] With the increase in the number of drones in flight, for the sake of drone flight safety, it is necessary to manage the drones, that is, to plan the flight routes of the drones so that each drone can fly according to its own flight route. The flight environment of the ultra-low altitude airspace where drones are located has gradually become more complex and changeable with the development of the drone industry. Therefore, in order to ensure the safety of drones during flight, it is necessary to plan the flight routes reasonably and safely. Therefore, a drone route planning method based on ground population density is proposed in the present invention.

[0069] A method for unmanned aerial vehicle (UAV) route planning based on ground crowd density is applied to a flight route planning system. The flight route planning system includes a target grid map corresponding to a target space. The target grid map includes a plurality of 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 interval between the target grid and an obstacle.

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

[0071] 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, ..., 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 grids, A m is the distance list corresponding to the mth target grid; j = 1, 2, ..., k; k is the number of obstacles in the target space; A mj is the distance between the mth target grid and the jth obstacle in the target space;

[0072] Step S020: MIN(A m ) is determined as the safety interval D corresponding to the mth target grid. m ; Wherein, MIN() is a preset minimum value determination function;

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

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

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

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

[0077] c1, c2, and c3 can be determined by the staff according to the existing grid safety level determination method, or they can be set by the staff themselves according to the flight standards or rules of civil aviation drones.

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

[0079] Step S100, in response to receiving a flight route planning request from a target UAV, determining a plurality of initial flight routes from the starting grid to the destination grid according to the starting grid coordinates of the starting grid and the destination grid coordinates of the destination grid included in the flight route planning request;

[0080] Further, step S100 includes steps S110 to 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 ) is determined as the target flight area; where x a is the horizontal coordinate of the starting grid coordinate; y a is the ordinate of the starting grid coordinate; x b is the horizontal coordinate of the target grid coordinate; y b is the ordinate of the destination grid coordinate;

[0082] Among them, the starting grid corresponding to the starting grid coordinates is the target grid of the starting position point of the target UAV in the target grid map; the destination grid corresponding to the destination grid coordinates is the target grid of the destination position point of the target UAV in the target grid map.

[0083] Step S120 , according to a preset flight route planning algorithm, starting from the starting grid, traversing each target grid in the target flight area, and ending at the destination grid, to determine a number of initial flight routes.

[0084] The preset flight route planning algorithm may adopt an existing path planning algorithm, such as a traditional A-star algorithm.

[0085] Step S200, determining a target key flight route corresponding to each target grid located between the starting grid and the destination grid on each initial flight route from the plurality of key flight routes according to the historical average population density corresponding to the plurality of target grids located between the starting grid and the destination grid on each initial flight route in the historical period;

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

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

[0088] Further, step S200 includes steps S210 to S240:

[0089] Step S210: Obtain the historical average population density corresponding to a number of target grids between the starting grid and the destination grid on each initial flight route during the historical period, and obtain a historical average population 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, and n is the number of initial flight routes; Q i is the list of historical average population density corresponding to the i-th initial flight route; g = 1, 2, ..., f(i); f(i) is the number of target grids between the starting grid and the destination grid on the i-th initial flight route; Q ig is the historical average population density corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route during the historical period;

[0090] The historical average population density is obtained by averaging the historical population densities corresponding to several historical moments in the target grid during the historical period.

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

[0092] Step S230: Obtain key flight route identifiers of several key flight routes of each key UAV, and obtain a key flight route identifier list set 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 is the key flight route identification list corresponding to the qth key UAV; p = 1, 2, ..., r(q); r(q) is the number of key flight routes of the qth key UAV; J qp is the key flight route identifier of the pth key flight route of the qth key UAV;

[0093] The key UAV is the UAV whose planned flight time period coincides with F.

[0094] Step S240, traverse the key flight route identifier list set J, if J qp The corresponding key flight routes include Q ig The corresponding target grid is J qp The corresponding key flight route is determined as Q ig The target critical flight path of the corresponding target grid.

[0095] Step S300, determining a density cost value corresponding to each target grid according to the number of target key flight routes corresponding to each target grid located between the starting grid and the destination grid on each initial flight route;

[0096] Further, step S300 includes step S310:

[0097] Step S310: Determine the density cost value Y corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route. ig =Q ig +N ig ; Among them, N ig Q ig The number of target critical flight paths of the corresponding target grid.

[0098] Step S400, determining a target flight route from a plurality of initial flight routes according to a density cost value corresponding to each target grid on each initial flight route;

[0099] Further, step S400 includes step S410-step S440:

[0100] Step S410: Determine the security level cost value and security level cost coefficient corresponding to each target grid on each initial flight route according to the grid security level of each target grid and the grid security level of the key grid corresponding to the target grid;

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

[0102] Wherein, step S410 includes steps S411 to S414:

[0103] Step S411: Obtain the grid security levels corresponding to a number of target grids between the starting grid and the destination grid on each initial flight route, 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 is the grid safety level list corresponding to the i-th initial flight route; V ig is the grid safety level corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route;

[0104] Step S412: According to V ig and V i(g+1) , determine the safety level change O corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route ig ;

[0105] In step S412, the security level change is determined by step S4121:

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

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

[0108] If V ig is c1, and V i(g+1) is c3, then

[0109] If V ig is c1, and V i(g+1) is c2, then

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

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

[0112] If V ig is c1, and V i(g+1) is c1, then

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

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

[0115] Step S413: Determine the safety level cost value corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route. Among them, e is a natural constant;

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

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

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

[0119] Among them, t is the preset security level cost weight coefficient.

[0120] Step S420, determining an estimated cost value of each target grid on each initial flight route according to the starting grid coordinates, the destination grid coordinates and each corresponding grid coordinate on each initial flight route;

[0121] Wherein, step S420 includes steps S421 to S427:

[0122] Step S421, obtain the grid coordinates corresponding to a number of target grids between the starting grid and the destination grid on each initial flight route, and obtain the initial flight route 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 is the initial flight route coordinate list corresponding to the i-th initial flight route; E ig is the grid coordinates corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route; E ig1 For E ig The horizontal axis of E ig2 For E ig The vertical coordinate of

[0123] Step S422: determine the number of cells along the i-th initial flight route from the starting grid to E ig The corresponding cumulative cost value H at the target grid ig =|E ig1 -x a |+|E ig2 -y a |;

[0124] Step S423: Determine the route along the i-th initial flight path from E ig The corresponding key cost 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 corresponding position coefficient T at the target grid ig =M ig / H ig Otherwise, determine E ig The corresponding position coefficient T at the target grid ig =H ig / M ig ;

[0126] Step S425: Determine E ig The corresponding cumulative cost coefficient of the target grid Among them, e is a natural constant;

[0127] Step S426: Determine E ig The corresponding target grid's key cost coefficient W ig =1-U ig ;

[0128] Step S427: According to U ig and W ig , determine E ig The estimated cost value Z of the corresponding target grid ig =U ig ×H ig +W ig ×M ig .

[0129] Step S430, determining a total cost value corresponding to each initial flight route according to the density cost values, estimated cost values, safety level cost values ​​and safety level cost coefficients of a number of target grids on each initial flight route;

[0130] Further, step S430 includes step S431:

[0131] Step S431: According to Y ig , Z ig , P ig and G ig , determine the total cost 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 smallest total cost as the target flight route.

[0133] The method for planning the route of a drone based on ground population density of the present invention determines several initial flight routes corresponding to the target drone according to the starting grid coordinates and the destination grid coordinates of the target drone, determines the density cost value corresponding to the target grid according to the corresponding population density in each target grid on each initial flight route, and then determines the safety level cost value, safety level cost coefficient and estimated cost value corresponding to the target grid according to the grid safety level of each target grid on each initial flight route and the grid safety level of the key grid corresponding to the target grid, thereby determining the total cost value corresponding to each initial flight route, and determining the initial flight route with the minimum total cost value as the target flight route of the target drone. By analyzing the population density in the area on the initial flight route of the target drone, the safety of the determined target flight route is improved, the serious consequences caused by sudden accidents during the flight of the drone are avoided, and the flight safety of the drone and the safety of the crowd in the high-density area on the ground are guaranteed.

[0134] 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.

[0135] 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.

[0136] 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 mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

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

[0138] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0139] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0140] 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 storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0141] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0142] 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).

[0143] The storage 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 the implementation of a network environment.

[0144] 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.

[0145] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via 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) via a network adapter.

[0146] 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.

[0147] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Program code for performing the operations of the present invention 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 cases involving 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., through the Internet using an Internet service provider).

[0152] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0153] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0154] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for UAV route planning based on ground population density, characterized in that: Applied to flight route planning system; The method comprises the following steps: Step S100, in response to receiving a flight route planning request from a target UAV, determining a plurality of initial flight routes from the starting grid to the destination grid according to the starting grid coordinates of the starting grid and the destination grid coordinates of the destination grid included in the flight route planning request; Step S200: determining a target key flight route corresponding to each target grid located between the starting grid and the destination grid on each of the initial flight routes from a plurality of key flight routes according to the historical average population density corresponding to a plurality of target grids located between the starting grid and the destination grid on each of the initial flight routes in a historical period; wherein the key flight route is a flight route planned to be flown by a key UAV within a planned flight time period corresponding to the key UAV; and the key UAV is a UAV whose planned flight time period coincides with the planned flight time period of the target UAV included in the flight route planning request; Step S300, determining a density cost value corresponding to each target grid according to the number of target key flight routes corresponding to each target grid located between the starting grid and the destination grid on each of the initial flight routes; Step S400: determining a target flight route from a plurality of initial flight routes according to a density cost value corresponding to each target grid on each initial flight route.

2. The method according to claim 1, characterized in that: The flight route planning system includes a target grid map corresponding to the target space, the target grid map includes a plurality of target grids, the target grid map corresponds to a grid coordinate system, each of the target grids corresponds to a grid coordinate in the grid coordinate system, each of the target grids corresponds to a grid safety level, and the grid safety level corresponding to the target grid is determined according to the safety interval between the target grid and the obstacle; Wherein, the 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 ) is determined as the target flight area; where x a is the horizontal coordinate of the starting grid coordinate; a is the ordinate of the starting grid coordinate; b is the horizontal coordinate of the target grid coordinate; b is the ordinate of the destination grid coordinate; the starting grid corresponding to the starting grid coordinate is the target grid of the starting position point of the target UAV in the target grid map; the destination grid corresponding to the destination grid coordinate is the target grid of the destination position point of the target UAV in the target grid map; Step S120, according to a preset flight route planning algorithm, starting from the starting grid, traversing each target grid in the target flight area, and ending at the destination grid, to determine a number of initial flight routes.

3. The method according to claim 2, characterized in that The step S200 includes: Step S210: Obtain the historical average population density corresponding to a number of target grids between the starting grid and the destination grid on each of the initial flight routes during a historical period, and obtain a historical average population density list set Q = (Q1, Q2, ..., Q i ,...,Q n );Q i =(Q i1 ,Q i2 ,...,Q ig ,...,Q if(i) ); wherein i = 1, 2, ..., n; n is the number of the initial flight routes; Q i is a list of historical average population densities corresponding to the i-th initial flight route; g = 1, 2, ..., f(i); f(i) is the number of target grids between the starting grid and the destination grid on the i-th initial flight route; Q ig is the historical average population density corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route during the historical period; the historical average population density is obtained by averaging the historical population densities corresponding to the target grid at several historical moments during the historical period; Step S220, obtaining the planned flight time period F=(F1, F2) of the target UAV included in the flight route planning request; wherein F1 is the start time of F, and F2 is the end time of F; Step S230: Obtain key flight route identifiers of several key flight routes of each key UAV, and obtain a key flight route identifier list set 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 is the key flight route identification list corresponding to the qth key UAV; p = 1, 2, ..., r(q); r(q) is the number of key flight routes of the qth key UAV; J qp is the key flight route identifier of the pth key flight route of the qth key UAV; the key UAV is a UAV whose planned flight time period coincides with F; the key flight route is the flight route that the key UAV plans to fly during the planned flight time period corresponding to the key UAV; Step S240, traverse the key flight route identifier list set J, if J qp The corresponding key flight routes include Q ig The corresponding target grid is J qp The corresponding key flight route is determined as Q ig The target critical flight path of the corresponding target grid.

4. The method according to claim 3, characterized in that: The step S300 includes: Step S310: Determine the density cost value Y corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route. ig =Q ig +N ig ; Among them, N ig Q ig The number of target critical flight paths of the corresponding target grid.

5. The method according to claim 4, characterized in that The step S400 includes: Step S410: Determine the safety level cost value and safety level cost coefficient corresponding to the target grid according to the grid safety level of each target grid on each of the initial flight routes and the grid safety level of the key grid corresponding to 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 route; Step S420, determining an estimated cost value of each target grid on each of the initial flight routes according to the starting grid coordinates, the destination grid coordinates and each corresponding grid coordinate on each of the initial flight routes; Step S430, determining a total cost value corresponding to each of the initial flight routes according to the density cost values, estimated cost values, safety level cost values ​​and safety level cost coefficients of the plurality of target grids on each of the initial flight routes; Step S440: determine the initial flight route with the smallest total cost as the target flight route.

6. The method according to claim 5, characterized in that The step S410 includes: Step S411: Obtain the grid security levels corresponding to a number of target grids between the starting grid and the destination grid on each of the initial flight routes, 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 is the grid safety level list corresponding to the i-th initial flight route; V ig is the grid safety level corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route; Step S412: According to V ig and V i(g+1) , determine the safety level change O corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route ig ; Step S413: Determine the safety level cost value corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route. Among them, e is a natural constant; Step S414: If V i(g+1) If c1 is c1, then determine the safety level cost coefficient G corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route ig =t×c3×O ig ; If V i(g+1) If c2 is c2, then determine the safety level cost coefficient G corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route ig =t×c2×O ig ; If V i(g+1) If c3 is c3, then determine the safety level cost coefficient G corresponding to the g-th target grid between the starting grid and the destination grid on the i-th initial flight route ig =t×c1×O ig ; Among them, t is the preset security level cost weight coefficient.

7. The method according to claim 6, characterized in that The step S412 includes: Step S4121: If V ig is c3, and V i(g+1) is c3, then If V ig is c2, and V i(g+1) is c3, then If V ig is c1, and V i(g+1) is c3, then If V ig is c1, and V i(g+1) is c2, then If V ig is c2, and V i(g+1) is c2, then If V ig is c3, and V i(g+1) is c2, then If V ig is c1, and V i(g+1) is c1, then If V ig is c2, and V i(g+1) is c1, then If V ig is c3, and V i(g+1) is c1, then O ig =c3.

8. The method according to claim 7, characterized in that The step S420 includes: Step S421: Obtain the grid coordinates corresponding to a number of target grids between the starting grid and the destination grid on each of the initial flight routes, and obtain an initial flight route 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 is a list of initial flight route coordinates corresponding to the i-th initial flight route; E ig is the grid coordinate corresponding to the gth target grid between the starting grid and the destination grid on the i-th initial flight route; E ig1 For E ig The horizontal axis of E ig2 For E ig The vertical coordinate of Step S422: determine the route from the starting grid to E along the i-th initial flight route. ig The corresponding cumulative cost value H at the target grid ig =|E ig1 -x a |+|E ig2 -y a |; Step S423: determine the route along the i-th initial flight path from E ig The key cost value 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 corresponding position coefficient T at the target grid ig =M ig / H ig Otherwise, determine E ig The corresponding position coefficient T at the target grid ig =H ig / M ig ; Step S425: Determine E ig The corresponding cumulative cost coefficient of the target grid Among them, e is a natural constant; Step S426: Determine E ig The corresponding target grid's key cost coefficient W ig =1-U ig ; Step S427: According to U ig and W ig , determine E ig The estimated cost value Z of the corresponding target grid ig =U ig ×H ig +W ig ×M ig .

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

10. The method according to claim 9, characterized in that The grid security level corresponding to the target grid is determined by 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, ..., A m ,...,A h );A m =(A m1 ,A m2 ,...,A mj ,...,A mk ), wherein m=1, 2, ..., h, and h is the number of target grids; A m is the distance list corresponding to the mth target grid; j=1,2,...,k; k is the number of obstacles in the target space; A mj is the distance between the mth target grid and the jth obstacle in the target space; Step S020: MIN(A m ) is determined as the safety interval D corresponding to the mth target grid. m ; Wherein, MIN() is a preset minimum value determination function; Step S030: If 0<D m ≤d1, then determine that the grid security level corresponding to the mth target grid is c1; If d1<D m ≤d2, then determine that the grid security level corresponding to the mth target grid is c2; If D m >d2, then determine that the grid security level corresponding to the mth target grid is c3; Among them, d1 and d2 are preset safety interval thresholds; c3>c2>c1>0.

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