Urban low-altitude unmanned aerial vehicle route autonomous allocation method

Abstract

The invention relates to an autonomous deployment method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: (1) initializing an unmanned aerial vehicle, an air route deployment algorithm, starting and ending point coordinates of each air route and weight factors alpha and beta, and establishing a Manhattan cubic mean G (X) model of a target ending point and a target starting point and a distance sum N (X) model of each leg; (2) calculating a Manhattan distance as a first satisfaction condition, carrying out second condition judgment on the distance of each leg, considering the isomerism of air route models at different depths, and completing optimal air route deployment from the starting point to the target terminal point; and (3) finally, comprehensively analyzing the airspace limitation of the flight segment where the unmanned aerial vehicle is located, whether conflicts exist or not and whether the risk degree is high or not, and completing rapid switching of routes of different flight segments by updating the Manhattan weight factor and the route weight factor in real time, so that intelligent danger escape under the optimal path is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of directional transportation, and particularly relates to an autonomous allocation method for urban low-altitude drone routes. Background Art

[0002] With the development of drone technology, it is widely used in industries such as smart agriculture, rescue operations, and logistics transportation, and has advantages such as high operation efficiency and high intelligence compared with traditional manual operations.

[0003] With the development of drone technology in logistics transportation, drones have a large deviation value during route transportation, autonomously repair the deviation and return to the route. This can not only save energy costs but also improve the efficiency of logistics transportation and distribution.

[0004] When drones fly on fixed routes, there will be a risk of collision when the deviation from the route is large. Therefore, there is an urgent need for a yaw detection method that can detect the situation of deviating from the route in time for intervention and compensation, so as to avoid the occurrence of collision risks. Summary of the Invention

[0005] In order to solve the above existing problems, the present invention proposes a deviation detection method for drones under fixed routes, which is applicable to drones operating under fixed routes.

[0006] The present invention is realized through the following technical solutions:

[0007] An autonomous allocation method for urban low-altitude drone routes includes the following steps:

[0008] S1. Initialize the drone, initialize the route allocation algorithm, and initialize the coordinates of the starting point and target end point of each route; establish the longitude, latitude, and altitude coordinates of the starting point of the local coordinate system of the drone corresponding to the spatial coordinate system.

[0009] S2. Take the starting point of the current route as the coordinate origin, and establish a horizontal X-axis, a vertical Y-axis, and a vertical Z-axis coordinate system; set the longitude, latitude, and altitude coordinates of the current drone take-off point, and the longitude, latitude, and altitude coordinates of the target end point.

[0010] S3. After determining the longitude, latitude, and altitude coordinates of the route end point, calculate the Manhattan distance from the starting point to the end point position; establish an autonomous allocation model, including the Manhattan distance from the starting point to the end point and the sum of the distances of all flight segments; standardize the Manhattan distance from the starting point to the end point and the sum of the distances of all flight segments, and establish a unified measurement mechanism; complete the change of different flight segment routes through the real-time update of the Manhattan weight factor and the route weight factor.

[0011] Further, it specifically includes the following steps:

[0012] (1) Initialize the autonomous allocation model \(T(X)=\alpha H(X)+\beta N(X)\), and initialize the Manhattan distance \(H(X)\) from the starting point to the ending point; where \(T(X)\) represents the total distance value of autonomous route allocation, \(N(X)\) represents the sum of the distances of all segments passed from the starting point to the ending point, \(\alpha\) represents the Manhattan weight factor, and \(\beta\) represents the route weight factor;

[0013] (2) Set the \(X\), \(Y\), and \(Z\) axes to be perpendicular to each other. Then, the \(X\) axis coincides with the route, and the forward direction is the positive direction; set the \(Y\) axis to be perpendicular to the \(X\) axis and the positive direction is out of the paper; set the \(Z\) axis to be perpendicular to the \(X\) axis and the positive direction is upward;

[0014] (3) Initialize the Manhattan distance parameters in the autonomous route allocation model: \(G(X)\), \(\delta\), \(M(X)\), \(Lh\), \(\alpha\); where \(G(X)\) represents the mean value from the target ending point to the first three points sorted from the starting point, \(\delta\) represents the reverse value factor, \(M(X)\) represents the reverse Manhattan value between the target point and the last segment point \((o,p)\), and \(Lh\) represents the \(h\) altitude value corresponding to the \(i\)-th route of \(H(X)\).

[0015] Furthermore, there are the following two cases:

[0016] The first case: When moving forward in the route planning at the same route altitude, \(G(X)\) is the mean value of the first three times:

[0017]

[0018] The second case: \(M(X)\) is the reverse value between the target point and the last route point \((o,p)\):

[0019]

[0020] where \(x\) i , \(y\) i represent the longitude and latitude coordinates of point \(i\); \(x\) j , \(y\) j represent the longitude and latitude coordinates of point \(j\); \(x\) o , \(y\) o represent the longitude and latitude coordinates of point \(o\); \(x\) p , \(y\) p represent the longitude and latitude coordinates of point \(p\);

[0021] According to the requirements of the Manhattan distance, adjust the reverse value factor \(\delta\) to achieve the weight of the proportion of the end route points.

[0022] Furthermore, there are the following two cases:

[0023] The first case: When moving forward in the route planning at the same route altitude, \(H(X)=G(X)+\delta M(X)\), that is:

[0024]

[0025] The second case: When planning routes at different altitudes, H(X) = G(X) + δM(X) + Lh, that is:

[0026]

[0027] where z i , z j represents the longitude and latitude coordinates of a point on the altitude Z-axis;

[0028] Adjust the Manhattan weight factor α according to the current airspace restrictions and whether there is a conflict risk to update the current route.

[0029] Furthermore, it specifically includes the following steps:

[0030] (1) Initialize the autonomous allocation model T(X) = αH(X) + βN(X), and initialize the sum of the routes from the starting point to the ending point N(X); where T(X) represents the total distance of autonomous route allocation, H(X) represents the Manhattan distance from the initialized starting point to the ending point, α represents the Manhattan weight factor, and β represents the route weight factor;

[0031] (2) Set the X, Y, and Z axes to be perpendicular to each other. Then, the X-axis coincides with the route, and the forward direction is the positive direction; set the Y-axis to be perpendicular to the X-axis and the positive direction is out of the paper; set the Z-axis to be perpendicular to the X-axis and the positive direction is upward;

[0032] (3) Initialize the route distance parameters from the initialized starting point to the ending point in the autonomous route allocation model: N(X), β.

[0033] Furthermore, there are the following two cases:

[0034] The first case: The sum of the distances of i segments from the starting point to the ending point at the same route altitude. The solution model is:

[0035]

[0036] The second case: The sum of the distances of the route from the starting point to the ending point at different altitude routes. The solution model is:

[0037]

[0038] where L i represents the distance of the i-th route, represents the sum of the distances of the first i routes, and L t represents the t altitude value corresponding to the i-th route of N(X);

[0039] Adjust the route weight factor β according to the current airspace restrictions and whether there is a conflict risk to update the current route.

[0040] Further, it specifically includes the following steps:

[0041] (1) Schematic diagram of Manhattan distance calculation at the same height, including the drone flying from point A (X a , Y a ) at section AP001 - AP002 to point B (X b , Y b ) at section AP002 - AP003. After substituting into the Manhattan model, the calculation yields:

[0042]

[0043] (2) Schematic diagram of Manhattan distance calculation at different heights, including the drone flying from point M (X m , Y m , Z m ) in the three - dimensional space to point N (X n , Y n , Z n ). After substituting into the Manhattan model, the calculation yields:

[0044]

[0045] (3) Flow chart of autonomous allocation calculation for the drone on multiple air routes. Through the total distance model of the autonomous air route allocation model:

[0046]

[0047] Among them, T(X) represents the total value of the distance of autonomous air route allocation; H(X) represents the Manhattan distance from the initialization starting point to the end point; α represents the Manhattan weight factor; N(X) represents the sum of the distances of all sections passed from the starting point to the end point; β represents the air route weight factor; x i , y i represent the longitude and latitude coordinates of point i; x j , y j represent the longitude and latitude coordinates of point j; x o , y o represent the longitude and latitude coordinates of point o; x p , y p represent the longitude and latitude coordinates of point p; L i represents the distance of the i - th air route; represents the sum of the distances of the first i air routes.

[0048] Compared with the existing technology, the beneficial effects of the present invention are:

[0049] The present invention relates to a method for autonomous allocation of low-altitude drone routes in cities. First, drones, route allocation algorithms, starting and ending coordinates of each route, and weight factors α and β are initialized, and a Manhattan cubic mean G(X) model of the target end point and the starting point and a sum-of-distances N(X) model of each flight segment are established. On this basis, the Manhattan distance is calculated as the first condition to satisfy, and the second condition of each flight segment distance is judged. At the same time, considering the heterogeneity of route models at different depths, the optimal route allocation from the starting point to the target end point is completed. Finally, by comprehensively analyzing the airspace restrictions, conflicts, and high-risk scenarios of the flight segments where the drones are located, and by real-time updating the Manhattan weight factor and the route weight factor, the rapid switching of different flight segment routes is completed, so as to achieve intelligent escape under the optimal path. Description of the Drawings

[0050] Figure 1 is the general flow chart of the autonomous route allocation of the present invention.

[0051] Figure 2 is the flow chart of calculating the Manhattan distance of the route of the present invention.

[0052] Figure 3 is the flow chart of the sum of all flight segments from the starting point to the end point of the route of the present invention.

[0053] Figure 4 is the schematic diagram of calculating the Manhattan distance at the same height of the route of the present invention.

[0054] Figure 5 is the schematic diagram of calculating the Manhattan distance at different heights of the route of the present invention.

[0055] Figure 6 is the flow chart of the autonomous allocation calculation of drones on multiple routes of the present invention.

[0056] Figure 7 is the schematic diagram of the optimal flight path of the drone route allocation of the present invention. Detailed Embodiment

[0057] The present invention will be further clarified below in conjunction with the drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0058] As Figure 1 shown, the present invention provides a method for autonomous allocation of low-altitude drone routes in cities, which includes the following steps:

[0059] 100. Initialize the drones, initialize the route allocation algorithm, and initialize the starting and target end coordinates of each route;

[0060] 101. Establish the longitude, latitude coordinates and altitude coordinates of the starting point corresponding to the local coordinate system of the drone in the space coordinate system;

[0061] 102. Establish a coordinate system with the starting point of the current flight path as the coordinate origin, including a horizontal X-axis, a vertical Y-axis, and a perpendicular Z-axis.

[0062] 103. Set the latitude, longitude, and altitude coordinates of the current UAV takeoff point, as well as the latitude, longitude, and altitude coordinates of the target end point.

[0063] 104. After determining the latitude, longitude, and altitude coordinates of the end point of the flight path, calculate the Manhattan distance from the starting point to the end point.

[0064] 105. Establish an autonomous allocation model, including the Manhattan distance from the starting point to the end point and the cumulative sum of all flight segment distances, expressed as:

[0065]

[0066] 106. Standardize the Manhattan distance from the starting point to the end point and the sum of all flight segment distances, and establish a unified measurement mechanism. The standardized model is:

[0067] 107. Complete the change of different flight segment routes through the real-time update of the Manhattan weight factor and the flight path weight factor.

[0068] As Figure 2 shown, when using the Manhattan to solve the distance process, the specific steps are as follows:

[0069] 200. Initialize the autonomous allocation model T(X) = αH(X) + βN(X), and initialize the Manhattan distance H(X) from the starting point to the end point.

[0070] 201. Set the X, Y, and Z axes to be perpendicular to each other. Then, the X-axis coincides with the flight path, and the forward direction is the positive direction; set the Y-axis to be perpendicular to the X-axis and the positive direction is out of the paper; set the Z-axis to be perpendicular to the X-axis and the positive direction is upward.

[0071] 202. Initialize the Manhattan distance parameters in the autonomous flight path allocation model: G(X), δ, M(X), Lh, α.

[0072] 203. The first case: When planning to move forward on the flight path at the same altitude, G(X) in the Manhattan distance is the average value of the previous three times.

[0073] 204. The second case: The Manhattan distance M(X) is the reverse value of the target point and the last flight path point (o, p):

[0074]

[0075] 205. According to the need of the Manhattan distance, adjust the reverse value factor δ to achieve the weight ratio of the end flight path point.

[0076] 206. The first case: When moving forward in the route planning at the same airway altitude, the Manhattan distance H(X) = G(X) + δM(X), that is:

[0077]

[0078] 207. The second case: When planning routes at different altitudes, the Manhattan distance H(X) = G(X) + δM(X) + Lh, that is:

[0079]

[0080] 208. Adjust the Manhattan weight factor α according to the current airspace restrictions and whether there is a conflict risk to update the current airway.

[0081] As Figure 3 shown, when calculating the sum of all flight segments, the specific steps are as follows:

[0082] 300. Initialize the autonomous deployment model T(X) = αH(X) + βN(X), and initialize the sum of the routes from the starting point to the ending point N(X);

[0083] 301. Set the X, Y, and Z axes to be perpendicular to each other. Then, the X-axis coincides with the airway, and the forward direction is the positive direction; set the Y-axis to be perpendicular to the X-axis and the positive direction is out of the paper; set the Z-axis to be perpendicular to the X-axis and the positive direction is upward;

[0084] 302. Initialize the distance parameters of the route from the starting point to the ending point in the autonomous route deployment model: N(X), β;

[0085] 303. The first case: The sum of the distances of i flight segments from the starting point to the ending point at the same airway altitude. The solution model is:

[0086]

[0087] 304. The second case: For routes at different altitudes, the sum of the distances of the route from the starting point to the ending point. The solution model is:

[0088]

[0089] 305. Adjust the route weight factor β according to the current airspace restrictions and whether there is a conflict risk to update the current airway.

[0090] As Figure 4 shown, the schematic diagram of calculating the Manhattan distance at the same altitude, including the drone at point A (X a , Y a ) on the flight segment AP001 - AP002 flying towards point B (X b , Y b), after substituting into the Manhattan model, the calculation results are as follows:

[0091]

[0092] As Figure 5 shown, the schematic diagram of calculating Manhattan distance at different heights, including the M point (X m , Y m , Z m ) of the drone in the three-dimensional space flying towards the N point (X n , Y n , Z n ). After substituting into the Manhattan model, the calculation results are as follows:

[0093]

[0094] As Figure 6 shown, the calculation flow chart of the autonomous allocation of the drone on multiple routes. Through the general formula of the autonomous route allocation model:

[0095]

[0096] where the overall Manhattan distance from the starting point to the ending point and H(X) take the average value of the sum of the Manhattan distances from the target ending point to the first three starting points; the N(X) is the sum of the distances of all the segments passed from the starting point to the ending point; the H(X) is the first condition satisfied in the total autonomous allocation formula T(X); the M(x) is a part of H(X) and is the Manhattan distance from the target point to the intersection of the nearest segment; the magnitude of the reverse value factor δ can change the magnitude of the Manhattan distance from the target point to the intersection of the nearest segment M(x) in the overall Manhattan distance from the starting point to the ending point and H(X) and the route selection of the total autonomous allocation distance T(X); after the first condition is satisfied, the second condition judgment of the sum of the distances of the passed segments N(X) is carried out; if the first condition is not satisfied, the parameters are re-initialized for allocation; the numerical magnitudes of the Manhattan weight factor α and the route weight factor β can be dynamically adjusted according to whether the current route conflicts with the dangerous route.

[0097] Among them, T(X) represents the total value of the distance of the autonomous route allocation; H(X) represents the Manhattan distance from the initialized starting point to the ending point, α represents the Manhattan weight factor, which is mainly a variable coefficient used to change the magnitude of the Manhattan distance; N(X) represents the sum of the distances of all the segments passed from the starting point to the ending point, β represents the route weight factor, which is mainly a weight coefficient used to adjust the ratio of the sum of the distances of the segments from the starting point to the ending point to the total distance; x i , y i represent the longitude and latitude coordinates of point i; x j , y j represent the longitude and latitude coordinates of point j; x o , y o represent the longitude and latitude coordinates of point o; xp , y p represent the longitude and latitude coordinates of point p; z m , z n represent the longitude and latitude coordinates of the point on the height Z - axis; G(X) represents the mean value of the first three points sorted from the starting point to the target end - point; M(X) is the reverse Manhattan value of the target point and the last flight - segment points (o, p); δ represents the reverse - value factor, and the change of the value determines the distance between the target point and the nearest flight segment; L i represents the distance of the i - th flight path; represents the sum of the distances of the first i flight paths; L t represents the t - height value corresponding to the i - th flight path of N(X); Lh represents the h - height value corresponding to the i - th flight path of H(X).

[0098] As Figure 7 shown, through the method of the present invention, the optimal flight path of the UAV route allocation is obtained.

[0099] The specific embodiments of the invention have been described above. It should be understood that the present invention is not limited to the above - mentioned specific embodiments, and those skilled in the art can make various deformations or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for autonomously deploying routes of low-altitude UAVs in cities, characterized in that: The following steps are included: S1. Initialize the UAV, initialize the route allocation algorithm, initialize the coordinates of the starting point and target end point of each route; establish the latitude and longitude coordinates and altitude coordinates of the starting point of the UAV local coordinate system in the spatial coordinate system; S2. Take the starting point of the current route as the coordinate origin, establish the horizontal X-axis, vertical Y-axis and vertical Z-axis coordinate systems; set the longitude and latitude and altitude coordinates of the current drone take-off point, and the longitude and latitude and altitude coordinates of the target endpoint; S3. After determining the longitude, latitude and altitude coordinates of the route endpoint, calculate the Manhattan distance from the starting point to the end point; establish an autonomous dispatching model, including the Manhattan distance from the starting point to the end point and the sum of the distances of all segments; standardize the Manhattan distance from the starting point to the end point and the sum of the distances of all segments, and establish a unified measurement mechanism; complete the change of routes of different segments through real-time updates of Manhattan weight factors and route weight factors.

2. The method for autonomously deploying routes of low-altitude urban UAVs according to claim 1 is characterized in that: The specific steps include: (1) Initialize the autonomous dispatch model T(X) = αH(X) + βN(X), and initialize the Manhattan distance H(X) from the start point to the end point; where T(X) represents the total distance of autonomous route dispatch, N(X) represents the sum of the distances of all segments from the start point to the end point, α represents the Manhattan weight factor, and β represents the route weight factor; (2) Set the X, Y, and Z axes to be perpendicular to each other, and then set the X axis to coincide with the route, with the forward direction as the positive direction; set the Y axis to be perpendicular to the X axis and with the positive direction out of the paper; set the Z axis to be perpendicular to the X axis and with the upward direction as the positive direction; (3) Initialize the Manhattan distance parameters in the autonomous route allocation model: G(X), δ, M(X), Lh, α; where G(X) represents the average of the three points from the target end point to the starting point, δ represents the reverse value factor, M(X) represents the reverse Manhattan value between the target point and the last segment point (o, p), and Lh represents the h altitude value corresponding to the i-th route of H(X).

3. The method for autonomously deploying routes of low-altitude urban UAVs according to claim 2 is characterized in that: There are two situations: The first case: When the route is planned at the same altitude, G(X) is the average of the previous three times: The second case: M(X) is the reverse value of the target point and the last waypoint (o,p): Among them, x i ,y i represents the longitude and latitude coordinates of point i; x j ,y j represents the longitude and latitude coordinates of point j; x o ,y o Represents the latitude and longitude coordinates of point o; x p ,y p Represents the longitude and latitude coordinates of point p; According to the needs of Manhattan distance, the reverse value factor δ is adjusted to achieve the weight of the terminal waypoint proportion.

4. The method for autonomously deploying routes of low-altitude urban UAVs according to claim 3 is characterized by: There are two situations: The first case: When the route is planned at the same altitude, H(X) = G(X) + δM(X), which is: The second case: When planning routes at different altitudes, H(X) = G(X) + δM(X) + Lh, which is: Among them, z i ,z j Represents the latitude and longitude coordinates of a point on the height Z axis; The Manhattan weight factor α is adjusted according to the current airspace restrictions and whether there is a risk of conflict to update the current route.

5. The method for autonomously deploying routes of low-altitude urban UAVs according to claim 1 is characterized in that: The specific steps include: (1) Initialize the autonomous dispatching model T(X) = αH(X) + βN(X), and initialize the sum of the routes from the starting point to the end point N(X); where T(X) represents the total distance of autonomous route dispatching, H(X) represents the Manhattan distance from the initial starting point to the end point, α represents the Manhattan weight factor, and β represents the route weight factor; (2) Set the X, Y, and Z axes to be perpendicular to each other, and then set the X axis to coincide with the route, with the forward direction as the positive direction; set the Y axis to be perpendicular to the X axis and with the positive direction out of the paper; set the Z axis to be perpendicular to the X axis and with the upward direction as the positive direction; (3) Initialize the route distance parameters from the starting point to the end point in the autonomous route allocation model: N(X), β.

6. The method for autonomously deploying routes of low-altitude urban UAVs according to claim 5 is characterized in that: There are two situations: The first case: the sum of the distances of the i segments from the starting point to the end point at the same route altitude. The solution model is: The second case: The sum of the distances from the starting point to the end point on routes at different altitudes. The solution model is: Among them, L i represents the distance of the ith route, represents the sum of the distances of the previous i routes, L t represents the t altitude value corresponding to the i-th route of N(X); The route weight factor β is adjusted according to the current airspace restrictions and whether there is a risk of conflict to update the current route.

7. The method for autonomously deploying routes of low-altitude urban UAVs according to claim 1 is characterized in that: The calculation flow chart of autonomous deployment of drones on multiple routes, and the total distance model of the autonomous route deployment model: Among them, T(X) represents the total distance of autonomous route allocation; H(X) represents the Manhattan distance from the initial starting point to the end point, α represents the Manhattan weight factor; N(X) represents the sum of the distances of all the segments from the starting point to the end point; β represents the route weight factor; x i ,y i represents the longitude and latitude coordinates of point i; x j ,y j represents the longitude and latitude coordinates of point j; x o ,y o Represents the longitude and latitude coordinates of point o; x p ,y p represents the longitude and latitude coordinates of point p; L i represents the distance of the ith route; Represents the sum of the distances of the previous i routes.

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