Automatic emergency flight route planning method for unmanned aerial vehicle

Abstract

An automatic emergency flight route planning method for an unmanned aerial vehicle. The method comprises the following steps: S100: on the basis of terrain, performing sampling between an unmanned aerial vehicle dock and a target, so as to acquire a plurality of sampling points, wherein the position of the unmanned aerial vehicle dock and the position of the target serve as a starting point and an ending point among the sampling points; S200: on the basis of the sampling points and terrain data, obtaining a plurality of pre-waypoints; and S300: optimizing the pre-waypoints to obtain waypoints, and sequentially connecting the waypoints to obtain a flight route trajectory. By means of the method, the reaction speed and execution efficiency of an unmanned aerial vehicle in an emergency response can be significantly improved, thereby providing powerful technical support for the emergency response.

Description

Method for automatic emergency route planning of unmanned aerial vehicle

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present application claims priority to the Chinese patent application No. 2024113201290, filed on September 23, 2024, and entitled "Method for automatic emergency route planning of unmanned aerial vehicle", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0003] The present application belongs to the technical field of unmanned aerial vehicle, and particularly relates to a method for automatic emergency route planning of unmanned aerial vehicle. BACKGROUND

[0004] In recent years, unmanned aerial vehicle (UAV) technology has made significant progress and is widely used in various fields, including agriculture, logistics, environmental monitoring, and emergency response. In emergency response, unmanned aerial vehicles are important tools for monitoring and reconnaissance tasks due to their high mobility, rapid deployment, and low cost.

[0005] In emergency situations, the effective use of unmanned aerial vehicles faces multiple technical challenges, especially automatic route planning and autonomous flight technology. Emergency response scenarios often include natural disasters (such as earthquakes, floods, and fires), accident rescue (such as traffic accidents and industrial accidents), and public safety events (such as terrorist attacks and mass gatherings). In these scenarios, unmanned aerial vehicles can quickly reach the disaster site and provide real-time image and data support. However, current emergency response unmanned aerial vehicle systems mostly rely on manual operation, which has problems such as long response time, low efficiency, and high difficulty of operation. How to realize automatic route planning of unmanned aerial vehicles in emergency situations and make unmanned aerial vehicles fly along the route has become a technical challenge that needs to be solved. Therefore, a technical solution is needed to realize automatic route planning of unmanned aerial vehicles in unattended situations to improve the efficiency and effectiveness of emergency response. SUMMARY

[0006] In order to achieve timely response and ensure flight safety and emergency speed, and solve the above technical problems, the present application discloses a method for automatic emergency route planning of unmanned aerial vehicle, which comprises the following steps:

[0007] S100: sampling between the unmanned aerial vehicle hangar and the target according to the terrain to obtain a plurality of sampling points, wherein the unmanned aerial vehicle hangar position and the target position are the starting point and the ending point of the sampling points;

[0008] S200: obtaining a plurality of pre-flight points according to the sampling points and terrain data;

[0009] S300: optimizing the pre-navigation points to obtain navigation points, connecting the navigation points in sequence to obtain a flight path trajectory.

[0010] By the above technical solution, the method can enable the unmanned aerial vehicle to have high automation degree and efficient flight path planning capability in the emergency response process, can automatically wake up the unmanned aerial vehicle in the unattended case, and automatically fly to the target location according to the early warning information, can generate the ground-effect flight path by using the terrain data, and optimize the same to enable the unmanned aerial vehicle to fly to the target location at the fastest speed, and ensure the flight safety and reliability in the complex environment. The method can significantly improve the reaction speed and execution efficiency of the unmanned aerial vehicle in the emergency response, and provide strong technical support for the emergency response. BRIEF DESCRIPTION OF DRAWINGS

[0011] Fig. 1 is a flow chart of a method for automatic emergency flight path planning of an unmanned aerial vehicle according to an embodiment of the present application;

[0012] Fig. 2 is a schematic diagram of a sampling point acquisition according to an embodiment of the present application;

[0013] Fig. 3 is a schematic diagram of a sampling point after height acquisition according to an embodiment of the present application;

[0014] Fig. 4 is a schematic diagram of a height extreme point according to an embodiment of the present application;

[0015] Fig. 5 is a schematic diagram of a slope maximum value according to an embodiment of the present application;

[0016] Fig. 6 is a schematic diagram of a pre-navigation point after screening according to an embodiment of the present application;

[0017] Fig. 7 is a schematic diagram of an emergency flight path navigation point according to an embodiment of the present application;

[0018] Fig. 8 is a schematic diagram of an emergency flight path generated by connecting according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order for those skilled in the art to understand the technical solutions disclosed in the present application, the technical solutions of various embodiments will be described below in conjunction with the embodiments and the related drawings. The described embodiments are part of the embodiments of the present application, but not all of the embodiments.

[0020] In this document, the phrase "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive or alternative to other embodiments. Those skilled in the art can understand that the embodiments described herein can be combined with other embodiments.

[0021] Referring to FIG. 1, in one embodiment, the present application discloses a method for automatic emergency route planning of a UAV, which comprises the following steps:

[0022] S100: sampling between a UAV hangar and a target according to a terrain to obtain a plurality of sampling points, wherein the UAV hangar position and the target position are taken as starting point and ending point of the sampling points.

[0023] S200: obtaining a plurality of pre-flight points according to the sampling points and terrain data.

[0024] S300: optimizing the pre-flight points to obtain flight points, connecting the flight points in sequence to obtain a route trajectory.

[0025] In this embodiment, in an emergency response scenario, the method can realize automatic route planning of a UAV according to an emergency target, so that the UAV flies to the destination to complete task execution and ensures the safety and rapid response of the entire task process. The method can realize automatic route planning in cooperation with the hangar, thereby improving the efficiency and effect of UAV emergency response in an emergency scenario. Since the route is optimized according to the terrain, it is ensured that the UAV can fly to the emergency target at the shortest path, thereby ensuring the flight safety of the UAV.

[0026] Through the UAV and the hangar as equipment, unattended operation is realized. Then when other equipment such as a camera or a person gives an alarm, the UAV in the hangar automatically flies to the target site for on-site detection. Before the UAV automatically flies, the system needs to automatically complete route planning through the UAV position and the target position. The method for route planning is to first sample between the hangar and the target according to the terrain to obtain a plurality of ground points; then add a relative height to the points and screen the feature points to obtain a plurality of pre-flight points; and finally optimize the pre-flight points into a flight route that enables the UAV to fly to the destination faster.

[0027] In other embodiments, the sampling points are obtained according to the following steps: the target position and the UAV hangar position are taken as two points on a plane, the line connecting the two points is divided into a plurality of segments according to a fixed value, and the division points are the sampling points.

[0028] In this embodiment, the height sampling points are calculated by interpolation according to the target position and the UAV hangar position. The target position and the UAV hangar position can be taken as two points on a plane, the line connecting the two points is divided into a plurality of segments according to a fixed value, and the division points are the sampling points, and the fixed value is the sampling distance. The UAV hangar position and the target point are also taken as starting and ending points of the sampling points.

[0029] In other embodiments, the specific calculation method of the sampling points is as follows:

[0030] S101: First calculate the yaw angle from the position of the UAV hangar to the target position.

[0031] S102: Obtain a sample point every other sample distance according to the direction of the yaw angle.

[0032] S103: Until the distance between the latest sample point and the target position is less than the sample distance.

[0033] For this embodiment, the specific calculation method is as follows: first calculate the yaw angle from the hangar to the target point, and then obtain a sample point every other sample distance (the distance is set during use, generally according to the accuracy of the terrain data) according to the direction of the yaw angle. Until the distance between the latest sample point and the target point is less than the sample distance. As shown in FIG. 2.

[0034] Yaw angle and sample point calculation: In aviation, the yaw angle is the horizontal angle of the aircraft nose relative to the north direction of the earth. The north direction is 0 degrees, and clockwise is positive. For example, if the aircraft nose is facing east, the yaw angle is 90 degrees.

[0035] The calculation of the yaw angle is a professional and conventional formula, and the calculation process is as follows. The bearing angle from point A to point B can be calculated using the following formula. Suppose the latitude and longitude of point A is (lat1, lon1), and the latitude and longitude of point B is (lat2, lon2), both in decimal degrees. Use the formula: θ = atan2(sin(Δλ)·cos(φ2), cos(φ1)·sin(φ2)-sin(φ1)·cos(φ2)·cos(Δλ))

[0036] Where φ1 and φ2 are the latitudes of points A and B respectively (in radians), and Δλ = λ2 - λ1, where λ1 and λ2 are the longitudes of points A and B respectively (in radians).

[0037] Convert the result θ to degrees and normalize it to the range of 0 to 360 degrees:

[0038] bearing = (θ·180 / π+360) mod 360

[0039] The calculation of the sample point is a professional and conventional formula, and the calculation process is as follows. To calculate the latitude and longitude of point C obtained by moving a distance L along a given bearing angle (azimuth) from point A, the dead reckoning method can be used. The specific steps and formulas are as follows:

[0040] 1. The latitude and longitude of point A is (lat1, lon1), the distance is L (in kilometers), and the yaw angle is bearing (in degrees).

[0041] 2. Convert the latitude and longitude of point A from degrees to radians:

[0042] φ1 = lat1 • π / 180

[0043] λ1 = lon1 • π / 180

[0044] 3. Convert the yaw angle from degrees to radians:

[0045] θ = bearing • π / 180

[0046] 4. The radius of the earth R is taken as an average of 6371 km.

[0047] 5. Calculate the latitude and longitude of point C (in radians) using the following equations:

[0048] φ2 = arcsin(sin(φ1) • cos(L / R) + cos(φ1) • sin(L / R) • cos(θ))

[0049] λ2 = λ1 + arctan2(sin(θ) • sin(L / R) • cos(φ1), cos(L / R) - sin(φ1) • sin(φ2))

[0050] 6. Convert the latitude and longitude of point C from radians back to degrees:

[0051] Lat2 = φ2 • 180 / π

[0052] Lon2 = λ2 • 180 / π

[0053] In other embodiments, the sampling distance is set according to the accuracy of the terrain data.

[0054] In other embodiments, the step S200 further comprises the following steps:

[0055] S201: Query the ground surface height H of all sampling points dem and then add a relative ground height H r Obtain the sampling point height H and sequentially form a sampling point height list.

[0056] S202: Connect the sampling points sequentially from the beginning to the end, calculate the slope of each line segment, and obtain an array containing the slopes.

[0057] S203: Loop through the array to obtain the height extreme point and the slope maximum point.

[0058] S204: The starting point, the obtained height extreme point and the slope maximum point, and the ending point together form a pre-navigation point list.

[0059] For this embodiment, the pre-flight points are obtained according to the sampling points and the terrain data, the sampling points are very dense, and the flight path of the unmanned aerial vehicle does not need so many flight points, so it is necessary to screen out the feature points as the pre-flight points, and the pre-flight points are mainly used to reduce the calculation amount of the next step while reflecting the terrain features to ensure flight safety.

[0060] Query the ground height (H dem ) of all sampling points, and then add a relative ground height (H r ) to obtain the sampling point height (H = H dem + H r ), and sequentially form a sampling point height list, and place the coordinates of the entire sampling point list in a coordinate system with the horizontal coordinate as the distance and the vertical coordinate as the height, as shown in FIG. 3, the solid line is the terrain, and the point is the sampling point after obtaining the height. The height is queried by querying the terrain data, and the terrain data uses the public data of the country or the public data of the American space agency, and is generally dem. And the specific acquisition of dem and reading of the data is not within the scope of discussion of the present patent. Dem definition: digital elevation model (DEM) or digital surface model (DSM) is a kind of 3D computer graphics representation, which is used to represent terrain data, representing the terrain or covered objects, usually the terrain of planets, moon or asteroids. "Global DEM" refers to a discrete global grid. DEM is often used in geographic information system (GIS), and is the most common basis for digital production of topographic maps. Digital terrain model (DTM) refers to the ground surface, while DEM and DSM may represent tree canopy or building roof.

[0061] Connect the sampling points in order from the beginning, calculate the slope of each line segment, and obtain an array of slopes. Assuming there are n points, there are n-1 line segments in total, the slope of each line segment is calculated (k = ΔH / ΔL. ΔH is the difference between the vertical coordinates of two points, that is, the height difference; ΔL is the difference between the horizontal coordinates of two points, that is, the distance difference), and a slope array slopes = [k1, k2, …, kn-2, kn-1] is obtained;

[0062] Loop through slopes, and set the current sequence number as i.

[0063] Calculate the height extreme point and the slope maximum value point.

[0064] The starting point, all extreme coordinates and the target point together form a pre-flight point list, as shown in FIG. 6, the solid line is the terrain, and the point is the screened pre-flight point.

[0065] The purpose of the calculation of the pre-flight point is to reduce the calculation amount of the last step, and the actual implementation can skip this step and directly use all the sampling points for the calculation of the final flight path.

[0066] H = H dem + Hr This H is equivalent to the height of the waypoint, and the UAV flies naturally above the ground. As shown in FIG. 3, the ground height H of the collection point is dem Add a relative ground height H r All points form a set almost parallel to the terrain above the terrain. For example, there is a sampling point a, and the ground elevation of the point is 200. A relative ground height of 100 is added to obtain the height (elevation) of point a, which is 300. If point a becomes a waypoint, the height of the UAV when flying to the point is 300, and the relative ground height is 100.

[0067] The sampling points obtained in step S100 are only the positions of the sampling points, and step S200 is used to obtain the heights of the sampling points.

[0068] The order in step S201 is the order of obtaining the sampling points in step S100, from the initial point to the target point.

[0069] In other embodiments, the height extreme point includes a height maximum point and a height minimum point.

[0070] In other embodiments, when the slope of the adjacent line segment changes from positive to negative, the point at which the two line segments intersect is a height maximum point; when the slope of the adjacent line segment changes from negative to positive, the point at which the two line segments intersect is a height minimum point.

[0071] In this embodiment, as shown in FIG. 4, the extreme value of the height is a very obvious feature point, indicating the local fluctuation. When the slope of the adjacent line segment changes from positive to negative, the point at which the two line segments intersect is a maximum point. Similarly, when the slope of the adjacent line segment changes from negative to positive, the point at which the two line segments intersect is a minimum. That is, when slopes[i-1]>0 and slopes[i]<=0, the sampling point with the serial number i is a height maximum point; when slopes[i-1]<0 and slopes[i]>=0, the sampling point with the serial number i is a height minimum point.

[0072] In other embodiments, the slope maximum point is a point at which the slope of the current line segment is greater than the slopes of the previous and subsequent line segments.

[0073] In this embodiment, as shown in FIG. 5, the maximum value of the slope indicates a protrusion in the region where the terrain rises or falls in the overall elevation, which is a very important terrain feature. The slope maximum point is found when the slope of the current line segment is greater than the slopes of the previous and subsequent line segments. That is, when slopes[i-1]>slopes[i] and slopes[i-1]>slopes[i-2], the sampling point with the serial number i is a slope maximum point.

[0074] In other embodiments, the step S300 further includes the following steps:

[0075] S301: Place the coordinates of the entire pre-navigation point list in a coordinate system with horizontal coordinate as distance and vertical coordinate as height, determine the navigation point from the starting point;

[0076] S302: Calculate the slope of the line connecting the current point with all subsequent points, and the point with the maximum slope is the next navigation point;

[0077] S303: Repeat the traversal to obtain all navigation points, and then obtain the final flight path.

[0078] For this embodiment, the best flight path is calculated: place the coordinates of the entire pre-navigation point list in a coordinate system with horizontal coordinate as distance and vertical coordinate as height, and determine the navigation point from the first point. Note that the first and last points of the pre-navigation point must be the first and last points of the final navigation point, as they represent the hangar (unmanned aerial vehicle takeoff point) and the target point, respectively.

[0079] Calculate the slope of the line connecting the current point with all subsequent points, and the point with the maximum slope is the next navigation point. Then repeat the traversal to obtain all navigation points to obtain the final flight path.

[0080] Specifically:

[0081] a) Start counting from i = 0, a total of n points, loop n-2 times. Denote the pre-navigation point array as points, and points[0] as the first navigation point.

[0082] b) Calculate the slope of the line connecting points[i] with all subsequent points, obtaining a slope array [ki+1, ki+2, …, kn-2, kn-1]. If the value of i is 0, it is equivalent to calculating the slope of n-1 line segments, and if the value of i is n-2, the slope array has only one value;

[0083] c) Find the maximum value kj = max[ki+1, ki+2, …, kn-2, kn-1] in the slope array, and the count of the maximum value is j, points[j] is the navigation point;

[0084] d) i = j, repeat step b) until the value of i is equal to n-2 to complete the loop, and obtain all navigation points, as shown in FIG. 7.

[0085] Wherein, the slope here is different from the slope calculated in the acquisition of the pre-navigation point. The slope in the acquisition of the pre-navigation point is the slope of the line connecting adjacent points in order, that is, if the points are 1, 2, 3, 4, 5, the calculated slopes are the slopes of the line segments 12, 23, 34, 45, respectively. The slope here is the slope of the line connecting the ith point with the following points, that is, the points are 1, 2, 3, 4, 5. When i is 0, the calculated slopes are the slopes of the line segments 12, 13, 14, 15, respectively; when i is 1, the calculated slopes are the slopes of the line segments 23, 24, 25, respectively.

[0086] e) connecting the navigation points in order to obtain the navigation track, as shown in FIG. 8.

[0087] In summary, the navigation line is equivalent to a straight line in the horizontal direction to the target, avoiding detours. All the sampling points are below the navigation line, ensuring that the unmanned aerial vehicle flying the navigation line will not crash into the mountain, ensuring safety. The number of navigation points of the navigation line is reduced as much as possible, making the flight trajectory of the unmanned aerial vehicle simpler, reducing the flight distance of the unmanned aerial vehicle, and enabling the unmanned aerial vehicle to maintain the fastest flight speed.

[0088] If the unmanned aerial vehicle directly ascends to the maximum height of the sampling point, flies horizontally, and then reduces the height to above the target, the flight distance is farther, and the vertical up-and-down flight of the unmanned aerial vehicle consumes a lot of power and is very slow.

[0089] The point with the maximum slope is taken as the next navigation point because it indicates that the line connecting the two points is above other points after the current point, thus ensuring the navigation line, the simple navigation track, the faster flight of the unmanned aerial vehicle, and the safety without crashing into the mountain.

[0090] Finally, it should be noted that many forms of variations can be made by those skilled in the art under the guidance of the present specification and without departing from the scope of protection of the claims of the present application, and these all belong to the protection of the present application. Industrial applicability

[0091] By using the above scheme, the terrain data can be used to generate the ground-following navigation line, which is optimized on this basis to enable the unmanned aerial vehicle to fly to the target location at the fastest speed, ensuring the flight safety and reliability in complex environments, and can significantly improve the response speed and execution efficiency of the unmanned aerial vehicle in emergency response, providing strong technical support for emergency response.

Claims

1. A method for automatic emergency route planning of a UAV, characterized in that, The method comprises the following steps: S100: sampling between a UAV hangar and a target according to a terrain to obtain a plurality of sampling points, wherein a position of the UAV hangar and a position of the target are starting point and ending point of the sampling points; S200: obtaining a plurality of pre-navigation points according to the sampling points and terrain data; S300: optimizing the pre-navigation points to obtain navigation points, connecting the navigation points in sequence to obtain a navigation track.

2. The method of claim 1, wherein, The sampling points are obtained according to the following steps: the position of the target and the position of the UAV hangar are regarded as two points on a plane, the two points are divided into a plurality of segments by a fixed value, and the division is the sampling point.

3. The method of claim 2, wherein, The specific calculation method of the sampling points is as follows: S101: first, calculate the yaw angle from the position of the UAV hangar to the position of the target; S102: obtain a sampling point every sampling distance according to the direction of the yaw angle; S103: until the distance between the latest sampling point and the position of the target is less than the sampling distance.

4. The method of claim 3, wherein, The sampling distance is set according to the accuracy of the terrain data.

5. The method of claim 1, wherein the step S200 further comprises the following steps: S201: Query the ground height H of all sampling points dem Then add a relative ground height H r Obtain the sampling point height H and sequentially form a sampling point height list; S202: connecting the sampling points in sequence to calculate the slope of each line segment to obtain an array containing the slope; S203: traversing the array to obtain a height extreme point and a slope maximum point; S204: the starting point, the obtained height extreme point and the slope maximum point and the ending point together form a pre-navigation point list.

6. The method of claim 5, wherein, The height extreme point includes a height maximum point and a height minimum point.

7. The method of claim 6, wherein, Wherein, When the slope of adjacent line segments changes from positive to negative, the point where the two line segments intersect is a height maximum point; when the slope of adjacent line segments changes from negative to positive, the point where the two line segments intersect is a height minimum point.

8. The method of claim 5, wherein, The slope maximum point is a point whose slope is greater than the slopes of the previous and subsequent line segments.

9. The method of claim 1, wherein, The step S300 further comprises the following steps: S301: placing the coordinates of the entire pre-navigation point list in a coordinate system with horizontal coordinate as distance and vertical coordinate as height, and determining the navigation point from the starting point; S302: calculating the slope of the line connecting the current point with all subsequent points, and the point with the maximum slope is the next navigation point; S303: repeating the traversal to obtain all navigation points, and further obtaining the final navigation track.

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