A method and system for low altitude flight path automation

Abstract

The present application relates to the technical field of low-altitude flight path planning, and particularly relates to a low-altitude flight path automatic generation method and system, comprising the following steps: S1: synchronously acquiring original wind field distribution data and original terrain elevation data of a flight airspace; S2: performing wind shear intensity analysis on the original wind field distribution data; at the same time, performing obstacle contour identification on the original terrain elevation data to generate an obstacle basic coordinate set; S3: calculating real-time risk coefficients of each obstacle; S4: performing iterative calculation to generate an initial obstacle avoidance path point sequence; and S5: under the constraint of maintaining a minimum safety height set based on task requirements, performing vertical direction compression on the path points to generate a final path instruction set. Through the fusion of wind shear analysis, obstacle risk assessment and energy consumption constraint path optimization, the present application realizes low-altitude flight path automatic generation suitable for dynamic complex environments, and improves the safety and executability of the path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-altitude flight path planning, and particularly relates to a low-altitude flight path automatic generation method and system. BACKGROUND

[0002] In application scenarios such as urban low-altitude logistics, emergency search and rescue, and patrol monitoring, unmanned aerial vehicles serve as autonomous aerial platforms, and are faced with high-risk flight environments such as complex terrain, dynamic obstacles, and severe wind field disturbances; in order to ensure flight safety and task completion quality, a flight path planning system needs to have dynamic sensing capability for wind field environments, spatial positioning capability for obstacles, and avoidance capability for risk areas.

[0003] Current path planning methods mostly rely on static maps or assume stable environments, and ignore the problems of wind shear influence and obstacle dynamic changes in the flight process, resulting in low matching degree of path planning results and real environments, and potential risks of path failure or flight accidents. Therefore, a low-altitude flight path automatic generation method and system are urgently needed to solve the above problems. SUMMARY

[0004] Based on the above purpose, the present application provides a low-altitude flight path automatic generation method and system.

[0005] A low-altitude flight path automatic generation method, comprising the following steps:

[0006] S1: synchronously acquiring original wind field distribution data and original terrain elevation data of a flight airspace by an airborne Doppler radar and a terrain scanner;

[0007] S2: performing wind shear intensity analysis on the original wind field distribution data to output a global wind shear intensity gradient value distribution map; and performing obstacle contour identification on the original terrain elevation data to generate an obstacle basic coordinate set;

[0008] S3: calculating real-time risk coefficients of each obstacle based on the obstacle basic coordinate set and in combination with real-time detection of mobile object azimuth data;

[0009] S4: inputting a start point coordinate, an end point coordinate, the wind shear intensity gradient value distribution map, and the real-time risk coefficients into a path generator to perform iterative calculation to generate an initial obstacle avoidance path point sequence;

[0010] S5: according to a current remaining power of the unmanned aerial vehicle and a total climbing height of the initial obstacle avoidance path point sequence, performing vertical direction compression on the path points to generate a final path instruction set while maintaining a minimum safety height constraint set based on task requirements.

[0011] Optionally, the S1 specifically comprises:

[0012] S11: controlling the airborne Doppler radar to scan the region in front of the flight path at a preset frequency, acquiring frequency offset data of reflected waves, and calculating wind speed vector information at a spatial position corresponding to each direction-finding unit based on the Doppler shift principle to form preliminary wind field vector data;

[0013] S12: controlling the terrain scanner to synchronously scan the ground surface region overlapping the direction-finding range of the Doppler radar in a laser ranging manner, acquiring absolute height data of ground surface reflection points, and converting the absolute height data into original elevation data in a standard terrain elevation coordinate system;

[0014] S13: aligning the preliminary wind field vector data collected in S11 and the original elevation data acquired in S12 according to time tags, and fusing the data based on spatial coordinates to generate original wind field distribution data and original terrain elevation data of the current flight airspace.

[0015] Optionally, the S2 specifically includes:

[0016] S21: discretizing the original wind field distribution data into a regular grid format, calculating wind speed vector differences between adjacent nodes at each grid node, and calculating a wind shear intensity gradient value at the node according to the following formula: wherein, is the wind shear intensity gradient value; is a wind speed vector component at the current grid node; is a wind speed vector component at an adjacent grid node; is a Euclidean distance between the current grid node and the adjacent grid node;

[0017] S22: performing calculation of the wind shear intensity gradient value for all grid nodes, collecting to form a wind shear intensity gradient value matrix, and generating a wind shear intensity gradient value distribution map based on the wind shear intensity gradient value matrix;

[0018] S23: rasterizing the original terrain elevation data, extracting a sudden boundary line based on elevation difference analysis of adjacent pixel units, and identifying an edge region with a continuous elevation difference greater than a set threshold as a potential obstacle profile;

[0019] S24: combining a current height profile of the aircraft, calculating three-dimensional spatial coordinates of a highest point of each identified profile region, and collecting to form an obstacle basic coordinate set.

[0020] Optionally, the S23 specifically includes:

[0021] S231: rasterizing the original terrain elevation data to construct a two-dimensional elevation matrix wherein is a row-column index number of a terrain grid;

[0022] S232: For each elevation point The elevation difference is calculated for the four adjacent grid cells (upper, lower, left, and right), using the following elevation difference formula:

[0023] ;

[0024] ;in, This represents the elevation difference between the current point and its right-hand neighbor in the horizontal direction. This represents the elevation difference between the current point and its vertically adjacent point.

[0025] S233: Define the maximum difference at each point as its local elevation response value. The calculation method is as follows: ;

[0026] S234: Set height change threshold To satisfy all The grid cells are labeled as candidate obstacle region cells.

[0027] Optionally, S24 specifically includes:

[0028] S241: Perform connected component analysis on the candidate obstacle region units marked in S23, extract the outer boundary contour of each connected region, and assign a unique number to each connected region. ,in It is a positive integer;

[0029] S242: For each numbered... For a connected region, traverse all the raster cells it contains in the two-dimensional elevation matrix. Select the cell with the highest elevation value and record its elevation. ;

[0030] S243: Obtain the row and column indices of the drone's current projection onto the terrain grid, combined with the grid resolution. UAV reference plane coordinates Calculate the absolute three-dimensional spatial coordinates of the highest point of the obstacle. ;

[0031] S244: Put all areas The highest point's three-dimensional coordinates The obstacles are numbered and archived to obtain a basic coordinate set.

[0032] Optionally, S3 specifically includes:

[0033] S31: Obtain the three-dimensional spatial coordinates of each obstacle within the obstacle base coordinate set. And obtain the current three-dimensional position of the drone, denoted as ;

[0034] S32: Real-time acquisition of the spatial coordinates of all moving objects within the flight airspace via onboard sensor systems. and speed of movement;

[0035] S33: For each obstacle, calculate the spatial distance to the drone. and spatial distance to the nearest moving object ;

[0036] S34: Calculate the real-time risk coefficient for each obstacle, using the following formula: ,in, For the first Real-time risk coefficient of each obstacle; To deal with obstacles The speed of the nearest moving object; For obstacles Spatial distance between the drone and the other drone; For obstacles The spatial distance between it and its nearest moving object; These are the preset risk weight coefficients.

[0037] Optionally, S4 specifically includes:

[0038] S41: Using the starting point coordinates, ending point coordinates, wind shear intensity gradient value distribution map, and real-time risk coefficient set as input, initialize the path generator, construct the spatial grid structure, and determine the search step size;

[0039] S42: Based on the wind shear intensity gradient value distribution map, mark the areas in the spatial grid where the wind shear intensity is greater than the set threshold, and determine the risk level of the grid where the obstacle is located based on the real-time risk coefficient;

[0040] S43: The A* path planning algorithm is used to search for path nodes in the spatial grid, starting from the starting point and gradually moving towards the destination.

[0041] S44: During the path planning process, risk and shear judgment are performed on each candidate path node. If the conditions are not met, backtrack and reselect until a continuous path point sequence from the starting point to the end point is formed, thereby obtaining the initial obstacle avoidance path point sequence.

[0042] Optionally, S44 specifically includes:

[0043] S441: Obtain the wind shear intensity gradient value of the grid where the candidate path node is located when the A* path planning algorithm selects the candidate path node each time with the risk level ;

[0044] S442: Set the wind shear intensity judgment threshold with the risk level judgment threshold for evaluating the node environmental risk

[0045] S443: Perform a joint judgment on the current candidate path node, and if any of the following conditions is met, the current node is considered to not meet the safe passage requirement

[0046] Condition one, that is, the wind shear intensity at the candidate node is too high

[0047] Condition two, that is, the risk level at the candidate node exceeds the upper limit of safety

[0048] S444: If the candidate node meets the safe passage requirement, add it to the path node sequence and continue to expand the path search to the next node. If it does not meet the requirement, remove the current node from the open list, back up to the previous node, and reselect the next passable candidate node.

[0049] Optionally, the S5 specifically includes:

[0050] S51: Obtain the three-dimensional space coordinates of each path point in the initial obstacle avoidance path point sequence, and calculate the total climb height of the path point sequence ;

[0051] S52: Obtain the current remaining power of the UAV , and calculate the maximum allowed climb height according to the UAV climb consumption characteristics ;

[0052] S53: Compare the total climb height with the maximum allowed climb height , if , then vertically and proportionally compress the height sequence of the path points, first calculate the compression coefficient , and then adjust the height of each path point according to the following formula: , wherein is the minimum safety height set by the task requirement, is the path point height after compression adjustment, is the height of the i-th path point ;

[0053] S54: Compress all the compressed path points that meet The path points are used to generate the final path instruction set in sequence.

[0054] A low-altitude flight path automated generation system, used to implement the aforementioned low-altitude flight path automated generation method, includes the following modules:

[0055] Flight environment perception module: used to acquire raw wind field distribution data and raw terrain elevation data of the flight airspace through airborne Doppler radar and terrain scanner;

[0056] Wind shear and obstacle recognition module: used to receive the original wind field distribution data, perform wind shear intensity gradient value analysis, and generate a wind shear intensity gradient value distribution map; at the same time, it performs elevation difference analysis on the original terrain elevation data, identifies obstacle outline regions, extracts the three-dimensional coordinates of the highest point of the obstacle by combining the current altitude profile of the aircraft, and outputs the basic coordinate set of the obstacle.

[0057] Risk assessment module: Used to receive the basic coordinate set of obstacles and the orientation and speed information of moving objects collected by airborne sensors, calculate the real-time risk coefficient of each obstacle, and generate a risk level map of the obstacle grid based on the risk level mapping rules;

[0058] Path planning module: Used to receive the starting point coordinates, the ending point coordinates, the wind shear intensity gradient value distribution map and the risk level map, and based on the A* path planning algorithm, iteratively generate an initial obstacle avoidance path point sequence in the constructed spatial grid structure;

[0059] Path compression and command generation module: It is used to receive the initial obstacle avoidance path point sequence and the aircraft's remaining power parameters, and perform vertical compression on the path point sequence according to the total climb altitude and minimum safe altitude requirements to generate the final path command set.

[0060] The beneficial effects of this invention are:

[0061] This invention constructs a spatial grid structure, uses the A* algorithm for iterative generation of path points, and combines adaptive adjustment of path point spacing in wind shear regions, high-risk area avoidance strategies, and vertical compression optimization under energy consumption constraints to form a closed-loop path planning mechanism. Compared with existing schemes that rely solely on static maps or simplified assumptions, this invention significantly improves environmental modeling accuracy, path safety, and energy adaptability, making it suitable for low-altitude autonomous flight missions in dynamic environments. Attached Figure Description

[0062] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only belong to the present application, and other drawings can also be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0063] Figure 1 The low-altitude flight path automatic generation method of the embodiment of the present application is shown in the figure.

[0064] Figure 2 The low-altitude flight path automatic generation system of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0065] The present application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0066] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiments can include specific features, structures or characteristics, but not necessarily every embodiment includes the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to realize this feature, structure or characteristic in combination with other embodiments (whether or not explicitly described).

[0067] Generally, the terms can be understood at least in part from the context of their use. For example, depending on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular or can be used to describe combinations of features, structures, or characteristics, in the plural. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but can instead, depending on the context, allow for the existence of other factors not necessarily explicitly described.

[0068] As shown in Figure 1 A low-altitude flight path automatic generation method, comprising the following steps:

[0069] S1: synchronously acquiring original wind field distribution data and original terrain elevation data of a flight airspace by an airborne Doppler radar and a terrain scanner;

[0070] S2: Perform wind shear intensity analysis on the original wind field distribution data, output the global wind shear intensity gradient value distribution map; at the same time, perform obstacle contour identification on the original terrain elevation data, generate the obstacle basic coordinate set;

[0071] S3: Based on the obstacle basic coordinate set, combined with the real-time detection of the moving object azimuth data, calculate the real-time risk coefficient of each obstacle, the real-time risk coefficient is positively correlated with the obstacle moving speed and inversely correlated with the distance from the unmanned aerial vehicle;

[0072] S4: Input the starting point coordinates, end point coordinates, wind shear intensity gradient value distribution map and real-time risk coefficient into the path generator, perform iterative calculation to generate an initial obstacle avoidance path point sequence;

[0073] S5: According to the current remaining power of the unmanned aerial vehicle and the total climbing height of the initial obstacle avoidance path point sequence, under the constraint of maintaining the minimum safety height set based on the task demand, the path points are compressed in the vertical direction to generate a final path instruction set.

[0074] S1 specifically includes:

[0075] S11: Control the airborne Doppler radar to scan the area in front of the flight path at a preset frequency, obtain the frequency offset data of the reflected wave, and calculate the wind speed vector information at the corresponding spatial position of each direction finding unit based on the Doppler shift principle, forming preliminary wind field vector data; The specific calculation formula is: , wherein, is the wind speed component in the direction of the direction finding unit; is the Doppler shift value received by the radar; is the wavelength of the Doppler radar transmitted wave; is the angle between the direction of the direction finding unit and the direction of the wind speed vector;

[0076] S12: Control the terrain scanner to synchronously scan the ground surface area overlapping the direction finding range of the Doppler radar in a laser ranging manner, obtain the absolute height data of the ground surface reflection points, and convert them into original elevation data in the standard terrain elevation coordinate system;

[0077] S13: Align the preliminary wind field vector data collected by S11 and the original elevation data obtained by S12 according to the time tag, and fuse the data based on the spatial coordinates, to generate the original wind field distribution data and the original terrain elevation data of the current flight airspace, providing basic data support for subsequent path planning; Through the above sub-steps, the synchronous collection and registration of wind field and terrain data in time and space dimensions can be realized, providing consistent data support with high temporal and spatial resolution for subsequent wind shear analysis and obstacle identification, improving the real-time and accuracy of flight path calculation.

[0078] S2 specifically includes:

[0079] S21: Discretize the original wind field distribution data into a regular grid format, calculate the wind speed vector difference between adjacent nodes at each grid node, and calculate the wind shear intensity gradient value at the node according to the following formula: wherein, is the wind shear intensity gradient value; is the wind speed vector component at the current grid node; is the wind speed vector component at the adjacent grid node; is the Euclidean distance between the current grid node and the adjacent grid node;

[0080] S22: Perform the calculation of the wind shear intensity gradient value for all grid nodes, aggregate to form a wind shear intensity gradient value matrix, and generate a wind shear intensity gradient value distribution map based on the wind shear intensity gradient value matrix;

[0081] S23: Rasterize the original terrain elevation data, extract the abrupt boundary line based on the elevation difference analysis of adjacent pixel units, and identify the edge region with a continuous elevation difference greater than a set threshold as a potential obstacle profile;

[0082] S24: Combine the current height profile of the aircraft, calculate the three-dimensional spatial coordinates of the highest point of each identified profile region, and aggregate to form a set of obstacle basic coordinates; through the above sub-steps, on the one hand, the quantitative analysis of the wind field shear intensity is realized, and a complete gradient distribution map is obtained for risk prediction; on the other hand, the obstacle boundary that may constitute a threat in the terrain is identified and the three-dimensional coordinates are extracted, which provides accurate spatial constraint basis for path obstacle avoidance and enhances the robustness and safety of path planning.

[0083] S23 specifically includes:

[0084] S231: Rasterize the original terrain elevation data to construct a two-dimensional elevation matrix wherein is the row and column index number of the terrain grid, represents the ground elevation value of the corresponding grid unit;

[0085] S232: Calculate the elevation difference of each elevation point and its adjacent upper, lower, left and right four grid units, specifically using the following elevation difference formula:

[0086] ;

[0087] ; wherein, represents the elevation difference between the current point and the right adjacent point in the horizontal direction; This represents the elevation difference between the current point and its vertically adjacent point.

[0088] S233: Define the maximum difference at each point as its local elevation response value. The calculation method is as follows: ;

[0089] S234: Set height change threshold To satisfy all The grid cells are marked as candidate obstacle region cells, serving as the basic input data for subsequent obstacle contour extraction. Through the above-mentioned elevation difference method, high-risk elevation difference areas can be identified based on the local change characteristics of the terrain data, and candidate obstacle areas can be clearly defined, providing structured data support for contour boundary analysis and three-dimensional coordinate extraction in subsequent steps.

[0090] S24 specifically includes:

[0091] S241: Perform connected component analysis on the candidate obstacle region units marked in S23, extract the outer boundary contour of each connected region, and assign a unique number to each connected region. ,in It is a positive integer;

[0092] S242: For each numbered... For a connected region, traverse all the raster cells it contains in the two-dimensional elevation matrix. Select the cell with the highest elevation value and record its elevation. ,in, The index of the raster row with the highest elevation in this region; The raster column index with the highest elevation in this region;

[0093] S243: Obtain the row and column indices of the drone's current projection onto the terrain grid, combined with the grid resolution. UAV reference plane coordinates Calculate the absolute three-dimensional spatial coordinates of the highest point of the obstacle. The formula is: ; ; ,in, The x and y coordinates of the highest point of the obstacle in a Cartesian coordinate system; This is the absolute elevation of the highest point of the obstacle. This represents the row and column indices of the UAV projection points in the elevation matrix; The grid cell side length;

[0094] S244: Put all areas The highest point's three-dimensional coordinates The numbered archive is obtained, and the obstacle base coordinate set is obtained; the above step accurately establishes the spatial reference data set of the highest point of the obstacle by extracting the highest grid unit of each candidate obstacle area in the two-dimensional elevation matrix and converting it into absolute three-dimensional coordinates, thereby providing a reliable vertical risk boundary for subsequent path planning.

[0095] S3 specifically includes:

[0096] S31: Obtain the three-dimensional spatial coordinates of each obstacle in the obstacle base coordinate set , and obtain the current three-dimensional position of the unmanned aerial vehicle, denoted as ;

[0097] S32: Real-time collection of spatial coordinates and motion speed of all moving objects in the flight airspace by the airborne sensing system

[0098] S33: For each obstacle, calculate the spatial distance from the unmanned aerial vehicle and the spatial distance from the nearest moving object , which are calculated by the following formulas, respectively:

[0099] ;

[0100] ;

[0101] S34: Calculate the real-time risk coefficient of each obstacle, and the formula is: , wherein is the real-time risk coefficient of the i-th obstacle; is the speed of the moving object closest to the obstacle ; is the spatial distance between the obstacle and the unmanned aerial vehicle; is the spatial distance between the obstacle and its nearest moving object; are preset risk weight coefficients, respectively; the above step dynamically evaluates the potential collision risk of each obstacle by combining the spatial position of the obstacle with the orientation and motion speed of the moving object detected in real time, and the obtained real-time risk coefficient provides a quantifiable and adjustable obstacle avoidance factor for path generation.

[0102] S4 specifically includes:

[0103] S41: Take the start point coordinates, end point coordinates, wind shear intensity gradient value distribution map, and real-time risk coefficient set as inputs, initialize the path generator, construct a spatial grid structure, and determine the search step size;

[0104] ​​S42: According to the wind shear intensity gradient value distribution map, mark the area in the spatial grid where the wind shear intensity is greater than the set threshold value, and label the risk level of the grid where the obstacle is located according to the real-time risk coefficient;

[0105] The step of labeling the risk level of the grid where the obstacle is located is as follows:

[0106] S421: Set two preset risk thresholds And , and satisfy ;

[0107] S422: For each cell containing an obstacle in the spatial grid, obtain the real-time risk coefficient of the obstacle corresponding to the grid ;

[0108] S423: Convert the real-time risk coefficient Into a discrete risk level according to the following piecewise function , the expression is:

[0109] ;

[0110] Wherein, is the real-time risk coefficient of the th obstacle; is the threshold between low risk and medium risk; is the threshold between medium risk and high risk; is the risk level of the grid where the th obstacle is located, wherein the value 1 represents low risk, 2 represents medium risk, and 3 represents high risk;

[0111] S424: Assign the risk level Corresponding to each obstacle to the grid cell where it is located, which is used to implement differentiated avoidance for different risk level areas during path planning.

[0112] S43: Using A* path planning algorithm, taking the starting point as the starting node in the spatial grid, gradually searching for path nodes to the end point;

[0113] S44: During path planning, perform risk and shear judgment on each candidate path node, and if the condition is not met, back off and reselect until a continuous path point sequence from the starting point to the end point is formed, and then obtain the initial obstacle avoidance path point sequence.

[0114] S44 specifically includes:

[0115] S441: When the A* path planning algorithm selects a candidate path node each time, obtain the wind shear intensity gradient value And the risk level Of the grid where the candidate path node is located;

[0116] S442: Set wind shear intensity judgment threshold and risk level judgment threshold for evaluating the node environment risk;

[0117] S443: Perform joint judgment on the current candidate path node, and if any of the following conditions is met, the current node is considered to not meet the safe travel requirement;

[0118] Condition one, that is, the wind shear intensity at the candidate node is too high;

[0119] Condition two, that is, the risk level at the candidate node exceeds the upper limit of safety;

[0120] S444: If the candidate node meets the safe travel requirement, it is added to the path node sequence, and the path search is continued to the next node. If it does not meet the requirement, the current node is removed from the open list, and the previous node is returned to reselect the next passable candidate node. The above steps can identify and exclude unsafe nodes in time during the path planning iteration process by performing explicit wind shear intensity and obstacle risk level judgment on each candidate path node, ensuring the safety and stability of the generated path in the coexistence scenario of wind field disturbance and obstacle threat.

[0121] S5 specifically includes:

[0122] S51: Obtain the three-dimensional space coordinates of each path point in the initial obstacle avoidance path point sequence, and calculate the total climb height of the path point sequence , the formula is: wherein, is the total number of path points, is the height of the th path point;

[0123] S52: Obtain the current remaining power of the UAV , and calculate the maximum allowed climb height according to the UAV climb consumption characteristics, the expression is: wherein, is the power consumption per unit climb height;

[0124] S53: Compare the total climb height with the maximum allowed climb height , if , the height sequence of the path point is compressed in the vertical direction at a constant ratio, first calculate the compression coefficient , then adjust the height of each path point according to the following formula: wherein, a minimum safety height set for task requirements, adjusting the path point height according to compression;

[0125] S54: generating a final path instruction set in sequence for all compressed path points meeting The above steps can ensure task safety while achieving energy consumption adaptation of the path, and improve the actual execution capability and endurance reliability of the unmanned aerial vehicle in low-altitude obstacle avoidance tasks, by performing vertical direction compression on the initial obstacle avoidance path point sequence under the power constraint and ensuring the minimum safety height constraint.

[0126] As shown in Figure 2 A low-altitude flight path automatic generation system for implementing the low-altitude flight path automatic generation method described above, comprising the following modules:

[0127] Flight environment perception module: for obtaining original wind field distribution data and original terrain elevation data of the flight airspace through an airborne Doppler radar and a terrain scanner;

[0128] Wind shear and obstacle identification module: for receiving the original wind field distribution data, performing wind shear intensity gradient value analysis, and generating a wind shear intensity gradient value distribution map; at the same time, performing elevation difference analysis on the original terrain elevation data to identify the obstacle contour area, extracting the three-dimensional coordinates of the highest point of the obstacle in combination with the current height profile of the aircraft, and outputting a basic obstacle coordinate set;

[0129] Risk assessment module: for receiving the basic obstacle coordinate set and the moving object azimuth and speed information collected by the airborne sensor, calculating the real-time risk coefficient of each obstacle, and generating a risk level map of the obstacle grid based on the risk level mapping rule;

[0130] Path planning module: for receiving the start point coordinates, end point coordinates, wind shear intensity gradient value distribution map and risk level map, and iteratively generating an initial obstacle avoidance path point sequence in the constructed spatial grid structure based on the A* path planning algorithm;

[0131] Path compression and instruction generation module: for receiving the initial obstacle avoidance path point sequence and the remaining power parameter of the aircraft, performing vertical direction compression on the path point sequence according to the total climb height of the path and the minimum safety height requirement, and generating a final path instruction set.

[0132] The present application encompasses any alternatives, modifications, equivalent methods and solutions made to the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details by those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0133] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method for low altitude flight path automation generation, characterized by, The method comprises the following steps: S1: synchronously acquiring original wind field distribution data and original terrain elevation data of a flight airspace by an airborne Doppler radar and a terrain scanner; S2: performing wind shear strength analysis on the original wind field distribution data to output a global wind shear strength gradient value distribution map; and simultaneously performing obstacle profile identification on the original terrain elevation data to generate an obstacle basic coordinate set; The S2 specifically comprises: S21: Discretize the original wind field distribution data into a regular grid format, calculate the wind speed vector difference between adjacent nodes at each grid node, and calculate the wind shear intensity gradient value at the node according to the following formula: wherein, is the wind shear intensity gradient value; is the wind speed vector component at the current grid node; is the wind speed vector component at the adjacent grid node; is the Euclidean distance between the current grid node and the adjacent grid node; S22: performing wind shear strength gradient value calculation on all grid nodes to form a wind shear strength gradient value matrix, and generating a wind shear strength gradient value distribution map based on the wind shear strength gradient value matrix; S23: performing rasterization processing on the original terrain elevation data, extracting a mutation boundary line based on elevation difference value analysis of adjacent pixel units, and identifying an edge region with a continuous elevation difference greater than a set threshold as a potential obstacle profile; S24: combining a current height profile of the aircraft, calculating three-dimensional space coordinates of a highest point of each identified profile region, and combining the three-dimensional space coordinates to form the obstacle basic coordinate set; The S24 specifically comprises: S241: performing connected component analysis on the candidate obstacle region units marked in S23, extracting the outer boundary contour line of each connected region, and assigning a unique number to each connected region wherein is a positive integer; S242: For each connected region numbered , traverse all the grid cells contained in it, select the cell with the largest elevation value in the two-dimensional elevation matrix , and record its elevation ; S243: Obtain the current row and column index of the UAV projected on the terrain grid, combined with the grid resolution With the UAV reference plane coordinates , calculate the absolute three-dimensional space coordinates of the highest point of the obstacle ; S244: Numbering and archiving the highest point three-dimensional coordinates of all regions to obtain the obstacle base coordinate set;​ S3: based on the obstacle basic coordinate set, combining real-time moving object direction data detected in real time, and calculating real-time risk coefficients of the obstacles; S4: inputting a start point coordinate, an end point coordinate, the wind shear strength gradient value distribution map, and the real-time risk coefficients into a path generator to perform iterative calculation to generate an initial obstacle avoidance path point sequence; The S4 specifically comprises: S41: inputting the start point coordinate, the end point coordinate, the wind shear strength gradient value distribution map, and the real-time risk coefficients as inputs, initializing the path generator, constructing a space grid structure, and determining a search step length; S42: according to the wind shear strength gradient value distribution map, marking a region with a wind shear strength greater than a set threshold in the space grid, and according to the real-time risk coefficients, marking a risk level of a grid where an obstacle is located; S43: using an A* path planning algorithm, taking the start point as a starting node in the space grid, and gradually searching for path nodes toward the end point; S44: performing risk and shear judgment on each candidate path node in the path planning process, and if the conditions are not met, backtracking and reselecting until a continuous path point sequence from the start point to the end point is formed, and then obtaining the initial obstacle avoidance path point sequence; S5: according to a current remaining power of the unmanned aerial vehicle and a total climbing height of the initial obstacle avoidance path point sequence, under a constraint of maintaining a minimum safety height set based on a task requirement, performing vertical direction compression on the path points to generate a final path instruction set; The S5 specifically comprises: S51: Obtain the three-dimensional space coordinates of each path point in the initial obstacle avoidance path point sequence, and calculate the total climbing height of the path point sequence ; S52: Obtain the current remaining power of the UAV , and calculate the maximum allowed climb height according to the UAV climb consumption characteristics ; S53: compare the total climb height with the maximum allowed climb height , if , then the height sequence of the path points is compressed in the vertical direction proportionally, first calculate the compression coefficient , then adjust the height of each path point according to the following formula: , where is the minimum safety height set for the task requirement, is the path point height after compression adjustment, is the height of the th path point; S54: generate the final path instruction set in order for all the compressed path points that satisfy the path point.

2. The method of claim 1, wherein, The S1 specifically comprises: S11: controlling the airborne Doppler radar to scan a region in front of a flight path at a preset frequency to acquire frequency offset data of reflected waves, and based on a Doppler shift principle, calculating wind speed vector information at a space position corresponding to each direction finding unit to form preliminary wind field vector data; S12: controlling the terrain scanner to synchronously scan a ground surface region overlapping a direction finding range of the Doppler radar in a laser ranging manner to acquire absolute height data of ground surface reflection points, and converting the absolute height data into original elevation data in a standard terrain elevation coordinate system; S13: Align the preliminary wind field vector data collected in S11 with the original elevation data obtained in S12 according to the time label, and fuse the data based on the spatial coordinates to generate the original wind field distribution data and the original terrain elevation data of the current flight airspace.

3. The method of claim 2, wherein, The S23 specifically comprises: S231: Rasterizing the original terrain elevation data to construct a two-dimensional elevation matrix wherein is the row and column index number of the terrain grid; S232: Calculate the elevation difference for each grid cell The elevation difference of each grid cell is calculated using the following formula: ； ; wherein, represents the elevation difference between the current point and the right neighboring point in the horizontal direction; represents the elevation difference between the current point and the lower neighboring point in the vertical direction; S233: define the maximum difference value of each point as its local relief response value , the calculation method is: ; S234: Set height change threshold All grid cells satisfying are marked as candidate obstacle region cells.

4. The method of claim 3, wherein, The S3 specifically comprises: S31: Obtain the three-dimensional space coordinates of each obstacle in the obstacle basic coordinate set , and obtain the current three-dimensional position of the unmanned aerial vehicle, denoted as ; S32: Collecting spatial coordinates of all moving objects in the flight airspace in real time through an airborne sensing system and motion speed; S33: for each obstacle, calculate the spatial distance from the drone and the spatial distance from the nearest moving object ; S34: calculate the real-time risk coefficient of each obstacle, the formula is: wherein, is the real-time risk coefficient of the th obstacle; is the speed of the mobile object closest to the obstacle ; is the spatial distance between the obstacle and the UAV; is the spatial distance between the obstacle and its closest mobile object; are respectively preset risk weight coefficients.

5. The method of claim 1, wherein, The S44 specifically comprises: S441: When the A* path planning algorithm selects a candidate path node each time, obtain the wind shear intensity gradient value of the grid where the candidate path node is located with the risk level ; S442: Set the wind shear intensity judgment threshold with the risk level judgment threshold for evaluating the node environmental risk; S443: Perform joint judgment on the current candidate path node. If any of the following conditions is met, it is considered that the current node does not meet the safety travel requirement; Condition one, i.e. the wind shear intensity is too high at the candidate node; Condition two, i.e. the risk level at the candidate node exceeds the upper safety limit; S444: If the candidate node meets the safety travel requirement, add it to the path node sequence and continue to expand the path search to the next node. If it does not meet the requirement, remove the current node from the open list, back to the previous node, and select the next passable candidate node.

6. A low-altitude flight path automated generation system for implementing a low-altitude flight path automated generation method according to any one of claims 1 to 5, characterized in that, Comprise the following modules: A flight environment perception module for obtaining original wind field distribution data and original terrain elevation data of a flight airspace through an airborne Doppler radar and a terrain scanner; A wind shear and obstacle identification module for receiving the original wind field distribution data, performing wind shear intensity gradient value analysis to generate a wind shear intensity gradient value distribution map, and performing elevation difference analysis on the original terrain elevation data to identify an obstacle contour area, extract the three-dimensional coordinates of the highest point of the obstacle in combination with the current height profile of the aircraft, and output a basic obstacle coordinate set; A risk assessment module for receiving the basic obstacle coordinate set and the moving object azimuth and speed information collected by the airborne sensor, calculating the real-time risk coefficient of each obstacle, and generating a risk level map of the obstacle grid based on a risk level mapping rule; A path planning module for receiving the start point coordinates, the end point coordinates, the wind shear intensity gradient value distribution map, and the risk level map, generating an initial obstacle avoidance path point sequence in the constructed spatial grid structure based on an A* path planning algorithm; A path compression and instruction generation module for receiving the initial obstacle avoidance path point sequence and the remaining power parameter of the aircraft, performing vertical compression on the path point sequence according to the total climbing height of the path and the minimum safety height requirement, and generating a final path instruction set.

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