Low-altitude flight path automatic generation method and system

Abstract

The invention relates to the technical field of low-altitude flight path planning, in particular to a low-altitude flight path automatic generation method and system, and the method comprises the following steps: S1, synchronously obtaining original wind field distribution data and original terrain elevation data of a flight airspace; s2, performing wind shear strength analysis on the original wind field distribution data; meanwhile, carrying out obstacle contour recognition on the original terrain elevation data to generate an obstacle basic coordinate set; s3, calculating a real-time risk coefficient of each obstacle; s4, executing iterative calculation to generate an initial obstacle avoidance path point sequence; and S5, under the condition that the minimum safety height constraint set based on task requirements is maintained, compressing the path points in the vertical direction to generate a final path instruction set. According to the method, wind shear analysis, obstacle risk assessment and energy consumption constraint path optimization are fused, so that automatic generation of the low-altitude flight path suitable for a dynamic complex environment is realized, and the safety and the performability of the path are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude flight path planning, and in particular to a method and system for automatically generating low-altitude flight paths. Background Art

[0002] In application scenarios such as urban low-altitude logistics, emergency search and rescue, and patrol monitoring, as an aerial autonomous platform, unmanned aerial vehicles (UAVs) face high-risk flight environments such as complex terrains, dynamic obstacles, and severe wind field disturbances. To ensure flight safety and the quality of task completion, the flight path planning system needs to have the ability to dynamically perceive the wind field environment, spatially locate obstacles, and avoid risk areas.

[0003] Currently, existing path planning methods mostly rely on static maps or assume a stable environment, ignoring the actual effects of wind shear and the dynamic changes of obstacles during flight, resulting in a low matching degree between the path planning results and the real environment, and there is a potential risk of path failure or flight accidents. Therefore, there is an urgent need for a method and system for automatically generating low-altitude flight paths to solve the above problems. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a method and system for automatically generating low-altitude flight paths.

[0005] A method for automatically generating low-altitude flight paths includes the following steps: S1: Synchronously obtain the original wind field distribution data and original terrain elevation data of the flight airspace through an onboard Doppler radar and a terrain scanner; S2: Perform wind shear intensity analysis on the original wind field distribution data and output a wind shear intensity gradient value distribution map of the entire region; at the same time, perform obstacle contour recognition on the original terrain elevation data to generate an obstacle basic coordinate set; S3: Based on the obstacle basic coordinate set, combine the azimuth data of moving objects detected in real time to calculate the real-time risk coefficient of each obstacle; S4: Input the starting point coordinates, ending point coordinates, wind shear intensity gradient value distribution map, and real-time risk coefficient into a path generator to perform iterative calculations to generate an initial obstacle avoidance path point sequence; S5: According to the current remaining battery power of the UAV 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 task requirements, compress the path points in the vertical direction to generate a final path instruction set.

[0006] Optionally, the S1 specifically includes: 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 spatial positions corresponding to each direction-finding unit based on the Doppler frequency shift principle to form preliminary wind field vector data; S12: Control the terrain scanner to synchronously scan the surface area overlapping with the direction-finding range of the Doppler radar by laser ranging, obtain the absolute height data of the surface reflection points, and convert it into the original elevation data in the 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 tags, and perform data fusion based on the spatial coordinates to generate the original wind field distribution data and the original terrain elevation data of the current flight airspace.

[0007] Optionally, the S2 specifically includes: 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 this node according to the following formula: , where 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: Perform the calculation of the wind shear intensity gradient value for all grid nodes, summarize and form a wind shear intensity gradient value matrix, and generate a wind shear intensity gradient value distribution map based on this wind shear intensity gradient value matrix; S23: Perform rasterization processing on the original terrain elevation data, analyze and extract the mutation boundary line based on the elevation difference between adjacent pixel units, and identify the edge area with a continuous height difference greater than the set threshold as the potential obstacle contour; S24: Combine the current height profile of the aircraft, calculate the three-dimensional spatial coordinates of the highest point for each identified contour area, and summarize them to form an obstacle basic coordinate set.

[0008] Optionally, the S23 specifically includes: S231: Perform rasterization processing on the original terrain elevation data to construct a two-dimensional elevation matrix , where is the row and column index number of the terrain grid; S232: Calculate the elevation difference between each elevation point and its adjacent upper, lower, left, and right four grid cells respectively, and specifically use the following elevation difference formula: ; ; Among them, represents the elevation difference between the current point and the right adjacent point in the horizontal direction; represents the elevation difference between the current point and the lower adjacent point in the vertical direction; S233: Define the maximum difference of each point as its local elevation difference response value , and the calculation method is: ; S234: Set the height change threshold , and mark all grid cells that satisfy as candidate obstacle area cells.

[0009] Optionally, the S24 specifically includes: S241: Perform connected component analysis on the candidate obstacle area cells marked in S23, extract the outer boundary contour line of each connected area, and assign a unique number to each connected area , where is a positive integer; S242: For each connected area numbered , traverse all the grid cells it contains, select the cell with the maximum elevation value in the two-dimensional elevation matrix , and record its elevation ; S243: Obtain the row and column indices of the current projection of the UAV on the terrain grid, combine the grid point resolution with the UAV reference plane coordinates , and calculate the absolute three-dimensional space coordinates of the highest point of the obstacle; S244: Archive the three-dimensional coordinates of the highest points of all areas by numbering to obtain the obstacle basic coordinate set.

[0010] Optionally, the S3 specifically includes: 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 UAV, denoted as ; S32: Real-time collect the space coordinates and movement speed of all moving objects in the flight airspace through the on-board sensing system; S33: For each obstacle, calculate the spatial distance from the UAV and the spatial distance from the nearest moving object; S34: Calculate the real-time risk coefficient of each obstacle, and the formula is: , where is the real-time risk coefficient of the th obstacle; is the speed of the moving object closest to the obstacle ; is the spatial distance between the obstacle and the drone; is the spatial distance between the obstacle and its closest moving object; are respectively the preset risk weight coefficients.

[0011] Optionally, the S4 specifically includes: S41: Taking the starting point coordinates, ending point coordinates, wind shear intensity gradient value distribution map, and real-time risk coefficient set as inputs, initializing the path generator, constructing a spatial grid structure, and determining the search step size; S42: According to the wind shear intensity gradient value distribution map, marking the areas in the spatial grid where the wind shear intensity is greater than the set threshold, and calibrating the risk level of the grid where the obstacle is located based on the real-time risk coefficient; S43: Using the A* path planning algorithm, starting from the starting point as the starting node in the spatial grid, and gradually searching for path nodes towards the end point; S44: During the path planning process, performing risk and shear judgment on each candidate path node. If the conditions are not met, then 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.

[0012] Optionally, the S44 specifically includes: S441: When the A* path planning algorithm selects a candidate path node each time, obtaining the wind shear intensity gradient value and risk level of the grid where the candidate path node is located; S442: Setting the wind shear intensity judgment threshold and the risk level judgment threshold for evaluating the node environment risk; S443: Performing a joint judgment on the current candidate path node. If any of the following conditions are simultaneously met, the current node is considered not to meet the safe passage requirements; Condition 1, , that is, the wind shear intensity at the candidate node is too high; Condition 2, , that is, the risk level at the candidate node exceeds the safety upper limit; S444: If the candidate node meets the safe passage requirements, add it to the path node sequence and continue to expand the path search to the next node; if not, remove the current node from the open list, backtrack to the previous node, and reselect the next passable candidate node.

[0013] Optionally, the S5 specifically includes: 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 remaining power of the UAV , and calculate the maximum allowable climbing height according to the climbing consumption characteristics of the UAV ; S53: Compare the total climbing height with the maximum allowable climbing height . If , perform an equal-proportion compression of the height sequence of the path points in the vertical direction. First, calculate the compression coefficient , and then adjust the height of each path point according to the following formula: , where is the minimum safety height set by the mission requirements, is the height of the path point after compression adjustment, and is the height of the th path point; S54: Generate the final path instruction set in order for all compressed path points that meet .

[0014] A low-altitude flight path automatic generation system for implementing the above-mentioned low-altitude flight path automatic generation method, including the following modules: Flight environment perception module: used to obtain the original wind field distribution data and original terrain elevation data of the flight airspace through the onboard Doppler radar and terrain scanner; Wind shear and obstacle identification 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, perform elevation difference analysis on the original terrain elevation data, identify the obstacle contour area, and extract the three-dimensional coordinates of the highest point of the obstacle in combination with the current altitude profile of the aircraft, and output the obstacle basic coordinate set; Risk assessment module: used to receive the obstacle basic coordinate set and the moving object azimuth and speed information collected by the onboard sensor, 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 rule; Path planning module: It is 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 iteratively generate an initial obstacle avoidance path point sequence in the constructed spatial grid structure based on the A* path planning algorithm; Path compression and instruction generation module: It is used to receive the initial obstacle avoidance path point sequence and the remaining battery power parameter of the aircraft, and perform vertical compression on the path point sequence according to the total climbing height of the path and the minimum safety height requirement to generate a final path instruction set.

[0015] Advantages of the present invention: In the present invention, by constructing a spatial grid structure, using the A* algorithm to iteratively generate path points, and combining the adaptive adjustment of path point spacing in the wind shear area, the high-risk area avoidance strategy and the vertical compression optimization under energy consumption constraints, a closed-loop path planning mechanism is formed. Compared with the existing solutions that only rely on static maps or simplified assumptions, it has significant improvements in environmental modeling accuracy, path safety and energy adaptability, and is applicable to low-altitude autonomous flight tasks in dynamic environments. Description of the drawings

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic diagram of the low-altitude flight path automatic generation method according to the embodiment of the present invention; Figure 2 It is a schematic diagram of the low-altitude flight path automatic generation system according to the embodiment of the present invention. Detailed implementation manners

[0018] The present invention will be described in detail below in combination with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0019] It should be noted that in the specification, references to "one embodiment", "an embodiment", "exemplary embodiments", "some embodiments", etc. indicate that the described embodiments may include a particular feature, structure, or characteristic, but not necessarily every embodiment includes that particular feature, structure, or characteristic. Additionally, when combining embodiments to describe a particular feature, structure, or characteristic, implementing such a feature, structure, or characteristic in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0020] Generally, terms can be understood, at least in part, from their use in context. For example, at least in part depending on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or can be used to describe a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but rather, at least in part depending on the context, can allow for the existence of other factors that are not necessarily explicitly described.

[0021] As Figure 1 shown, a method for automatically generating a low-altitude flight path includes the following steps: S1: Synchronously obtain the original wind field distribution data and the original terrain elevation data of the flight airspace through an on-board Doppler radar and a terrain scanner; S2: Perform wind shear intensity analysis on the original wind field distribution data and output a wind shear intensity gradient value distribution map of the entire region; at the same time, perform obstacle contour recognition on the original terrain elevation data to generate an obstacle base coordinate set; S3: Based on the obstacle base coordinate set, combine the azimuth data of moving objects detected in real time to calculate the real-time risk coefficient of each obstacle. The real-time risk coefficient is positively correlated with the moving speed of the obstacle and inversely correlated with the distance from the unmanned aerial vehicle; S4: Input the starting coordinate, ending coordinate, wind shear intensity gradient value distribution map, and real-time risk coefficient into a path generator to perform iterative calculations to generate an initial obstacle avoidance path point sequence; 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 mission requirements, compress the path points in the vertical direction to generate a final path instruction set.

[0022] S1 specifically includes: S11: Control the on-board Doppler radar to scan the area in front of the flight path at a preset frequency, obtain the frequency shift data of the reflected wave, and calculate the wind speed vector information at the corresponding spatial positions of each direction measurement unit based on the Doppler frequency shift principle to form preliminary wind field vector data. The specific calculation formula is: , where is the wind speed component in the direction pointed by the direction measurement unit; is the Doppler frequency shift value received by the radar; is the wavelength of the transmitted wave of the Doppler radar; is the angle between the direction of the direction-finding unit and the direction of the wind speed vector; S12: Control the terrain scanner to synchronously scan the surface area overlapping with the direction-finding range of the Doppler radar in a laser ranging mode, obtain the absolute height data of the surface reflection points, and convert it into the original elevation data in the 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 tags, and perform data fusion 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, it is possible to realize the synchronous acquisition and registration of wind field and terrain data in the time and space dimensions, provide consistent data support with high spatio-temporal resolution for subsequent wind shear analysis and obstacle identification, and improve the real-time performance and accuracy of flight path calculation.

[0023] S2 specifically includes: 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 this node according to the following formula: , where 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: Perform the calculation of the wind shear intensity gradient value for all grid nodes, summarize and form a wind shear intensity gradient value matrix, and generate a wind shear intensity gradient value distribution map based on this matrix; S23: Rasterize the original terrain elevation data, analyze and extract the mutation boundary line based on the elevation difference between adjacent pixel units, and identify the edge area with a continuous height difference greater than the set threshold as the potential obstacle contour; S24: Combine the current altitude profile of the aircraft, calculate the three-dimensional spatial coordinates of the highest point of each identified contour area, and summarize them to form an obstacle basic coordinate set; Through the above sub-steps, on the one hand, it realizes the quantitative analysis of the wind field shear intensity, obtains a complete gradient distribution map for risk prediction; on the other hand, it identifies the obstacle boundaries that may pose a threat in the terrain and extracts the three-dimensional coordinates, providing an accurate spatial constraint basis for path obstacle avoidance and enhancing the robustness and safety of path planning.

[0024] S23 specifically includes: S231: Rasterize the original terrain elevation data to construct a two-dimensional elevation matrix , where is the row and column index number of the terrain raster, represents the surface elevation value of the corresponding raster cell; S232: Calculate the elevation differences between each elevation point and its four adjacent raster cells above, below, left, and right respectively. The following elevation difference formula is specifically used: ; ; where represents the elevation difference between the current point and the right adjacent point in the horizontal direction; represents the elevation difference between the current point and the lower adjacent point in the vertical direction; S233: Define the maximum difference of each point as its local elevation difference response value , and the calculation method is: ; S234: Set the height change threshold , and mark all raster cells that satisfy as candidate obstacle area cells, which are used as the input basic data for subsequent obstacle contour extraction; Through the above elevation difference method processing, it is possible to calibrate the high-risk elevation difference area based on the local change characteristics of the terrain data, clearly define the candidate obstacle area, and provide structured data support for the contour boundary analysis and three-dimensional coordinate extraction in the subsequent steps.

[0025] S24 specifically includes: S241: Conduct connected component analysis on the candidate obstacle area cells marked in S23, extract the outer boundary contour line of each connected area, and assign a unique number to each connected area , where is a positive integer; S242: For each connected area numbered , traverse all the raster cells it contains, select the cell with the maximum elevation value in the two-dimensional elevation matrix , and record its elevation , where is the row index of the raster with the maximum elevation in this area; is the column index of the raster with the maximum elevation in this area; S243: Obtain the row and column indices of the current projection of the UAV on the terrain raster, and combine the grid point resolution and the UAV reference plane coordinates to calculate the absolute three-dimensional space coordinates of the highest point of the obstacle. The formula is: ; ; , where are the horizontal and vertical coordinates of the highest point of the obstacle in the plane rectangular coordinate system; is the absolute elevation value of the highest point of the obstacle; are the row and column indices of the UAV projection point in the elevation matrix; is the side length of the grid cell; S244: Number and file the three-dimensional coordinates of the highest points of all regions to obtain the obstacle basic coordinate set; The above steps accurately establish a spatial reference data set of the highest points of obstacles by extracting the highest grid cells of each candidate obstacle region in the two-dimensional elevation matrix and converting them into absolute three-dimensional coordinates, providing a reliable vertical risk boundary for subsequent path planning.

[0026] S3 specifically includes: S31: Obtain the three-dimensional spatial coordinates of each obstacle in the obstacle basic coordinate set, and obtain the current three-dimensional position of the UAV, denoted as ; S32: Real-time collect the spatial coordinates and motion speeds of all moving objects in the flight airspace through the on-board sensing system; S33: For each obstacle, calculate the spatial distance from the UAV and the spatial distance from the nearest moving object, and calculate them respectively using the following formulas: ; ; S34: Calculate the real-time risk coefficient of each obstacle, and the formula is: , where is the real-time risk coefficient of the th obstacle; is the speed of the moving object closest to the obstacle ; is the spatial distance between the obstacle and the UAV; is the spatial distance between the obstacle and its nearest moving object; are the preset risk weight coefficients respectively; The above steps can dynamically evaluate the potential collision risks of each obstacle by combining the spatial positions of the obstacles with the azimuth and motion speeds of the real-time detected moving objects, and the obtained real-time risk coefficients provide quantifiable and adjustable obstacle avoidance factors for path generation.

[0027] S4 specifically includes: S41: Initialize the path generator, construct a spatial grid structure, and determine the search step by taking the starting coordinates, ending coordinates, wind shear intensity gradient value distribution map, and real-time risk coefficient set as inputs. S42: Mark the areas in the spatial grid where the wind shear intensity is greater than the set threshold according to the wind shear intensity gradient value distribution map, and calibrate the risk level of the grid where the obstacle is located based on the real-time risk coefficient. The steps of calibrating the risk level of the grid where the obstacle is located are as follows: S421: Set two preset risk thresholds and such that ; S422: For each cell in the spatial grid that contains an obstacle, obtain the real-time risk coefficient of the corresponding obstacle in this cell; S423: Convert the real-time risk coefficient to a discrete risk level according to the following piecewise function, and its expression is: ; where is the real-time risk coefficient of the th obstacle; is the demarcation threshold between low risk and medium risk; is the demarcation threshold between medium risk and high risk; is the risk level of the grid where the th obstacle is located, where the value 1 represents low risk, 2 represents medium risk, and 3 represents high risk; S424: Assign the risk level corresponding to each obstacle to the grid cell where it is located, which is used to implement differential avoidance for areas with different risk levels during path planning.

[0028] S43: Use the A* path planning algorithm to search for path nodes step by step from the starting point as the starting node in the spatial grid. S44: During the path planning process, perform risk and shear judgment on each candidate path node. If the conditions are not met, backtrack and reselect until a continuous sequence of path points from the starting point to the ending point is formed, thereby obtaining an initial obstacle avoidance path point sequence.

[0029] S44 specifically includes: 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 this candidate path node is located; S442: Set the wind shear intensity judgment threshold and the risk level judgment threshold for evaluating the risk of the node environment; S443: Perform a joint judgment on the current candidate path node. If any of the following conditions is met simultaneously, the current node is considered not to meet the safe passage requirements; Condition 1, i.e., the wind shear intensity at the candidate node is too high; Condition 2, i.e., the risk level at the candidate node exceeds the safety upper limit; S444: If the candidate node meets the safe passage requirements, add it to the path node sequence and continue to expand the path search to the next node; if not, remove the current node from the open list, backtrack to the previous node, and reselect the next passable candidate node; through the above steps, by performing clear judgments on the wind shear intensity and obstacle risk level of each candidate path node, unsafe nodes can be identified and excluded in a timely manner during the path planning iteration process, ensuring the passage safety and planning stability of the generated path in the scenario where wind field disturbances and obstacle threats coexist.

[0030] S5 specifically includes: 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 , and the formula is: , where is the total number of path points, is the height of the th path point; S52: Obtain the current remaining battery power of the UAV , and calculate the maximum allowable climbing height according to the climbing consumption characteristics of the UAV, and its expression is: , where is the power consumption per unit climbing height; S53: Compare the total climbing height with the maximum allowable climbing height . If , then perform an equal-proportion compression on 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, and the formula is: , where is the minimum safety height set by the mission requirements, is the height of the path point after compression adjustment; S54: Compress all the compressed ones and satisfy The path points are used to generate the final path instruction set in sequence; through the above steps, by vertically compressing the initial obstacle avoidance path point sequence under the power constraint and ensuring the minimum safe altitude constraint, it is possible to achieve energy consumption adaptation of the path while ensuring mission safety, and improve the actual execution ability and endurance reliability of the UAV low-altitude obstacle avoidance mission.

[0031] As Figure 2 shown, a low-altitude flight path automatic generation system for implementing the above-mentioned low-altitude flight path automatic generation method includes the following modules: Flight environment perception module: used to obtain the original wind field distribution data and original terrain elevation data of the flight airspace through the onboard Doppler radar and terrain scanner; 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, perform elevation difference analysis on the original terrain elevation data, identify the obstacle contour area, extract the three-dimensional coordinates of the highest point of the obstacle in combination with the current altitude profile of the aircraft, and output the obstacle basic coordinate set; Risk assessment module: used to receive the obstacle basic coordinate set and the moving object azimuth and speed information collected by the onboard sensor, 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 rule; Path planning module: used to receive the starting point coordinates, ending point coordinates, wind shear intensity gradient value distribution map and 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; Path compression and instruction generation module: used to receive the initial obstacle avoidance path point sequence and the remaining battery power parameter of the aircraft, and perform vertical compression on the path point sequence according to the total path climb height and minimum safe altitude requirements to generate the final path instruction set.

[0032] The present invention covers any substitutions, modifications, equivalent methods and solutions made within the spirit and scope of the present invention. In order to enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without these detailed descriptions. In addition, in order to avoid unnecessary confusion to the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0033] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An automated method for generating low-altitude flight paths, characterized in that, It includes the following steps: S1: Synchronously obtain the original wind field distribution data and the original terrain elevation data of the flight airspace through an airborne Doppler radar and a terrain scanner; S2: Perform wind shear intensity analysis on the original wind field distribution data and output a wind shear intensity gradient value distribution map of the entire region; at the same time, perform obstacle contour recognition on the original terrain elevation data to generate an obstacle basic coordinate set; S3: Based on the obstacle basic coordinate set, combine the azimuth data of moving objects detected in real time to calculate the real-time risk coefficient of each obstacle; S4: Input the starting point coordinates, ending point coordinates, wind shear intensity gradient value distribution map, and real-time risk coefficient into a path generator to perform iterative calculations to generate an initial obstacle avoidance path point sequence; 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 mission requirements, compress the path points in the vertical direction to generate a final path instruction set.

2. The automatic generation method of a low-altitude flight path according to claim 1, wherein The specific content of S1 includes: 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 positions of each direction measurement unit based on the Doppler frequency shift principle to form preliminary wind field vector data; S12: Control the terrain scanner to synchronously scan the surface area overlapping with the direction measurement range of the Doppler radar in a laser ranging manner, obtain the absolute height data of the surface reflection points, and convert it into the original elevation data in the standard terrain elevation coordinate system; S13: Align the preliminary wind field vector data collected in S11 and the original elevation data obtained in S12 according to the time tags, and perform data fusion 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. An automated low-altitude flight path generation method according to claim 1, characterized in that The specific content of S2 includes: 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 this node according to the following formula: , where 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: Calculate the wind shear intensity gradient values for all grid nodes, summarize them to form a wind shear intensity gradient value matrix, and generate a wind shear intensity gradient value distribution map based on this wind shear intensity gradient value matrix; S23: Perform rasterization processing on the original terrain elevation data, analyze and extract the mutation boundary line based on the elevation difference between adjacent pixel units, and identify the edge area with a continuous height difference greater than the set threshold as the potential obstacle contour; S24: Combine the current height profile of the aircraft, calculate the three-dimensional spatial coordinates of the highest point for each identified contour area, and summarize them to form an obstacle basic coordinate set.

4. The automated low-altitude flight path generation method according to claim 3, characterized in that, The specific content of S23 includes: S231: Perform rasterization processing on the original terrain elevation data to construct a two-dimensional elevation matrix , where is the row-column index number of the terrain raster; S232: For each elevation point calculate the elevation differences with the four adjacent grid cells above, below, left, and right respectively, specifically using the following elevation difference formula: ； ; wherein, represents the elevation difference between the current point and the right adjacent point in the horizontal direction; represents the elevation difference between the current point and the lower adjacent point in the vertical direction; S233: Define the maximum difference of each point as its local height difference response value , and the calculation method is as follows: ; S234: Set the height change threshold value , and mark all grid cells that meet as candidate obstacle area cells.

5. The automated low-altitude flight path generation method according to claim 4, wherein The specific content of S24 includes: S241: Perform connected component analysis on the candidate obstacle region units marked in S23, extract the outer boundary contour lines of each connected region, and assign a unique number to each connected region , where is a positive integer; S242: For each connected region numbered , traverse all the grid cells it contains, select the cell with the maximum elevation value in the two-dimensional elevation matrix , and record its elevation ; S243: Obtain the row and column indices where the drone is currently projected onto the terrain grid, and combine with the grid point resolution and the reference plane coordinates of the drone , and calculate the absolute three-dimensional space coordinates of the highest point of the obstacle ; S244: Number and file the three-dimensional coordinates of the highest points of all areas to obtain the obstacle base coordinate set. ​ 6. The automated low-altitude flight path generation method according to claim 5, characterized in that The specific content of S3 includes: S31: Obtain the three-dimensional spatial coordinates of each obstacle in the obstacle basic coordinate set , and obtain the current three-dimensional position of the UAV, denoted as ; S32: Real-time collect the spatial coordinates and movement speeds of all moving objects within the flight airspace through the airborne sensing system and movement speeds; S33: Calculate the spatial distance to the UAV for each obstacle and the spatial distance to the nearest moving object ; S34: Calculate the real-time risk coefficient of each obstacle, with the formula: , where is the real-time risk coefficient of the th obstacle; is the speed of the moving object closest to the obstacle ; is the spatial distance between the obstacle and the drone; is the spatial distance between the obstacle and its closest moving object; are the preset risk weight coefficients respectively.

7. The automated generation method of a low-altitude flight path according to claim 1, characterized in that, The specific content of S4 includes: S41: Take the starting point coordinates, ending 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; S42: According to 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 calibrate the risk level of the grid where the obstacle is located based on the real-time risk coefficient; S43: Use the A* path planning algorithm to search for path nodes step by step from the starting point as the starting node in the spatial grid towards the ending point; S44: During the path planning process, perform risk and shear judgment on each candidate path node. If the conditions are not met, roll back and reselect until a continuous sequence of path points from the starting point to the ending point is formed, thereby obtaining an initial obstacle avoidance path point sequence.

8. The automated low-altitude flight path generation method according to claim 7, characterized in that, The specific steps of S44 include: S441: When each candidate path node is selected by the A* path planning algorithm, obtain the wind shear intensity gradient value of the grid where the candidate path node is located and the risk level ; S442: Set the judgment threshold for wind shear intensity and the judgment threshold for risk level , which is used to evaluate the environmental risk of the node; S443: Perform a combined judgment on the current candidate path node. If any of the following conditions are met simultaneously, the current node is considered not to meet the safe passage requirements; Condition 1, that is, the wind shear intensity at the candidate node is too high; Condition 2, that is, the risk level at the candidate node exceeds the safety upper limit; S444: If the candidate node meets the safe passage requirements, add it to the path node sequence and continue to expand the path search to the next node; if not, remove the current node from the open list, roll back to the previous node, and reselect the next passable candidate node.

9. The automatic generation method of a low-altitude flight path according to claim 1, characterized in that The specific steps of S5 include: S51: Obtain the three-dimensional spatial 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 drone , and calculate the maximum allowable climbing height according to the climbing power consumption characteristics of the drone ; S53: Compare the total climbing height with the maximum allowable climbing height . If , vertically compress the height sequence of the path points proportionally. First, calculate the compression coefficient , and then adjust the height of each path point according to the following formula: , where is the minimum safety height set for the mission requirements, is the height of the path point after compression adjustment, is the th height of the path point; S54: Generate the final path instruction set in sequence for all the path points after compression and meeting .

10. An automated low-altitude flight path generation system for implementing an automated low-altitude flight path generation method according to any one of claims 1-9, characterized in that, It includes the following modules: Flight environment perception module: Used to obtain the original wind field distribution data and original terrain elevation data of the flight airspace through the onboard Doppler radar and terrain scanner; 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, perform elevation difference analysis on the original terrain elevation data, identify the obstacle contour area, extract the three-dimensional coordinates of the highest point of the obstacle in combination with the current altitude profile of the aircraft, and output the obstacle base coordinate set; Risk assessment module: Used to receive the obstacle base coordinate set and the azimuth and speed information of moving objects collected by the onboard 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 rule; Path planning module: Used to receive the starting point coordinates, ending point coordinates, the wind shear intensity gradient value distribution map and the risk level map, and iteratively generate an initial obstacle avoidance path point sequence in the constructed spatial grid structure based on the A* path planning algorithm; Path compression and instruction generation module: Used to receive the initial obstacle avoidance path point sequence and the remaining battery power parameter of the aircraft, perform vertical compression on the path point sequence according to the total path climb height and the minimum safe height requirements, and generate the final path instruction set.

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