Unmanned aerial vehicle true altitude flight path planning method, system, device and medium for complex terrain

Abstract

The application discloses a complex-terrain-oriented unmanned aerial vehicle true-height flight path planning method, system, device and medium. The complex-terrain-oriented unmanned aerial vehicle true-height flight path planning method comprises the following steps: acquiring multi-source geographic data, wherein the multi-source geographic data comprises at least one of terrain elevation data, building top elevation data, no-fly area data, high-voltage line data and geographic feature data; performing target obstacle identification on the multi-source geographic data corresponding to a planning area to obtain target obstacle data corresponding to the planning area, and performing two-dimensional grid conversion based on the target obstacle data and the geographic feature data to obtain converted multi-source geographic data, wherein the converted multi-source geographic data comprises obstacle grids, priority grids and ordinary grids; performing path search on the converted multi-source geographic data to obtain two-dimensional flight path data; and obtaining three-dimensional unmanned aerial vehicle true-height flight paths based on the two-dimensional flight path data, the terrain elevation data and the building top elevation data.

Description

Technical Field

[0001] This application relates to the technical fields of unmanned aerial vehicles (UAVs), and in particular to a method, system, device and medium for UAV true altitude flight path planning for complex terrain. Background Technology

[0002] Currently, when drones perform missions in complex terrains such as mountains and cities, the undulating terrain may cause the actual distance between the drone and the ground or obstacles to be too small, increasing the risk of collisions. Therefore, adapting to complex terrain and ensuring a safe distance between the drone and the ground has become a key research focus in drone flight path planning.

[0003] One related technology involves experienced pilots planning flight routes through on-site surveys and manual point marking, which is inefficient. Another related technology uses terrain elevation data for simple path planning, but its reliance on a single data source leads to a large deviation between the planning results and the actual environment. Yet another related technology relies on basic sensors or a single data source for limited obstacle identification and avoidance, and cannot automatically adjust the flight altitude according to terrain changes and environmental factors, making it difficult to meet the requirements for true-altitude flight in complex environments. Summary of the Invention

[0004] The embodiments of this application aim to at least partially address one of the technical problems in the related art. To this end, the embodiments of this application propose a method, system, device, and medium for UAV true-altitude flight path planning in complex terrain, improving the safety and efficiency of UAV flight path planning.

[0005] This application provides a method for planning true-altitude flight paths for unmanned aerial vehicles (UAVs) in complex terrain, comprising: acquiring multi-source geographic data, wherein the multi-source geographic data includes at least one of terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data; identifying target obstacles in the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area, and performing two-dimensional raster transformation based on the target obstacle data and geographic feature data to obtain transformed multi-source geographic data, wherein the transformed multi-source geographic data includes obstacle raster, priority raster, and ordinary raster; performing path search on the transformed multi-source geographic data to obtain two-dimensional flight path data; and obtaining a three-dimensional UAV true-altitude flight path based on the two-dimensional flight path data, terrain elevation data, and building top elevation data.

[0006] In some implementations, the multi-source geographic data corresponding to the planning area is transformed into two-dimensional raster data to obtain transformed multi-source geographic data. This includes: standardizing the multi-source geographic data corresponding to the planning area to obtain processed multi-source geographic data, wherein the processed multi-source geographic data includes at least one of processed terrain elevation data, processed building top elevation data, processed no-fly zone data, processed high-voltage line data, and processed geographic feature data; identifying the processed terrain elevation data, processed building top elevation data, processed no-fly zone data, and processed high-voltage line data to obtain target obstacle data; and performing two-dimensional raster transformation based on the target obstacle data corresponding to the planning area and the processed geographic feature data to obtain transformed multi-source geographic data.

[0007] In some implementations, the processed terrain elevation data, processed building top elevation data, processed no-fly zone data, and processed high-voltage line data are subjected to identification processing to obtain target obstacle data. This includes: acquiring minimum true altitude flight altitude data, maximum true altitude flight altitude data, and minimum true altitude flight range data; performing identification processing based on the processed no-fly zone data, processed terrain elevation data, minimum true altitude flight altitude data, and minimum true altitude flight range data to determine target no-fly zone data; performing identification processing based on the processed building top elevation data, maximum true altitude flight altitude data, minimum true altitude flight range data, and a first safety value to determine target building data; performing identification processing based on the processed high-voltage line data, maximum true altitude flight altitude data, minimum true altitude flight range data, and a second safety value to determine target high-voltage line data; and obtaining target obstacle data based on the target no-fly zone data, target building data, and target high-voltage line data.

[0008] In some implementations, the transformed multi-source geographic data includes obstacle rasters, priority rasters, and ordinary rasters; the transformed multi-source geographic data is obtained by performing two-dimensional raster transformation based on the target obstacle data corresponding to the planning area and the processed geographic feature data, including: performing grid encoding processing on the planning area to obtain a grid area; and performing two-dimensional raster transformation on the target obstacle data and processed geographic feature data corresponding to the grid area to obtain obstacle rasters, priority rasters, and ordinary rasters.

[0009] In some implementations, path search is performed on the transformed multi-source geographic data to obtain two-dimensional flight route data, including: path search is performed on priority grids and ordinary grids based on evaluation functions and cost functions to obtain two-dimensional flight route data, wherein the evaluation function represents the first cost from the current node to the planned endpoint, and the cost function represents the second cost from the planned starting point to the current node. The evaluation function is obtained based on Manhattan distance, and the cost function is obtained based on Euclidean distance.

[0010] In some implementations, a three-dimensional UAV true-altitude flight path is obtained based on two-dimensional flight path data, terrain elevation data, and building top elevation data. This includes: performing linear interpolation on the two-dimensional flight path data based on terrain elevation data and building top elevation data to obtain flight path elevation profile data; obtaining the target flight altitude corresponding to the flight path elevation profile data based on the flight path elevation profile data, minimum true-altitude flight altitude data, and a first safety value; and obtaining the three-dimensional UAV true-altitude flight path based on the target flight altitude corresponding to the flight path elevation profile data.

[0011] In some implementations, a true-altitude flight path for a three-dimensional UAV is obtained based on the target flight altitude corresponding to the flight path elevation profile data, including: acquiring waypoint data of the planning area; adjusting the target flight altitude corresponding to the flight path elevation profile data based on the waypoint data to obtain the true-altitude flight path for the three-dimensional UAV.

[0012] This application provides a UAV true altitude flight path planning system for complex terrain. The system includes: an acquisition module for acquiring multi-source geographic data, wherein the multi-source geographic data includes at least one of terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data; a conversion module for identifying target obstacles in the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area, and performing two-dimensional raster conversion based on the target obstacle data and geographic feature data to obtain converted multi-source geographic data, wherein the converted multi-source geographic data includes obstacle raster, priority raster, and ordinary raster; a search module for performing path search on the converted multi-source geographic data to obtain two-dimensional flight path data; and an acquisition module for obtaining a three-dimensional UAV true altitude flight path based on the two-dimensional flight path data, terrain elevation data, and building top elevation data.

[0013] An embodiment of this application provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0014] Embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0015] In the above embodiments, the UAV true altitude flight path planning method for complex terrain includes: acquiring multi-source geographic data, wherein the multi-source geographic data includes at least one of terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data; identifying target obstacles in the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area, and performing two-dimensional raster transformation based on the target obstacle data and geographic feature data to obtain transformed multi-source geographic data, wherein the transformed multi-source geographic data includes obstacle raster, priority raster, and ordinary raster; performing path search on the transformed multi-source geographic data to obtain two-dimensional flight path data; and obtaining a three-dimensional UAV true altitude flight path based on the two-dimensional flight path data, terrain elevation data, and building top elevation data. By fusing multi-source geographic information and unifying it into a two-dimensional grid for efficient path search, a globally optimized two-dimensional planar flight path is first obtained. Then, true altitude mapping is performed based on terrain elevation and building top elevation to finally generate a three-dimensional UAV true altitude flight path. This effectively balances planning efficiency and flight safety. While significantly reducing the computational complexity of direct three-dimensional spatial search, it can accurately ensure the safe true altitude distance between the UAV and undulating ground and various obstacles. It solves the problems of high collision risk and poor adaptability in complex terrain environments, and realizes safe, efficient and reliable autonomous flight path planning for UAVs in complex scenarios such as mountainous areas, urban building clusters, and high-voltage power line corridors. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a method for planning true altitude flight paths for unmanned aerial vehicles (UAVs) in complex terrain, provided for the implementation of this application;

[0017] Figure 2 A flowchart illustrating the first stage of another UAV true altitude flight path planning method for complex terrain provided in this application embodiment;

[0018] Figure 3 A flowchart illustrating phase two and phase three of another UAV true altitude flight path planning method for complex terrain provided in this application embodiment;

[0019] Figure 4 This is a schematic diagram of the two-dimensional route planning results provided in the embodiments of this application;

[0020] Figure 5 This is a schematic diagram of the three-dimensional route planning results provided in the embodiments of this application;

[0021] Figure 6 A schematic diagram of a UAV true altitude flight path planning system for complex terrain, provided as another embodiment of this application;

[0022] Figure 7 A block diagram of an electronic device provided for another embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] Currently, when drones perform missions in complex terrains such as mountains and cities, the undulating terrain may cause the actual distance between the drone and the ground or obstacles to be too small, increasing the risk of collisions. Therefore, adapting to complex terrain and ensuring a safe distance between the drone and the ground has become a key research focus in drone flight path planning.

[0025] Related technologies rely on manual route planning, where experienced pilots plan routes through on-site surveys and manual point marking. This method depends on the pilot's personal experience and familiarity with the environment, and while it remains a widely used basic method in the industry, it is inefficient. Another related technology is based on automatic planning using single terrain data (such as path search using the A* algorithm and shortest path planning using Dijkstra's algorithm). This technology uses terrain elevation data for simple path planning, mainly generating paths in a two-dimensional plane, lacking comprehensive consideration of three-dimensional space. Yet another related technology is based on simple obstacle avoidance planning techniques, using fixed safety distances or simple collision detection algorithms. It relies on basic sensors or a single data source for limited obstacle identification and avoidance, making it difficult to cope with complex and changing environmental conditions.

[0026] The aforementioned technologies do not offer a complete solution specifically for true altitude flight path planning in complex terrain environments. In particular, they are significantly lacking in the fusion of multi-source heterogeneous geographic data and the dynamic conversion of relative altitude and altitude. The specific limitations are as follows: (1) Low planning efficiency: Manual planning requires a lot of manpower and time. It usually requires 2 pilots and 1 flight path marker, and it takes a week to complete one planning session. Even the relevant automatic planning technologies have problems such as low computational efficiency and complex parameter adjustment when dealing with complex terrain environments. (2) Insufficient planning accuracy: Manual planning is prone to omissions in complex terrain environments, and the planning error rate is high. Due to the single data source and simplified model, the relevant automatic planning technologies also have the problem of large deviations between the planning results and the actual environment. (3) Safety hazards: The relevant solutions cannot effectively identify obstacles such as restricted flight zones, high-voltage lines, and tall buildings, resulting in a high risk of collisions with buildings / mountains and a high accident rate. Especially in complex urban environments, due to the dense buildings and large terrain undulations, the safety hazards are more prominent. (4) Poor data integration capability: Relying on a single data source (such as terrain elevation), it is difficult to uniformly process heterogeneous geographical data such as building top elevation, dynamic no-fly zones, and high-voltage lines, resulting in a large deviation between the planning results and the actual environment. (5) Insufficient true altitude flight capability: At present, most route planning technologies lack the ability to accurately control true altitude (vertical height above the ground), and cannot automatically adjust the flight altitude according to terrain changes and environmental factors, making it difficult to meet the true altitude flight requirements in complex environments. (6) Poor adaptability to complex environments: In complex terrain environments such as cities and mountains, related technologies are difficult to handle various factors such as terrain undulations, building obstruction, and electromagnetic interference, making it difficult to actually execute planned routes or posing safety hazards.

[0027] In view of the above-mentioned technical limitations, this application aims to solve the following specific technical problems: (1) How to provide an efficient method for planning the true altitude flight path of UAVs: solve the problem of time-consuming and labor-intensive manual planning (2 pilots + 1 person for flight path marking + one week), and break through the bottleneck of low calculation efficiency and complex parameter adjustment of related automatic planning technologies in complex terrain environments. (2) How to achieve effective fusion of multi-source heterogeneous geographic data: solve the problem of unified processing of various data types such as terrain elevation, building top elevation, legal no-fly zones, and high-voltage lines, improve the matching degree between planning results and actual environment, and reduce the planning error rate. (3) How to ensure that UAVs always maintain safe true altitude flight in complex terrain environments: solve the problem of the lack of precise control of true altitude (vertical height above the ground) in related technologies, and realize the ability to automatically adjust flight altitude according to terrain changes and environmental factors. (4) How to achieve intelligent obstacle avoidance and special area avoidance: solve the problem that related solutions cannot effectively identify and avoid obstacles such as legal no-fly zones, high-voltage lines, and tall buildings, reduce the risk of crashing into buildings / mountains, and improve flight safety. (5) How to build a complete true altitude flight path planning system: solve the problem of lack of systematicness in the planning area determination, two-dimensional path generation, and three-dimensional height optimization of related technologies, and provide a full-process solution from data input to flight path output. (6) How to improve the adaptability to complex environments: solve the problem of poor adaptability of related technologies in complex terrain environments such as cities and mountains, and realize the comprehensive processing capability of various complex factors such as terrain undulation and building obstruction.

[0028] Therefore, this application proposes a true-altitude flight path planning method for UAVs in complex terrain. It systematically solves the entire process problem from multi-source data fusion to 3D flight path generation through a three-stage processing framework (including multi-source data fusion and processing, 2D flight path planning, and 3D height optimization). In the first stage, multi-source data fusion and processing are used to identify areas that cannot be avoided by vertical avoidance as obstacles. The second stage is used to construct a 2D planning map and plan the flight path based on the planning map. The third stage is used to obtain the elevation information on the 2D flight path, perform vertical avoidance and flight path smoothing optimization, and generate a 3D flight path that meets the true-altitude requirements.

[0029] Figure 1 This is a flowchart illustrating a method for planning the true altitude flight path of an unmanned aerial vehicle (UAV) in complex terrain, as provided in the embodiments of this application.

[0030] like Figure 1 As shown, the UAV true altitude flight path planning method 100 for complex terrain includes, for example, steps S110-S140.

[0031] Step S110: Obtain multi-source geographic data, wherein the multi-source geographic data includes at least one of the following: terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data.

[0032] For example, terrain elevation data is used to reflect the ground undulation of the planning area, with a vertical accuracy of less than or equal to 10 meters; building top elevation data is used to reflect the top height information of buildings within the planning area, with a vertical accuracy of less than or equal to 1 meter; no-fly zone data is used to define the legally restricted airspace for drone flights, including polygonal planar boundary information and its associated restricted height information; high-voltage line data is used to identify the spatial location and height information of overhead power transmission lines; geographic feature data includes rivers, roads, etc., and the areas corresponding to geographic feature data have higher priority than other areas when planning flight routes.

[0033] Step S120: Target obstacle identification is performed on the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area. Based on the target obstacle data and geographic feature data, a two-dimensional raster transformation is performed to obtain the transformed multi-source geographic data. The transformed multi-source geographic data includes obstacle raster, priority raster and ordinary raster.

[0034] For example, the planning area is determined based on the planning start point and planning end point. The planning area is processed by two-dimensional grid encoding to obtain the grid area corresponding to the planning area. The grid area may include a square grid. The converted multi-source geographic data includes obstacle grids, priority grids and ordinary grids. By processing the multi-source geographic data, target obstacle data and processed geographic feature data can be obtained. Two-dimensional grid conversion is performed based on the target obstacle data to obtain obstacle grids. Two-dimensional grid conversion is performed based on the processed geographic feature data to obtain priority grids. The remaining grids in the planning area other than obstacle grids and priority grids are ordinary grids.

[0035] Step S130: Perform path search on the transformed multi-source geographic data to obtain two-dimensional flight route data.

[0036] For example, path search is based on an improved A* algorithm (the improved A* algorithm is implemented based on a heuristic function, an evaluation function, a cost function, and a search strategy, where the heuristic function represents the sum of the evaluation function and the cost function, and the search strategy adopts a priority grid-first search method). The evaluation function represents the cost from the current node to the planned endpoint, and the cost function represents the cost from the planned starting point to the current node. The evaluation function is obtained based on Manhattan distance, and the cost function is obtained based on Euclidean distance. The search terminates when the endpoint grid is found.

[0037] Step S140: Based on the two-dimensional flight path data, terrain elevation data, and building top elevation data, obtain the three-dimensional UAV true altitude flight path.

[0038] For example, linear interpolation of multiple sampling points is performed on the two-dimensional flight path data based on terrain elevation data and building top elevation data to obtain a profile map corresponding to the two-dimensional flight path. The multiple sampling points are spaced equally. The actual flight altitude of the UAV at each sampling point is calculated based on the terrain elevation data and building top elevation data. The actual flight altitude of the UAV is adjusted according to the altitude of the route points input by the user to obtain the three-dimensional UAV true altitude flight path.

[0039] As can be seen, the UAV true altitude flight path planning method for complex terrain proposed in this application achieves efficient path search by fusing multi-source geographic information and unifying it into a two-dimensional grid. It first obtains a globally optimized two-dimensional planar flight path, and then performs true altitude mapping based on terrain elevation and building top elevation to finally generate a three-dimensional UAV true altitude flight path. This method effectively balances planning efficiency and flight safety. While significantly reducing the computational complexity of direct three-dimensional spatial search, it can accurately ensure the safe true altitude distance between the UAV and undulating ground and various obstacles. It solves the problems of high collision risk and poor adaptability in complex terrain environments, and realizes safe, efficient and reliable autonomous flight path planning for UAVs in complex scenarios such as mountainous areas, urban building clusters, and high-voltage power line corridors.

[0040] Phase 1 is used for multi-source data fusion and processing, mainly to integrate, standardize and identify various types of geographic data, and provide basic data support for subsequent route planning.

[0041] In one example, target obstacle identification is performed on multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area. Then, a two-dimensional raster transformation is performed based on the target obstacle data and the geographic feature data to obtain transformed multi-source geographic data. This includes: standardizing the multi-source geographic data corresponding to the planning area to obtain processed multi-source geographic data, wherein the processed multi-source geographic data includes at least one of processed terrain elevation data, processed building top elevation data, processed no-fly zone data, processed high-voltage line data, and processed geographic feature data; identifying the processed terrain elevation data, processed building top elevation data, processed no-fly zone data, and processed high-voltage line data to obtain target obstacle data; and performing a two-dimensional raster transformation based on the target obstacle data corresponding to the planning area and the processed geographic feature data to obtain the transformed multi-source geographic data.

[0042] Specifically, standardization processing includes coordinate transformation and data format conversion, while identification processing includes obstacle identification. Obstacle identification on the processed no-fly zone data and processed terrain elevation data includes determining the no-fly zone based on the processed no-fly zone data and processed terrain elevation data, and identifying the target no-fly zone data. Obstacle identification on the processed building top elevation data includes determining the target building data based on the building top safety redundancy value. Obstacle identification on the processed high-voltage line data includes determining the target high-voltage line data based on the safety redundancy value. The three-dimensional target obstacle data is then projected into two dimensions to obtain the transformed multi-source geographic data.

[0043] For example, unified standardization processing can be carried out on multi-source geographic data from different sources and in different formats, including coordinate system conversion and data and format conversion. The coordinate system conversion adopts the WGS84 coordinate system, and the data format conversion can be carried out by converting vector data, raster data and text data into a unified spatial database format.

[0044] In one example, the processed terrain elevation data, processed building top elevation data, processed no-fly zone data, and processed high-voltage line data are processed to identify and obtain target obstacle data. This includes: acquiring minimum true altitude flight altitude data, maximum true altitude flight altitude data, and minimum true altitude flight range data; performing identification and processing based on the processed no-fly zone data, processed terrain elevation data, minimum true altitude flight altitude data, and minimum true altitude flight range data to determine target no-fly zone data; performing identification and processing based on the processed building top elevation data, maximum true altitude flight altitude data, minimum true altitude flight range data, and a first safety value to determine target building data; performing identification and processing based on the processed high-voltage line data, maximum true altitude flight altitude data, minimum true altitude flight range data, and a second safety value to determine target high-voltage line data; and obtaining target obstacle data based on the target no-fly zone data, target building data, and target high-voltage line data.

[0045] Specifically, a true altitude threshold can be calculated based on the processed no-fly zone data (e.g., 300m-400m, where 300m is the lower limit and 400m is the upper limit), minimum true altitude flight altitude data, and minimum true altitude flight range data. Comparing the processed terrain elevation data with the first true altitude threshold determines the target no-fly zone data (e.g., a height where the processed terrain elevation data is greater than the first true altitude threshold is the target no-fly zone data). A building threshold can be obtained based on the maximum true altitude flight altitude data, the first safety value, and the minimum true altitude flight range data. Comparing the processed building top elevation data with the second true altitude threshold determines the target building data. A high-voltage line threshold can be obtained based on the maximum true altitude flight altitude data, the second safety value, and the minimum true altitude flight range data. Comparing the processed high-voltage line data with the third true altitude threshold determines the target high-voltage line data. The target no-fly zone data, target building data, and target high-voltage line data are used as target obstacle data.

[0046] For example, for no-fly zones, the no-fly zone defined by altitude is converted into a two-dimensional area that needs to be avoided under true altitude flight conditions, as shown in formula (1):

[0047] h1= min_altitude- H_min–δ(1)

[0048] Where h1 represents the first true altitude threshold, min_altitude represents the processed no-fly zone data (processed no-fly zone lower limit), H_min represents the minimum true altitude flight altitude data (e.g., 100m), and δ represents the minimum true altitude flight range data (e.g., 20m).

[0049] For example, if the altitude range of 300m-400m in a certain area is a no-fly zone, then min_altitude=300, H_min represents the minimum true altitude flight altitude (e.g., 100m, meaning that the altitude must be at least 100m above the ground when flying), and δ represents the minimum true altitude flight range (e.g., 20m). Then h1=300-100-20=180m. All areas with terrain elevation data higher than 180m are no-fly zones (target no-fly zone data).

[0050] The area within the no-fly zone that meets the condition that the ground elevation (processed terrain elevation data) is greater than h1 is used as the two-dimensional obstacle avoidance zone (target no-fly data). Since the no-fly zone is an altitude concept, it is difficult to determine which no-fly zones need to be bypassed and which need to be avoided when flying at true altitude. In this way, the no-fly zones that cannot be avoided by adjusting altitude are identified and two-dimensional obstacle avoidance is performed. The remaining areas are avoided by adjusting altitude in the third stage.

[0051] For the elevation data of the building top, since the flight is based on true height, only the avoidance of super tall buildings needs to be considered. The rest can be passed by adjusting the height and flying at true height, as shown in formula (2):

[0052] h2 = True altitude flight limit - Minimum true altitude flight range data - Building top safety redundancy value (2)

[0053] Where h2 represents the second true height threshold, the upper limit of true height flight represents the maximum true height flight height data, the building top safety redundancy value represents the first safety value (e.g., 20m), the minimum true height flight range data is 20m, and the area within the planning area that meets the processed building top elevation data > h2 is taken as the two-dimensional obstacle avoidance area (target building data).

[0054] For high-voltage line data, since the flight is at true altitude, only the avoidance of ultra-high-voltage lines needs to be considered. The rest can be passed by adjusting the altitude to fly at true altitude, as shown in formula (3):

[0055] h3 = True altitude flight limit - Minimum true altitude flight range data - High-voltage line safety redundancy value (3)

[0056] Where h3 represents the third true altitude threshold, the upper limit of true altitude flight represents the maximum true altitude flight altitude data, the high-voltage line safety redundancy value represents the second safety value (e.g., 30m), the minimum true altitude flight range data is, for example, 20m, and the area within the planning area that meets the condition that the processed high-voltage line data is greater than h3 is taken as the two-dimensional obstacle avoidance area (target high-voltage line data).

[0057] Phase 2 is used to generate a two-dimensional raster map based on the data processed in Phase 1, and to perform path planning using the A* algorithm.

[0058] In one example, the transformed multi-source geographic data includes obstacle rasters, priority rasters, and ordinary rasters. Based on the target obstacle data corresponding to the planning area and the processed geographic feature data, a two-dimensional raster transformation is performed to obtain the transformed multi-source geographic data, including: performing grid encoding processing on the planning area to obtain grid areas; and performing two-dimensional raster transformation on the target obstacle data and processed geographic feature data corresponding to the grid areas to obtain obstacle rasters, priority rasters, and ordinary rasters.

[0059] Specifically, by performing grid encoding on the planning area according to the grid interval, a uniform grid area corresponding to the planning area can be obtained. The grid area can include square grids. The grid encoding process adopts two-dimensional encoding. The two-dimensional grid conversion includes projection processing. The target obstacle data corresponding to the grid area is projected to obtain the obstacle grid. Based on the processed geographic feature data, the priority grid is also projected. The remaining grids in the planning area other than the obstacle grid and the priority grid are ordinary grids. The passage cost is set for the priority grid based on the attenuation factor. The passage cost of the obstacle grid is set to infinity. The priority grid is set to a lower passage cost (e.g., if the attenuation factor is 0.5, the passage cost is 50% of that of the ordinary grid). The ordinary grid is set to a basic passage cost (e.g., calculated according to the cost function, and the obtained cost function value is used as the basic passage cost).

[0060] For example, based on the processed target obstacle data and priority area information (processed geographic feature data), a two-dimensional raster map is constructed. The raster attributes include obstacle raster (no-fly zone, super tall building, high-voltage line conversion surface), ordinary raster, and priority raster (river and road area). The map resolution is dynamically adjusted according to the planning accuracy requirements, typically set to 10 meters × 10 meters. A quadtree hierarchical storage structure is adopted to achieve efficient map query and update.

[0061] In one example, path search is performed on the transformed multi-source geographic data to obtain two-dimensional flight route data, including: path search is performed on priority grids and ordinary grids based on evaluation function and cost function to obtain two-dimensional flight route data. The evaluation function represents the first cost from the current node to the planning endpoint, and the cost function represents the second cost from the planning starting point to the current node. The evaluation function is obtained based on Manhattan distance, and the cost function is obtained based on Euclidean distance.

[0062] Specifically, an improved A* algorithm (where the evaluation function uses Manhattan distance, the cost function uses Euclidean distance, and the search strategy prioritizes grid-first search) is used for two-dimensional path planning. The evaluation function... As shown in formula (4):

[0063] (4)

[0064] in,( The coordinates of the current node, ( Indicates the coordinates of the destination of the plan.

[0065] Cost function As shown in formula (5):

[0066] (5)

[0067] in,( The coordinates of the current node, ( The coordinates represent the starting point of the planning process. The heuristic function represents the sum of the evaluation function and the cost function.

[0068] For priority grid cells, the passage cost is The product of the attenuation factor (e.g., 0.5) and the grid distance, for a normal grid, is: For obstacle grids, the passage cost is infinite.

[0069] Path search is performed on the transformed multi-source geographic data, starting from the planning origin. Priority is given to expanding priority rasters as the current node. (If no priority raster exists, the evaluation function value and cost function value are selected; for ordinary rasters...) For priority grids, for Attenuation factor (e.g., 0.5) The node with the smallest sum of grid distances (heuristic function value) (ordinary grid) is selected as the current node. The search terminates when the destination grid is reached, and obstacle grids are automatically avoided.

[0070] In the above embodiments, based on the A* algorithm, a decay factor is introduced for the priority grid, and a search strategy of priority grid expansion is adopted for path search. Paths along geographical feature data such as rivers and roads are selected first, making full use of geographical features to improve flight safety. While ensuring the optimality of the path, the probability of conforming to safe geographical features such as rivers and roads can be significantly increased, realizing safe route planning that actively conforms to geographical features.

[0071] Phase 3 is used to optimize the three-dimensional altitude based on the two-dimensional flight path obtained in Phase 2, combined with terrain elevation and building elevation data, to ensure that the UAV always maintains safe true altitude flight.

[0072] In one example, a three-dimensional UAV true-altitude flight path is obtained based on two-dimensional flight path data, terrain elevation data, and building top elevation data. This includes: performing linear interpolation on the two-dimensional flight path data based on terrain elevation data and building top elevation data to obtain flight path elevation profile data; obtaining the target flight altitude corresponding to the flight path elevation profile data based on the flight path elevation profile data, minimum true-altitude flight altitude data, and a first safety value; and obtaining the three-dimensional UAV true-altitude flight path based on the target flight altitude corresponding to the flight path elevation profile data.

[0073] Specifically, linear interpolation is performed on multiple sampling points of the two-dimensional flight path data, with the same sampling interval between the multiple sampling points. Linear interpolation is then performed based on terrain elevation data and building top elevation data to obtain flight path elevation profile data. The actual flight altitude is calculated based on the minimum true altitude flight altitude data and the first safety value for the sampling points corresponding to the flight path elevation profile data, thereby obtaining the target flight altitude corresponding to the flight path elevation profile data and thus obtaining the three-dimensional UAV true altitude flight path.

[0074] For example, along a two-dimensional flight path, ground elevation and building top elevation data are obtained by sampling at fixed intervals. The elevation data is processed by linear interpolation to construct a flight path elevation profile (flight path elevation profile data) to intuitively show the terrain change characteristics. For each sampling point, the actual flight altitude (target flight altitude) is calculated as shown in formula (6) to ensure that the flight altitude meets the true altitude flight requirements, avoid all super tall buildings, and set a reasonable climb / descent rate according to the terrain change characteristics to ensure flight safety.

[0075] Actual flight altitude = max{terrain elevation data + minimum true altitude flight altitude data, building top elevation data + first safety value} (6)

[0076] In one example, a true-altitude flight path for a 3D UAV is obtained based on the target flight altitude corresponding to the flight path elevation profile data. This includes: acquiring waypoint data for the planning area; and adjusting the target flight altitude corresponding to the flight path elevation profile data based on the waypoint data to obtain the true-altitude flight path for the 3D UAV.

[0077] Specifically, based on the target altitude of the waypoints input by the user (e.g., 100m) and the calculated actual safe flight altitude (target flight altitude, e.g., 80m), the target flight altitude of the final three-dimensional flight path is determined, and the true altitude flight path of the three-dimensional UAV is obtained.

[0078] The UAV true altitude flight path planning method for complex terrain proposed in this application achieves the following: (1) Significantly improved planning efficiency: Manual planning requires 2 pilots and 1 flight path marker to work together and takes about 1 week to complete the flight path planning for complex terrain. This application, through a three-stage processing framework and a highly automated processing flow, only requires 1 operator and can complete the flight path planning of the same complexity within 1 minute, reducing the planning time by 99.8% and the manpower requirement by 66.7%. (2) Significantly improved planning accuracy: The planning error rate of manual planning or simple automatic planning systems is as high as 30%, mainly manifested in problems such as insufficient safe distance between the flight path and obstacles and unstable true altitude maintenance. This application, through precise multi-source data fusion and scientific obstacle identification processing, significantly improves the planning accuracy, reduces the planning error rate to below 1%, and improves the matching degree between the flight path and the actual environment to 99%. (3) Significantly enhanced flight safety: In complex terrain environments, the UAV flight accident rate of related flight path planning systems is relatively high, mainly due to collisions with obstacles and failure to maintain true altitude. This application, through scientific obstacle identification processing and three-dimensional height optimization design, significantly improves flight safety. (4) Strong data integration capability: Most related technologies can only process single types of geographic data, resulting in poor data integration capability and low matching degree between planning results and actual environment. This application can process multiple heterogeneous geographic data such as terrain elevation, building top elevation, legal no-fly zones, high-voltage lines, and river and road networks simultaneously through a multi-source heterogeneous geographic data fusion model. (5) Wide adaptability: Related technologies are usually only applicable to specific terrain conditions, such as plains or simple mountainous areas, and have poor adaptability in complex urban environments or steep mountainous areas. This application can adapt to complex urban environments, mountains, hills, plains and other terrain conditions through a three-stage processing framework and dynamic adjustment strategy. (6) High degree of automation: Most related technologies require manual intervention in multiple links, such as data preprocessing, path adjustment and height setting, resulting in low degree of automation. This application realizes full-process automation from multi-source data fusion and two-dimensional path planning to three-dimensional height optimization.

[0079] Figure 2 This is a flowchart illustrating the first stage of another UAV true altitude flight path planning method for complex terrain provided in this application.

[0080] like Figure 2 As shown, the first stage of the UAV true altitude flight path planning method for complex terrain includes steps 1-5.

[0081] Phase 1: Acquiring Avoidance Zone Data

[0082] Step 1, Request route planning (altitude is relative to the starting point altitude).

[0083] For example, the user sends a route planning request to the decision module, specifying the altitude as a relative altitude to the route origin.

[0084] Step 2: Request the elevation data of the top of the starting building.

[0085] For example, the decision module inputs the starting point coordinates into the Geographic Information System (GIS) service, requests the building top elevation data of the starting point of the flight route, and the GIS returns the building top elevation data of the starting point.

[0086] Step 3: Convert the relative starting point height into altitude.

[0087] Step 4: Call the API to calculate the planning area.

[0088] For example, the decision module calls the relevant algorithm to calculate the minimum outer rectangle and expands it by 5000 meters. The algorithm returns the planning area and true height threshold to the decision module.

[0089] Step 5: Request airspace core data.

[0090] For example, the decision module inputs the planning area and true height threshold into the GIS service, and the GIS service returns obstacle area information, including: no-fly zone data, building data with a height exceeding the true height threshold, high-voltage line data with a height exceeding the true height threshold, restricted flight zone (sensitive area) data, river data, and road data.

[0091] Figure 3 The flowcharts for stage two and stage three of another UAV true altitude flight path planning method for complex terrain provided in the embodiments of this application are shown.

[0092] like Figure 3 As shown, the UAV true altitude flight path planning method for complex terrain includes, for example, steps 6-12 in stages two and three.

[0093] Phase Two: Generating Planning Maps and 2D Paths

[0094] Step 6: Initialize the planning environment.

[0095] For example, the decision module feeds a dynamically loaded dataset into the algorithm to generate a planning grid map.

[0096] Step 7, map initialization.

[0097] Step 8, Route planning.

[0098] For example, the algorithm generates a two-dimensional route (consisting of two-dimensional waypoints) without altitude and returns the two-dimensional route result to the decision module.

[0099] Phase 3: Obtain waypoint elevation data and plan flight routes based on relative terrain elevation.

[0100] Step 9: Request ground elevation and building top elevation information along the flight path.

[0101] For example, the decision module inputs the two-dimensional flight path results into the GIS service, and the GIS service returns the elevation data on the flight path, generating ground elevation data and building top elevation information on the flight path.

[0102] Step 10: Input elevation and other information.

[0103] For example, the decision module passes elevation information to the algorithm.

[0104] Step 11: Vertical obstacle avoidance algorithm, trajectory optimization to generate a 3D flight path.

[0105] For example, the algorithm generates a three-dimensional flight path based on waypoint elevation and building height information, and returns the planning results to the decision module.

[0106] Step 12, return to the final route.

[0107] For example, the decision module returns the final route to the user.

[0108] The UAV true altitude flight path planning method for complex terrain proposed in this application adopts: (1) a three-stage processing framework, including: multi-source data fusion and processing stage: receiving various types of geographic data, performing standardization processing, and identifying obstacles and priority areas; two-dimensional flight path planning stage: constructing a two-dimensional grid map, using an improved A* algorithm for path planning, avoiding obstacles, and prioritizing safe areas; three-dimensional height optimization stage: acquiring terrain and building elevation data, calculating the actual flight altitude, and ensuring true altitude flight safety. (2) a true altitude-based obstacle identification method, including: no-fly zone conversion processing: calculating the safety threshold using the formula h= min_altitude - H_min - δ, and obtaining the areas that need to be avoided within the no-fly zone; super-tall building identification: defining super-tall buildings as "building top elevation + building top safety redundancy value 20m > true altitude flight limit - minimum true altitude flight range data 10m"; high-voltage line processing: adding a 10m safety extension to convert the high-voltage line into a surface and processing it as a building. (3) a three-dimensional height optimization algorithm, including: sampling along the two-dimensional flight path to obtain ground elevation and building top elevation data,

[0109] The safe flight altitude for each sampling point is calculated using the formula "Actual flight altitude = max{terrain elevation + minimum true flight altitude, building elevation + building top safety redundancy value 20m}", and basic smoothing is performed to ensure that altitude changes conform to the flight characteristics of the UAV.

[0110] For ease of understanding, this application provides the following specific embodiment:

[0111] (1) Application Background

[0112] Scenario: Relevant departments in a city need to conduct regular drone inspections of the city center area.

[0113] Challenges: The area is densely built (many high-rise buildings), has complex terrain (undulating), and contains obstacles such as no-fly zones and high-voltage power lines. Traditional manual planning is inefficient (taking about one week and requiring three people to work together), and existing automated planning technologies struggle to effectively process multi-source heterogeneous data (terrain, buildings, no-fly zones, high-voltage power lines), making it impossible to guarantee the safety and accuracy of true-altitude flight and resulting in a high risk of accidents (collisions with buildings, high-voltage power lines, or accidental entry into no-fly zones).

[0114] (2) Implementation process

[0115] Phase 1: Multi-source data fusion and obstacle recognition

[0116] a. Input: Planning area: starting point [106.5, 29.5, 100.0], ending point [106.7, 29.5, 100.0], 4 waypoints. Flight requirements: true altitude flight range [40m, 120m], safe distance 25m. Multi-source geographic data: terrain elevation data (accuracy ≤10 meters), building top elevation data (accuracy ≤1 meter), legal no-fly zone data (3, including boundaries and height restrictions), high-voltage line data (location, height), river and road data (for priority areas).

[0117] b. Data standardization: Convert all input data to the WGS84 coordinate system and a unified spatial database format.

[0118] c. Obstacle recognition and transformation:

[0119] No-fly zone handling: The core algorithm h = min_altitude - H_min - δ (where H_min = 100m, δ = 20m) is applied. This calculates the areas within each no-fly zone that satisfy the condition that the ground elevation is greater than h, identifying no-fly zone areas that cannot be avoided by altitude adjustment alone (two-dimensional planar obstacle avoidance is required). The output is a MultiPolygon geometric set.

[0120] Supertall Building Identification: Based on the definition of building top elevation + 20m > true altitude flight limit (120m) - 10m, all supertall buildings (i.e., buildings with top elevation > 90m) are filtered out. The two-dimensional projection area of ​​these supertall buildings is obtained as obstacles.

[0121] High-voltage line handling: After adding a 10m safety extension to the high-voltage line data, it is converted into a planar obstacle (the handling method is the same as that for super high-rise buildings).

[0122] d. Output (stage one results, such as...) Figure 4 As shown): The integrated second-stage input data includes: the planning area boundary (Polygon); coordinates and elevations of 4 waypoints; identified obstacle areas: 3 no-fly zones requiring 2D obstacle avoidance, 2 super-tall building projection areas (GeometryCollection), and 1 high-voltage line conversion surface (Polygon); data on restricted flight zones (GeometryCollection); river and road data; and elevation information of building tops at waypoints. The following is the solution data for stage one:

[0123]

TrueAltitudeRoutePlanning (2D)

[0124] Phase Two: 2D Raster Map Construction and Path Planning

[0125] a. Input: Integrated data output from Phase 1 (TrueAltitudeRoutePlanning2DInput).

[0126] b. Processing procedure:

[0127] Constructing a raster map: Based on information such as obstacle areas and priority areas (rivers, roads), construct a two-dimensional raster map. The map resolution is set to 10 meters x 10 meters. Raster attributes are labeled as: obstacle raster (no-fly zone, super tall buildings, high-voltage line conversion surface), ordinary raster, and priority raster.

[0128] A* Path Planning: This algorithm uses an improved A* algorithm to search for the optimal 2D path from the starting point to the destination point on a grid map. Evaluation Function: Manhattan distance (|x1-x2| + |y1-y2|) is used to improve search efficiency. Cost Function: The toll cost is calculated using Euclidean distance (√[(x1-x2)² + (y1-y2)²]). Obstacle Avoidance: The algorithm automatically avoids all grid cells marked as obstacles. Termination Condition: The search terminates when the destination grid cell is reached.

[0129] c. Output (Phase Two Result): 2D Route Planning Result (TrueAltitudeRoutePlanning2DOutput): Status: Success (200). The planned 2D route (LineString) includes the starting point, ending point, waypoints, and coordinates (longitude, latitude) of the planned intermediate waypoints. The following is the solution data for Phase Two:

[0130]

True Altitude Route Planning (2D)

[0131] Phase 3: 3D Height Optimization and True Altitude Flight Line Generation

[0132] a. Input: The two-dimensional flight path (LineString) output from stage two, and: the ground elevation data (GeometryCollectionofLineString, Z value is elevation) along the route, the building top elevation data (GeometryCollectionofLineString, Z value is elevation) along the route, the true flight altitude range [40m, 120m], and the target altitude (100m) of the 4 waypoints.

[0133] b. Processing procedure:

[0134] Elevation information sampling and interpolation: Ground elevation and building top elevation are obtained by sampling at fixed intervals along a two-dimensional flight path. Linear interpolation is used to process the data and construct an elevation profile of the flight path.

[0135] High-level safety design: For each sampling point along the flight path, the actual safe flight altitude (altitude) is calculated: Actual flight altitude = max{ terrain elevation + minimum true altitude flight altitude (40m), building elevation + building top safety redundancy (20m)}. This calculation ensures that the UAV, at any location: if below is the ground, the flight altitude ≥ ground height + 40m (meeting the minimum true altitude). If below is a building, the flight altitude ≥ building top height + 20m (safely flying over the building). Simultaneously, the altitude of all points is within the true altitude range of [40m, 120m].

[0136] Altitude Conversion and Control: Based on the user-input target altitude (100m) of the route points and the calculated actual safe flight altitude, dynamic altitude adjustments are performed. Terrain undulations are considered, and reasonable climb / descent rate constraints are set to ensure smooth and safe flight. Multi-layered safety redundancy mechanisms (such as safety distances and altitude buffer zones) are applied.

[0137] c. Output (stage three results, such as...) Figure 5As shown, the yellow, purple, and orange dots represent different target altitudes of the generated 3D true altitude route (yellow dot represents altitude 1, orange dot represents altitude 2, and purple dot represents altitude 3): 3D True Altitude Route Planning 3D Output: Status: Success (200). The final generated 3D route (LineString) contains the latitude and longitude of all waypoints and the optimized altitude. For example: Starting point [106.5, 29.5, 100.0] (maintaining target height), transit point 1 [106.5596053, 29.46450623, 100.0] (maintaining target height), transit point 2 [106.56012137, 29.46405671, 73.17165351] (adjusted to 73.17m due to the +20m elevation requirement of the building's top below), ... (other points are dynamically adjusted according to terrain and building conditions), transit point 4 [106.7, 29.5, 100.0] (maintaining target height). The following is the solution data for Stage 3:

[0138]

TrueAltitudeRoutePlanning (3D)

[0139] The final 3D true altitude flight path is shown below:

[0140]

True Altitude Route Planning (3D)

[0141] In the above embodiments, the following effects are achieved: (1) Efficiency: Only one operator is needed to complete the route planning for the entire complex urban area, which takes 0.737 seconds. Compared with manual planning (2 pilots + 1 marker + 1 week), the efficiency is improved by 99.99%, and the manpower is reduced by 66.7%. (2) Accuracy: The generated route accurately avoids all identified no-fly zones, super tall buildings (through height adjustment or planar obstacle avoidance) and high-voltage lines (through planar obstacle avoidance). The route is highly matched with the actual environment (terrain undulation, building distribution), and the planning error rate is <1%. (3) Safety: Through scientific multi-source data fusion, obstacle identification and three-dimensional height optimization, the UAV is ensured to maintain safe true-altitude flight (height above ground / height above obstacles ≥40m / 20m) throughout the entire flight process, which significantly reduces the risk of collision and the accident rate is much lower than that of traditional solutions. (4) Adaptability: It successfully copes with the complex challenges of dense buildings, undulating terrain and multiple obstacles in the urban environment, and verifies the strong adaptability and reliability of the present invention in complex urban environments.

[0142] The UAV true altitude flight path planning method and system proposed in this application for complex terrain solves the technical problem of difficult unified processing of different types of geographic data in related technologies by: (1) Multi-source heterogeneous geographic data fusion and processing: Designing a unified data access interface and processing flow, which can simultaneously receive and process multiple data types such as terrain elevation, building top elevation, no-fly zones, high-voltage lines, and river and road networks. It adopts data standardization processing technologies such as coordinate system conversion, data format conversion, and timestamp synchronization, and designs special processing algorithms for different types of data (no-fly zones, super high-rise buildings, high-voltage lines, rivers and roads) to achieve efficient data utilization. (2) Three-stage processing framework: It ensures the comprehensiveness and accuracy of route planning, and systematically solves the whole process problem from multi-source data fusion to three-dimensional route generation. It decomposes the complex route planning process into three stages: multi-source data fusion and processing, two-dimensional route planning, and three-dimensional height optimization. Each stage focuses on solving specific problems. In stage one, a special no-fly zone conversion algorithm and obstacle recognition method are used; in stage two, an improved A* algorithm is used for path planning; in stage three, the safe flight altitude is calculated by the formula "actual flight altitude = max{terrain elevation + minimum true flight altitude, building elevation + building top safety redundancy value 20m}". Through the core interface design, seamless connection and data flow between each stage are realized. (3) Obstacle recognition and avoidance strategy based on true altitude: It solves the technical problem of obstacle recognition and avoidance in true altitude flight mode. Based on the characteristics of true altitude flight, it only performs two-dimensional avoidance on obstacles that cannot be avoided by altitude adjustment (no-fly zone, super tall building, high voltage line). The safety threshold calculation method of h = min_altitude - H_min - δ is used to identify no-fly zone; super tall building is defined as "building top elevation + building top safety redundancy value 20m > true altitude flight upper limit - minimum true altitude flight range data 10m"; the safety extension of high voltage line is increased by 10m and transformed into a surface.

[0143] Figure 6 A schematic diagram of a UAV true altitude flight path planning system for complex terrain provided as another embodiment of this application.

[0144] like Figure 6 As shown, the UAV true altitude flight path planning system 600 for complex terrain includes: acquisition module 610, conversion module 620, search module 630, and acquisition module 640.

[0145] The acquisition module 610 is used to acquire multi-source geographic data, wherein the multi-source geographic data includes at least one of the following: terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data;

[0146] The conversion module 620 is used to identify target obstacles in the multi-source geographic data corresponding to the planning area, obtain target obstacle data corresponding to the planning area, and perform two-dimensional raster conversion based on the target obstacle data and the geographic feature data to obtain the converted multi-source geographic data, wherein the converted multi-source geographic data includes obstacle raster, priority raster and ordinary raster.

[0147] Search module 630 is used to perform path search on the transformed multi-source geographic data to obtain two-dimensional flight route data;

[0148] The module 640 is used to obtain the true altitude flight path of a three-dimensional UAV based on two-dimensional flight path data, terrain elevation data, and building top elevation data.

[0149] For example, the conversion module 620 is further configured to standardize the multi-source geographic data corresponding to the planning area to obtain processed multi-source geographic data, wherein the processed multi-source geographic data includes at least one of processed terrain elevation data, processed building top elevation data, processed no-fly zone data, processed high-voltage line data, and processed geographic feature data; the processed terrain elevation data, processed building top elevation data, processed no-fly zone data, and processed high-voltage line data are identified to obtain target obstacle data; and a two-dimensional raster conversion is performed based on the target obstacle data corresponding to the planning area and the processed geographic feature data to obtain the converted multi-source geographic data.

[0150] For example, the conversion module 620 is further configured to acquire minimum true altitude flight altitude data, maximum true altitude flight altitude data, and minimum true altitude flight range data; perform identification processing based on the processed no-fly zone data, processed terrain elevation data, minimum true altitude flight altitude data, and minimum true altitude flight range data to determine target no-fly zone data; perform identification processing based on the processed building top elevation data, maximum true altitude flight altitude data, minimum true altitude flight range data, and a first safety value to determine target building data; perform identification processing based on the processed high-voltage line data, maximum true altitude flight altitude data, minimum true altitude flight range data, and a second safety value to determine target high-voltage line data; and obtain target obstacle data based on the target no-fly zone data, target building data, and target high-voltage line data.

[0151] For example, the transformed multi-source geographic data includes obstacle rasters, priority rasters, and ordinary rasters; the transformation module 620 is also used to perform grid encoding processing on the planning area to obtain a grid area; and to perform two-dimensional grid transformation on the target obstacle data and the processed geographic feature data corresponding to the grid area to obtain obstacle rasters, priority rasters, and ordinary rasters.

[0152] For example, the search module 630 is also used to perform path search on priority grids and ordinary grids based on the evaluation function and the cost function to obtain two-dimensional route data, wherein the evaluation function represents the first cost from the current node to the planned endpoint, and the cost function represents the second cost from the planned starting point to the current node. The evaluation function is obtained based on Manhattan distance, and the cost function is obtained based on Euclidean distance.

[0153] For example, the obtaining module 640 is further configured to perform linear interpolation processing on the two-dimensional flight path data based on terrain elevation data and building top elevation data to obtain flight path elevation profile data; based on the flight path elevation profile data, minimum true altitude flight altitude data, and a first safety value, obtain the target flight altitude corresponding to the flight path elevation profile data; and based on the target flight altitude corresponding to the flight path elevation profile data, obtain the three-dimensional UAV true altitude flight path.

[0154] For example, the acquisition module 640 is also used to acquire waypoint data of the planning area; and adjust the target flight altitude corresponding to the route elevation profile data based on the waypoint data to obtain the true altitude route of the three-dimensional UAV.

[0155] It is understandable that the specific functions of the UAV true altitude flight path planning system 600 for complex terrain can be referred to the above-mentioned method for UAV true altitude flight path planning for complex terrain, and will not be repeated here.

[0156] Figure 7 A block diagram of an electronic device provided for another embodiment of this application.

[0157] An embodiment of this application provides an electronic device, which includes: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by one or more processors, which are executed by one or more processors to cause the one or more processors to implement the steps of the method of any of the above embodiments.

[0158] like Figure 7 As shown, for ease of understanding, an embodiment of this application illustrates a specific electronic device 700.

[0159] Electronic device 700 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 700 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0160] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0161] Multiple components in electronic device 700 are connected to input / output (I / O) interface 705. These components include: input unit 706, such as a keyboard or mouse; output unit 707, such as various types of displays or speakers; storage unit 708, such as a disk or optical disk; and communication unit 709, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0162] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods described above. For example, in some embodiments, any one or more of the methods described above can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of any one or more of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform any one or more of the methods described above by any other suitable means (e.g., by means of firmware).

[0163] Embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0164] With the rapid development of UAV technology and the continuous expansion of its application fields, the demand for high-precision, high-safety, and high-efficiency flight path planning technology is increasing. The UAV true-altitude flight path planning system and method for complex terrain provided in this application are particularly suitable for urban and mountainous environments with complex terrain and dense buildings. It can be widely applied to: UAV inspections in complex urban environments; aerial surveying and mapping of complex terrains such as mountains and hills; and special missions with strict requirements for flight altitude.

[0165] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this application, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0166] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0167] In the description of this application, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this application, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0168] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0169] Furthermore, the terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this application can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this application, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly and specifically defined in the embodiments.

[0170] In this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0171] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

Claims

1. A method for true altitude flight path planning for unmanned aerial vehicles (UAVs) in complex terrain, characterized in that, The method includes: Acquire multi-source geographic data, wherein the multi-source geographic data includes terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data, wherein the geographic feature data indicates the area where the priority passage object is located; Target obstacle identification is performed on the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area. The target obstacle data includes target no-fly data, target building data, and target high-voltage line data. Two-dimensional raster transformation is performed based on the target obstacle data and the geographic feature data to obtain transformed multi-source geographic data, wherein the transformed multi-source geographic data includes obstacle raster obtained based on the target obstacle data, priority raster obtained based on the geographic feature data, and ordinary raster; Based on the transformed multi-source geographic data, with the search constraints of avoiding the obstacle grid and increasing the priority of the priority grid, a path search is performed on the priority grid and the ordinary grid to obtain two-dimensional flight path data; Linear interpolation is performed on the two-dimensional flight path data based on the terrain elevation data and the building top elevation data to obtain flight path elevation profile data; based on the flight path elevation profile data, minimum true altitude flight altitude data, and a first safety value, the target flight altitude corresponding to the flight path elevation profile data is obtained, wherein the first safety value is a preset value of distance from the building top elevation data; The target flight altitude is adjusted based on the input waypoint altitude data to obtain the true altitude flight path of the three-dimensional UAV.

2. The method according to claim 1, characterized in that, The process involves identifying target obstacles from multi-source geographic data corresponding to the planning area to obtain target obstacle data for the planning area, and then performing a two-dimensional raster transformation based on the target obstacle data and the geographic feature data to obtain the transformed multi-source geographic data, including: The multi-source geographic data corresponding to the planning area is standardized to obtain processed multi-source geographic data, wherein the processed multi-source geographic data includes at least one of the following: processed terrain elevation data, processed building top elevation data, processed no-fly zone data, processed high-voltage line data, and processed geographic feature data. The processed terrain elevation data, the processed building top elevation data, the processed no-fly zone data, and the processed high-voltage line data are identified and processed to obtain the target obstacle data; Based on the target obstacle data corresponding to the planning area and the processed geographic feature data, a two-dimensional raster transformation is performed to obtain the transformed multi-source geographic data.

3. The method according to claim 2, characterized in that, The process of identifying and processing the processed terrain elevation data, the processed building roof elevation data, the processed no-fly zone data, and the processed high-voltage line data to obtain target obstacle data includes: Obtain minimum true altitude flight altitude data, maximum true altitude flight altitude data, and minimum true altitude flight range data; Based on the processed no-fly zone data, the processed terrain elevation data, the minimum true altitude flight altitude data, and the minimum true altitude flight range data, the target no-fly data is determined through identification processing. The target building data is determined by performing identification processing based on the processed building top elevation data, the maximum true altitude flight height data, the minimum true altitude flight range data, and the first safety value. Based on the processed high-voltage line data, the maximum true altitude flight altitude data, the minimum true altitude flight range data, and the second safety value, the target high-voltage line data is determined. Based on the target no-fly zone data, the target building data, and the target high-voltage line data, the target obstacle data is obtained.

4. The method according to claim 2, characterized in that, The process of converting the target obstacle data corresponding to the planning area and the processed geographic feature data into two-dimensional raster data yields the converted multi-source geographic data, including: The planned area is processed by grid coding to obtain the grid area; The target obstacle data and the processed geographic feature data corresponding to the grid area are transformed into two-dimensional raster data to obtain the obstacle raster, the priority raster, and the ordinary raster.

5. The method according to claim 4, characterized in that, The path search for the priority grid and the ordinary grid includes: Path search is performed on the priority grid and the ordinary grid based on the evaluation function and the cost function, wherein the evaluation function represents the first cost from the current node to the planning endpoint, and the cost function represents the second cost from the planning starting point to the current node. The evaluation function is obtained based on Manhattan distance, and the cost function is obtained based on Euclidean distance.

6. The method according to claim 1, characterized in that, The target flight altitude is adjusted based on the input waypoint altitude data to obtain a three-dimensional UAV true altitude flight path, including: Obtain the elevation data of waypoints input by the user for the planned area; The target flight altitude corresponding to the route elevation profile data is adjusted based on the waypoint elevation data to obtain the true altitude route of the three-dimensional UAV.

7. A true-altitude flight path planning system for unmanned aerial vehicles (UAVs) in complex terrain, characterized in that: The system includes: The acquisition module is used to acquire multi-source geographic data, wherein the multi-source geographic data includes terrain elevation data, building top elevation data, no-fly zone data, high-voltage line data, and geographic feature data, wherein the geographic feature data indicates the area where the priority passage object is located; The conversion module is used to identify target obstacles in the multi-source geographic data corresponding to the planning area to obtain target obstacle data corresponding to the planning area. The target obstacle data includes target no-fly data, target building data, and target high-voltage line data. Based on the target obstacle data and the geographic feature data, a two-dimensional raster conversion is performed to obtain the converted multi-source geographic data. The converted multi-source geographic data includes obstacle raster obtained based on the target obstacle data, priority raster obtained based on the geographic feature data, and ordinary raster. The search module is used to perform path search on the priority grid and the ordinary grid based on the transformed multi-source geographic data, with the search constraints being avoiding the obstacle grid and increasing the priority of the priority grid, to obtain two-dimensional flight path data; The acquisition module is used to perform linear interpolation processing on the two-dimensional flight path data based on the terrain elevation data and the building top elevation data to obtain flight path elevation profile data; based on the flight path elevation profile data, minimum true altitude flight altitude data, and a first safety value, to obtain the target flight altitude corresponding to the flight path elevation profile data, wherein the first safety value is a preset value of distance from the building top elevation data; and to adjust the target flight altitude based on the input waypoint altitude data to obtain the three-dimensional UAV true altitude flight path.

8. An electronic device having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-6.

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