A method and system for planning a concealed ground-hugging flight path for a drone
By employing 3D spatial meshing and hybrid Riemann tensor optimization, the UAV path planning method solves the multi-dimensional balance problem in complex environments, enabling UAVs to fly covertly in complex terrains and improving energy efficiency and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING UNIV OF INFORMATION SCI & TECH
- Filing Date
- 2026-03-20
- Publication Date
- 2026-06-09
AI Technical Summary
Existing UAV path planning technologies fail to achieve precise quantification of environmental characteristics, dynamic adaptation of planning strategies, and intelligent optimization of search algorithms. They cannot meet the multi-dimensional balance requirements of UAVs for covert, low-altitude flight in complex interference environments. In particular, they are difficult to achieve a comprehensive improvement in energy efficiency, terrain-following concealment, and interference avoidance safety when facing complex terrain.
By employing three-dimensional spatial meshing, the hybrid Riemann tensor of each voxel is calculated. Combined with wind speed, terrain, and threat source data, the path planning is optimized through Riemann geometric distance and path cost scalar values, achieving deep coupling and dynamic adjustment of environmental factors, and selecting the optimal flight path.
It accurately matches the three-dimensional spatial motion requirements of UAVs, improves the effectiveness of sampling, avoids blind searching, generates the optimal path that meets multiple constraints, and satisfies the requirements of UAVs for stealthy flight in complex environments.
Smart Images
Figure CN121877017B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) path planning technology, specifically relating to a method and system for planning UAV stealthy, low-altitude flight paths. Background Technology
[0002] UAV path planning is a core technology for autonomous UAV flight, with significant application value in fields such as terrain exploration, environmental monitoring, and low-altitude reconnaissance. In particular, the path planning for covert, low-altitude flight in complex terrain and interference environments directly determines the stealth, safety, and endurance of UAV missions. Therefore, UAV path planning has become an important research direction for UAV technology upgrades.
[0003] Existing technologies typically reduce the 3D environment to a 2D mesh for processing, ignoring the actual undulations of the terrain. When facing terrain obstacles such as mountains, they are often simplified into insurmountable vertical obstacles for horizontal avoidance, resulting in a singular planning objective, usually only pursuing the shortest path in a geometric sense. This approach has several key drawbacks. First, the environmental factors are not comprehensively represented, often only considering Euclidean distance and ignoring the direct impact of slope on energy consumption, the directional effect of wind fields, and the core value of environmental disturbance exposure, failing to accurately quantify multi-dimensional environmental constraints. Second, the planning weight mechanism is rigid, relying on fixed empirical weights to integrate multiple factors, and cannot dynamically adjust according to real-time environmental characteristics such as wind speed fluctuations, terrain steepness, and disturbance distribution density, resulting in poor strategy adaptability. Third, the search algorithm is disconnected from the environment; traditional random sampling algorithms uniformly sample and connect in straight lines in Euclidean space, failing to consider the anisotropic costs brought by wind and slope, resulting in blind and inefficient searches that are difficult to converge to the global optimum. In addition, some geodesic-related studies only couple a single environmental factor, lacking an adaptive decision-making mechanism, and cannot adapt to the multi-constraint operation requirements of UAVs.
[0004] In summary, existing UAV path planning technologies fail to achieve precise quantification of environmental characteristics, dynamic adaptation of planning strategies, and intelligent optimization of search algorithms. They cannot meet the stringent requirements of UAVs for energy efficiency, terrain-following concealment, and interference avoidance safety in complex interference environments, and are also difficult to adapt to the actual operational requirements of long-range, long-duration ground exploration and low-altitude reconnaissance missions. Summary of the Invention
[0005] This invention provides a method and system for planning stealthy, low-altitude flight paths for unmanned aerial vehicles (UAVs), aiming to plan the optimal trajectory that balances safety and energy efficiency for long-duration, long-distance stealthy, low-altitude reconnaissance missions of UAVs.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0007] The first aspect of this invention provides a method for planning a stealthy, low-altitude flight path for an unmanned aerial vehicle (UAV), comprising:
[0008] The UAV flight area is meshed in three-dimensional space to form several voxels; the mixed Riemann tensor of each voxel is calculated based on the wind speed characteristics, terrain characteristics and threat source data in the UAV flight environment.
[0009] The node cost scalar value of each voxel is calculated based on the hybrid Riemann tensor, and the sampling probability density of the voxel is determined by the node cost scalar value. The starting position of the UAV flight is used as the search node. The sampling area is determined based on the search node, the target position of the UAV flight, and the search radius. Within the sampling area, voxels are sampled according to the sampling probability to obtain sampling candidate points. The Riemann geometric distance between the search node and the sampling candidate points is calculated based on the hybrid Riemann tensor. The n sampling candidate points with the smallest Riemann geometric distance are used as the next search node. The sampling is repeated until the target position of the UAV flight is reached to obtain the search tree.
[0010] Calculate the path cost scalar value for each search flight path in the search tree; the search flight path is the path from the starting position of the UAV flight through interconnected search nodes to the target position of the UAV flight; based on the path cost scalar value, select the theoretical flight path from all search flight paths in the search tree, and perform interpolation smoothing on the theoretical flight path to obtain the final flight execution path.
[0011] Furthermore, based on wind speed characteristics, terrain features, and threat source data in the UAV's flight environment, the mixed Riemann tensor for each voxel is calculated, specifically including:
[0012] Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the wind speed fluctuation coefficient of the UAV flight area is calculated. Topographic relief coefficient and exposure coefficient ;
[0013] An analysis and judgment matrix is constructed based on the relative emphasis of wind speed characteristics, terrain features, and threat source data. The eigenvector corresponding to the largest eigenvalue of the analysis and judgment matrix is then solved. ; for eigenvectors Normalization is performed to obtain the basic weight vector; the basic weight vector and wind speed fluctuation coefficient are then used to obtain the basic weight vector. Topographic relief coefficient and exposure coefficient Calculate and obtain the path planning weights;
[0014] The wind speed features, terrain features, and threat source data in each voxel are transformed into exposure tensors, slope tensors, and wind force tensors using tensor mapping functions; the exposure tensors, slope tensors, and wind force tensors are then fitted into hybrid Riemann tensors using path planning weights.
[0015] Furthermore, based on the wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the wind speed fluctuation coefficient of the UAV flight area is calculated. Topographic relief coefficient and exposure coefficient Specifically, it includes:
[0016] The wind speed vector at the center point of each voxel is extracted from the wind speed characteristics of the UAV flight environment, and the wind speed fluctuation coefficient is calculated based on the wind speed vector. The formula is as follows:
[0017]
[0018] In the formula, The total number of voxels in the drone's flight area. For the first Wind speed vector at the center of an individual element This represents the average wind speed in the drone's flight area.
[0019] The elevation of each voxel is extracted from the terrain features in the UAV flight environment, and the terrain undulation coefficient is calculated based on the elevation of each voxel. The formula is as follows:
[0020]
[0021]
[0022] In the formula, For the first The angle between the slope of the earth's surface and the horizontal plane in individual elements. The average slope of all voxels in the drone's flight area; For the first The position coordinates of the voxels Elevation;
[0023] The location coordinates and transmission power of threat sources are extracted from threat source data in the UAV flight environment. The threat source and its corresponding power are then calculated based on the location coordinates. Three-dimensional spatial distance between individual elements; based on threat source and first Exposure values are calculated based on the three-dimensional spatial distance between individual elements and the emission power of the threat source. The formula is as follows:
[0024]
[0025] In the formula, For the first Individual exposure values, Let k be the transmission power of the k-th threat source. Let be the gain of the transmitting antenna of the k-th threat source in the direction of the drone. The operating wavelength of the k-th threat source. This is the radar cross-section of the drone. Pi For the k-th threat source and the The three-dimensional spatial distance between individual elements Let K be the system loss factor of the kth threat source, K be the total number of threat sources in the UAV flight environment, and k be the index of the threat source in the UAV flight environment.
[0026] The first The exposure value of an individual voxel is compared with the exposure threshold to determine whether the voxel belongs to the hazardous voxel category. The ratio of the number of hazardous voxels to the total number of voxels in the UAV flight area is used as the exposure coefficient. .
[0027] Furthermore, an analysis and judgment matrix is constructed based on wind speed characteristics, terrain features, and the relative emphasis of threat source data, specifically including:
[0028]
[0029] In the formula, To analyze and judge the matrix, To set a value representing the relative importance of wind speed features to terrain features, Set a value to indicate the relative importance of wind speed characteristics to threat source data. Set a value to indicate the relative importance of terrain features to threat source data.
[0030] Furthermore, based on the fundamental weight vector and wind speed fluctuation coefficient... Topographic relief coefficient and exposure coefficient The path planning weights are calculated, specifically including:
[0031]
[0032]
[0033] , ,
[0034] In the formula, For path planning weights, As for wind power weight, For slope weight, For exposure weight, For normalization operations, Based on the weight vector, As the basic weight of wind power, As the basic weight of slope, As the base weight for exposure; , and Coupling factor; It is the sum of the element-wise multiplication of the base weights and the coupling factors.
[0035] Furthermore, tensor mapping functions are used to transform the wind speed features, terrain features, and threat source data in each voxel into exposure tensors, slope tensors, and wind force tensors, specifically including:
[0036]
[0037]
[0038]
[0039]
[0040]
[0041] In the formula, It is the identity matrix. For the exposure tensor, The first corresponding to the threat source data Individual exposure values, For the slope tensor, This is the slope penalty coefficient. The terrain normal vector in the terrain features. , and These are the x-axis, y-axis, and z-axis components of the terrain normal vector, respectively. For matrix transpose, For wind tensor; This is the wind penalty coefficient. This refers to the wind speed vector in the wind speed characteristics. , and These are the x-axis, y-axis, and z-axis components of the wind speed vector, respectively.
[0042] Furthermore, the exposure tensor, slope tensor, and wind tensor are fitted into a hybrid Riemann tensor using path planning weights, specifically including:
[0043]
[0044] In the formula, For wind tensor, For the slope tensor, For the exposure tensor; As for wind power weight, For slope weight, For exposure weight, For mixed Riemann tensors.
[0045] Furthermore, the node cost scalar value for each voxel is calculated based on the hybrid Riemann tensor, and the sampling probability density of the voxel is determined from the node cost scalar value; the formula is as follows:
[0046]
[0047] In the formula, Let be the scalar value of the node cost. For sampling probability density, It is a natural exponential function; For mixed Riemann tensors.
[0048] Furthermore, the sampling area is determined based on the search node, the target location of the UAV flight, and the search radius, specifically including:
[0049] A three-dimensional spherical initial sampling area is defined with the search node as the center and the search radius as the radius of the sphere. At the same time, the initial spherical sampling area is moved towards the target position by a set offset displacement to obtain the sampling area, guided by the target position of the UAV.
[0050] Furthermore, the Riemann geometric distance between the search node and the sampled candidate point is calculated based on the hybrid Riemann tensor, specifically including:
[0051]
[0052] In the formula, The Riemann geometric distance between the search node and the sampled candidate points. Let be the coordinate vector of the search node. Let be the coordinate vector of the sampling candidate points. Let Riemann tensor be the mixture of the midpoints between the search node and the sampled candidate points. This is the matrix transpose.
[0053] Furthermore, the path cost scalar value for each search flight path in the search tree is calculated, specifically including:
[0054]
[0055] In the formula, From the starting position of the drone flight to the search node The path cost scalar value, From the starting position of the drone flight to the search node The path cost scalar value, Add a new search node to search for flight paths. To search for new search nodes along the flight path Nearby search nodes, The first derivative of the UAV flight trajectory parameter curve. Let be the mixture Riemannian metric tensor corresponding to path point s. The parameter curve of the drone's flight trajectory; Let be the infinitesimal length of the path.
[0056] Furthermore, the theoretical flight path is interpolated and smoothed to obtain the final flight execution path, specifically including:
[0057]
[0058]
[0059] In the formula, These are the trajectory points of the flight execution path. Let J be the discrete path points on the theoretical flight path, and J be the total number of discrete path points on the theoretical flight path. Let b be the B-order B-spline basis function for the j-th discrete path point; Here is the normalized parameter for the position of the B-spline curve. , , and For parameters The interval division point, For the first Discrete path points B-spline basis functions For the first Discrete path points B-spline basis functions.
[0060] A second aspect of the present invention provides a stealthy, low-altitude flight path planning system for unmanned aerial vehicles (UAVs), comprising:
[0061] The data acquisition module performs meshing of the UAV flight area in three-dimensional space to form several voxels; based on the wind speed characteristics, terrain features and threat source data in the UAV flight environment, it calculates the mixed Riemann tensor of each voxel;
[0062] The path search module calculates the node cost scalar value of each voxel based on the hybrid Riemann tensor, and determines the sampling probability density of the voxel based on the node cost scalar value. The starting position of the UAV flight is used as the search node. The sampling area is determined based on the search node, the target position of the UAV flight, and the search radius. Within the sampling area, voxels are sampled according to the sampling probability to obtain sampling candidate points. The Riemann geometric distance between the search node and the sampling candidate points is calculated based on the hybrid Riemann tensor. The n sampling candidate points with the smallest Riemann geometric distance are used as the next search node. The sampling is repeated until the target position of the UAV flight is reached to obtain the search tree.
[0063] The path selection module calculates the path cost scalar value of each search flight path in the search tree; the search flight path is the path from the starting position of the UAV flight, through interconnected search nodes to the target position of the UAV flight; based on the path cost scalar value, the theoretical flight path is selected from all search flight paths in the search tree, and the theoretical flight path is interpolated and smoothed to obtain the final flight execution path.
[0064] A third aspect of the present invention provides an electronic terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the UAV covert ground-hugging flight path planning method described in the first aspect.
[0065] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0066] This invention meshes the UAV flight area into voxels in three-dimensional space, abandoning the traditional method of dimensionality reduction of the three-dimensional environment. It fully preserves the three-dimensional spatial characteristics of the flight environment, accurately matching the three-dimensional spatial motion requirements of UAVs flying close to the ground, and avoiding planning deviations caused by the simplification of three-dimensional features such as terrain undulations. Simultaneously, it calculates a hybrid Riemann tensor for each voxel, achieving deep coupling of three core environmental factors: wind speed characteristics, terrain characteristics, and threat source data. This transforms the directional influence of the environment into mathematical tensor features, overcoming the limitation of traditional scalar cost fields that cannot distinguish the cost differences between different flight directions.
[0067] This invention calculates the node cost scalar value of each voxel using a hybrid Riemann tensor, transforming the multidimensional environmental cost information contained in the tensor into a single scalar value. This solves the problem that multidimensional costs are difficult to directly use for determining sampling probability. By determining the sampling probability density of voxels using the node cost scalar value, the sampling probability is strongly correlated with the actual passage cost of the voxel. This results in lower-cost voxels having higher sampling probabilities and higher-cost voxels having lower sampling probabilities. This fundamentally avoids the problem of blind sampling in high-cost regions inherent in traditional uniform sampling, guiding sampling points to concentrate in areas more favorable to UAV flight, significantly improving sampling effectiveness and reducing the ineffective consumption of computational resources.
[0068] This invention calculates the Riemann geometric distance between search nodes and sampled candidate points based on the hybrid Riemann tensor, replacing the traditional Euclidean distance method. The Riemann geometric distance accurately reflects the actual travel cost between two points, taking into account wind speed, terrain, and threat sources, rather than simply the geometric spatial distance. This avoids generating invalid nodes that are geometrically close but have high actual flight costs. The n sampled candidate points with the smallest Riemann geometric distance are selected as the next search nodes, ensuring that the growth of the search tree always progresses along the low-cost direction. The search tree gradually built during the iteration process can conform to the environmental cost characteristics defined by the hybrid Riemann tensor, ensuring that the paths connected by all nodes in the search tree conform to the physical constraints of UAV flight. This avoids forcibly traversing high-cost areas such as headwinds or steep slopes, achieving a more intelligent and physically feasible path exploration in complex anisotropic environments.
[0069] This invention calculates a scalar value of path cost for each search flight path in the search tree. This scalar value comprehensively reflects the voxel node cost from the starting position to the target position, objectively quantifying the overall travel cost of each path. Based on this, theoretical flight paths are selected from all paths in the search tree. This breaks through the limitation of traditional path planning that only pursues a single objective, and can select the optimal path that achieves a comprehensive balance in multiple dimensions such as wind speed adaptation, terrain conformity, and threat avoidance, thus meeting the requirements of UAVs for ground-hugging stealth flight under multiple constraints. Attached Figure Description
[0070] Figure 1 This is a flowchart of the UAV covert ground-hugging flight path planning method provided in Embodiment 1 of the present invention;
[0071] Figure 2 This is a flowchart of the phase-one adaptive weight optimization mechanism provided in Embodiment 1 of the present invention;
[0072] Figure 3 This is a schematic diagram of the process for constructing a hybrid Riemannian metric tensor field in stage two, as provided in Embodiment 1 of the present invention.
[0073] Figure 4 This is a flowchart illustrating the three-stage improved Riemann RRT* algorithm provided in Embodiment 1 of the present invention;
[0074] Figure 5 This is a result diagram of the UAV path planning provided in Embodiment 1 of the present invention;
[0075] Figure 6 This is a flight contour map of the planned path provided in Embodiment 1 of the present invention;
[0076] Figure 7 This is a schematic diagram of the flight altitude profile of the planned path provided in Embodiment 1 of the present invention. Detailed Implementation
[0077] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.
[0078] Example 1
[0079] like Figure 1 As shown, this embodiment provides a method for planning the stealthy, low-altitude flight path of an unmanned aerial vehicle (UAV), including:
[0080] like Figure 2 As shown, the UAV flight area is meshed in three-dimensional space to form several voxels; based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the mixed Riemann tensor of each voxel is calculated, specifically including:
[0081] Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the wind speed fluctuation coefficient of the UAV flight area is calculated. Topographic relief coefficient and exposure coefficient Specifically, it includes:
[0082] The wind speed vector at the center point of each voxel is extracted from the wind speed characteristics of the UAV flight environment, and the wind speed fluctuation coefficient is calculated based on the wind speed vector. The formula is as follows:
[0083]
[0084] In the formula, The total number of voxels in the drone's flight area. For the first Wind speed vector at the center of an individual element This represents the average wind speed in the drone's flight area.
[0085] The elevation of each voxel is extracted from the terrain features in the UAV flight environment, and the terrain undulation coefficient is calculated based on the elevation of each voxel. The formula is as follows:
[0086]
[0087]
[0088] In the formula, For the first The angle between the slope of the earth's surface and the horizontal plane in individual elements. The average slope of all voxels in the drone's flight area; For the first The position coordinates of the voxels Elevation;
[0089] The location coordinates and transmission power of threat sources are extracted from threat source data in the UAV flight environment. The threat source and its corresponding power are then calculated based on the location coordinates. Three-dimensional spatial distance between individual elements; based on threat source and first Exposure values are calculated based on the three-dimensional spatial distance between individual elements and the emission power of the threat source. The formula is as follows:
[0090]
[0091] In the formula, For the first Individual exposure values, Let k be the transmission power of the k-th threat source. Let be the gain of the transmitting antenna of the k-th threat source in the direction of the drone. The operating wavelength of the k-th threat source. This is the radar cross-section of the drone. Pi For the k-th threat source and the The three-dimensional spatial distance between individual elements Let K be the system loss factor of the kth threat source, K be the total number of threat sources in the UAV flight environment, and k be the index of the threat source in the UAV flight environment.
[0092] The first The exposure value of an individual voxel is compared with the exposure threshold to determine whether the voxel belongs to the hazardous voxel category. The ratio of the number of hazardous voxels to the total number of voxels in the UAV flight area is used as the exposure coefficient. .
[0093] By calculating the corresponding values based on the wind speed characteristics, terrain features, and threat source data in the drone's flight environment... , , The coefficient transforms the originally abstract and multidimensional raw environmental data into quantifiable and calculable feature indicators, accurately extracting the core characteristic patterns of three types of environmental factors and solving the problem of insufficient characterization of environmental factors by traditional technologies. This approach abandons empirical and vague judgments about the environment, and uses objective quantitative coefficients to reflect the degree of wind speed fluctuation, the undulating characteristics of terrain, and the exposure risk of threat sources.
[0094] An analysis and judgment matrix is constructed based on the relative emphasis of wind speed characteristics, terrain features, and threat source data. The formula is as follows:
[0095]
[0096] In the formula, To analyze and judge the matrix, To set a value representing the relative importance of wind speed features to terrain features, Set a value to indicate the relative importance of wind speed characteristics to threat source data. Set a value to indicate the relative importance of terrain features to threat source data.
[0097] Solve for the eigenvector corresponding to the largest eigenvalue of the analysis and judgment matrix. ; for eigenvectors Normalization is performed to obtain the basic weight vector; the basic weight vector and wind speed fluctuation coefficient are then used to obtain the basic weight vector. Topographic relief coefficient and exposure coefficient The path planning weights are calculated using the following formula:
[0098]
[0099]
[0100] , ,
[0101] In the formula, For path planning weights, As for wind power weight, For slope weight, For exposure weight, For normalization operations, Based on the weight vector, As the basic weight of wind power, As the basic weight of slope, As the base weight for exposure; , and Coupling factor; It is the sum of the element-wise multiplication of the base weights and the coupling factors.
[0102] An analysis and judgment matrix is constructed based on the relative emphasis of wind speed, terrain, and threat source data. A basic weight vector is obtained by solving for and normalizing the feature vectors, allowing the basic weights to accurately match the inherent requirements of the task for different environmental factors, reflecting the task-oriented nature of path planning. Based on this, combined with... , , The computational path planning weights achieve a deep integration of the basic weights inherent to the mission requirements and the quantitative coefficients of actual environmental characteristics. This transforms static basic weights into adaptive weights that can dynamically adjust with environmental features, fundamentally changing the traditional planning model based on fixed empirical weights. This allows path planning weights to be specifically adjusted according to changes in the actual environmental characteristics of the flight area, enabling planning strategies in different environments to automatically favor more critical environmental factors, thus improving the weights' adaptability to both mission and environment.
[0103] like Figure 3 As shown, the wind speed features, terrain features, and threat source data in each voxel are transformed into exposure tensors, slope tensors, and wind force tensors using tensor mapping functions. Specifically, this includes:
[0104]
[0105]
[0106]
[0107]
[0108]
[0109] In the formula, It is the identity matrix. For the exposure tensor, The first corresponding to the threat source data Individual exposure values, For the slope tensor, This is the slope penalty coefficient. The terrain normal vector in the terrain features. , and These are the x-axis, y-axis, and z-axis components of the terrain normal vector, respectively. For matrix transpose, For wind tensor; This is the wind penalty coefficient. This refers to the wind speed vector in the wind speed characteristics. , and These are the x-axis, y-axis, and z-axis components of the wind speed vector, respectively.
[0110] The exposure tensor, slope tensor, and wind tensor are fitted into a hybrid Riemann tensor using path planning weights, specifically including:
[0111]
[0112] In the formula, For wind tensor, For the slope tensor, For the exposure tensor; As for wind power weight, For slope weight, For exposure weight, For mixed Riemann tensors.
[0113] For each voxel, a hybrid Riemann tensor is calculated, achieving deep coupling of three core environmental factors: wind speed characteristics, terrain characteristics, and threat source data. This transforms the directional influence of the environment into mathematical tensor features, overcoming the limitation of traditional scalar cost fields that cannot distinguish the cost differences of different flight directions.
[0114] like Figure 4 As shown, the node cost scalar value of each voxel is calculated based on the hybrid Riemann tensor, and the sampling probability density of the voxel is determined from the node cost scalar value; the formula is as follows:
[0115]
[0116] In the formula, Let be the scalar value of the node cost. For sampling probability density, It is a natural exponential function; For mixed Riemann tensors.
[0117] The starting position of the UAV flight is used as the search node. A three-dimensional spherical initial sampling area is defined with the search node as the center and the search radius as the radius of the sphere. At the same time, the spherical initial sampling area is moved towards the target position by a set offset displacement to obtain the sampling area, guided by the target position of the UAV flight.
[0118] The sampling probability density of voxels is determined by the node cost scalar value, making the sampling probability strongly correlated with the actual passage cost of the voxel. This results in low-cost voxels having a higher sampling probability and high-cost voxels having a lower sampling probability. This fundamentally avoids the problem of blind sampling in high-cost areas in traditional uniform sampling. It can guide sampling points to concentrate in areas that are more friendly to UAV flight, greatly improve the effectiveness of sampling, and reduce the ineffective consumption of computing resources.
[0119] Within the sampling region, voxels are sampled according to the sampling probability to obtain candidate sampling points; the Riemann geometric distance between the search node and the candidate sampling point is calculated based on the mixed Riemann tensor, expressed as:
[0120]
[0121] In the formula, The Riemann geometric distance between the search node and the sampled candidate points. Let be the coordinate vector of the search node. Let be the coordinate vector of the sampling candidate points. Let Riemann tensor be the mixture of the midpoints between the search node and the sampled candidate points. This is the matrix transpose.
[0122] The n candidate points with the smallest Riemann geometric distance are used as the next search nodes, and the sampling is repeated until the target position of the UAV is reached to obtain the search tree.
[0123] This embodiment calculates the Riemann geometric distance between search nodes and sampled candidate points based on the hybrid Riemann tensor, replacing the traditional Euclidean distance method. The Riemann geometric distance accurately reflects the actual travel cost between two points, taking into account wind speed, terrain, and threat sources, rather than simply the geometric spatial distance. This avoids generating invalid nodes that are geometrically close but have high actual flight costs. The n sampled candidate points with the smallest Riemann geometric distance are selected as the next search nodes, ensuring that the growth of the search tree always progresses along the low-cost direction. The search tree gradually built during the iteration process can conform to the environmental cost characteristics defined by the hybrid Riemann tensor, ensuring that the paths connected by all nodes in the search tree conform to the physical constraints of UAV flight. This avoids forcibly traversing high-cost areas such as headwinds or steep slopes, achieving a more intelligent and physically feasible path exploration in complex anisotropic environments.
[0124] The search flight path is the path from the starting position of the UAV's flight, through interconnected search nodes, to the target position of the UAV's flight; the path cost scalar value of each search flight path in the search tree is calculated; the formula is:
[0125]
[0126] In the formula, From the starting position of the drone flight to the search node The path cost scalar value, From the starting position of the drone flight to the search node The path cost scalar value, Add a new search node to search for flight paths. To search for new search nodes along the flight path Nearby search nodes, The first derivative of the UAV flight trajectory parameter curve. Let be the mixture Riemannian metric tensor corresponding to path point s. The parameter curve of the drone's flight trajectory; Let be the infinitesimal length of the path.
[0127] Based on the path cost scalar value, the theoretical flight path is selected from all search flight paths in the search tree. The theoretical flight path is then interpolated and smoothed to obtain the final flight execution path, which specifically includes:
[0128]
[0129]
[0130] In the formula, These are the trajectory points of the flight execution path. Let J be the discrete path points on the theoretical flight path, and J be the total number of discrete path points on the theoretical flight path. Let b be the B-order B-spline basis function for the j-th discrete path point; Here is the normalized parameter for the position of the B-spline curve. , , and For parameters The interval division point, For the first Discrete path points B-spline basis functions For the first Discrete path points B-spline basis functions.
[0131] The theoretical flight path in this embodiment consists of interconnected search nodes, which is a discrete path form and cannot be directly adapted to the flight control execution requirements of the UAV. Interpolation and smoothing are applied to the theoretical flight path to transform the discrete node path into a continuous and smooth final flight execution path. This aligns with the flight dynamics characteristics of the UAV, avoiding flight instability caused by sudden path changes. Simultaneously, it ensures the path better conforms to terrain undulations, meeting the trajectory smoothness and practical operational requirements for UAV's low-altitude stealth flight, allowing the planned path to be directly output to the flight control system for execution.
[0132] like Figure 5 As shown in Figure 6, this embodiment uses 3rd order B-spline smoothing to finally output the flight path executed by the UAV flight control system. From the perspective of terrain following, Figure 6 intuitively shows that the planned path can accurately follow the natural undulation of the terrain, achieving close conformity with the terrain and forming a good concealment effect by taking advantage of the natural cover of the terrain. Figure 7, from the dimension of flight altitude profile, clearly shows the stable change characteristics of flight altitude, without obvious altitude abrupt changes, and always maintaining a flight altitude state close to the ground surface. This fully confirms that the planned path fully meets the core mission requirements of UAV close-to-the-ground concealment flight, and also reflects the good performance of this path planning method in terms of terrain adaptability and flight concealment.
[0133] Example 2
[0134] This embodiment provides a UAV covert low-altitude flight path planning system. The UAV covert low-altitude flight path planning system is used to execute the UAV covert low-altitude flight path planning method described in Embodiment 1. The UAV covert low-altitude flight path planning system includes:
[0135] The data acquisition module performs meshing of the UAV flight area in three-dimensional space to form several voxels; based on the wind speed characteristics, terrain features and threat source data in the UAV flight environment, it calculates the mixed Riemann tensor of each voxel;
[0136] The path search module calculates the node cost scalar value of each voxel based on the hybrid Riemann tensor, and determines the sampling probability density of the voxel based on the node cost scalar value. The starting position of the UAV flight is used as the search node. The sampling area is determined based on the search node, the target position of the UAV flight, and the search radius. Within the sampling area, voxels are sampled according to the sampling probability to obtain sampling candidate points. The Riemann geometric distance between the search node and the sampling candidate points is calculated based on the hybrid Riemann tensor. The n sampling candidate points with the smallest Riemann geometric distance are used as the next search node. The sampling is repeated until the target position of the UAV flight is reached to obtain the search tree.
[0137] The path selection module calculates the path cost scalar value of each search flight path in the search tree; the search flight path is the path from the starting position of the UAV flight, through interconnected search nodes to the target position of the UAV flight; based on the path cost scalar value, the theoretical flight path is selected from all search flight paths in the search tree, and the theoretical flight path is interpolated and smoothed to obtain the final flight execution path.
[0138] Example 3
[0139] This embodiment provides an electronic terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the UAV covert ground-hugging flight path planning method described in Embodiment 1.
[0140] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0141] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0142] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0143] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0144] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for planning a stealthy, low-altitude flight path for an unmanned aerial vehicle (UAV), characterized in that, include: The flight area of the UAV is meshed in three-dimensional space to form several voxels; Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, calculate the mixed Riemann tensor for each voxel; The node cost scalar value of each voxel is calculated based on the hybrid Riemann tensor, and the sampling probability density of the voxel is determined by the node cost scalar value. The starting position of the UAV flight is used as the search node. The sampling area is determined based on the search node, the target position of the UAV flight, and the search radius. Within the sampling area, voxels are sampled according to the sampling probability to obtain sampling candidate points. The Riemann geometric distance between the search node and the sampling candidate points is calculated based on the hybrid Riemann tensor. The n sampling candidate points with the smallest Riemann geometric distance are used as the next search node. The sampling is repeated until the target position of the UAV flight is reached to obtain the search tree. Calculate the scalar value of the path cost for each search flight path in the search tree; The search flight path is the path from the starting position of the UAV flight through interconnected search nodes to the target position of the UAV flight. The theoretical flight path is selected from all search flight paths in the search tree according to the path cost scalar value, and the theoretical flight path is interpolated and smoothed to obtain the final flight execution path.
2. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the mixed Riemann tensor for each voxel is calculated, specifically including: Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the wind speed fluctuation coefficient of the UAV flight area is calculated. Topographic relief coefficient and exposure coefficient ; An analysis and judgment matrix is constructed based on the relative emphasis of wind speed characteristics, terrain features, and threat source data. The eigenvector corresponding to the largest eigenvalue of the analysis and judgment matrix is then solved. ; for eigenvectors Normalization is performed to obtain the basic weight vector; the basic weight vector and wind speed fluctuation coefficient are then used to obtain the basic weight vector. Topographic relief coefficient and exposure coefficient Calculate and obtain the path planning weights; The wind speed features, terrain features, and threat source data in each voxel are transformed into exposure tensors, slope tensors, and wind force tensors using tensor mapping functions; the exposure tensors, slope tensors, and wind force tensors are then fitted into hybrid Riemann tensors using path planning weights.
3. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 2, characterized in that, Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, the wind speed fluctuation coefficient of the UAV flight area is calculated. Topographic relief coefficient and exposure coefficient ; Specifically, it includes: The wind speed vector at the center point of each voxel is extracted from the wind speed characteristics of the UAV flight environment, and the wind speed fluctuation coefficient is calculated based on the wind speed vector. The formula is as follows: ； In the formula, The total number of voxels in the drone's flight area. For the first Wind speed vector at the center of an individual element This represents the average wind speed in the drone's flight area. The elevation of each voxel is extracted from the terrain features in the UAV flight environment, and the terrain undulation coefficient is calculated based on the elevation of each voxel. The formula is as follows: ； ； In the formula, For the first The angle between the slope of the earth's surface and the horizontal plane in individual elements. The average slope of all voxels in the drone's flight area; For the first The position coordinates of the voxels Elevation; The location coordinates and transmission power of threat sources are extracted from threat source data in the UAV flight environment. The threat source and its corresponding power are then calculated based on the location coordinates. Three-dimensional spatial distance between individual elements; based on threat source and first Exposure values are calculated based on the three-dimensional spatial distance between individual elements and the emission power of the threat source. The formula is as follows: ； In the formula, For the first Individual exposure values, Let k be the transmission power of the k-th threat source. Let be the gain of the transmitting antenna of the k-th threat source in the direction of the drone. The operating wavelength of the k-th threat source. This is the radar cross-section of the drone. Pi For the k-th threat source and the The three-dimensional spatial distance between individual elements Let K be the system loss factor of the kth threat source, K be the total number of threat sources in the UAV flight environment, and k be the index of the threat source in the UAV flight environment. The first The exposure value of an individual voxel is compared with the exposure threshold to determine whether the voxel belongs to the hazardous voxel category. The ratio of the number of hazardous voxels to the total number of voxels in the UAV flight area is used as the exposure coefficient. .
4. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 2, characterized in that, An analysis and judgment matrix is constructed based on wind speed characteristics, terrain features, and the relative emphasis of threat source data, specifically including: ； In the formula, To analyze and judge the matrix, To set a value representing the relative importance of wind speed features to terrain features, Set a value to indicate the relative importance of wind speed characteristics to threat source data. Set a value to indicate the relative importance of terrain features to threat source data.
5. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 2, characterized in that, Composed of basic weight vector and wind speed fluctuation coefficient Topographic relief coefficient and exposure coefficient The path planning weights are calculated, specifically including: ； ； ， ， ； In the formula, For path planning weights, As for wind power weight, For slope weight, For exposure weight, For normalization operations, Based on the weight vector, As the basic weight of wind power, As the basic weight of slope, As the base weight for exposure; , and Coupling factor; It is the sum of the element-wise multiplication of the base weights and the coupling factors.
6. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, The node cost scalar value for each voxel is calculated using the hybrid Riemann tensor, and the sampling probability density of the voxel is determined from the node cost scalar value; the formula is as follows: ； In the formula, Let be the scalar value of the node cost. For sampling probability density, It is a natural exponential function; For mixed Riemann tensors.
7. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, The Riemann geometric distance between the search node and the sampled candidate point is calculated based on the hybrid Riemann tensor, specifically including: ； In the formula, The Riemann geometric distance between the search node and the sampled candidate points. Let be the coordinate vector of the search node. Let be the coordinate vector of the sampling candidate points. Let Riemann tensor be the mixture of the midpoints between the search node and the sampled candidate points. This is the matrix transpose.
8. The method for planning a covert, low-altitude flight path for an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, The theoretical flight path is interpolated and smoothed to obtain the final flight execution path, specifically including: ； ； In the formula, These are the trajectory points of the flight execution path. Let J be the discrete path points on the theoretical flight path, and J be the total number of discrete path points on the theoretical flight path. Let b be the B-order B-spline basis function for the j-th discrete path point; Here is the normalized parameter for the position of the B-spline curve. , , and For parameters The interval division point, For the first Discrete path points B-spline basis functions For the first Discrete path points B-spline basis functions.
9. A stealthy, low-altitude flight path planning system for unmanned aerial vehicles (UAVs), characterized in that, include: The data acquisition module performs meshing of the UAV flight area in three-dimensional space to form several voxels; Based on wind speed characteristics, terrain features, and threat source data in the UAV flight environment, calculate the mixed Riemann tensor for each voxel; The path search module calculates the node cost scalar value of each voxel based on the hybrid Riemann tensor, and determines the sampling probability density of the voxel based on the node cost scalar value. The starting position of the UAV flight is used as the search node. The sampling area is determined based on the search node, the target position of the UAV flight, and the search radius. Within the sampling area, voxels are sampled according to the sampling probability to obtain sampling candidate points. The Riemann geometric distance between the search node and the sampling candidate points is calculated based on the hybrid Riemann tensor. The n sampling candidate points with the smallest Riemann geometric distance are used as the next search node. The sampling is repeated until the target position of the UAV flight is reached to obtain the search tree. The path selection module calculates the scalar value of the path cost for each search flight path in the search tree; The search flight path is the path from the starting position of the UAV flight through interconnected search nodes to the target position of the UAV flight. The theoretical flight path is selected from all search flight paths in the search tree according to the path cost scalar value, and the theoretical flight path is interpolated and smoothed to obtain the final flight execution path.
10. An electronic terminal, characterized in that, It includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the UAV covert ground-hugging flight path planning method according to any one of claims 1 to 8.