Dynamic planning method and system for flight path of unmanned aerial vehicle

Abstract

The invention provides an unmanned aerial vehicle flight path dynamic planning method and system, and relates to the technical field of flight path planning, and the method comprises the steps: obtaining the starting and ending points of the flight of an unmanned aerial vehicle, and planning an initial unmanned aerial vehicle flight path based on the starting and ending points of the flight of the unmanned aerial vehicle and a preset constraint; constructing an unmanned aerial vehicle flight environment model based on a preset flight area map, the elevation information and the obstacle information; route optimization is carried out based on the initial unmanned aerial vehicle flight route and a flight environment model, and an unmanned aerial vehicle initial obstacle avoidance route is generated; the unmanned aerial vehicle initial obstacle avoidance route is optimized based on a preset iterative optimization model, and an optimized unmanned aerial vehicle obstacle avoidance route is obtained; and updating the optimized unmanned aerial vehicle obstacle avoidance route to the initial unmanned aerial vehicle flight route to generate a final unmanned aerial vehicle flight route. According to the invention, the flight time and cost are saved, the adaptability to the environment, especially the urban complex environment, is enhanced, and the safety is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flight path planning, in particular to a dynamic planning method and system for a flight path of a UAV. BACKGROUND

[0002] Path planning is a process of guiding a UAV to fly from one location to another, which needs to consider the flight capability of the UAV, obstacles, flight environment and other factors. Path planning technology generally includes global path planning and local path planning. Global path planning is to plan an optimal path from a starting point to an ending point for a UAV in a known environment, while local path planning is to plan a safe path for a UAV to avoid obstacles in an unknown or dynamic environment.

[0003] The adjustment and optimization of the flight path of a UAV are mainly based on the above two key technologies, one is the optimization of the path algorithm itself, and the other is the optimization based on external variables such as environmental model elements (e.g. roads, green areas) and the like. The existing optimization methods have insufficient comprehensive constraints, only considering safety, without considering other constraints of the UAV itself, weather and temporary flight area regulations and the like. Secondly, the existing optimization methods have low space utilization efficiency, only reusing the space above roads, green areas and the like, without fully considering other low buildings and the like. Moreover, the existing optimization methods lack a relatively systematic quantitative analysis and calculation process.

[0004] Therefore, there is an urgent need for a dynamic planning method and system for a flight path of a UAV to solve the above technical problems. SUMMARY

[0005] The present application relates to the technical field of flight path planning, in particular to a dynamic planning method and system for a flight path of a UAV.

[0006] In a first aspect, the present application provides a dynamic planning method for a flight path of a UAV, comprising:

[0007] obtaining a starting point and an ending point of a flight of a UAV, and planning an initial flight route of the UAV based on the starting point and the ending point of the flight of the UAV and preset constraints;

[0008] constructing a flight environment model of the UAV based on a preset flight area map, elevation information and obstacle information;

[0009] optimizing the initial flight route of the UAV based on the initial flight route and the flight environment model to generate an initial obstacle-avoiding route of the UAV;

[0010] optimizing the initial obstacle-avoiding route of the UAV based on a preset iterative optimization model to obtain an optimized obstacle-avoiding route of the UAV;

[0011] The optimized UAV obstacle avoidance route is updated into the initial UAV flight route to generate a final UAV flight route.

[0012] In a second aspect, the application further provides a UAV flight path dynamic planning system, comprising:

[0013] An acquisition unit is configured to acquire start and end points of UAV flight and plan an initial UAV flight route based on the start and end points of UAV flight and preset constraints;

[0014] A construction unit is configured to construct a UAV flight environment model based on a preset flight area map, elevation information and obstacle information;

[0015] An optimization unit is configured to perform route optimization based on the initial UAV flight route and the flight environment model to generate an initial UAV obstacle avoidance route;

[0016] An iteration unit is configured to optimize the initial UAV obstacle avoidance route based on a preset iteration optimization model to obtain an optimized UAV obstacle avoidance route;

[0017] An update unit is configured to update the optimized UAV obstacle avoidance route into the initial UAV flight route to generate a final UAV flight route.

[0018] The application has the following advantages:

[0019] The application selects to fly over low ground reference objects such as roads and green lands in priority when there are local flight obstacles or constraints in the flight path on the basis of the shortest straight flight distance of the UAV, greatly reduces the flight path length and guarantees safety. The constraints and the environment model consider various factors such as UAV self-constraints, flight area system constraints and temporary regulations, and the flight cost model constructed by the application not only saves flight time and flight cost but also strengthens adaptability to the environment, especially complex urban environment and safety guarantee.

[0020] Other features and advantages of the application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0022] Figure 1 The flow chart of the UAV flight path dynamic programming method described in the embodiments of the present application is shown in the figure.

[0023] Figure 2 The structure diagram of the UAV flight path dynamic programming system described in the embodiments of the present application is shown in the figure.

[0024] In the figure: 701, acquisition unit; 702, construction unit; 703, optimization unit; 704, iteration unit; 705, update unit. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0026] It should be noted that: similar labels and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0027] Embodiment 1:

[0028] The present embodiment provides a UAV flight path dynamic programming method.

[0029] Referring to Figure 1 , the figure shows that the method includes steps S1, S2, S3, S4 and S5.

[0030] Step S1, obtain the start and end points of the UAV flight, and plan an initial UAV flight route based on the start and end points of the UAV flight and a preset constraint;

[0031] It can be understood that this step not only guarantees the geometric optimality (shortest distance) of the UAV path planning, but also incorporates safety and compliance into the constraint conditions in the initial stage, reducing the complexity of subsequent optimization. The technical effect is to significantly improve the reliability and feasibility of the initial flight path of the UAV, and lay a solid foundation for subsequent dynamic obstacle avoidance and path optimization. In this step, step S1 includes steps S11, S12, S13 and S14.

[0032] Step S11, obtain the start and end point information of the UAV according to the task demand, and perform coordinate initialization processing, wherein the start and end point three-dimensional coordinate data is obtained by converting data in a preset unified geographic reference system;

[0033] It can be understood that when the UAV performs a task, it usually receives "takeoff point" and "destination" descriptions at the task level, which can be marked points on a map, address information or geographic coordinates. In order to use this information for subsequent path planning, it needs to be converted to a preset geographic reference system. The significance of such a unified reference system is to ensure that all geographic data involved in the calculation (such as maps, obstacle information, and weather information) have a consistent spatial reference, avoiding path deviation or planning errors due to non-uniform coordinate systems. Through conversion, the start and end points are processed into three-dimensional coordinate data with longitude, latitude and height, realizing unified representation of two-dimensional plane position and flight height.

[0034] Step S12, perform height analysis processing according to the start and end point three-dimensional coordinate data, and obtain the initial climb or descent path of the UAV by comparing the minimum safe flight height constraint of the UAV;

[0035] It can be understood that this step combines three-dimensional coordinate data with the minimum safe flight height constraint of the unmanned aerial vehicle, so as to determine the height adjustment strategy of the unmanned aerial vehicle in the take-off and landing stage. Among them, when the start and end point height is lower than the preset minimum safe flight height, the unmanned aerial vehicle must climb at the beginning of the task execution to ensure that the flight process does not touch the ground obstacles or low-altitude risk areas; on the contrary, if the end point is lower than the safe height, the unmanned aerial vehicle needs to perform a landing segment before reaching the destination, so that it can safely descend to the task target position. In this process, the system not only makes a simple height difference comparison, but also combines the performance parameters of the unmanned aerial vehicle (such as the maximum climb rate, the maximum descent rate, and the energy consumption constraint) to reasonably segment the path, so as to avoid too steep climb or descent that leads to decreased flight stability or excessive energy consumption. For example, in the urban scene, the determination of the minimum safe flight height often takes into account the average height of buildings and the height limit requirements of laws and regulations, so the climb segment and the descent segment of the path are actually a dynamic calibration of the environmental safety boundary.

[0036] Among them, the determination of the initial route is divided into two cases:

[0037] When the starting point is not below the required minimum flight height, the unmanned aerial vehicle determines the flight route according to the height of the arrival point.

[0038] If the arrival point is not below the minimum flight height, the unmanned aerial vehicle flies directly from the starting point to the arrival point, and the flight path at this time is the starting point-arrival point.

[0039] If the arrival point is below the minimum flight height, the unmanned aerial vehicle needs to fly directly to the point directly above the arrival point first, and then descend to the arrival point, and the flight path at this time is the starting point-point directly above the arrival point-arrival point.

[0040] When the starting point is below the required minimum flight height, the unmanned aerial vehicle needs to first ascend to the required minimum flight height, i.e., to the minimum flight height point, and then determine the flight route of the next segment according to the height of the arrival point.

[0041] If the arrival point is not below the minimum flight height, the unmanned aerial vehicle flies directly from the minimum flight height point to the point, and the flight path at this time is the starting point-minimum flight height point-arrival point.

[0042] If the arrival point is below the minimum flight height, the unmanned aerial vehicle first flies horizontally from the minimum flight height point to the point directly above the arrival point, and then descends to the arrival point, and the flight path at this time is the starting point-minimum flight height point-point directly above the arrival point-arrival point.

[0043] Step S13, based on the initial climb or descent path of the unmanned aerial vehicle, the initial unmanned aerial vehicle flight route is obtained through Euclidean space distance calculation;

[0044] Understandably, this step provides a benchmark for subsequent obstacle avoidance and optimization: before obstacles, restricted flight zones, or environmental constraints are added, the system already has a computationally optimal original straight trajectory, and any subsequent adjustments can be compared based on this path, thereby ensuring that the final route finds the optimal balance between safety and efficiency.

[0045] Step S2: Construct a UAV flight environment model based on a preset flight area map, elevation information, and obstacle information;

[0046] Understandably, this step involves calling a pre-defined flight area map to convert two-dimensional geographic information data into a basic spatial framework. Elevation data is then overlaid onto this framework, giving the model terrain undulations and building height characteristics. Based on this, obstacle information (including high-rise buildings, bridges, tower cranes, temporary facilities, etc.) is coupled to form a unified three-dimensional environmental representation with the original geographic model. Unlike traditional methods that rely solely on two-dimensional maps, this model can comprehensively reflect the spatial structure characteristics of the UAV's flight environment. Especially in complex urban scenarios, it can dynamically calculate the safe altitude around obstacles into the initial flight path using linear interpolation or other spatial fitting methods, resulting in a more accurate environmental adaptability model. In this step, step S2 includes steps S21 and S22.

[0047] Step S21: Convert the preset flight area map into a new format, and then process the converted data into a three-dimensional model. The starting point of the UAV flight is used as the origin of the coordinate system. The preset obstacle information is sent to the initial three-dimensional model for coupling processing to obtain a three-dimensional environment model. The three-dimensional environment model is a model that couples all obstacle information within the flight area map.

[0048] Understandably, this step first involves converting the flight area map data format, for example, converting vector maps, raster maps, or GIS data into a unified data structure suitable for 3D modeling (such as 3D point clouds, mesh data, or voxel units). Then, a 3D coordinate system is established using the UAV's takeoff point as the origin. This approach allows for mission-centric model building, ensuring simplified coordinate calculations and minimized errors in subsequent path planning. Next, known obstacle information (such as building heights, shape boundaries, power towers, etc.) is loaded into the initial 3D model, and a coupling algorithm seamlessly overlays the obstacles onto the area map. This coupling is not merely a simple geometric overlay but may also involve maintaining spatial topological relationships, such as the relative positions of obstacles and terrain, and the calculation of shadow areas or buffer zones, thereby ensuring the model's integrity and computability.

[0049] Step S22: Add elevation information to the three-dimensional environment unit model, and perform preset safe height interpolation on all obstacles and the initial UAV flight path based on linear interpolation to obtain the UAV flight environment model.

[0050] Understandably, after the 3D environment unit model is constructed, this step involves the system incorporating elevation information, including a digital elevation model (DEM) of the natural terrain and digital surface models (DSMs) of buildings and man-made structures. This elevation data, superimposed on the model, accurately reflects the relative height distribution of the ground surface and obstacles. Subsequently, based on the UAV's initial straight-line path, the system uses linear interpolation to calculate the safe height between obstacles and the flight path. The significance of linear interpolation is that even if the path segment crosses multiple obstacles of different heights, a continuous safe height curve can be generated on the path, rather than a simple "point-to-point" comparison. In other words, the system establishes a dynamic safety buffer zone between the flight path and obstacles, enabling the path to automatically adjust the safe height requirements according to the obstacle heights in different sections.

[0051] Step S3: Optimize the route based on the initial UAV flight path and flight environment model to generate the initial obstacle avoidance route for the UAV;

[0052] Understandably, this step first loads an initial straight-line path into the flight environment model and performs spatial matching between the path and the environment, identifying potential obstacles, height-restricted areas, or controlled airspace along the path. Subsequently, based on the UAV's performance parameters (such as maximum climb rate, range, and minimum turning radius) and environmental constraints (such as safe altitude and wind speed), the system establishes flight constraints, defining the boundaries of path optimization. Next, the system constructs an obstacle avoidance route option library near the detour segments. These candidate routes may prioritize low-risk areas such as roads and green spaces to ensure that the overall flight distance is kept as short as possible while avoiding obstacles. Then, a cost model is used to quantitatively evaluate different candidate routes, comprehensively considering multiple factors such as time, energy consumption, and safety, to select the segment with the lowest cost as the initial obstacle avoidance route. This step adjusts the path to a dynamically optimal solution that balances safety, efficiency, and compliance through multi-objective optimization. The technical effect is that the initial obstacle avoidance route ensures that the UAV can successfully avoid environmental risks while maximizing the efficiency advantage of the straight-line route, providing a structurally reasonable and cost-balanced basic route for subsequent higher-level iterative optimizations. In this step, step S3 includes steps S31, S32 and S33.

[0053] Step S31: Set constraints based on the UAV flight environment model and the initial UAV flight path to obtain the UAV flight constraints;

[0054] Understandably, this step first correlates empirical constraints accumulated from historical flight missions with the current UAV's flight performance parameters (such as maximum range, endurance, rate of climb, rate of descent, and flight speed) to select the parameters with the highest matching degree to the current mission, serving as the basic constraints for UAV operation. This step ensures that the planned path does not exceed the UAV's physical limits. Secondly, combined with the flight environment model, the system identifies whether there are obstacles, military control zones, airport no-fly zones, etc., within the area traversed by the initial path, thereby setting flight area constraints. This ensures the compliance and safety of the UAV within its spatial range. Finally, the system incorporates real-time meteorological and dynamic environmental factors, including wind speed, wind direction, temperature, and even temporary airspace closures, generating environmental constraints that allow path planning to dynamically adapt to short-term environmental fluctuations. This step provides a clear boundary condition framework for subsequent obstacle avoidance and optimization, ensuring that all generated paths not only meet efficiency requirements but are also fully feasible at the physical, environmental, and legal levels, greatly improving the safety and adaptability of UAV flight missions.

[0055] Step S32: Based on the UAV flight environment model and the initial UAV flight route, process the data to construct an obstacle avoidance route option library;

[0056] Understandably, this step first involves segment-by-segment detection of the initial flight path based on the flight environment model, identifying which segments overlap with obstacles, restricted flight zones, or areas that do not meet safe altitude requirements. These segments are marked as "obstacle avoidance segments." Subsequently, the system generates multiple candidate alternative paths in the surrounding space of these dangerous segments. These paths often prioritize flying along roads, green spaces, or above low-rise buildings to take advantage of their relatively open and low-risk spatial advantages. Furthermore, the generation of each candidate path considers not only geometric feasibility but also the flight constraints of the UAV, such as minimum turning radius, range limitations, and communication link stability. The resulting option library resembles a "path candidate pool," where each candidate path has independent geometric parameters and constraint suitability.

[0057] Step S33: Construct a cost model based on the UAV's flight constraints and obstacle avoidance route option library, and determine the UAV's initial obstacle avoidance route based on the cost model.

[0058] As is understandable, this step first calls upon the obstacle avoidance route option library obtained in the previous step, and, in conjunction with the established flight constraints, performs multi-dimensional cost calculations on each candidate path. The cost model is as follows:

[0059] J=W1·C1+W2·C2+W3·C3+W4·C4

[0060] Where J is the total value of the agency, W1, W2, W3, and W4 are the weights of time cost, expense cost, security cost, and other costs, respectively, the sum of W1, W2, W3, and W4 is 1, and W1, W2, W3, and W4 are all greater than or equal to 0; C1 is the time cost, C2 is the expense cost, C3 is the security cost, and C4 is the other cost.

[0061] The formula for calculating the time cost is as follows:

[0062]

[0063] Where C1 represents the time cost, T1 represents the estimated flight time of the newly planned obstacle avoidance route, and T2 represents the estimated flight time of the corresponding obstacle avoidance section in the original planned route. D1 represents the total length of the horizontal segment in the newly planned obstacle avoidance route, D2 represents the total length of the vertical segment in the newly planned obstacle avoidance route, D3 represents the total length of the inclined segment in the newly planned obstacle avoidance route, and v1, v2, and v3 are the average speeds of the UAV when flying horizontally, vertically, and inclined, respectively, estimated based on factors such as UAV flight performance and pilot habits.

[0064] The formula for calculating costs is as follows:

[0065]

[0066] Where C2 represents the cost, Q1 represents the estimated total cost of the newly planned obstacle avoidance route, and Q2 represents the estimated total cost of the corresponding obstacle avoidance section in the original planned route. Each route segment is divided into horizontal, vertical, and inclined segments, and the cost is also divided into three parts accordingly: D1 represents the total length of the horizontal segment in the newly planned obstacle avoidance route, D2 represents the total length of the vertical segment in the newly planned obstacle avoidance route, D3 represents the total length of the inclined segment in the newly planned obstacle avoidance route, P1 represents the average flight cost of the horizontal segment in the newly planned obstacle avoidance route, P2 represents the average flight cost of the vertical segment in the newly planned obstacle avoidance route, and P3 represents the average flight cost of the inclined segment in the newly planned obstacle avoidance route.

[0067] The formula for calculating security costs is as follows:

[0068]

[0069] Where C3 represents the safety cost, S1 represents the safety factor estimate of the newly planned obstacle avoidance route, and S2 represents the safety factor estimate of the corresponding obstacle avoidance route portion in the original planned route. A safety risk comparison database is constructed based on actual conditions, and S1 and S2 are derived from this database, as shown in the table below:

[0070]

[0071] Step S4: Optimize the initial obstacle avoidance route of the UAV based on the preset iterative optimization model to obtain the optimized obstacle avoidance route of the UAV;

[0072] Understandably, this step uses the beginning and end endpoints of the obstacle avoidance route segments as nodes, dividing the current optimal route into several segments, and iteratively calculating whether two adjacent obstacle avoidance segments in the entire route can be merged. When the distance between two adjacent obstacle avoidance segments is less than a preset value, the cost of merging the two obstacle avoidance segments for obstacle avoidance may be less than the cost of merging them individually. This step combines the latest environmental perception (sudden wind field changes, temporary control updates) to dynamically refresh the sub-segment cost parameters during iteration, ensuring that the optimization results are robust to time-varying constraints. This step elevates the original "individualistic" local obstacle avoidance scheme to a "cross-segment collaborative" global reorganization. By merging and deduplicating, unnecessary turnarounds and redundant climb / descent segments are reduced, which can shorten the total flight distance, reduce energy consumption, and improve safety margin. Without sacrificing compliance, it significantly reduces the total cost function and outputs a smoother and more efficient optimized obstacle avoidance route, providing a near-globally optimal candidate solution for the final route update.

[0073] Step S5: Update the optimized drone obstacle avoidance route to the initial drone flight route to generate the final drone flight route.

[0074] Understandably, in this step, the optimized local obstacle avoidance segment replaces the corresponding conflict section in the initial flight path, while keeping the unaffected straight path portion unchanged. This "local replacement-global update" strategy has two significant advantages: firstly, it avoids the computational redundancy caused by full flight path reconstruction, ensuring that the final route remains as close as possible to the straight-line baseline, thereby maintaining overall flight efficiency; secondly, by directly embedding obstacle avoidance segments validated through cost models and iterative optimization, it ensures that the path possesses the highest safety and adaptability in critical areas. During this process, the system will also perform another legality and consistency check to ensure that the replaced flight path is geometrically continuous, legally compliant, and performance-executable.

[0075] Example 2:

[0076] like Figure 2 As shown, this embodiment provides a dynamic planning system for the flight path of an unmanned aerial vehicle (UAV). See [link / reference]. Figure 2 The system shown includes an acquisition unit 701, a construction unit 702, an optimization unit 703, an iteration unit 704, and an update unit 705.

[0077] The acquisition unit 701 is used to acquire the start and end points of the UAV flight and plan the initial UAV flight route based on the start and end points of the UAV flight and preset constraints.

[0078] The construction unit 702 is used to construct a UAV flight environment model based on a preset flight area map, elevation information, and obstacle information.

[0079] Optimization unit 703 is used to optimize the route based on the initial UAV flight route and flight environment model, and generate the initial obstacle avoidance route of the UAV.

[0080] Iteration unit 704 is used to optimize the initial obstacle avoidance route of the UAV based on a preset iterative optimization model to obtain the optimized obstacle avoidance route of the UAV.

[0081] The update unit 705 is used to update the optimized drone obstacle avoidance route into the initial drone flight route, generating the final drone flight route.

[0082] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A dynamic programming method for the flight path of an unmanned aerial vehicle (UAV), characterized in that, include: Obtain the start and end points of the drone flight, and plan the initial drone flight route based on the start and end points of the drone flight and preset constraints; A UAV flight environment model is constructed based on a pre-set flight area map, elevation information, and obstacle information. Based on the initial UAV flight path and flight environment model, the route is optimized to generate the initial obstacle avoidance route for the UAV. The initial obstacle avoidance route of the UAV is optimized based on a preset iterative optimization model to obtain the optimized UAV obstacle avoidance route; The optimized drone obstacle avoidance route is updated into the initial drone flight route to generate the final drone flight route.

2. The dynamic planning method for UAV flight paths according to claim 1, characterized in that... The process of planning the initial drone flight route based on the start and end points of the drone flight and preset constraints includes: According to the mission requirements, the take-off and end-point information of the UAV is obtained and the coordinate initialization process is performed. In this process, the data is transformed using a preset unified geographic reference system to obtain the three-dimensional coordinate data of the take-off and end-points. Based on the three-dimensional coordinate data of the start and end points, the altitude is analyzed and processed. By comparing with the minimum safe flight altitude constraint of the UAV, the initial climb or landing path of the UAV is obtained. Based on the initial ascent or descent path of the UAV, the initial UAV flight path is obtained through Euclidean spatial distance calculation.

3. The dynamic planning method for UAV flight paths according to claim 1, characterized in that... A UAV flight environment model is constructed based on a pre-set flight area map, elevation information, and obstacle information, including: The preset flight area map is converted into a new format, and the converted data is processed to build a three-dimensional model. The starting point of the UAV flight is used as the origin of the coordinate system, and the preset obstacle information is sent to the initial three-dimensional model for coupling processing to obtain a three-dimensional environment model. The three-dimensional environment model is a model that couples all obstacle information within the flight area map. Elevation information is added to the three-dimensional environment unit model, and a preset safe height is interpolated for all obstacles and the initial UAV flight path based on linear interpolation to obtain the UAV flight environment model.

4. The dynamic planning method for UAV flight paths according to claim 1, characterized in that... Based on the initial UAV flight path and flight environment model, route optimization is performed to generate the initial obstacle avoidance route for the UAV, including: Based on the UAV flight environment model and the initial UAV flight path, the constraints are set to obtain the UAV flight constraints. Based on the UAV flight environment model and the initial UAV flight route, a library of obstacle avoidance route options is constructed. A cost model is constructed based on the flight constraints of the UAV and the obstacle avoidance route option library, and the initial obstacle avoidance route of the UAV is determined based on the cost model.

5. The dynamic planning method for UAV flight paths according to claim 1, characterized in that... Constraints are set based on the UAV flight environment model and the initial UAV flight path, including: Based on the preset historical constraints, correlation analysis was performed with flight performance parameters, environmental adaptability parameters, control and communication parameters, and body size parameters, and the historical constraints with the highest correlation were used as the operational parameter constraints. Based on the UAV flight environment model and the initial UAV flight path, the obstacles, military control zones, and no-fly zones traversed by the initial UAV flight path are determined, and the flight area constraints are obtained. Flight environment constraints are set based on the wind direction, wind speed, and temperature along the initial UAV flight path obtained in real time, thus obtaining the flight environment constraints.

6. A dynamic planning system for the flight path of an unmanned aerial vehicle (UAV), characterized in that, include: The acquisition unit is used to acquire the start and end points of the UAV flight and plan the initial UAV flight route based on the start and end points of the UAV flight and preset constraints. The building unit is used to construct a UAV flight environment model based on a preset flight area map, elevation information, and obstacle information; The optimization unit is used to optimize the route based on the initial UAV flight path and flight environment model, and generate the initial obstacle avoidance route for the UAV. An iterative unit is used to optimize the initial obstacle avoidance route of the UAV based on a preset iterative optimization model, so as to obtain the optimized obstacle avoidance route of the UAV. The update unit is used to update the optimized drone obstacle avoidance route into the initial drone flight route, generating the final drone flight route.

7. The UAV flight path dynamic planning system according to claim 6, characterized in that, The acquisition unit includes: The first acquisition subunit is used to acquire the start and end point information of the UAV according to the task requirements and perform coordinate initialization processing. In this process, the three-dimensional coordinate data of the start and end points are obtained by data conversion using a preset unified geographic reference system. The second acquisition subunit is used to perform altitude analysis and processing based on the three-dimensional coordinate data of the start and end points, and obtain the initial climb or landing path of the UAV by comparing it with the minimum safe flight altitude constraint of the UAV. The third acquisition subunit is used to obtain the initial UAV flight path by calculating the Euclidean distance based on the initial climb or landing path of the UAV.

8. The UAV flight path dynamic planning system according to claim 6, characterized in that, The building unit includes: The first construction subunit is used to convert the preset flight area map into a new format and process the converted data into a three-dimensional model. The starting point of the UAV flight is used as the origin of the coordinate system, and the preset obstacle information is sent to the initial three-dimensional model for coupling processing to obtain a three-dimensional environment model. The three-dimensional environment model is a model that couples all obstacle information in the flight area map. The second construction sub-unit is used to add elevation information to the three-dimensional environment unit model and to perform preset safe height interpolation on all obstacles and the initial UAV flight path based on the linear interpolation method to obtain the UAV flight environment model.

9. The UAV flight path dynamic planning system according to claim 6, characterized in that, The optimization unit includes: The first optimization subunit is used to set constraints based on the UAV flight environment model and the initial UAV flight path to obtain the UAV flight constraints. The second optimization subunit is used to process the UAV flight environment model and the initial UAV flight route to construct an obstacle avoidance route option library. The third optimization subunit is used to construct a cost model based on the UAV's flight constraints and obstacle avoidance route option library, and to determine the UAV's initial obstacle avoidance route based on the cost model.

10. The UAV flight path dynamic planning system according to claim 6, characterized in that, The first optimization subunit includes: The fourth optimization subunit is used to perform correlation analysis with flight performance parameters, environmental adaptability parameters, control and communication parameters and body size parameters based on preset historical constraints, and to take the historical constraints with the highest correlation as the operating parameter constraints. The fifth optimization subunit is used to determine the obstacles, military control zones, and no-fly zones that the initial UAV flight path passes through based on the UAV flight environment model and the initial UAV flight path, and obtain the flight area constraints. The sixth optimization subunit is used to set flight environment constraints based on the wind direction, wind speed and temperature on the initial UAV flight path acquired in real time, and obtain the flight environment constraints.

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