An unmanned aerial vehicle path planning method based on an ellipse tangent line graph method

Abstract

The application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method based on an ellipse tangent graph method, which comprises the following steps: a flight environment modeling step, which is used for constructing an elliptical obstacle model by taking buildings as obstacles, and constructing a three-dimensional flight environment model based on the elevation information of a flight area and the distribution of the obstacle model; a three-dimensional path acquisition step, which is used for path searching and outputting a flight path; and a three-dimensional path smoothing step, which is used for smoothing the path points of the flight path output by the three-dimensional path acquisition step to obtain a flight trajectory for unmanned aerial vehicle control. The application effectively solves the comprehensive decision problem among path length, motion smoothness and energy consumption of a traditional method by taking energy consumption as a core optimization target of path planning, thereby remarkably prolonging the task range of the unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method based on an ellipse tangent graph method. BACKGROUND

[0002] Unmanned aerial vehicles (UAVs) are widely used in various fields such as traffic inspection, disaster rescue, cargo transportation, and target reconnaissance. Path planning, as a core part of autonomous control of unmanned aerial vehicle systems, is an important foundation for ensuring efficient task execution and plays a key role in improving the application of unmanned aerial vehicles.

[0003] In the prior art, in the path planning method based on graph, the Voronoi diagram method divides the free space into multiple convex polygon regions to improve planning efficiency, and its advantage lies in relatively low computational complexity and the ability to ensure that the path maintains the maximum safety distance from obstacles. However, this planning method based on the center axis often leads to a significant deviation of the generated path length from the theoretical optimal value, and performs poorly in application scenarios that require precise paths. In contrast, the A* algorithm exhibits superior performance in complex environments due to its heuristic search strategy, and by comprehensively evaluating the path cost and heuristic function, it can effectively find the optimal or near-optimal path. However, this algorithm needs to maintain open and closed lists when expanding nodes, and when the complexity of the environment increases, it will face the problems of explosive growth of nodes and excessive memory occupation, usually requiring strategies such as hierarchical search and jump point optimization to improve performance. Another classic tangent graph method uses a more intuitive planning approach by calculating the common tangent lines between obstacle polygons to construct a path network. This method is simple to implement and can guarantee path connectivity, but the generated path often has defects such as excessive zigzagging and proximity to obstacles. Although researchers have proposed using circular or elliptical inflation models to smooth the path, these improvement schemes are currently only applicable to two-dimensional space and are difficult to meet the planning needs of three-dimensional moving bodies such as unmanned aerial vehicles.

[0004] In addition, existing intelligent optimization algorithms include GA, ACO, and PSO, etc. These methods have global optimization capabilities, but they are time-consuming in complex environments and are prone to local optima.

[0005] The endurance of unmanned aerial vehicles is limited by battery capacity and power efficiency, and inappropriate energy management may lead to task interruption, equipment crash, or data loss. However, in actual applications, existing research on unmanned aerial vehicle path planning mainly focuses on obstacle avoidance ability, path length, and planning time, and the power consumption problem of unmanned aerial vehicles is often ignored, which cannot guarantee the endurance during task execution. SUMMARY

[0006] The embodiment of the present application provides a UAV path planning method based on an ellipse tangent graph method, which effectively solves the comprehensive decision problem among path length, motion smoothness and energy consumption of a traditional method by taking energy consumption as a core optimization target of path planning, and significantly prolongs the task range of the UAV.

[0007] To achieve the above object, the embodiment of the present application provides a UAV path planning method based on an ellipse tangent graph method, comprising:

[0008] A flight environment modeling step determines a flight area, obtains geographical position information and size structure information of a building in the flight area, constructs an elliptical obstacle model by taking the building as an obstacle, and constructs a three-dimensional flight environment model based on the elevation information of the flight area and the distribution of the obstacle model;

[0009] A three-dimensional path obtaining step connects a path ST from a starting point S to a terminal point T, performs collision detection on the path ST based on the three-dimensional flight environment model,

[0010] If no collision occurs, the UAV is controlled to fly along the path ST;

[0011] If collision occurs, a two-dimensional reference path point is generated by performing a path point obtaining step, and it is judged whether the height of the starting point S and the terminal point T is the same;

[0012] If the same, a flight path bypassing the obstacle is generated based on a bypassing strategy or a climbing strategy;

[0013] If different, an alternative path is generated, and a flight path with the lowest energy consumption in the alternative path is output based on a flight energy consumption model, wherein n path points between the starting point S and the terminal point T are obtained based on the two-dimensional reference path point, each path point is configured with a bypassing strategy and a climbing strategy, and an alternative path is generated, the UAV flight movement according to the alternative path is decomposed into a take-off stage, a landing stage and a horizontal flight stage, the energy consumption of the take-off stage , the energy consumption of the landing stage , and the energy consumption of the horizontal flight stage The flight energy consumption model is constructed, and the flight path with the lowest energy consumption in the alternative path is output by using the flight energy consumption model, and the flight energy consumption model is represented as the following calculation model:

[0014] ;

[0015] A three-dimensional path smoothing step performs smoothing processing on the path points of the flight path output by the three-dimensional path obtaining step, and obtains a flight trajectory for UAV control.

[0016] In some embodiments, the energy consumption of the take-off stage is calculated based on the following calculation model:

[0017]

[0018] wherein, is the ground height, is the flight height of the UAV, represents the vertical upward velocity component, respectively represent the propeller working efficiency, the motor working efficiency and the electronic speed controller working efficiency, is the total mass of the UAV, is the gravity acceleration, represents the air density, taking the value of 1.225 ; is the flight time, is the temperature gradient below the tropopause, taking the value of -0.0065 , R is the gas constant in air, taking the value of 287.05287 , is the propeller disc area, is the correction factor, is the standard temperature, taking the value of 288.12 K, i.e. 15℃.

[0019] The above calculation model is based on the energy change of the UAV from the ground height to the flight height to model the energy consumption, converts the energy required by the UAV from the ground to the target height into an integral expression, considers various actual influencing factors such as the change of air density with height, the propulsion system efficiency, the flight speed, and the temperature change term is used to simulate the standard atmospheric temperature gradient, representing the influence of the environment on the air density and the lift, and the efficiency parameter term corrects the loss of the UAV propulsion system, calculates the instantaneous power consumption of the whole process by integration, obtains the total energy consumption estimation result of the take-off stage, realizes more accurate estimation of the energy demand of the UAV in the vertical take-off stage, and improves the authenticity and adaptability of the energy consumption prediction.

[0020] In the above embodiment, if the ground temperature distribution and the wind speed model are relatively complex, a segmented temperature function or fitting based on measured data can be introduced to replace the standard temperature gradient term.

[0021] In the above embodiment, the propeller working efficiency, the motor working efficiency and the electronic speed controller working efficiency can be obtained through actual flight test or manufacturer-provided characteristic curve, or when the accurate value cannot be obtained, a typical constant is set for approximate estimation.

[0022] ​​​​In some embodiments, the energy consumption of the landing phase is calculated based on the following calculation model:

[0023] ,

[0024] wherein, respectively represent the vertical downward velocity component.

[0025] The above calculation model accumulates the instantaneous power of the UAV during the process of lowering from the target flight height to the ground by integration. The temperature change term is the same as that in the take-off phase and is used to represent the temperature change with the decrease of height over time. Compared with the traditional static estimation method, this model is more in line with the real aerodynamic characteristics and can be used to optimize the landing attitude and path selection, thereby reducing unnecessary energy consumption and system load and improving system energy efficiency and flight safety.

[0026] In some embodiments, the energy consumption of the horizontal flight phase is calculated based on the following calculation model:

[0027] ,

[0028] wherein, is the horizontal flight distance, is the horizontal velocity component, is the windward area; is the zero drag lift coefficient; is the induced drag factor, , represents the average value of the air density at the current path point height and the air density at the next path point height , the air density at the current path point height and the air density at the next path point height can be calculated based on the following calculation model:

[0029] .

[0030] The above calculation model models the horizontal flight energy consumption as two parts, one of which is the air resistance power consumption, i.e. the energy consumed by the UAV to resist the air resistance in the forward direction in unit time, and the other of which is the power required to maintain the lift, i.e. the consumption to overcome the gravity to maintain the lift. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling the two parts, and the propulsion system efficiency is used as the denominator for correction. The energy consumption of the horizontal flight phase is obtained by modeling

[0031] In the above embodiments, the windward area , zero drag lift coefficient , induced drag factor When it cannot be measured, it can be estimated according to the UAV platform combined with the model and the standard parameter measurement, or it can be fine-tuned by using an approximate constant combined with the flight scene.

[0032] In some embodiments, the path point acquisition step includes:

[0033] The three-dimensional flight environment model is projected into a two-dimensional plane obstacle graph, and an improved ellipse tangent graph method is used to generate two-dimensional reference path points from the starting point S to the ending point T.

[0034] In some embodiments, the path point acquisition step further includes:

[0035] The path initialization step initializes a path node set, a candidate node queue, and a collision obstacle set to empty sets. The path node set is represented as Path and is used to record valid nodes in the current path. The candidate node queue is represented as C and is used to temporarily store path nodes to be expanded. The collision obstacle set is represented as B and is used to record obstacles that collide with obstacles in path search;

[0036] The bidirectional search path step adds the starting point S to the path node set Path, adds the ending point T to the candidate node queue C as a preset ending point to perform a path collision detection step, generates a first candidate path set from the starting point S to the ending point T, and adds the ending point T to the path node set Path, adds the starting point S to the candidate node queue C as a preset ending point to perform a path collision detection step, iteratively generates a second candidate path set from the ending point T to the starting point S, establishes a bidirectional path topology graph based on the first candidate path set and the second candidate path set, and records collision-free path nodes and their connection relationships;

[0037] The optimal path screening step combines the first candidate path set and the second candidate path set, calculates the total length of each path through a path cost matrix, selects a collision-free path with the smallest total length in the bidirectional path topology graph using the Dijkstra algorithm, and outputs two-dimensional reference path points of the global shortest path;

[0038] In the collision detection process, a tangent graph method is used to generate a candidate temporary path, and a preset heuristic rule is used to select a temporary path in the candidate temporary path for path generation, ensuring that the path is reasonably detoured between obstacles and has high planning efficiency, and avoiding falling into local optimization.

[0039] In some embodiments, the path collision detection step includes:

[0040] obtaining an end node O in the path node set Path, obtaining a target node D in the candidate node queue C, connecting the end node O and the target node D to obtain a path segment OD for collision detection, if collision is detected, re-planning the path until the path reaches a preset terminal point and no obstacle collision occurs, and generating a candidate path. When performing reverse iteration, the preset terminal point represents the starting point S.

[0041] When performing forward and reverse iteration by using the path collision detection step, the path optimization quality in a complex obstacle dense environment can be improved.

[0042] In some embodiments, the path collision detection step further comprises:

[0043] obtaining an end node O in the path node set Path;

[0044] performing collision detection on the path segment OD, if the path segment OD does not intersect with an obstacle, moving the target node D from the candidate node queue C to the path node set Path, otherwise,

[0045] determining a first collision obstacle Obs, obtaining a first tangent and a second tangent of the end node O and the collision obstacle Obs, and obtaining a third tangent and a fourth tangent of the terminal point T and the collision obstacle Obs, generating two candidate temporary paths based on an intersection point N1 of the first tangent and the third tangent and an intersection point N1' of the second tangent and the fourth tangent;

[0046] determining an optimal intersection point N* between the intersection point N1 and the intersection point N1' based on a preset heuristic rule, as a relay point of the current candidate path;

[0047] connecting the optimal intersection point N* and the end node O for collision detection, if the optimal intersection point N* does not intersect with an obstacle, adding the optimal intersection point N* to the path node set Path, and taking the optimal intersection point N* as a new end node O for cyclic iteration, otherwise, adding the optimal intersection point N* to the candidate node queue C, and adding the collision obstacle Obs to the collision obstacle set B;

[0048] if the candidate node queue C is not empty, obtaining a target node D' in the candidate node queue C, and repeating the above steps to connect the end node O and the target node D' for collision detection.

[0049] In some embodiments, in the flight environment modeling step, a top view outer contour of a building is modeled as an ellipse to obtain an obstacle model, so as to represent a no-fly zone of the unmanned aerial vehicle, and the obstacle model is represented as:

[0050] ,

[0051] In the above formula, and respectively represent the semi-major axis and semi-minor axis of the ellipse; represents the inclination angle of the semi-major axis and the positive direction of the horizontal axis in the two-dimensional plane obstacle map; represents the safety distance between the unmanned aerial vehicle and the obstacle; represents the center coordinates of the obstacle; represents the height of the obstacle.

[0052] Based on the obstacle model, based on the actual geometric boundary characteristics of the obstacle in the flight area, the modeling efficiency and path algorithm compatibility are improved by approximating to an ellipse. In a three-dimensional space, the obstacle region is an upper and lower closed elliptic cylinder, which limits the flyable path of the unmanned aerial vehicle, simplifies the obstacle representation form in the flight environment, and improves the modeling speed and algorithm analysis efficiency. The modeling method has high parameterization degree, can realize the approximate expression of the diversified shape of the building, and can dynamically adjust the no-fly boundary by configuring the safety distance, so as to realize the flight safety strategy adapting to different task risk levels. Compared with the polygon or voxel modeling method, the scheme has low calculation overhead, continuous representation, and is convenient for geometric operation and shortest path search in the path planning algorithm.

[0053] In some embodiments, the three-dimensional path smoothing step uses a cubic B-spline curve for smoothing.

[0054] Compared with the related art, the unmanned aerial vehicle path planning method based on the ellipse tangent graph method provided by the embodiments of the present application expands the ellipse tangent graph method to a three-dimensional scene, converts the building obstacle into an elliptical obstacle model, enhances the adaptability to irregular building structures, realizes effective acquisition of a two-dimensional reference path, and realizes modeling and path search of a complex three-dimensional flight environment in combination with the height of the obstacle and the two-dimensional reference path. The scheme with the minimum energy consumption is selected through energy consumption model calculation, the flexibility and adaptability of path planning are enhanced, and the scheme is especially suitable for typical urban air traffic scenes such as urban canyons and buildings with height difference or multi-level obstacles, and avoids path collision problems.

[0055] Details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects and advantages of the present application more apparent. BRIEF DESCRIPTION OF DRAWINGS

[0056] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0057] Figure 1 is a flowchart of the unmanned aerial vehicle path planning method according to the embodiments of the present application;

[0058] Figure 2is a flow chart of a three-dimensional path acquisition step according to an embodiment of the present application;

[0059] Figure 3 is a flow chart of a path point acquisition step according to an embodiment of the present application;

[0060] Figure 4 is a two-dimensional plane obstacle map for a low-density obstacle distribution;

[0061] Figure 5 is a two-dimensional plane obstacle map for another low-density obstacle distribution;

[0062] Figure 6 is a two-dimensional plane obstacle map for a high-density obstacle distribution;

[0063] Figure 7 is a path planning comparison effect schematic diagram under C1 distribution;

[0064] Figure 8 is a path planning comparison effect schematic diagram under C2 distribution;

[0065] Figure 9 is a path planning comparison effect schematic diagram under C2 distribution;

[0066] Figure 10 is a top view effect schematic diagram of Figure 7 ;

[0067] Figure 11 is a top view effect schematic diagram of Figure 8 ;

[0068] Figure 12 is a top view effect schematic diagram of Figure 9 . DETAILED DESCRIPTION

[0069] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is described and explained below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0070] It is obvious that the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can also be applied to other similar situations according to the drawings without creative labor. In addition, it can be understood that although the efforts made in this development process may be complex and lengthy, some design, manufacture or production changes based on the technology disclosed in the present application are only routine technical means for those skilled in the art related to the disclosure of the present application, and should not be understood as insufficient disclosure of the present application.

[0071] The embodiment of the present application provides a UAV path planning method based on an ellipse tangent graph method, Figures 1 to 3 The flow chart of the UAV path planning method according to the embodiment of the present application is shown in Figures 1 to 3 The flow chart includes the following steps:

[0072] The flight environment modeling step S1 determines the flight area, obtains the geographical position information and size structure information of the buildings in the flight area, constructs an elliptical obstacle model by taking the buildings as obstacles, and constructs a three-dimensional flight environment model based on the elevation information of the flight area and the distribution of the obstacle model.

[0073] The three-dimensional path acquisition step S2 connects the path ST from the starting point S to the ending point T, performs collision detection on the path ST based on the three-dimensional flight environment model,

[0074] If no collision occurs, the UAV is controlled to fly along the path ST.

[0075] If collision occurs, the path point acquisition step S21 generates a two-dimensional reference path point, and judges whether the heights of the starting point S and the ending point T are the same.

[0076] If the same, the flight path that bypasses the obstacle is generated based on the bypass strategy or the climbing strategy.

[0077] If different, an alternative path is generated and the flight path with the lowest energy consumption in the alternative path is output based on a flight energy consumption model, wherein n path points between the starting point S and the ending point T are obtained based on the two-dimensional reference path point, each path point is configured with a bypass strategy and a climbing strategy, and The alternative path is generated, the UAV flight is decomposed into a take-off stage, a landing stage and a horizontal flight stage according to the alternative path, the energy consumption of the take-off stage , the energy consumption of the landing stage , and the energy consumption of the horizontal flight stage The flight energy consumption model is constructed, and the flight path with the lowest energy consumption in the alternative path is output by using the flight energy consumption model.

[0078] The three-dimensional path smoothing step S3 smoothes the path points of the flight path output by the three-dimensional path acquisition step, to obtain a flight trajectory for unmanned aerial vehicle control, improve the smoothness of the unmanned aerial vehicle flight trajectory, eliminate sharp turns in the path, ensure the stability of the flight, and reduce sharp turns and shaking during the flight.

[0079] In the above embodiment, the bypass strategy is to bypass from the current path point to the next path point based on the two-dimensional reference path, mark the next path point as a new starting point and update it as the starting point S, and then execute the path point acquisition step S21; the obstacle avoidance strategy is to climb above the obstacle from the current path point, mark the obstacle above as a new starting point and update it as the starting point S, and then execute the path point acquisition step.

[0080] Based on the above steps, the present application extends the tangent graph method of an ellipse to a three-dimensional scene, converts the building obstacle into an elliptical obstacle model, enhances the adaptability to irregular building structures, realizes effective acquisition of a two-dimensional reference path, combines the obstacle height and the two-dimensional reference path, realizes modeling and path search of a complex three-dimensional flight environment, introduces bypass and climbing strategies in the path search process to determine whether to fly directly or execute path adjustment, uses path point combination logic to arrange and combine n path points, arrange and combine n path points, and then calculate the energy consumption by an energy consumption model to select the scheme with the minimum energy consumption, enhance the flexibility and adaptability of path planning, and is especially suitable for typical urban air traffic scenarios such as urban canyons, buildings, etc. with height differences or multi-level obstacles, to avoid path collision problems.

[0081] In the above embodiment, the flight energy consumption model is represented by the following calculation model:

[0082] which improves the autonomous decision-making and path planning ability of the unmanned aerial vehicle in a complex environment;

[0083] ,

[0084] ,

[0085] ,

[0086] wherein, is the ground height, is the flight height of the unmanned aerial vehicle, represents the vertical upward velocity component respectively represent the vertical downward velocity component, , , respectively represent the propeller working efficiency, motor working efficiency and electronic speed regulator working efficiency, M is the total mass of the UAV, g is the acceleration of gravity, , ρ is the air density, taking the value of 1.225 ; t is the flight time, is the temperature gradient below the tropopause, taking the value of -0.0065 R is the gas constant in air, taking the value of 287.05287 , A is the propeller disc area, is the correction factor, which can be dynamically adjusted over time, T is the standard temperature, taking the value of 288.12 K, i.e. 15℃, is the horizontal flight distance, is the horizontal velocity component, is the windward area; is the zero drag lift coefficient; is the induced drag factor, , ρ is the air density at the current path point height and the air density at the next path point height is the average value of the air density at the current path point height and the air density at the next path point height can be calculated based on the following calculation model:

[0087] .

[0088] The energy consumption of the above take-off phase The energy consumption calculation model is based on the energy change of the UAV from the ground height to the flight height during take-off, and the energy required for the UAV to climb from the ground to the target height is converted into an integral expression. The air density changes with height, the propulsion system efficiency, the flight speed and other factors are considered. The temperature change term is used to simulate the standard atmospheric temperature gradient, which represents the influence of the environment on the air density and lift. The efficiency parameter term corrects the loss of the UAV propulsion system, and the instantaneous power consumption of the whole process is calculated by integration to obtain the total energy consumption estimation result of the take-off phase, which realizes more accurate estimation of the energy demand of the UAV in the vertical take-off phase and improves the authenticity and adaptability of the energy consumption prediction.

[0089] The energy consumption of the above landing phase The computational model uses an integral to represent the instantaneous power accumulation of the UAV as it descends from the target flight altitude to the ground. The temperature change term is similar to that in the takeoff phase, used to represent the temperature change as the altitude decreases over time. Compared with traditional static estimation methods, this model is closer to real aerodynamic characteristics and can be used to optimize landing attitude and path selection, thereby reducing unnecessary energy consumption and system load, and improving system energy efficiency and flight safety.

[0090] In the above embodiments, if the known ground temperature distribution and wind speed model are complex, a piecewise temperature function or fitting based on measured data can be introduced to replace the standard temperature gradient term.

[0091] In the above embodiments, the propeller efficiency, motor efficiency, and electronic speed governor efficiency can be obtained through actual flight tests or characteristic curves provided by the manufacturer, or, when precise values ​​are not available, can be approximated by setting typical constants.

[0092] Energy consumption during the aforementioned level flight phase The computational model divides the energy consumption of horizontal flight into two parts: air resistance power consumption, which is the energy consumed by the UAV to counteract air resistance in its forward direction per unit time; and the power required to maintain lift, which is the energy consumed to overcome gravity and maintain lift. Modeling both parts yields the energy consumption during the horizontal flight phase, which is then corrected using propulsion system efficiency as the denominator. This model comprehensively simulates the actual energy requirements of the UAV during complex flights, aiding in the energy efficiency ranking and optimal selection of paths for different horizontal segments. Compared to traditional simplified models that only estimate drag energy consumption, this implementation is more practical for engineering applications, particularly suitable for long-distance, varying-altitude mission path planning, enhancing the system's refined energy budgeting capabilities.

[0093] In the above embodiments, the windward area Zero drag lift coefficient Induced resistance factor When it is impossible to obtain the data through actual measurement, it can be estimated based on the UAV platform combined with the model and the Fengdong measurement standard parameters, or fine-tuned by using approximate constants combined with the flight scenario.

[0094] In the preferred embodiment of this application, It is 0.8; It is 0.8; It is 0.8; It is 0.94; It is 0.75 square meters; It is 0.13; It weighs 15 kilograms; It is 1.313 square meters; It is 0.015; It is 15 m / s, based on The horizontal velocity component, the vertically downward velocity component, and the vertically upward velocity component are calculated.

[0095] Based on the above calculation model, the application establishes a refined energy consumption model based on aerodynamics and energy conversion relationship based on the horizontal flight stage, the take-off stage, and the landing stage, accurately quantifies the actual energy consumption of the path from each flight stage, considers the energy required to overcome gravity in vertical motion and the influence of air resistance in horizontal flight, and combines the working efficiency of key components such as propellers, motors, and electronic speed controllers, to improve the authenticity and engineering applicability of energy consumption calculation.

[0096] In some embodiments, the path point acquisition step S21 comprises:

[0097] The three-dimensional flight environment model is projected into a two-dimensional plane obstacle map, and an improved ellipse tangent graph method is used to generate two-dimensional reference path points from the starting point S to the ending point T.

[0098] In some embodiments, the path point acquisition step S21 further comprises:

[0099] The path initialization step S201 initializes a path node set, a candidate node queue, and a collision obstacle set to empty sets. The path node set is denoted as Path, which is used to record the effective nodes in the current path. The candidate node queue is denoted as C, which is used to temporarily store the path nodes to be expanded. The collision obstacle set is denoted as B, which is used to record the obstacles that collide with the obstacles in the path search.

[0100] The bidirectional search path step S202 adds the starting point S to the path node set Path, adds the ending point T to the candidate node queue C as a preset ending point to perform the path collision detection step S4, generates a first candidate path set from the starting point S to the ending point T, and adds the ending point T to the path node set Path, adds the starting point S to the candidate node queue C as a preset ending point to perform the path collision detection step, iteratively generates a second candidate path set from the ending point T to the starting point S, establishes a bidirectional path topology graph based on the first candidate path set and the second candidate path set, and records the collision-free path nodes and their connection relationships. Based on this, the bidirectional search strategy is used for path planning, the starting point S and the ending point T are taken as target points, the path collision detection step is performed simultaneously, and the obstacle avoidance candidate paths from the starting point S to the ending point T and from the ending point T to the starting point S are found.

[0101] The optimal path screening step S203 combines the first candidate path set and the second candidate path set, calculates the total length of each path through a path cost matrix, each element in the path cost matrix representing the length of a collision-free path segment from a node to another node, such as a weighted length, and assigning an infinite value if there is an obstacle blocking, and selecting a collision-free path with the minimum total length in the bidirectional path topology graph by using the Dijkstra algorithm, and outputting the two-dimensional reference path points of the global shortest path;

[0102] In the collision detection process, the tangent graph method is used to generate candidate temporary paths, that is, tangent paths, and a temporary path in the candidate temporary paths is selected based on a preset heuristic rule for path generation, ensuring that the path is reasonable to detour between obstacles and has high planning efficiency, and avoiding falling into local optimization.

[0103] Based on the above steps, a two-dimensional path search framework between the starting point and the ending point is constructed based on the elliptical tangent graph method, and a bidirectional search strategy is introduced to improve the path convergence efficiency, effectively reducing the redundant search range, and significantly improving the search efficiency and search performance. Initializing the empty path set and the candidate set helps to accurately track the path evolution process. By compressing the three-dimensional terrain to a two-dimensional graph and introducing a topology graph construction method, the obstacle boundary information can be converted into path connectivity. The use of Dijkstra algorithm combined with cost matrix makes the path generation not only consider obstacle avoidance, but also has path optimization capability. The generation of candidate paths is realized through "tangent strategy + heuristic selection", which makes the path bending position more controllable and continuous.

[0104] In the above embodiments, the path cost matrix can be extended to a weighted cost function containing flight energy consumption, corner penalty, etc.

[0105] In some embodiments, the path collision detection step S4 includes: obtaining the end node O in the path node set Path, obtaining the end node in the candidate node queue C as the target node D, connecting the end node O and the target node D to obtain the path segment OD for collision detection, if collision is detected, re-planning the path until the path reaches the preset endpoint and there is no obstacle collision, and generating a candidate path. When performing reverse iteration, the preset endpoint represents the starting point S.

[0106] When performing forward and reverse iteration using the path collision detection step S4, the path optimization quality in a complex obstacle dense environment can be improved.

[0107] The path collision detection step S4 further includes:

[0108] S401: Obtain the end node O in the path node set Path, represented as O=Path[end], as the starting node of the current expanded path;

[0109] S402: Collision detection is performed on the path segment OD. If the path segment OD does not intersect with the obstacle, the target node D is removed from the candidate node queue C and added to the path node set Path. Otherwise, step S403 is performed to correct the path.

[0110] S403: A first collision obstacle Obs is determined. A first tangent and a second tangent of the end node O and the collision obstacle Obs are obtained. A third tangent and a fourth tangent of the terminal point T and the collision obstacle Obs are obtained. Two candidate temporary paths are generated based on the intersection point N1 of the first tangent and the third tangent and the intersection point N1' of the second tangent and the fourth tangent. The two candidate temporary paths are represented as Path1: S→N1→T and Path2: S→N1'→T.

[0111] S404: Based on a preset heuristic rule, an optimal intersection point N* is determined between the intersection point N1 and the intersection point N1' as a relay point of the current candidate path.

[0112] S405: Collision detection is performed on the connection between the optimal intersection point N* and the end node O. If there is no collision, the optimal intersection point N* is added to the path node set Path. The optimal intersection point N* is used as a new end node O for a loop iteration. Otherwise, the optimal intersection point N* is added to the candidate node queue C. The collision obstacle Obs is added to the collision obstacle set B.

[0113] S406: If the candidate node queue C is not empty, a target node D' in the candidate node queue C is obtained. The steps S402 to S405 are repeated. Collision detection is performed on the connection between the end node O and the target node D'.

[0114] Based on the above steps, the embodiment of the present application adopts a double-point tangent intersection to construct an obstacle-avoiding candidate path. The intersection point is used to guide the path generation. The heuristic selection mechanism is used to dynamically select an optimal path. When there is an unprocessed path point in the candidate node queue, the steps S402 to S405 can be repeated until a complete path reaching the terminal point without collision is successfully generated.

[0115] In some embodiments, the preset heuristic rule is configured to include:

[0116] A first condition: selecting a sub-path with the least tangent collision with the start point S;

[0117] A second condition: selecting a sub-path with less tangent collision with the terminal point T;

[0118] A third condition: selecting a sub-path with a shorter length;

[0119] Based on the above preset heuristic rules, the S→N1 or S→N1' segment of each path is subjected to collision detection based on the first condition, the number of intersections with obstacles is recorded, the number of collision obstacles is counted and compared to ensure that the starting segment of the path avoids complex obstacle regions as much as possible and reduces the risk of initial path failure;

[0120] If the number of collision obstacles in the first condition is the same, the N1→T or N1'→T segment is subjected to collision detection based on the second condition, the number of intersections with obstacles is recorded, the number of collision obstacles is counted and compared to ensure that the end of the path is close to the target with minimal obstacle interference;

[0121] If the number of collision obstacles in the second condition is also the same, the shortest path is pursued under the premise of obstacle avoidance, thereby optimizing flight time and energy consumption.

[0122] Based on the above-mentioned cyclic collision detection process, the optimal path in two-dimensional space is solved according to the principle of tangent intersection.

[0123] In some embodiments, in the flight environment modeling step S1, the overhead outer contour of the building is modeled as an ellipse to obtain an obstacle model to represent the no-fly zone of the unmanned aerial vehicle, and the obstacle model is represented as:

[0124] ,

[0125] In the above formula, and respectively represent the semi-major axis and semi-minor axis of the ellipse; is the inclination angle of the semi-major axis to the positive direction of the horizontal axis in the two-dimensional plane obstacle map; represents the safety distance between the unmanned aerial vehicle and the obstacle; represents the center coordinates of the obstacle; represents the height of the obstacle.

[0126] Based on the obstacle model, based on the actual geometric boundary characteristics of the obstacle in the flight area, by approximating to an ellipse, the modeling efficiency and path algorithm compatibility are improved, in three-dimensional space, the obstacle region is an upper and lower closed elliptic cylinder, which limits the flyable path of the unmanned aerial vehicle, simplifies the obstacle representation form in the flight environment, and improves the modeling speed and algorithm analysis efficiency. This modeling method has high parameterization degree, can realize the approximate expression of the diversified shape of the building, can dynamically adjust the no-fly boundary by configuring the safety distance, and can realize the flight safety strategy suitable for different task risk levels. Compared with the polygon or voxel modeling method, this scheme has low calculation overhead, continuous representation, and is convenient for geometric operation and shortest path search in path planning algorithm.

[0127] In the above embodiments, the ellipse parameters , The building outer rectangle can be fitted, or can be extracted based on actual building geometry survey data. If the building structure is a rectangle or a regular polygon, an envelope ellipse can be used to approximate the boundary. For a multi-story building with a large height variation, the envelope ellipse model can be replaced by a multi-segment height function to construct a layered ellipse model. The safety distance The safety distance The safety distance can be set as a dynamic parameter related to factors such as flight speed, wind speed, autonomous control accuracy, etc., to realize differentiated flight safety strategies.

[0128] The two-dimensional plane obstacle map of the embodiments of the present application takes three typical obstacle distributions as examples, Figure 4 The two-dimensional plane obstacle map of the embodiments of the present application takes three typical obstacle distributions as examples, Figure 5 The two-dimensional plane obstacle map of the embodiments of the present application takes three typical obstacle distributions as examples, Figure 6 The two-dimensional plane obstacle map of the embodiments of the present application takes three typical obstacle distributions as examples, Figures 4 to 6 In the figure, the circular point is used to indicate the starting point S, and the triangular point is used to indicate the ending point T.

[0129] Considering that buildings are mostly irregular polygons, the sharp corners of the polygon can cause the path planning to generate a jagged path, and when judging whether the path intersects with the obstacle, complex edge and vertex intersection detection needs to be handled; based on the above obstacle model, the embodiments of the present application approximate the building as an ellipse, and the radius of the ellipse can contain the actual outer contour of the building and the safety distance, avoiding the path being close to the obstacle, improving the fault tolerance, and the continuous curvature feature of the ellipse can naturally guide the algorithm to generate a smoother path, simplifying the calculation, improving the efficiency, and ensuring the smoothness and safety of the path.

[0130] In some embodiments, the three-dimensional path smoothing step S3 uses a cubic B-spline curve for smoothing. Specifically:

[0131] The path point of the flight path is represented as: The basis function of the cubic B-spline curve is represented as the following calculation model:

[0132] ,

[0133] Then, the cubic B-spline curve between and is represented as: and

[0134] .

[0135] The technical effects of the embodiments of the present application will be described and explained below through preferred embodiments. ​

[0136] The preferred embodiments of the present application are based on Figures 4 to 6 obstacle distribution, randomly setting the height of obstacles in C1 distribution, C2 distribution and C3 distribution, and the height of the obstacles is set in the low-altitude feasible area range (i.e. 20-120 m), obtaining three-dimensional obstacle distribution, under the three three-dimensional obstacle distributions respectively, the path planning algorithm (denoted as ECTG) of the embodiments of the present application is compared and analyzed with A* algorithm, RRT (Rapidly -exploring Random Tree) algorithm, PSO (Particle Swarm Optimization) algorithm and VFH (Vector Field Histogram) algorithm respectively, and the path planning comparison effect diagram as shown in Figures 7 to 9 Figure 7 is the path planning comparison effect diagram under C1 distribution, Figure 8 is the path planning comparison effect diagram under C2 distribution, Figure 9 is the path planning comparison diagram under C2 distribution, Figures 10 to 12 are respectively Figures 7 to 9 are respectively

[0137] By analyzing the performance of the evaluation algorithm in three evaluation dimensions of path length, turning number and energy consumption, the data summary is shown in Table 1.

[0138] Table 1-Data summary

[0139]

[0140] Based on the above analysis, due to the path planning method of the present application considering the tangent path generated when avoiding obstacles and the path length, the quality of path planning is improved, and in C1, C2 and C3 three obstacle distribution scenarios, the shortest path with the least number of turns can be generated, and the more complex the environment is, the more advantageous it is. Compared with the present application, the RRT algorithm is based on random sampling method to generate path points, and does not set a mechanism to improve the quality of the path, which cannot guarantee the optimality of the path and easily leads to frequent turning and detour of the unmanned aerial vehicle, the A* algorithm is a deterministic search algorithm based on grid, which can generate a feasible path from the starting point to the end point, but the quality of the generated path is not excellent; the VFH algorithm is a local path planning algorithm, the search range is limited, and it focuses on avoiding visible obstacles in front, ignoring the overall path length.

[0141] ​Compared with the above-mentioned algorithms, the embodiments of the present application perform excellently in key indicators such as path length, steering times and energy consumption, wherein the average path length is reduced by 8.7%, 18.7%, 13.4% and 4.1% respectively compared with the A* algorithm, the RRT algorithm, the PSO algorithm and the VFH algorithm, the average steering times are reduced by 68.8%, 82.1%, 82.8% and 75.0% respectively, and the average energy consumption is reduced by 51.6%, 34.0%, 59.1% and 55.3% respectively, which shows that the present application can more efficiently find a short path close to the optimal one, and the generated path is smoother, which is beneficial to the stable flight of the unmanned aerial vehicle and reduces the control burden, especially in terms of energy consumption, which performs outstandingly, helps to prolong the endurance time of the unmanned aerial vehicle, and provides strong support for the optimization of battery management strategy in actual tasks, and the advantages are more significant with the increase of environmental complexity.

[0142] It should be noted that the steps shown in the above flow or the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0143] The technical features of the above-described embodiments can be combined arbitrarily, and to make the description concise, all possible combinations of the technical features in the above-described embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present disclosure.

[0144] The above-described embodiments only express several implementation manners of the present application, the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An unmanned aerial vehicle path planning method based on an ellipse tangent graph method, characterized in that, Comprise: Flight environment modeling step, determine the flight area, obtain the geographical position information, size structure information of the building in the flight area, build the elliptical obstacle model as the obstacle, build the three-dimensional flight environment model based on the elevation information of the flight area and the distribution of the obstacle model; Three-dimensional path acquisition step, connect the path ST from the starting point S to the terminal point T, and perform collision detection on the path ST based on the three-dimensional flight environment model, If no collision occurs, control the UAV to fly along the path ST; If collision occurs, execute the path point acquisition step to generate two-dimensional reference path points, and judge whether the height of the starting point S and the terminal point T is the same; If the same, generate a flight path that bypasses the obstacle based on the bypass strategy or the climbing strategy; If not, generating an alternative path and outputting a flight path with lowest energy consumption in the alternative path based on a flight energy consumption model, wherein n path points between a start point S and an end point T are obtained based on the two-dimensional reference path points, each path point is configured with a detour strategy and a climbing strategy, and an alternative path is generated The flight energy consumption model is constructed, and the flight path with the lowest energy consumption in the alternative path is output by using the flight energy consumption model, and the flight energy consumption model is represented as the following calculation model: The flight energy consumption model is constructed, and the flight path with the lowest energy consumption in the alternative path is output by using the flight energy consumption model, and the flight energy consumption model is represented as the following calculation model: The flight energy consumption model is constructed, and the flight path with the lowest energy consumption in the alternative path is output by using the flight energy consumption model, and the flight energy consumption model is represented as the following calculation model: The flight energy consumption model is constructed, and the flight path with the lowest energy consumption in the alternative path is output by using the flight energy consumption model, and the flight energy consumption model is represented as the following calculation model: ； Three-dimensional path smoothing step, the path points of the flight path output by the three-dimensional path acquisition step are smoothed to obtain the flight trajectory for UAV control, The path point acquisition step comprises: projecting the three-dimensional flight environment model into a two-dimensional plane obstacle map, and generating two-dimensional reference path points from the starting point S to the terminal point T by using an improved ellipse tangent graph method; the path point acquisition step further comprises: Path initialization step, initialize a path node set, a candidate node queue and a collision obstacle set to empty set, the path node set is represented as Path, the candidate node queue is represented as C, and the collision obstacle set is represented as B; Bidirectional search path step, add the starting point S to the path node set Path, add the terminal point T to the candidate node queue C as a preset terminal point, execute the path collision detection step, generate a first candidate path set from the starting point S to the terminal point T, and add the terminal point T to the path node set Path, add the starting point S to the candidate node queue C as a preset terminal point, execute the path collision detection step, iteratively generate a second candidate path set from the terminal point T to the starting point S, establish a bidirectional path topology graph based on the first candidate path set and the second candidate path set, record the collision-free path nodes and their connection relationship, and use bidirectional search measurement to plan the path, with the starting point S and the terminal point T as target points, simultaneously perform the path collision detection step, and find the obstacle avoidance candidate path from the starting point S to the terminal point T and from the terminal point T to the starting point S; Optimal path screening step, merge the first candidate path set and the second candidate path set, calculate the total length of each path through a path cost matrix, each element in the path cost matrix represents the collision-free path length from a node to another node, based on the weighted length, if there is an obstacle, assign an infinite value, select the collision-free path with the minimum total length in the bidirectional path topology graph by using Dijkstra algorithm, and output the two-dimensional reference path points of the global shortest path; In the collision detection process, the tangent graph method is used to generate a candidate temporary path, and a temporary path in the candidate temporary path is selected based on a preset heuristic rule for path generation; The path collision detection step comprises: An end node O in the path node set Path is obtained, an end node in the candidate node queue C is taken as a target node D, a path segment OD connecting the end node O and the target node D is obtained to perform collision detection, if collision is detected, the path is re-planned until the path reaches a preset end point and no obstacle collision occurs, and a candidate path is generated; The path collision detection step further comprises: S401: An end node O in the path node set Path is obtained as a starting node of a current extended path; S402: The path segment OD is subjected to collision detection, if no intersection with an obstacle occurs, the target node D is removed from the candidate node queue C to the path node set Path, otherwise, step S403 is performed to correct the path; S403: A first collision obstacle Obs is determined, a first tangent line and a second tangent line of the end node O and the collision obstacle Obs are obtained, a third tangent line and a fourth tangent line of an end point T and the collision obstacle Obs are obtained, and a second candidate temporary path is generated based on an intersection point N1 of the first tangent line and the third tangent line and an intersection point N1' of the second tangent line and the fourth tangent line; S404: An optimal intersection point N* is determined between the intersection point N1 and the intersection point N1' based on a preset heuristic rule, as a relay point of the current candidate path; S405: The optimal intersection point N* and the end node O are connected to perform collision detection, if no intersection with an obstacle occurs, the optimal intersection point N* is added to the path node set Path, and the optimal intersection point N* is taken as a new end node O for cyclic iteration, otherwise, the optimal intersection point N* is added to the candidate node queue C, and the collision obstacle Obs is added to the collision obstacle set B; S406: If the candidate node queue C is not empty, a target node D' in the candidate node queue C is obtained, and the above steps are repeated to connect the end node O and the target node D' to perform collision detection; When the candidate node queue exists an unprocessed path point, the above steps S402 to S405 can be repeatedly performed until a complete path reaching the end point and having no collision is successfully generated. 2.The elliptical tangent graph-based path planning method for UAVs according to claim 1, wherein, Energy consumption during take-off phase is calculated based on the following calculation model: ， wherein, is the ground height, is the flight height of the UAV, represents the vertical upward velocity component, , , respectively represent the propeller efficiency, the motor efficiency and the electronic speed controller efficiency, is the total mass of the UAV, is the gravity acceleration, , represents the air density, taking the value 1.225 ; is the flight time, is the temperature gradient below the tropopause, taking the value -0.0065 , R is the gas constant in air, taking the value 287.05287 , is the propeller disc area, is the correction factor, is the standard temperature, taking the value 288.12 K. 3.The method of claim 2, wherein, Energy consumption of the landing phase is calculated based on the following calculation model: ， wherein respectively denote the vertical downward velocity components. 4.The method of claim 3, wherein, Energy consumption of horizontal flight phase is calculated based on the following calculation model: ， wherein is the horizontal flight distance, is the horizontal velocity component, is the wind- facing area; is the zero-drag lift coefficient; is the induced drag factor, , denotes the average of the air density at the current waypoint height and the air density at the next waypoint height . 5.The elliptical tangent graph based path planning method for UAVs according to claim 1, wherein, In the flight environment modeling step, a top view outer contour of a building is modeled as an ellipse to obtain an obstacle model, and the obstacle model is represented as: ， In the above formula, and respectively represent the semi-major axis and semi-minor axis of the ellipse; is the inclination angle of the semi-major axis with the positive direction of the horizontal axis in the two-dimensional plane obstacle map; represents the safety distance between the UAV and the obstacle; represents the center coordinates of the obstacle; represents the height of the obstacle.

6. The elliptical tangent graph based UAV path planning method according to any one of claims 1 to 4, wherein, The three-dimensional path smoothing step adopts a cubic B-spline curve for smoothing processing.

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