Fuzzy comprehensive evaluation-based path optimization method for coping with meteorological threats by unmanned aerial vehicle

Abstract

The invention relates to the technical field of path optimization when an unmanned aerial vehicle deals with meteorological threats, in particular to a path optimization method when the unmanned aerial vehicle deals with the meteorological threats based on fuzzy comprehensive evaluation. According to the method, path planning is carried out based on a fusion global path guiding mechanism, dynamic weight adjustment based on meteorological threats, three-dimensional highly-layered potential field modeling, velocity potential field introduction, fuzzy logic control potential field adjustment and an improved artificial potential field algorithm of a random disturbance and re-planning strategy. And outputting the flight path of the unmanned aerial vehicle meeting the multi-constraint condition, and performing visual verification in a simulation environment. The improved artificial potential field algorithm adopted by the invention fuses A global path guidance and a fuzzy logic dynamic weight adjustment mechanism, so that the unmanned aerial vehicle can respond to meteorological changes in real time, enhance the repulsive force in a high-risk area, optimize the path length in a safe area, and realize dynamic balance of safety and economy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path optimization when an unmanned aerial vehicle (UAV) responds to meteorological threats, in particular to a path optimization method for an UAV to respond to meteorological threats based on fuzzy comprehensive evaluation. BACKGROUND

[0002] With the wide application of UAVs in logistics, inspection, surveying and mapping, emergency rescue and other fields, the autonomous flight safety and path planning capability of UAVs in complex meteorological environments have become key technical challenges. Traditional UAV path planning methods are mostly based on geometric constraints or static environment modeling, and do not fully take into account the influence of dynamic meteorological factors, resulting in poor performance of the flight path under actual meteorological threats.

[0003] Currently common path planning algorithms such as artificial potential field (APF), A Star, genetic algorithm, etc. have been applied to a certain extent in three-dimensional path planning; however, the traditional artificial potential field method has limitations such as local minimum value problem, lack of global path guidance, and difficulty in handling dynamic multi-threat environments, especially when facing wind speed mutation, wind shear, turbulence, icing and other types of meteorological threats, the planned path often cannot achieve a balance between safety and economy.

[0004] The modeling of meteorological threats in existing research is mostly limited to a single factor (such as wind speed), lacking a systematic and multi-index comprehensive evaluation system; in addition, the spatio-temporal variability and uncertainty of meteorological data also increase the complexity of path planning; how to dynamically integrate multi-source meteorological information into path decision-making and achieve adaptive avoidance in three-dimensional space is still a technical problem to be solved in the field of intelligent flight control of UAVs. Therefore, there is an urgent need for a path optimization method for an UAV to respond to meteorological threats based on fuzzy comprehensive evaluation. SUMMARY

[0005] The purpose of the present application is to provide a path optimization method for an UAV to respond to meteorological threats based on fuzzy comprehensive evaluation, to solve the problems raised in the background.

[0006] To solve the above technical problems, the present application provides the following technical solution: a path optimization method for an UAV to respond to meteorological threats based on fuzzy comprehensive evaluation, comprising the following steps: S1. Three-dimensional modeling of the UAV flight path is performed using a spherical vector coding method, and the flight trajectory of the UAV is described by a vector composed of amplitude, horizontal turning angle and vertical height angle in three-dimensional space with the starting point as the reference; S2, discretize the flight airspace into a uniform grid structure in three-dimensional space, each grid node corresponds to a flyable position, and the unmanned aerial vehicle can select a next node set consisting of 25 adjacent nodes centered on the current node at any time from the current node; and construct a path cost function to analyze the planned unmanned aerial vehicle flight path, the path cost function includes distance cost, angle cost, height cost and threat cost; S3, quantitatively evaluate the meteorological threat by fuzzy comprehensive evaluation method, map each threat to a unified meteorological threat index through the membership function and threat level division table, the meteorological threat includes wind speed, wind shear, turbulence probability and ice accretion index; S4, path planning based on improved artificial potential field algorithm with fusion of global path guidance mechanism, dynamic weight adjustment based on meteorological threat, three-dimensional height layered potential field modeling, velocity potential field introduction, fuzzy logic control potential field adjustment and random disturbance and re-planning strategy, output the unmanned aerial vehicle flight path that meets the multiple constraint conditions, and visualize and verify in the simulation environment.

[0007] Further, the flight trajectory of the unmanned aerial vehicle is composed of a plurality of path points, and a path section composed of any two adjacent path points corresponds to a vector composed of an amplitude, a horizontal turning angle and a vertical height angle, the amplitude represents the length of the corresponding path section. The horizontal turning angle in the vector corresponding to the path section composed of the i-th path point and the i+1-th path point in the unmanned aerial vehicle flight path is denoted as . Wherein, represents the projection point of the i-1-th path point in the unmanned aerial vehicle flight path on the horizontal plane; represents the projection point of the i-th path point in the unmanned aerial vehicle flight path on the horizontal plane; represents the projection point of the i+1-th path point in the unmanned aerial vehicle flight path on the horizontal plane; represents to the vector composed of to the vector composed of ; represents and respectively corresponding to the product of the vector module length; The horizontal turning angle in the vector corresponding to the path section composed of the i-th path point and the i+1-th path point in the unmanned aerial vehicle flight path is denoted as . Wherein, This represents the altitude value corresponding to the (i+1)th path point in the drone's flight path; This represents the altitude value corresponding to the i-th path point in the drone's flight path; express The corresponding vector magnitude.

[0008] Furthermore, the algorithm formulas involved in the analysis of the UAV flight path based on the distance cost in the path cost function in S2 are as follows: in, This represents the distance cost corresponding to the drone's flight path; N represents the difference between the number of path points corresponding to the drone's flight path and 1. This represents the length of the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path; where i ∈ [1, N]. The algorithm formulas involved in the process of analyzing the UAV flight path based on the angle cost in the path cost function in S2 are as follows: in, This represents the angle cost corresponding to the drone's flight path. This represents the horizontal turning angle cost corresponding to the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path. This represents the elevation angle cost corresponding to the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path. This indicates the preset value of the maximum horizontal turning angle corresponding to the drone's flight path; This represents the horizontal turning angle in the vector corresponding to the path segment formed by the (i+1)th path point and the (i+2)th path point in the UAV's flight path; μ1 represents the preset maximum altitude angle value corresponding to the UAV's flight path; μ2 represents the weighting coefficient of the horizontal turning angle cost; μ1 represents the weighting coefficient of the altitude angle cost. The algorithm formulas involved in the process of analyzing the UAV flight path based on the altitude cost in the path cost function in S2 are as follows: in, This represents the altitude cost corresponding to the drone's flight path; Let $\frac{i}{i+1}$ be the altitude cost of the path segment formed by the $i$-th path point and the $i+1$-th path point in the UAV's flight path. Let be the height of the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path above the ground. These represent the minimum and maximum height values, respectively.

[0009] Furthermore, the threat cost in the path cost function is composed of the obstacle threat index and the weather threat index; The algorithm formulas involved in the process of analyzing the UAV flight path based on the obstacle threat index in the path cost function in S2 are as follows: Where k represents the number of obstacles within the corresponding planning area; T k R represents the obstacle threat index during the process of a drone moving from the i-th path point to the (i+1)-th path point in its flight path; k Let S represent the diameter of the nearest obstacle during the drone's flight path from the i-th path point to the (i+1)-th path point; let S represent the minimum safe distance between the drone and the obstacle; and let d represent the radius of the circumscribed sphere of the drone. k This represents the distance between the center of the nearest obstacle and the point on the path from the i-th path point to the (i+1)-th path point in the drone's flight path.

[0010] Furthermore, the meteorological threat index includes wind speed threat, wind shear threat, turbulence index corresponding to turbulence threat, and icing index; S3 specifically includes: S31. Process the data and calculate the wind shear threat, turbulence probability and icing index of the corresponding UAV at the corresponding path nodes. S32. Referring to the preset threat level classification table, the calculated data is fuzzified to obtain the threat assessment of each meteorological factor; S33. Set a membership function to map the fuzzy evaluation obtained in step S32 to the threat level of the corresponding meteorological factors, and obtain the wind speed threat level, wind shear threat level, turbulence threat level and icing threat level of the corresponding UAV at the corresponding path nodes; the threat level includes level I corresponding to minor threat, level II corresponding to mild threat, level III corresponding to severe threat and level IV corresponding to disaster threat. S34. The meteorological threat index of the corresponding UAV at the corresponding path node is calculated by weighted summation. The weight coefficients corresponding to the wind speed threat level, wind shear threat level, turbulence threat level and icing threat level are respectively the preset first weight threshold, second weight threshold, third weight threshold and fourth weight threshold.

[0011] Furthermore, when obtaining the wind shear threat to the corresponding UAV, the horizontal wind speed scalar of each path node is obtained, and the partial derivatives of the horizontal wind speed scalar of the corresponding path node in the X-axis, Y-axis and Z-axis directions in three-dimensional space are calculated by the finite difference method. Then, the wind shear threat of the corresponding path node is equal to the square root of the sum of the squares of the partial derivatives of the horizontal wind speed scalar of the corresponding path node in the X-axis, Y-axis and Z-axis directions in three-dimensional space. The algorithm formula for obtaining the turbulence index experienced by the corresponding drone is as follows: Where L represents the turbulence index experienced by the corresponding UAV at the corresponding path node; This indicates the vertical wind shear of the corresponding drone at the corresponding path node; This represents the horizontal temperature gradient of the corresponding UAV at the corresponding path node. PG represents the horizontal wind speed gradient of the corresponding UAV at the corresponding path node; PG represents the turbulence threat of the corresponding UAV at the corresponding path node; the value of PG is equal to the turbulence probability of mapping the turbulence index L to the range (0,1). The algorithm formula for obtaining the icing index of the corresponding drone is as follows: Where F represents the icing index of the corresponding UAV at the corresponding path node; T represents the relative humidity of the corresponding UAV at the corresponding path node; and H represents the relative temperature of the corresponding UAV at the corresponding path node.

[0012] Furthermore, the improved artificial potential field algorithm in S4 includes: Introducing A The global path generated by the algorithm serves as a sequence of gravity-guided points. The meteorological threat index dynamically adjusts the weighting coefficient of the repulsive potential field; High-dimensional potential fields are modeled based on meteorological risk stratification; A velocity potential field term is introduced to predict threats ahead and respond in advance; The fuzzy logic controller adjusts the range and intensity of the potential field based on the threat level. When the potential gradient approaches zero, a random perturbation is applied, and a rolling window replanning mechanism is initiated.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) This invention uses spherical vector encoding and height potential field modeling to achieve smooth constraints on the turning angle, altitude angle and flight altitude of the UAV, supports hierarchical obstacle avoidance and altitude optimization in three-dimensional space, and is especially suitable for complex multi-obstacle environments; (2) This invention establishes a multi-index fuzzy comprehensive evaluation system that includes wind speed, wind shear, turbulence probability and icing index, thereby achieving systematic and hierarchical quantification of meteorological threats and overcoming the problems of single threat modeling and one-sided evaluation in traditional methods. (3) The improved artificial potential field algorithm used in this invention integrates A The global path guidance and fuzzy logic dynamic weight adjustment mechanism enable UAVs to respond to weather changes in real time, enhance repulsion in high-risk areas, optimize path length in safe areas, and achieve a dynamic balance between safety and economy. (4) The present invention adjusts the potential field parameters in real time through a fuzzy logic controller, so that the path planning results are based on the latest meteorological information, thereby improving the timeliness and reliability of flight decision-making. Attached Figure Description

[0014] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the path optimization method for UAVs responding to weather threats based on fuzzy comprehensive evaluation, as proposed in this invention. Figure 2 This is a schematic diagram of the horizontal steering angle and altitude angle in an embodiment of the present invention; Figure 3 This is a schematic diagram of the search node in an embodiment of the present invention; Figure 4 This is a schematic diagram of obstacle distance in an embodiment of the present invention; Figure 5 This is a schematic representation of threat level classification in an embodiment of the present invention; Figure 6 This is a schematic diagram of the artificial potential field algorithm in an embodiment of the present invention; Figure 7 This is a schematic diagram of the three-dimensional path planned by the traditional ADF algorithm in this embodiment of the invention; Figure 8 This is a schematic diagram of the three-dimensional path planned by the improved ADF algorithm in this embodiment of the invention; Figure 9 This is a schematic diagram showing the potential field distribution and threat level of key nodes during the path planning process in an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 This embodiment provides a path optimization method for UAVs responding to weather threats based on fuzzy comprehensive evaluation, including the following steps: S1. The UAV flight path is modeled in three dimensions using spherical vector coding. The UAV flight trajectory is described in three dimensions using a vector composed of amplitude, horizontal turning angle and vertical elevation angle, with the starting point as the reference. The flight trajectory of the UAV consists of multiple waypoints. Any path segment formed by two adjacent waypoints corresponds to a vector consisting of amplitude, horizontal turning angle and vertical altitude angle. The amplitude represents the length of the corresponding path segment. In this embodiment, the mathematical expression for describing the flight path of the UAV is adopted instead of the traditional rectangular coordinate system, which is more cumbersome. The spherical vector encoding is more suitable for the flight characteristics of UAVs. That is, N+1 path points are selected in the search space, and the flight trajectory is described by amplitude, turning angle and altitude angle for each path segment. This approach allows researchers to not need to care about the coordinates of the points passed by the flight trajectory, but only need to know the position of the initial point, and can deduce the complete flight trajectory based on the flight path. like Figure 2 As shown, the horizontal turning angle in the vector corresponding to the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path is denoted as... ; in, This represents the projection of the (i-1)th path point in the UAV's flight path onto the horizontal plane. This represents the projection of the i-th path point in the UAV's flight path onto the horizontal plane. This represents the projection of the (i+1)th path point in the UAV's flight path onto the horizontal plane. express to The vector formed by time; express to The vector formed by time; express and The product of the magnitudes of the corresponding vectors; Let the horizontal turning angle in the vector corresponding to the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path be denoted as . ; in, This represents the altitude value corresponding to the (i+1)th path point in the drone's flight path; This represents the altitude value corresponding to the i-th path point in the drone's flight path; express The corresponding vector magnitude.

[0017] S2. Discretize the flight airspace in three-dimensional space into a uniform grid structure, where each grid node corresponds to a flyable position. At any given time, the UAV can select the next node set consisting of 25 adjacent nodes centered on the current node. Figure 3 As shown, the UAV's flight environment is in a complex three-dimensional environment. Considering the large search space of the subsequent algorithm, the airspace part of the three-dimensional environment can be simplified into nodes to be searched with a sufficiently large resolution. This is done to simplify the search space of the subsequent algorithm. For example, if the UAV is at position (x, y, z) at time t, without considering turning back, the searchable space can be simplified to 25 adjacent search nodes. A path cost function is constructed to analyze the planned UAV flight path. The path cost function includes distance cost, angle cost, altitude cost, and threat cost. In this embodiment, the UAV flight path planning problem can be understood as an optimization problem under certain constraints. The constraints related to UAV flight are mainly reflected in distance cost, angle cost, altitude cost, and threat cost. These constraints can reasonably characterize the flight motion state limitations of the UAV, so that the planned flight route can be applied in practice. The algorithm formulas involved in the process of analyzing the UAV flight path based on the distance cost in the path cost function in S2 are as follows: in, This represents the distance cost corresponding to the drone's flight path; N represents the difference between the number of path points corresponding to the drone's flight path and 1. This represents the length of the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path; where i ∈ [1, N]. The algorithm formulas involved in the process of analyzing the UAV flight path based on the angle cost in the path cost function in S2 are as follows: in, This represents the angle cost corresponding to the drone's flight path. This represents the horizontal turning angle cost corresponding to the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path. This represents the elevation angle cost corresponding to the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path. This indicates the preset value of the maximum horizontal turning angle corresponding to the drone's flight path; This represents the horizontal turning angle in the vector corresponding to the path segment formed by the (i+1)th path point and the (i+2)th path point in the UAV's flight path; μ1 represents the preset maximum altitude angle corresponding to the UAV's flight path; μ2 represents the weighting coefficient of the horizontal turning angle cost; μ1 and μ2 can be adjusted according to mission requirements. When a mission is more sensitive to the UAV's flight turning angle, μ1 can be increased to increase the cost brought by the turning angle, and vice versa.

[0018] The algorithm formulas involved in the process of analyzing the UAV flight path based on the altitude cost in the path cost function in S2 are as follows: in, This represents the altitude cost corresponding to the drone's flight path; Let $\frac{i}{i+1}$ be the altitude cost of the path segment formed by the $i$-th path point and the $i+1$-th path point in the UAV's flight path. Let be the height of the path segment formed by the i-th path point and the (i+1)-th path point in the UAV's flight path above the ground. These represent the minimum and maximum height values, respectively.

[0019] The threat cost in the path cost function consists of the obstacle threat index and the weather threat index. The threats to UAVs during flight mainly include obstacle threats and weather threats. Obstacle threats are a common research subject, while this embodiment innovatively incorporates weather threats into the threat system. This can make up for the deficiency that UAVs cannot accurately plan routes when dealing with weather threats.

[0020] like Figure 4 As shown, the algorithm formulas involved in the analysis of the UAV flight path based on the obstacle threat index in the path cost function in step S2 are as follows: Where k represents the number of obstacles within the corresponding planning area; T k R represents the obstacle threat index during the process of a drone moving from the i-th path point to the (i+1)-th path point in its flight path; k Let S represent the diameter of the nearest obstacle during the drone's flight path from the i-th path point to the (i+1)-th path point; let S represent the minimum safe distance between the drone and the obstacle; and let d represent the radius of the circumscribed sphere of the drone. k This represents the distance between the center of the nearest obstacle and the point on the path from the i-th path point to the (i+1)-th path point in the drone's flight path.

[0021] The meteorological threat index includes wind speed threat, wind shear threat, turbulence index corresponding to turbulence threat, and icing index. Wind poses a direct threat to UAVs. When the wind speed is high and the direction is opposite to or at a large angle to the UAV, the speed and stability of the UAV will be greatly restricted. For ease of study, this embodiment directly uses wind speed data when characterizing the impact of wind on UAVs. When the resolution is insufficient, the wind speed is linearly interpolated according to the search node to fill the gap.

[0022] S3. The meteorological threat is quantitatively assessed by the fuzzy comprehensive evaluation method. The various threats are mapped to a unified meteorological threat index by the membership function and the threat level classification table. The meteorological threats include wind speed, wind shear, turbulence probability and icing index. S3 specifically includes: S31. Process the data and calculate the wind shear threat, turbulence probability and icing index of the corresponding UAV at the corresponding path nodes. S32, see reference as follows Figure 5 The preset threat level classification table shown will fuzz the calculated data to obtain a threat assessment for each meteorological factor; S33. Set a membership function to map the fuzzy evaluation obtained in step S32 to the threat level of the corresponding meteorological factors, and obtain the wind speed threat level, wind shear threat level, turbulence threat level and icing threat level of the corresponding UAV at the corresponding path nodes; the threat level includes level I corresponding to minor threat, level II corresponding to mild threat, level III corresponding to severe threat and level IV corresponding to disaster threat. S34. The meteorological threat index of the corresponding UAV at the corresponding path node is calculated by weighted summation. The weight coefficients corresponding to the wind speed threat level, wind shear threat level, turbulence threat level and icing threat level are respectively the preset first weight threshold, second weight threshold, third weight threshold and fourth weight threshold.

[0023] When obtaining the wind shear threat to the corresponding UAV, the horizontal wind speed scalar of each path node is obtained. The partial derivatives of the horizontal wind speed scalar of the corresponding path node in the X-axis, Y-axis and Z-axis directions in three-dimensional space are calculated by the finite difference method. Then the wind shear threat of the corresponding path node is equal to the square root of the sum of the squares of the partial derivatives of the horizontal wind speed scalar of the corresponding path node in the X-axis, Y-axis and Z-axis directions in three-dimensional space. The algorithm formula for obtaining the turbulence index experienced by the corresponding drone is as follows: Where L represents the turbulence index experienced by the corresponding UAV at the corresponding path node; This indicates the vertical wind shear of the corresponding drone at the corresponding path node; This represents the horizontal temperature gradient of the corresponding UAV at the corresponding path node. PG represents the horizontal wind speed gradient of the corresponding UAV at the corresponding path node; PG represents the turbulence threat of the corresponding UAV at the corresponding path node; the value of PG is equal to the turbulence probability of mapping the turbulence index L to the range (0,1). The algorithm formula for obtaining the icing index of the corresponding drone is as follows: Where F represents the icing index of the corresponding UAV at the corresponding path node; T represents the relative humidity of the corresponding UAV at the corresponding path node; and H represents the relative temperature of the corresponding UAV at the corresponding path node.

[0024] S4. An improved artificial potential field algorithm based on a fusion global path guidance mechanism, dynamic weight adjustment based on meteorological threats, three-dimensional height-layered potential field modeling, velocity potential field introduction, fuzzy logic control of potential field adjustment, and random disturbance and replanning strategy is used for path planning. The algorithm outputs a UAV flight path that meets multiple constraints and performs visualization verification in a simulation environment.

[0025] The improved artificial potential field algorithm in S4 includes: Introducing A The global path generated by the algorithm serves as a sequence of gravity-guided points. The meteorological threat index dynamically adjusts the weighting coefficient of the repulsive potential field; High-dimensional potential fields are modeled based on meteorological risk stratification; A velocity potential field term is introduced to predict threats ahead and respond in advance; The fuzzy logic controller adjusts the range and intensity of the potential field based on the threat level. When the potential gradient approaches zero, a random perturbation is applied, and a rolling window replanning mechanism is initiated.

[0026] This embodiment takes into account the limitations of traditional thermal potential field algorithms, mainly in that the conventional artificial potential field algorithm is a typical heuristic algorithm, which uses the repulsive force F of virtual obstacles on the drone. 斥 And the attractiveness of the target to the drone F 引 Methods to enable drones to automatically avoid obstacles and eventually approach the target (e.g.) Figure 6 (As shown). This method is characterized by its simple mathematical principles, fast computation speed, low hardware requirements, and ease of understanding. However, it also faces some challenges when dealing with more complex problems. For example, the environmental information on which this method is based is local, lacks global search capabilities, and is prone to getting trapped in local extrema and getting stuck in a cycle. Therefore, this embodiment attempts to improve the Artificial Potential Field (APF) algorithm so that the resulting algorithm can be more effectively applied to path planning in three-dimensional space.

[0027] The improvement strategies for the artificial potential field algorithm in this embodiment include: (1) Integrating global path guidance mechanism To solve the local minima problem, we introduced A in APF. Global path guidance for the algorithm. First, using A The algorithm plans an initial safe path in a three-dimensional grid space and uses it as a "guide path" in the potential field. In the gravitational function of the APF, not only the final target point is considered, but also the sequence of path points is introduced as an intermediate gravitational source, so that the UAV has a clear direction of escape when it gets stuck in a local minimum.

[0028] (2) Adjustment of dynamic potential field weights Based on the established fuzzy comprehensive evaluation system, we quantify meteorological threats such as wind speed, wind shear, turbulence probability, and icing index into dynamic weights and integrate them into the repulsive potential field function. Specifically, the weight coefficients of each threat are dynamically adjusted according to real-time meteorological data, so that the UAV experiences a stronger repulsive force in high-risk areas, thereby achieving adaptive obstacle avoidance and weather avoidance.

[0029] (3) High-dimensional potential field modeling For three-dimensional flight environments, we extended the altitude dimension of the traditional APF (Advanced Persistent Flight Parameter) and constructed a hierarchical potential field model. Different altitude layers correspond to different meteorological risk levels (such as low-altitude wind shear and high-altitude icing). By setting altitude-related potential field functions, we guide UAVs to select the optimal flight altitude layer, further improving the safety and economy of the flight path.

[0030] (4) Introduce velocity potential field and smoothness constraint To improve the feasibility and smoothness of the path, we introduce a velocity potential term into the potential field, enabling the UAV to respond to threats ahead in advance during high-speed flight. Simultaneously, combining the steering angle and altitude angle constraints in spherical vector encoding, we add an angle change penalty term during the potential field gradient descent process to prevent abrupt path changes or altitude fluctuations.

[0031] (5) Potential field adjustment of fuzzy logic control Using the established membership functions and threat level classification table, we designed a fuzzy logic controller to adjust the range and intensity of the potential field in real time. For example, when the probability of turbulence exceeds a threshold, the system automatically expands the repulsive force radius in that area, enhancing the UAV's avoidance capabilities.

[0032] (6) Random perturbation mechanism and reprogramming strategy To address the uncertainties in complex weather environments, we introduced the concept of simulated annealing, applying small-amplitude random perturbations when the potential field gradient approaches zero to help the UAV escape local minima. Simultaneously, a rolling window replanning mechanism was implemented, updating the potential field data at regular time steps to ensure the path is always based on the latest weather information.

[0033] Figure 7 and Figure 8 The paper presents a comparison of the three-dimensional paths planned using the traditional APF algorithm and the improved APF algorithm proposed in this study under the same complex terrain and weather conditions. Figure 9 This further presents the potential field distribution (total potential field distribution at 250m altitude (geographic coordinates)) and threat level visualization results of key nodes during the path planning process. Preliminary simulation results show that after introducing fuzzy evaluation of meteorological threats and a dynamic weight adjustment mechanism, the improved algorithm can significantly reduce the average meteorological threat value of the areas traversed by the flight path, demonstrating a stronger meteorological avoidance capability. Meanwhile, to avoid high-risk meteorological areas, the path length is slightly increased, reflecting the trade-off between safety and economy. These results preliminarily verify the effectiveness of the proposed modeling framework and improvement strategy, laying the foundation for the next stage of algorithm optimization and systematic experiments.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A path optimization method for an unmanned aerial vehicle (UAV) to cope with a weather threat based on fuzzy comprehensive evaluation, characterized in that, The method comprises the following steps: S1, three-dimensional modeling of the flight path of the unmanned aerial vehicle is performed by using a spherical vector coding mode, and a vector composed of an amplitude, a horizontal steering angle and a vertical height angle is used to describe the flight trajectory of the unmanned aerial vehicle in the three-dimensional space with a starting point as a reference; S2, the flight airspace in the three-dimensional space is discretized into a uniform grid structure, each grid node corresponds to a flyable position, and the unmanned aerial vehicle can select a next node set composed of 25 adjacent nodes centered on the current node from the current node at any time; a path cost function is constructed to analyze the planned flight path of the unmanned aerial vehicle, the path cost function comprises a distance cost, an angle cost, a height cost and a threat cost; S3, meteorological threats are quantitatively evaluated by using a fuzzy comprehensive evaluation method, each threat is mapped into a unified meteorological threat index by using a membership function and a threat grade division table, and the meteorological threats comprise a wind speed, a wind shear, a turbulence probability and an ice accumulation index; S4, the improved artificial potential field algorithm based on the fusion of a global path guiding mechanism, dynamic weight adjustment based on meteorological threats, three-dimensional height layered potential field modeling, velocity potential field introduction, fuzzy logic control potential field adjustment and random disturbance and re-planning strategies is used for path planning, a flight path of the unmanned aerial vehicle meeting multiple constraint conditions is output, and visual verification is performed in a simulation environment. 2.The path optimization method for an unmanned aerial vehicle to cope with a weather threat based on fuzzy comprehensive evaluation according to claim 1, characterized in that: The flight trajectory of the unmanned aerial vehicle is composed of a plurality of path points, and a path section formed by any two adjacent path points corresponds to a vector composed of an amplitude, a horizontal steering angle and a vertical height angle, and the amplitude represents the length of the corresponding path section; A horizontal turning angle in a path section vector corresponding to a path section formed by the i-th path point and the i+1-th path point in the flight path of the unmanned aerial vehicle is denoted as ; wherein, denotes the projection point of the i-1th waypoint in the horizontal plane of the UAV flight path; denotes the projection point of the ith waypoint in the horizontal plane of the UAV flight path; denotes the projection point of the i+1th waypoint in the horizontal plane of the UAV flight path; denotes denotes denotes the product of the respective vector lengths.​​​​​ A horizontal turning angle in a path segment vector corresponding to a path segment formed by the i-th path point and the i+1-th path point in the flight path of the unmanned aerial vehicle is denoted as ; wherein, represents a height value corresponding to the i+1th waypoint in the UAV flight path; represents a height value corresponding to the ith waypoint in the UAV flight path; represents corresponding vector length. 3.The method of claim 2, wherein, In the process of analyzing the flight path of the unmanned aerial vehicle according to the distance cost in the path cost function in S2, the algorithm formula involved is as follows: wherein, represents the distance cost corresponding to the flight path of the UAV; N represents the difference between the number of path points corresponding to the flight path of the UAV and 1; represents the length of the path segment formed by the ith path point and the (i+1)th path point in the flight path of the UAV; the i∈[1, N]. In the process of analyzing the flight path of the unmanned aerial vehicle according to the angle cost in the path cost function in S2, the algorithm formula involved is as follows: wherein, denotes an angle cost corresponding to the UAV flight path; denotes a horizontal turning angle cost corresponding to a path segment formed by the ith path point and the (i+1)th path point in the UAV flight path; denotes a height angle cost corresponding to a path segment formed by the ith path point and the (i+1)th path point in the UAV flight path; denotes a preset maximum horizontal turning angle value corresponding to the UAV flight path; denotes a horizontal turning angle in a vector corresponding to a path segment formed by the (i+1)th path point and the (i+2)th path point in the UAV flight path; denotes a preset maximum height angle value corresponding to the UAV flight path; μ1 denotes a weighting coefficient of the horizontal turning angle cost; and μ2 denotes a weighting coefficient of the height angle cost. In the process of analyzing the flight path of the unmanned aerial vehicle according to the height cost in the path cost function in S2, the algorithm formula involved is as follows: wherein, represents a height cost corresponding to the UAV flight path; is a height cost of a path segment formed by the i-th path point and the i+1-th path point in the UAV flight path, is a height of the path segment formed by the i-th path point and the i+1-th path point in the UAV flight path from the ground, respectively represent a set minimum and maximum height values.

4. The path optimization method for unmanned aerial vehicles to cope with weather threats based on fuzzy comprehensive evaluation according to claim 3, characterized in that: The threat cost in the path cost function is composed of an obstacle threat index and a meteorological threat index; In the process of analyzing the flight path of the unmanned aerial vehicle according to the obstacle threat index in the path cost function in S2, the algorithm formula involved is as follows: wherein k represents the number of obstacles in the corresponding planning area; T k represents the obstacle threat index in the process of moving from the i-th path point to the i+1-th path point in the flight path of the unmanned aerial vehicle; R k represents the diameter of the nearest obstacle in the process of moving from the i-th path point to the i+1-th path point in the flight path of the unmanned aerial vehicle; S represents the set minimum safety distance between the unmanned aerial vehicle and the obstacle; d represents the radius of the corresponding circumscribed sphere of the unmanned aerial vehicle; d k represents the distance from the center of the nearest obstacle in the process of moving from the i-th path point to the i+1-th path point in the flight path of the unmanned aerial vehicle.

5. The path optimization method for unmanned aerial vehicles to cope with weather threats based on fuzzy comprehensive evaluation according to claim 4, characterized in that, The meteorological threat index comprises a wind speed threat, a wind shear threat, a turbulence index corresponding to a turbulence threat and an ice accumulation index; S3 specifically comprises: S31, data is processed, and the wind shear threat, the turbulence probability and the ice accumulation index corresponding to the respective path nodes of the corresponding unmanned aerial vehicle are calculated; S32, the calculated data is fuzzed by referring to a preset threat grade division table to obtain a threat evaluation of each meteorological factor; S33, a membership function is set, the fuzzed evaluation obtained in step S32 is mapped into a threat grade of the corresponding meteorological factor to obtain a wind speed threat grade, a wind shear threat grade, a turbulence threat grade and an ice accumulation threat grade corresponding to the respective path nodes of the corresponding unmanned aerial vehicle; the threat grade comprises a slight threat corresponding to grade I, a mild threat corresponding to grade II, a serious threat corresponding to grade III and a disaster threat corresponding to grade IV. S34, the corresponding unmanned aerial vehicle is calculated in the corresponding path node corresponding to the weather threat index in the form of weighted sum; the weight coefficients corresponding to the wind speed threat level, the wind shear threat level, the bump threat level and the ice accretion threat level are respectively the first weight threshold, the second weight threshold, the third weight threshold and the fourth weight threshold. 6.The method for path optimization of UAV coping with weather threats based on fuzzy comprehensive evaluation according to claim 5, characterized in that: When the wind shear threat received by the corresponding unmanned aerial vehicle is obtained, the horizontal wind speed scalar of each path node is obtained, and the partial derivatives of the horizontal wind speed scalar of the corresponding path node in the X-axis direction, the Y-axis direction and the Z-axis direction in the three-dimensional space are calculated by the finite difference method, then the wind shear threat of the corresponding path node is equal to the square root of the sum of squares of the partial derivatives of the horizontal wind speed scalar of the corresponding path node in the X-axis direction, the Y-axis direction and the Z-axis direction in the three-dimensional space; The algorithm formula related to the bump index received by the corresponding unmanned aerial vehicle is as follows: wherein L represents a bumpiness index experienced by the respective drone at the respective path node; represents a vertical shear of the corresponding wind speed at the respective path node for the respective drone; represents a horizontal temperature gradient at the respective path node for the respective drone; represents a horizontal wind speed gradient at the respective path node for the respective drone; PG represents a bumpiness threat at the respective path node for the respective drone; the value of PG is equal to a bumpiness probability mapping the bumpiness index L to the interval (0, 1); The algorithm formula related to the ice accretion index received by the corresponding unmanned aerial vehicle is as follows: Wherein, F represents the ice accretion index corresponding to the corresponding path node of the corresponding unmanned aerial vehicle; T represents the relative humidity corresponding to the corresponding path node of the corresponding unmanned aerial vehicle; H represents the relative temperature corresponding to the corresponding path node of the corresponding unmanned aerial vehicle.

7. The path optimization method for unmanned aerial vehicles to cope with weather threats based on fuzzy comprehensive evaluation according to claim 1, characterized in that: The improved artificial potential field algorithm in S4 includes: Introduction A The algorithm generates a global path as a sequence of gravitational attractors. The weight coefficient of the repulsive potential field is dynamically adjusted according to the weather threat index; The height potential field is modeled according to the weather risk layering; The velocity potential field term is introduced to predict the threat in front and respond in advance; The fuzzy logic controller adjusts the range and intensity of the potential field according to the threat level; When the gradient of the potential field approaches zero, a random disturbance is applied, and a rolling window re-planning mechanism is started.

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