Unmanned aerial vehicle path planning method and system capable of resisting environmental disturbance

Abstract

The invention discloses an anti-environmental disturbance unmanned aerial vehicle path planning method and system, and relates to the technical field of unmanned aerial vehicle path planning. Acquiring a terrain model of a flight area of the unmanned aerial vehicle, dividing and setting a static threat area in the terrain model, and initializing a starting point coordinate and a terminal point coordinate of the unmanned aerial vehicle; respectively constructing a global disturbance environment mathematical model and a local disturbance area mathematical model; performing adaptive correction on the motion model of the unmanned aerial vehicle to reflect the actual motion state of the unmanned aerial vehicle in the disturbance environment; adding a disturbance quality evaluation function in a dynamic window method for random influence of a global disturbance environment on the position of the unmanned aerial vehicle; a dangerous distance evaluation function is introduced for the regularity influence of a local disturbance area on the position of the unmanned aerial vehicle, the unmanned aerial vehicle close to a local disturbance center is dynamically punished through the function, and the flight stability of the unmanned aerial vehicle and the decision-making ability of autonomous separation from the disturbance area are both considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system resisting environmental disturbance. BACKGROUND

[0002] In recent years, with the continuous improvement of the autonomous ability and intelligent level of unmanned aerial vehicles, they have been widely applied in scenarios such as regional patrol, photogrammetry, smart agriculture, and disaster monitoring, which require a lot of manpower and resources. As a basic supporting technology for the application of unmanned aerial vehicles, the core goal of unmanned aerial vehicle path planning technology is to generate an optimal collision-free flight path for unmanned aerial vehicles in complex environments.

[0003] However, in actual application, unmanned aerial vehicles are often in an environment with incomplete or dynamic information, and part of the map information is unknown. Under this background, it is urgent to build a path planning method that takes into account the global path optimality and local dynamic obstacle avoidance capability. Based on this demand, the industry generally adopts the strategy of combining global path planning and local path planning, such as Chinese patent documents with publication numbers CN119690092A and CN115328208A. Among them, global path planning is responsible for the overall planning of the macro path, providing the overall flight direction guidance for the unmanned aerial vehicle; local path planning then dynamically adjusts the path in the path execution process to adapt to environmental changes.

[0004] Dynamic Window Approach (DWA) is a commonly used method in the field of local path planning for unmanned aerial vehicles, but this method still has obvious shortcomings in dynamic and complex disturbance environments. Traditional DWA samples the velocity space under the premise of "fixed linear and angular velocity", and the ideal optimal path combination calculated by it often deviates from the actual optimal path in the disturbance environment, which may result in path detours, local optimal dilemma, poor stability, low robustness and other problems. SUMMARY

[0005] The purpose of the present application is to provide an unmanned aerial vehicle path planning method and system resisting environmental disturbance, which can effectively enhance the adaptability of the dynamic window method in the disturbance environment, has the function of evaluating and selecting the disturbance quality, and can significantly improve the stability and robustness.

[0006] According to a first aspect of the embodiments of the present disclosure, an unmanned aerial vehicle path planning method resisting environmental disturbance is provided, comprising the following steps:

[0007] Obtaining a terrain model of the flight area of the unmanned aerial vehicle, dividing and setting a static threat area in the terrain model, and initializing the start point coordinates and end point coordinates of the unmanned aerial vehicle;

[0008] A global disturbance environment mathematical model and a local disturbance region mathematical model are respectively constructed to quantitatively represent the influence of different types of environmental disturbances on the flight of the unmanned aerial vehicle;

[0009] Based on the global disturbance environment mathematical model and the local disturbance region mathematical model, the motion model of the unmanned aerial vehicle is adaptively corrected to reflect the actual motion state of the unmanned aerial vehicle in the disturbed environment;

[0010] A double-scene disturbance dynamic window method DDR-DWA is designed: for the random influence of the global disturbance environment on the position of the unmanned aerial vehicle, a disturbance quality evaluation function is added to the dynamic window method, the consistency of the disturbance offset and the ideal sampling result is measured through the function, and then the stability of the unmanned aerial vehicle in the global disturbance environment is enhanced; for the regular influence of the local disturbance region on the position of the unmanned aerial vehicle, on the basis of the disturbance quality evaluation function, a dangerous distance evaluation function is introduced, the unmanned aerial vehicle close to the local disturbance center is dynamically punished through the function, and the flight stability of the unmanned aerial vehicle and the decision-making ability of autonomously leaving the disturbance region are considered.

[0011] According to a second aspect of the embodiments of the present disclosure, an unmanned aerial vehicle path planning system against environmental disturbances is provided, comprising:

[0012] An initialization module obtains a terrain model of a flight region of the unmanned aerial vehicle, divides and sets a static threat region in the terrain model, and initializes a start point coordinate and an end point coordinate of the unmanned aerial vehicle;

[0013] A disturbance environment modeling module respectively constructs a global disturbance environment mathematical model and a local disturbance region mathematical model to quantitatively represent the influence of different types of environmental disturbances on the flight of the unmanned aerial vehicle;

[0014] A dynamic window method improvement module comprises:

[0015] A motion model correction unit adaptively corrects the motion model of the unmanned aerial vehicle based on the global disturbance environment mathematical model and the local disturbance region mathematical model to reflect the actual motion state of the unmanned aerial vehicle in the disturbed environment;

[0016] A DDR-DWA design unit adds a disturbance quality evaluation function to the dynamic window method for the random influence of the global disturbance environment on the position of the unmanned aerial vehicle, measures the consistency of the disturbance offset and the ideal sampling result through the function, and then enhances the stability of the unmanned aerial vehicle in the global disturbance environment; for the regular influence of the local disturbance region on the position of the unmanned aerial vehicle, on the basis of the disturbance quality evaluation function, a dangerous distance evaluation function is introduced, the unmanned aerial vehicle close to the local disturbance center is dynamically punished through the function, and the flight stability of the unmanned aerial vehicle and the decision-making ability of autonomously leaving the disturbance region are considered.

[0017] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and run on the memory, wherein the processor implements the method for path planning of an unmanned aerial vehicle against environmental disturbances when executing the program.

[0018] According to a fourth aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, having a computer program stored thereon, wherein the program is executed by a processor to implement the method for path planning of an unmanned aerial vehicle against environmental disturbances.

[0019] The above technical solutions adopted by the present application have the following advantages compared with the prior art:

[0020] 1. The present application introduces a disturbance quality evaluation function, which can effectively identify the direction and intensity of the disturbance. In the trajectory sampling process, trajectories that are consistent with the ideal flight path direction and are beneficial to the disturbance are preferentially selected, thereby significantly suppressing the small fluctuations and oscillations in the path. This mechanism greatly improves the smoothness of the path, making the generated trajectory more consistent with the actual dynamics of the unmanned aerial vehicle and the flight requirements.

[0021] 2. The present application can dynamically adjust the flight behavior of the unmanned aerial vehicle by constructing a dangerous distance evaluation function, and actively avoids the disturbance center or high-risk area. In a local disturbance environment, this mechanism greatly reduces the risk of the unmanned aerial vehicle falling into a local disturbance trap, and improves the adaptability to the environment and the safety of flight.

[0022] 3. The present application not only avoids the adverse effects of disturbances, but also "borrows the disturbance" at appropriate positions to achieve auxiliary propulsion by sampling trajectories consistent with the disturbance direction, thereby improving energy utilization efficiency and shortening path length, and exhibiting intelligent behavior of "rational use of disturbance", which is different from the traditional method of regarding disturbance as an obstacle.

[0023] 4. In the simulation of multiple groups of disturbance environments, the present application presents shorter path length and fewer iteration times. This advantage is due to the guiding effect of the disturbance evaluation mechanism on high-quality sampled trajectories, which improves the rationality of the sampled trajectories from the source, avoids frequent redundant correction and path rollback, and thereby improves the overall path planning efficiency.

[0024] 5. Compared with the traditional DWA method, the present application shows stronger adaptability in an environment with double local disturbance regions and dynamic obstacles. The dual mechanism based on disturbance perception and position strategy joint regulation enables the unmanned aerial vehicle to maintain control over the global path in a complex disturbance field, and exhibits excellent stability and robustness. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an implementation of the application and, together with the description, serve to explain the application.

[0026] Figure 1 is a schematic diagram of an environment gridding provided by the present application;

[0027] Figure 2 is a schematic diagram of a local disturbance scenario shown by an embodiment of the present application;

[0028] Figure 3 is a schematic diagram of different speed combinations shown by an embodiment of the present application;

[0029] Figure 4 is a schematic diagram of a typical combination sampling trajectory shown by an embodiment of the present application;

[0030] Figure 5 is a schematic diagram of a disturbance quality evaluation shown by an embodiment of the present application;

[0031] Figure 6 is a flowchart of a DDR-DWA shown by an embodiment of the present application;

[0032] Figure 7 is a schematic diagram of a simulation environment grid shown by an embodiment of the present application;

[0033] Figure 8 is a route map of different methods in an environment with a global disturbance and a single local disturbance region shown by an embodiment of the present application;

[0034] Figure 9 is a speed change curve in an iterative process of different methods in an environment with a global disturbance and a single local disturbance region shown by an embodiment of the present application;

[0035] Figure 10 is a route map of different methods in an environment with a global disturbance and multiple local disturbance regions shown by an embodiment of the present application; Figure 11 is a speed change curve in an iterative process of different methods in an environment with a global disturbance and multiple local disturbance regions shown by an embodiment of the present application;

[0036] Figure 12 is a route map of different methods in an environment with a global disturbance, a single local disturbance region, and dynamic obstacles shown by an embodiment of the present application;

[0037] Figure 13 is a speed change curve in an iterative process of different methods in an environment with a global disturbance, a single local disturbance region, and dynamic obstacles shown by an embodiment of the present application. DETAILED DESCRIPTION

[0038] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0040] It is also to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0041] It should be noted that the flowchart and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should also be noted that each block in the flowchart and block diagrams and / or combinations of blocks in the flowchart and block diagrams can be implemented by a combination of hardware and software, as will be appreciated by those skilled in the art. Furthermore, the flowchart and block diagrams in the drawings show the possible implementation of the methods and systems according to the embodiments of the present disclosure. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each of the blocks of the flowchart and / or block diagrams and combinations of blocks in the flowchart and / or block diagrams can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and

[0042] Embodiment one:

[0043] The embodiment provides an unmanned aerial vehicle path planning method resistant to environmental disturbances, comprising the following steps:

[0044] S1. Obtain a terrain model, set a static threat area, and initialize the starting point and ending point of the unmanned aerial vehicle;

[0045] Specifically, first, a high-precision terrain model of the unmanned aerial vehicle task area is obtained, and the terrain data is analyzed and processed with the aid of geographic information tools; then, according to the task requirements and environmental characteristics, the static threat area such as buildings, no-fly zones, etc. is accurately delineated and set in the terrain model; finally, the starting point coordinates and ending point coordinates of the unmanned aerial vehicle are initialized and set in combination with the task target and actual scene conditions, providing the basic environmental and task parameters for subsequent path planning.

[0046] S2. Disturbance environment modeling, design a simplified and efficient global disturbance environment and local disturbance area mathematical model;

[0047] As shown in Figure 1 , the binary nature of the grid method can conveniently divide the planning area and obstacles. To simulate the flight state of the unmanned aerial vehicle and simplify the obstacle-related processing, the grid method is used to model the environment. On this basis, a two-dimensional rectangular coordinate system is established to facilitate the implementation of the unmanned aerial vehicle path planning task.

[0048] In actual application scenarios, the flight of the unmanned aerial vehicle will be disturbed by various external factors such as air flow and wind force, resulting in a deviation between the actual flight trajectory and the predicted trajectory derived by the ideal linear velocity and angular velocity. Therefore, the existing local path planning scheme based on the dynamic window method (DWA) often has a certain degree of adaptability gap between the actual operation effect and the theoretical model when facing a disturbed environment, and it is difficult to fully meet the flight requirements in a disturbed environment. Considering that the global disturbance environment (such as turbulent wind, gust, etc. covering the entire flight area) itself has high complexity and randomness, directly modeling it in detail will greatly increase the complexity of the method and reduce the operation efficiency, and is not conducive to the verification of the method effect. Based on this, the present application abstracts the disturbance environment as a random model in a two-dimensional coordinate system, simulates various disturbance factors that affect the trajectory of the unmanned aerial vehicle in a non-ideal environment through the model, and ensures the reasonableness of the disturbance simulation while taking into account the verifiability and operation efficiency. The global disturbance environment mathematical model is constructed by disturbance velocity v area (t) and disturbance core angle θ area (t), and is represented as:

[0049]

[0050] Wherein, v max area is the global disturbance velocity peak; θ max area is the global disturbance core angle; Rand represents a uniformly distributed random number in the interval; w0 is π / 6; the sine function is added to increase the periodic modulation on the basis of random disturbance, so as to simulate the periodic random change in the disturbance environment.

[0051] As shown in Figure 2 , to simulate more complex environmental disturbances, the present application further constructs a model of a local strong disturbance scene on the basis of the global disturbance environment, typical scenes including small-scale atmospheric circulation, dust devil, etc. The local disturbance area is distributed in a circle, and in the same horizontal plane, the disturbance velocity has significant radial and tangential coupling characteristics.

[0052] The local strong disturbance scene set by the application draws lessons from the local wind field structure of two-dimensional atmospheric circulation: specifically, there is a certain strength of centripetal contraction movement around the disturbance center, and a tangential circulation rotating in the counterclockwise direction. Considering that the modeling process of real atmospheric circulation is extremely complex and there are many uncertain factors, if the real modeling method is directly used, it will greatly increase the operation load of the method and waste computing resources. Therefore, in order to improve the overall efficiency and save computing resources for the local path planning method, the application simplifies the modeling of the local disturbance scene: the disturbance area is set as a circular area with a fixed position as the center and a limited radius, and the arrow direction in the figure is used to represent the direction of the disturbance speed.

[0053] The tangential velocity v of the local disturbance area tang (t) follows the nonlinear principle and can be expressed as a variable of the radius r from the disturbance center:

[0054]

[0055] Wherein, v max-a represents the maximum disturbance speed in the local disturbance area, r represents the distance from the current position to the disturbance center, R peak is the distance from the disturbance speed peak position to the disturbance center, R max represents the maximum radius of the local disturbance area. The expression ensures that the tangential velocity reaches a peak near r=R peak , and gradually decreases to 0 at the disturbance boundary, simulating the speed distribution characteristics of the scale disturbance. In order to reflect the centripetal motion characteristics of the local disturbance area, the radial velocity can be expressed as:

[0056] v radial (t) = C·v tang (t) (3)

[0057] Wherein, C is a constant proportional coefficient, which determines the strength of the centripetal contraction. In order to express the influence of the local disturbance area in the rectangular coordinate system, the tangential velocity and the radial velocity are combined into a two-dimensional velocity vector. The disturbance speed v local (t) in the local disturbance area and the direction angle θ local (t) are as follows:

[0058]

[0059] S3. Dynamic window method improvement and path planning:

[0060] The Dynamic Windowing Algorithm (DWA) implements local path planning and autonomous obstacle avoidance by discretely sampling the drone's current speed, position, and motion parameters and dynamically planning them. Within this method's logical framework, the drone's trajectory consists of a series of straight line segments generated by sampling velocity control. Its motion form (such as trajectory curvature and direction) is primarily determined by the combination of linear velocity and angular velocity.

[0061] During velocity sampling, linear and angular velocities must simultaneously meet multiple constraints: they must comply with the drone's physical performance limitations (including maximum / minimum linear velocity, maximum / minimum angular velocity, maximum acceleration / deceleration, and maximum angular acceleration / deceleration), and they must also be constrained by the "reachable range of motion" under the current motion state (i.e., they must match the speed variation range supported by the drone's current position and attitude). Therefore, within the set time resolution t, speed variations are confined to a specific range, ultimately resulting in a finite number of viable speed combinations.

[0062] By discretely sampling the velocity space, the dynamic window method maps all feasible velocity combinations into a set of corresponding candidate trajectories, and then evaluates and selects these trajectories: the sampled trajectory generated by the optimal velocity combination is used as the execution trajectory of the drone path planning at the next moment. Through such successive iterations, the entire path planning process is finally completed. Figure 3 The distribution of trajectories generated by different speed combinations is shown. Figure 4 This is a schematic diagram of the sampling trajectory corresponding to the typical speed combination.

[0063] Specifically, the dynamic window method achieves real-time path planning through three core steps: the first step is speed sampling, which generates potential feasible speed combinations based on the above constraints; the second step is trajectory prediction, which performs forward simulation on each speed combination based on the UAV motion model to infer its possible flight trajectory in the future; the third step is trajectory selection, which quantitatively scores all candidate trajectories through an evaluation function combined with multi-dimensional factors (such as the directional deviation between the trajectory and the target point, the distance to the obstacle, the flight speed, etc.), and finally selects the trajectory with the highest score as the execution path for the current iteration cycle.

[0064] ①Speed ​​sampling space

[0065] The dynamic window method has three different constraints to limit the speed of the UAV. Although there are many combinations of linear velocity and angular velocity (v, ω) in theory, only a few of them meet the UAV dynamic and kinematic constraints and are practically feasible. Therefore, to ensure the effectiveness and feasibility of trajectory generation, it is necessary to reasonably limit the speed sampling range. Let V m represents the set of feasible velocities that satisfy the UAV's dynamic and kinematic constraints at the current moment. The sampling speed under this constraint can be expressed as:

[0066] V m = {(v, ω) | v e (v min , v max ), ω e (ω min , ω max )} (6)

[0067] where v min denotes the minimum linear velocity; v max denotes the maximum linear velocity; ω min denotes the minimum angular velocity; and ω max denotes the maximum angular velocity. In an iteration period, let T denote the sampling number, the velocity range that the UAV can reach in the period is also dynamically limited by the maximum acceleration and the maximum deceleration on the velocity variation amplitude. Let V d denote the feasible velocity set satisfying the UAV acceleration and deceleration constraints at the current time, the sampling velocity under the constraints can be expressed as:

[0068] V d = {(v, ω) | v e (v - v b · T, v + v a · T), ω e (ω - ω b · T, ω + ω a · T)} (7)

[0069] where v a denotes the maximum linear acceleration; v b denotes the maximum linear deceleration; ω a denotes the maximum angular acceleration; and ω b denotes the maximum angular deceleration. In order to ensure that the UAV can brake in time and maintain a sufficient safety distance when approaching the obstacle, a constraint condition must be set for the braking velocity of the UAV. The constraint ensures that the UAV can complete deceleration braking within the remaining distance to avoid collision. Let V a denote the feasible velocity set satisfying the UAV braking velocity constraint at the current time, the sampling velocity under the constraints can be expressed as:

[0070]

[0071] where d obs denotes the minimum Euclidean distance between the UAV and the obstacle.

[0072] ② UAV motion model

[0073] The dynamic window approach generates a corresponding trajectory for each sampling speed based on the motion model, and therefore the motion model of the UAV needs to be established. Considering that the interval between adjacent sampling time points is short and the displacement is small, the motion of the UAV can be approximated as uniform linear motion. Under this assumption, the pose (x(t), y(t), θ(t)) of the UAV at time t can be represented as:

[0074]

[0075] where v(t) represents the linear velocity of the UAV at time t; ω(t) represents the linear velocity of the UAV at time t; and θ(t) represents the heading angle of the UAV at time t.

[0076] ③Evaluation function

[0077] On the basis of the motion model of the UAV and the velocity sampling space, the trajectories generated by each sampling speed are quantitatively scored using the evaluation function, and the optimal trajectory and its corresponding speed combination are selected according to the evaluation results for the actual path execution of the UAV. The evaluation function can be represented as:

[0078] G(v, ω) = σ (a head(v, ω) + β dist(v, ω) + γ vel(v, ω)) (10)

[0079] where σ represents the normalization process; head(v, ω) represents the azimuth angle evaluation function, which is used to evaluate the angle difference between the end point of the trajectory and the target point, and the smaller the angle difference, the higher the score; dist(v, ω) represents the obstacle distance evaluation function, which is used to evaluate the minimum distance between the UAV and the obstacle, and the larger the distance, the higher the score; vel(v, ω) represents the speed evaluation function, which is used to evaluate the flight speed of the UAV, and the larger the speed, the higher the score; and a, β, and γ are the weights of the above three evaluation functions.

[0080] The advantage of DWA is its adaptability to dynamic environments. Through real-time path planning, the UAV can efficiently and safely navigate in complex scenarios. The final output of this method, i.e., the linear and angular velocities of the optimal trajectory, directly affects the motion attitude and direction of the UAV, thereby ensuring the robustness and reliability of the system in various situations.

[0081] S31. Dynamic window method and its improvement, according to the disturbed environment, the motion model of the UAV is modified;

[0082] In view of the many problems existing in DWA in the disturbed environment, the DWA is improved.

[0083] Unmanned aerial vehicle motion model revision: in complex flight environment, environmental disturbance can cause the position of unmanned aerial vehicle trajectory sampling target point to deviate from the ideal expected position. The position deviation puts forward higher requirements for the dynamic response speed, trajectory correction accuracy and algorithm robustness of dynamic window method (DWA). In order to solve the problem, the invention is based on the unmanned aerial vehicle motion model, adopts the dynamic expression derived from the translation equation and Newton's law of motion, and constructs the correction model specially used for representing the influence of disturbance on unmanned aerial vehicle. Considering that the influence mechanism of disturbance on the flight attitude angle (such as pitch angle, roll angle) and angular velocity of unmanned aerial vehicle is extremely complex, if it is included in modeling, the calculation complexity will be greatly increased, therefore, the model temporarily ignores such influencing factors, in order to preferentially guarantee the operation efficiency of the whole method, and ensure that the real-time path planning requirements can be met. In a single sampling, the position offset (Δx area (t),Δy area (t)) of unmanned aerial vehicle caused by global disturbance environment and the position offset (Δx local (t),Δy local (t)) of unmanned aerial vehicle caused by local disturbance region, i.e. disturbance offset, can be represented as:

[0084]

[0085] Therefore, the sampling position (x area (t),y area (t)) of unmanned aerial vehicle under the influence of global disturbance environment and the sampling position (x local (t),y local (t)) of unmanned aerial vehicle under the influence of local disturbance region can be represented as:

[0086]

[0087] Since the disturbance speed in the local disturbance region shows a certain spatial regularity, in order to more accurately depict the actual motion state of unmanned aerial vehicle, the concept of composite linear speed v combine is introduced. It can be represented as:

[0088] v combine (t)=v(t)+v o ·cos(θ(t)-θ local (t)) (15)v combine can approximately reflect the consistency of the linear speed of unmanned aerial vehicle and the local disturbance speed, and the greater the speed, the higher the consistency, which is helpful for subsequent simulation analysis and evaluation of the performance of the method.

[0089] S32. A dual disturbance-resistant dynamic window approach (DDR-DWA) is designed to cope with the random influence of global disturbance environment on the position of the UAV. A disturbance quality evaluation function is added to enhance the stability of the UAV flying in the disturbance environment. A dangerous distance evaluation function is additionally introduced to cope with the regular influence of local disturbance area on the position of the UAV, and the stability of the UAV and the autonomous decision-making ability are taken into account.

[0090] In a disturbance environment, the evaluation of the trajectory of the UAV should not only depend on its own motion state, but also fully take into account the influence of disturbance factors on the flight trajectory. This is because the disturbance will directly change the actual flight path of the UAV, and the evaluation based on the state of itself cannot reflect the true trajectory quality.

[0091] The traditional dynamic window approach (DWA) is based on the deterministic motion model in an ideal environment to carry out trajectory prediction and scoring, and does not consider external dynamic factors such as disturbance, which leads to the problem that the "optimal" trajectory selected by it is easy to deviate from the target point, or excessively close to the obstacle, or even may cause the flight efficiency to decrease and even safety risks due to the sharp increase in attitude control difficulty.

[0092] In view of the fact that the disturbance environment will significantly change the actual trajectory of the UAV, in order to improve the flight stability of the UAV in the disturbance environment, the present application adds a disturbance quality evaluation function to the original evaluation function of DWA. The core function of this function is to measure the consistency of "the position deviation caused by disturbance" and "the ideal sampling result", so as to cope with the problem that the path may deviate greatly from the original predicted path due to disturbance.

[0093] It should be noted that the evaluation function of the traditional DWA is based on the score of the end point of the last iteration in the iteration period, and after the best trajectory and speed combination are selected, the linear speed and angular speed that can reach the end point of the sampling are used to continue flying. If in a disturbance environment, the score benchmark of the disturbance quality evaluation function is still set as "the end point of this iteration", a large error may be caused after the superposition of multiple disturbances. In order to avoid this problem, the evaluation logic is adjusted: the consistency between "the disturbance deviation of the first sampling" and "the ideal trajectory of the second sampling" in the current iteration period is verified to enhance the stability of local path planning, and the corresponding disturbance quality evaluation function disturb(v, ω) is:

[0094]

[0095] Wherein, (Delta x (1), Delta y (1)) represents the disturbance offset at the initial sampling, (x (1), y (1)) represents the ideal initial sampling position of the unmanned aerial vehicle, (x (2), y (2)) represents the ideal second sampling position of the unmanned aerial vehicle. The deviation degree of the disturbance offset and the ideal trajectory is judged by calculating the cosine value of the angle between the disturbance offset and the trajectory, close to 1 indicates that the disturbance direction is close to the ideal motion trajectory, close to-1 indicates that the disturbance deviates from the ideal trajectory direction, and it is easy to produce a larger disturbance to the subsequent flight of the unmanned aerial vehicle. The disturbance quality evaluation schematic diagram is shown in Figure 1. Figure 5

[0096] In the global disturbance environment, the dynamic window method evaluation function of formula (10) can be modified as:

[0097] G1 (v, omega) = sigma (alpha * head (v, omega) + beta * dist (v, omega) + gamma * vel (v, omega) + epsilon * disturb (v, omega)) (17)

[0098] Wherein, epsilon represents the weight of the disturbance quality evaluation function.

[0099] The application proposes an improved scheme of evaluation function fusing disturbance consistency judgment aiming at the problem of insufficient path stability of traditional dynamic window method (DWA) in the disturbance environment. The scheme breaks through the limitation of taking the "sampling end point in the iteration period" as the only scoring basis of the traditional method, and innovatively introduces the "consistency measurement between continuous sampling trajectories" mechanism: by quantitatively evaluating the matching degree of adjacent sampling trajectories, the disturbance on the trajectory selection is effectively reduced, and the stability of the path selection process and the robustness of the whole algorithm are significantly enhanced.

[0100] In the local disturbance area, the unmanned aerial vehicle will be affected by the disturbance with regularity: the deeper the unmanned aerial vehicle goes into the local disturbance area, the lower the probability of successfully leaving the area, and the problem of flight out of control and falling into local optimal solution is prone to occur. In order to solve this problem, the application further introduces a dangerous distance evaluation function on the basis of the evaluation function shown in formula (17). The core function of the function is to enhance the ability of the unmanned aerial vehicle to leave the local disturbance area, and the design logic is that the closer the distance between the unmanned aerial vehicle and the center of the local disturbance area, the greater the punishment degree of the function, so as to forcibly guide the unmanned aerial vehicle away from the disturbance core and ensure the safety of the autonomous flight of the unmanned aerial vehicle. The corresponding dangerous distance evaluation function is shown in the following formula:

[0101]

[0102] Wherein, K is a proportional coefficient, and the greater the value of K represents the greater the punishment degree with the decrease of r. In the local disturbance area, the dynamic window method evaluation function of formula (17) can be modified as:

[0103] ​

[0104] where ζ represents the weight of the dangerous distance evaluation function. Combining it with the global disturbance evaluation function, the dual-scenario disturbance-resistant dynamic window method (DDR-DWA) can be obtained, which can be expressed as:

[0105]

[0106] where Ω cyclone represents the influence range of the local disturbance area.

[0107] Preferably, the present application combines the nodes in the global path planning of the LazyTheta* method as the path reference points of DDR-DWA on the basis of the above improvement, and the implementation process of the specific method is as shown in Figure 6 .

[0108] In summary, the dual-scenario disturbance-resistant dynamic window method (DDR-DWA) based on the disturbance perception and evaluation mechanism provides a more efficient and more robust solution for the path planning of unmanned aerial vehicles in complex disturbance environments. First, by introducing the disturbance quality evaluation function, DDR-DWA can effectively screen the trajectory sampling results, suppress the small fluctuations in the flight process, and significantly improve the path smoothness and flight stability. Second, the addition of the dangerous distance evaluation function enhances the ability of the method to cope with local strong disturbance areas, reduces the risk of the unmanned aerial vehicle being trapped in the disturbance center, and further improves the robustness and safety of the path planning. At the same time, the method can also make full use of disturbance resources to achieve "borrowing flight" under reasonable guidance, optimize the path length and energy utilization efficiency; and by improving the trajectory sampling quality through the disturbance guiding mechanism, the overall convergence speed of the method is also faster and the path redundancy is less.

[0109] To evaluate the performance of the dual-scenario disturbance-resistant dynamic window method (DDR-DWA), the present application carries out experimental simulation and result analysis under the control conditions of "same terrain environment" and "uniform initial information of unmanned aerial vehicles" to ensure the fairness of comparison and the reliability of conclusions.

[0110] The hardware and software environment parameters of this simulation are as follows: the operating system uses Windows 1164 bits, the device memory is 16 GB, the central processing unit is i5-1135G7 (clock frequency 2.4 GHz), and the simulation software selects MATLAB R2016a. The specific settings of the simulation environment are referred to Figure 7(Schematic diagram of simulation environment grid): The overall size of the environment is 40x40m, in which the black block represents static obstacles; the purple triangle represents dynamic obstacles, and the red line marks the uniform motion route from the starting point to the terminal point; the dotted line is the global static path planned by the LazyTheta* algorithm based on the grid map, which serves as the global reference benchmark for the path planning of the unmanned aerial vehicle. To further ensure the objectivity of the experiment, the standard parameters of all comparative methods are kept consistent: the starting point coordinates of the unmanned aerial vehicle are uniformly set to (4.5, 5.5), and the terminal point coordinates are uniformly set to (28.5, 32.5); the center coordinates of the local disturbance region are set to (14.5, 13.5), and the radius is set to 4m; the maximum number of iterations of each method is limited to 1000 times. In addition, the initial parameters of the unmanned aerial vehicle flight (such as maximum speed, acceleration and deceleration, etc.) are shown in Table 1.

[0111] Table 1 Initial information of unmanned aerial vehicle

[0112]

[0113] Comparison and analysis of global disturbance and single local disturbance region environment results: To verify the actual application effect of the method proposed in the application, the traditional dynamic window method DWA, the dynamic window method ObstacleDualDisturbance-ResistantDynamicWindowApproach, abbreviated as ODDR-DWA, which improves the dual-scene disturbance-resistant evaluation function but regards the local disturbance region as an obstacle, the dynamic window method Disturbance-ResistantDynamicWindowApproach, abbreviated as DR-DWA, which only improves the global disturbance-resistant evaluation function, and the dual-scene disturbance-resistant dynamic window method (DDR-DWA) proposed in the application are compared and experimented. The experimental environment is set to "global disturbance environment and single local disturbance region acting together", and the differences in path planning results of the four methods are analyzed.

[0114] Figure 8The path planning graphs of the four methods in the above environment are shown, and each method is as follows: traditional DWA: although it can complete the path planning task in the disturbed environment, the generated trajectory has more subtle fluctuations due to the absence of a disturbance quality evaluation function, and in multiple iterations after leaving the local disturbance area, there is obvious path redundancy. This shows that the traditional DWA has poor path quality in the disturbed environment, not only is the trajectory redundancy significant, but the robustness and stability of the method itself also have obvious limitations. ODDR-DWA: the small fluctuations of the path are significantly improved, and the overall trajectory is more stable. This method regards the local disturbance area with uncertain influence as an obstacle, which is a conservative strategy. By actively excluding potential dangerous areas to ensure flight safety, it reduces the risk of falling into local optimization to some extent. However, this strategy also leads to increased path redundancy, especially when the local disturbance range is large, the path redundancy problem is more prominent, making the overall path planning efficiency low and difficult to meet the demand for "efficient and robust" path planning. DR-DWA: the path quality in the global disturbance environment is improved compared to traditional DWA, but in the experiment, the UAV falls into local optimization. The reason is that this method only improves the global disturbance evaluation function, and lacks a dangerous distance evaluation function for the local disturbance area: in the local disturbance environment, although the disturbance quality evaluation function enables the method to "select a sampling trajectory with the same direction as the disturbance direction", the disturbance speed direction in the local disturbance area changes regularly in a "centripetal contraction" manner, eventually leading to the evolution of the UAV trajectory towards the disturbance source, and then falling into local optimization. This result not only shows the insufficient robustness and safety of DR-DWA in complex disturbance environments, but also confirms that in the local disturbance area, the disturbance quality evaluation function and the dangerous distance evaluation function should be used together to ensure flight safety while maintaining path stability. The invention DDR-DWA: since the method itself integrates dual-scene disturbance evaluation functions, in the global disturbance environment, the path quality is significantly improved compared to traditional DWA; in the local disturbance area, the distance between the UAV and the disturbance source is effectively avoided by the dangerous distance evaluation function, effectively avoiding the UAV falling into the local disturbance area. By comparing the path graphs, it can be seen that DDR-DWA not only has good path planning ability and direction guidance, but also significantly reduces path redundancy, and its robustness is significantly better than the other three comparison methods.

[0115] Table 2 Index data of results of different methods

[0116]

[0117] To further analyze the performance differences of the four methods in path planning, Figure 9The speed change curve in the flight process of the unmanned aerial vehicle is presented (the red line represents the angular velocity, and the blue line represents the linear velocity), and Table 2 lists the path planning result data corresponding to each method, and the specific analysis is as follows: the traditional DWA: in the iteration process, the disturbance factor is not considered, although the overall path planning can be completed at a high linear speed, but due to the lack of disturbance adaptation mechanism, a large number of small fluctuations appear in the path. Finally, the path generated by the traditional DWA has a total length of 38.44 m, and the path planning task is completed through 659 iterations. ODDR-DWA: although there is a certain fluctuation in the linear velocity and the angular velocity, the overall path stability is significantly enhanced. This method avoids the disturbance region as an obstacle to ensure the flight safety of the unmanned aerial vehicle, but also limits the path selection range. The total length of the generated path increases to 41.31 m, and 681 iterations are required to complete the task. It can be seen that although ODDR-DWA improves the local flight safety, it has obvious disadvantages in path efficiency. DR-DWA: in order to analyze the effect of the local disturbance resistance evaluation function, the synthetic speed v combine is used instead of its own linear speed for comparison in the experiment. The results show that DR-DWA gradually approaches the "maximum disturbance speed position" in the local disturbance environment, resulting in a continuous rise in v combine ; in this speed state, the flight trajectory of the unmanned aerial vehicle gradually follows the disturbance speed, and the attitude angle deviates from the target direction, finally the path planning task fails. This shows that although the DR-DWA introduces the disturbance quality evaluation function, it improves the adaptability to the global disturbance environment, but lacks sufficient safety constraint mechanism in the strong disturbance environment, and cannot guarantee the smooth completion of the flight task. DDR-DWA of the application: when the unmanned aerial vehicle enters the local disturbance region, the synthetic speed v combine steadily rises and tends to be stable, and the unmanned aerial vehicle does not reach the position of the maximum disturbance speed. Although it may be slightly inferior to DR-DWA in disturbance utilization efficiency, it can finally safely escape from the local disturbance environment, fully embodying the dual consideration of "disturbance quality evaluation" and "body motion control" of DDR-DWA. Finally, DDR-DWA completes the task with a path length of 36.43 m and an iteration number of 574, showing the comprehensive advantages in path efficiency, robustness and disturbance environment adaptability.

[0118] In summary, combined with the comprehensive analysis of the path graph and the speed change curve, it can be seen that DDR-DWA realizes the dual goals of "path quality improvement" and "disturbance utilization efficiency optimization" in the disturbance environment, effectively avoids the path oscillation and local optimization problem, and fully verifies the better performance and practical application potential of the method in the complex disturbance environment.

[0119] Global disturbance and multiple local disturbance area environment result comparison analysis: in order to further test the performance of the method in more complex environment, the experiment adds a local disturbance area (the center coordinates are set as (24.5, 31.5)), and the path planning results of traditional DWA, ODDR-DWA, DR-DWA and DDR-DWA of the application are compared and analyzed.

[0120] Figure 10 The path planning diagrams of the four methods in the more complex environment of "double local disturbance area" are shown, and the performances of each method are as follows: traditional DWA: although it can complete the path planning task, the path has significant fluctuation characteristics; and after passing through the newly added local disturbance area, the situation of "missing the end point and turning back" occurs. The reason is that the traditional DWA takes "ideal sample speed combination" as input, and the strong disturbance of the local disturbance environment will cause large position deviation of the sample result; since the method does not consider the influence caused by disturbance, the unmanned aerial vehicle is forced to turn back after missing the target point, and finally a large amount of redundant path is formed, which not only reduces the planning efficiency and path quality, but also further highlights the limitations of traditional DWA in disturbance environment. ODDR-DWA: compared with traditional DWA, the path stability is improved, and the small fluctuation of the path is obviously inhibited. However, in the process of avoiding two local disturbance areas, more significant path redundancy is generated. Compared with the results in the environment of "single local disturbance area", the problem of redundant path is further aggravated. It can be seen that when the disturbance area is more complex and larger in range, the path planning efficiency of ODDR-DWA is obviously insufficient; further deduction can be made that if the local disturbance area covers most or even all of the feasible path between "start point-end point" in space, the method has the risk of planning failure, and may not be able to complete the path planning task. DR-DWA: due to the limitation of the method mechanism, the performance in the scene of "double local disturbance area" is similar to that in the scene of "single local disturbance area". After entering the local disturbance area, the unmanned aerial vehicle will still be in the local optimal state and cannot complete the path planning task. DDR-DWA of the application: even in the complex environment of "double local disturbance area", it can still generate a flight trajectory with "shorter path and smaller fluctuation", which fully shows the excellent disturbance adaptation ability and path optimization performance.

[0121] Table 3 index data of results of different methods

[0122]

[0123] Figure 11The speed change curve of the unmanned aerial vehicle in the four method planning processes is presented, and the path planning result data corresponding to each method is summarized in Table 3. The results in the “single local disturbance region” environment are compared and analyzed as follows: traditional DWA: relying on the original evaluation function to select the optimal sampling result, the overall linear speed is relatively stable, but in the local disturbance region, the linear speed and angular speed fluctuate obviously due to the position change caused by disturbance. Finally, the total length of the path planned by the traditional DWA is 42.13 m, and the iteration number is 778 times. Compared with the “single local disturbance region” environment, the path length increases by 3.69 m (9.59% increase), and the iteration number increases by 119 times (18.05% increase). The complexity of the disturbance environment significantly affects the planning efficiency and path quality. ODDR-DWA: because the local disturbance region is regarded as an obstacle to be avoided, the overall path is longer. The total length of the generated path is 45.98 m, and the iteration number is 838 times. Compared with the “single local disturbance region” environment, the path length increases by 4.67 m (11.30% increase), and the iteration number increases by 157 times (23.05% increase). The path redundancy problem is further aggravated with the increase of the number of disturbance regions, and the planning efficiency continues to decline. The DDR-DWA of the application: it exhibits more stable dynamic response characteristics in the disturbance environment, and successfully balances between “disturbance adaptation” and “path optimization”. The total length of the generated path is 36.90 m, and the iteration number is 523 times. Compared with the “single local disturbance region” environment, the path length only increases by 0.47 m (1.29% increase), and the iteration number decreases by 25 times (4.30% decrease), which is the best in path length control and planning efficiency.

[0124] In summary, in the more complex environment of “double local disturbance region”: the traditional DWA can complete path planning, but it performs poorly in disturbance adaptability, path quality and stability; ODDR-DWA regards the disturbance region as an obstacle, resulting in significant path redundancy and low efficiency; and the DDR-DWA proposed by the application, by considering disturbance evaluation and planning robustness, exhibits better performance and stronger practical value in various complex disturbance environments.

[0125] Comparison and analysis of results in the presence of dynamic obstacles, global disturbance and single local disturbance region environment:

[0126] To test the dynamic obstacle avoidance ability of each method in the disturbance environment, the traditional DWA, ODDR-DWA, DR-DWA and DDR-DWA of the application are placed in the “single local disturbance region + dynamic obstacle” composite environment to compare their path planning results. The dynamic obstacle setting rules for each method are as follows:

[0127] For DWA, DR-DWA, and DDR-DWA: To test the improved method's obstacle avoidance performance within a local disturbance region, a dynamic obstacle was placed within the local disturbance region, with its starting coordinates set to (17.5, 13.5) and its ending coordinates set to (14.5, 9.5). For ODDR-DWA: Because this method considers the local disturbance region an obstacle and cannot perform dynamic obstacle avoidance within it, the dynamic obstacle's motion range was adjusted to a global disturbance environment, with its starting coordinates set to (17.5, 12.5) and its ending coordinates set to (14.5, 8.5) to test the method's obstacle avoidance performance within a global disturbance environment.

[0128] Figure 12 The path planning results of four methods are presented: DWA, ODDR-DWA, and DDR-DWA all successfully avoid dynamic obstacles and complete the path planning task. While DR-DWA achieves dynamic obstacle avoidance, it gets stuck in a local optimum and fails to complete the path planning. As can be seen from the path diagrams, each method exhibits significant collision avoidance behavior. To further analyze the performance differences during obstacle avoidance, the following analysis will be conducted in conjunction with the speed curve.

[0129] Table 4 Index data of the results of different methods

[0130]

[0131] Figure 13The speed change curve of the UAV in the planning process of different methods is presented, and the total path length and iteration number corresponding to each method are given in Table 4. In combination with the results in the environment of “single local disturbance region and no dynamic obstacle”, the specific analysis is as follows: traditional DWA: during about the 200th to 300th iteration, the linear speed appears a significant drop, and the minimum is close to 0 m / s. The core reason for this phenomenon is that in the trajectory combination of the UAV at this stage, there is no feasible trajectory that can move away from the obstacle by adjusting the angle speed with high linear speed, and only the attitude angle can be adjusted by greatly reducing the linear speed. To delve into the root cause, the traditional DWA lacks effective evaluation of the disturbance offset; when the dynamic obstacle approaches, it cannot adjust the strategy in time in combination with the actual environmental disturbance and its own motion state, resulting in that the UAV is too close to the obstacle during the planning process, all high linear speed trajectories do not meet the preset braking speed constraint, and finally the attitude can only be adjusted by greatly reducing the speed or even temporarily stopping to ensure the safety of obstacle avoidance. Although this method finally completes the task, the number of path planning iterations increases significantly: compared with the environment of “no dynamic obstacle”, the total path length only increases by 0.21 m (an increase of 0.54%), but the iteration number increases by 86 times (an increase of 13.05%), which exposes obvious deficiencies in flight efficiency and safety. ODDR-DWA, DR-DWA and DDR-DWA: although the linear speed of the three methods also appears a phased decline during obstacle avoidance, they always maintain a certain speed margin, which reserves more dynamic adjustment space for sudden disturbances. With the effective overcoming of the disturbance influence and the dynamic adjustment of the control strategy, the three methods all guarantee the safety and continuity of the flight path. Among them, ODDR-DWA and DDR-DWA successfully complete the path planning task, and compared with the results in the environment of “no dynamic obstacle”: ODDR-DWA total path length increases by 0.83 m (an increase of 2.00%), and the iteration number increases by 35 times (an increase of 5.13%); DDR-DWA total path length increases by 0.40 m (an increase of 1.09%), and the iteration number increases by 26 times (an increase of 4.52%). Although the path growth rate of the two is slightly higher than that of the traditional DWA, the iteration number growth rate is much lower than that of the DWA, which fully verifies that the double-scene anti-disturbance evaluation function has excellent dynamic obstacle avoidance ability in the environment of “global + local disturbance”.

[0132] In summary, in the disturbance environment containing dynamic obstacles: the traditional DWA exposes the problem of insufficient path control accuracy when facing dynamic obstacles, and the environmental adaptability and task efficiency are poor; ODDR-DWA and DDR-DWA with double-scene anti-disturbance evaluation function show stronger response ability to dynamic obstacles, further highlighting the comprehensive application potential of the two methods in complex environments, and DDR-DWA performs better in the balance between path length and iteration efficiency.

[0133] Example Two

[0134] The embodiment provides an unmanned aerial vehicle path planning system resisting environmental disturbance, comprising:

[0135] An initialization module acquires a terrain model of an unmanned aerial vehicle flight area, divides and sets a static threat area in the terrain model, and initializes a starting point coordinate and an ending point coordinate of the unmanned aerial vehicle;

[0136] A disturbed environment modeling module respectively constructs a global disturbed environment mathematical model and a local disturbed area mathematical model to quantitatively represent influences of different types of environmental disturbances on unmanned aerial vehicle flight;

[0137] A dynamic window method improvement module comprises:

[0138] A motion model correction unit adaptively corrects a motion model of the unmanned aerial vehicle based on the global disturbed environment mathematical model and the local disturbed area mathematical model to reflect an actual motion state of the unmanned aerial vehicle in the disturbed environment;

[0139] A DDR-DWA design unit adds a disturbance quality evaluation function to the dynamic window method for random influences of the global disturbed environment on unmanned aerial vehicle position, measures consistency of disturbance deviation and ideal sampling results through the function, and further enhances stability of unmanned aerial vehicle flight in the global disturbed environment; for regular influences of the local disturbed area on unmanned aerial vehicle position, a dangerous distance evaluation function is introduced on the basis of the disturbance quality evaluation function, the function dynamically punishes the unmanned aerial vehicle close to the local disturbed center, and both flight stability of the unmanned aerial vehicle and decision-making ability of the unmanned aerial vehicle to autonomously escape the disturbed area are considered.

[0140] Embodiment three:

[0141] An electronic device comprises a memory, a processor and a computer program stored in the memory and running on the memory, and the processor implements the above-mentioned unmanned aerial vehicle path planning method resisting environmental disturbance when executing the program, comprising:

[0142] An initialization module acquires a terrain model of an unmanned aerial vehicle flight area, divides and sets a static threat area in the terrain model, and initializes a starting point coordinate and an ending point coordinate of the unmanned aerial vehicle;

[0143] A disturbed environment modeling module respectively constructs a global disturbed environment mathematical model and a local disturbed area mathematical model to quantitatively represent influences of different types of environmental disturbances on unmanned aerial vehicle flight;

[0144] A motion model correction unit adaptively corrects a motion model of the unmanned aerial vehicle based on the global disturbed environment mathematical model and the local disturbed area mathematical model to reflect an actual motion state of the unmanned aerial vehicle in the disturbed environment;

[0145] A dual-scenario anti-disturbance dynamic window method DDR-DWA is designed: for the random influence of the global disturbance environment on the position of the UAV, a disturbance quality evaluation function is added in the dynamic window method, the consistency of the disturbance offset and the ideal sampling result is measured through the function, and then the stability of the UAV flying in the global disturbance environment is enhanced; for the regular influence of the local disturbance region on the position of the UAV, on the basis of the disturbance quality evaluation function, a dangerous distance evaluation function is introduced, the UAV close to the center of the local disturbance region is dynamically punished through the function, and the decision-making ability of the UAV to fly stably and autonomously escape from the disturbance region is considered.

[0146] Embodiment four:

[0147] A computer readable storage medium has a computer program stored thereon, the program being executed by a processor to implement the anti-environment disturbance UAV path planning method described above, comprising:

[0148] Obtain a terrain model of a UAV flight region, divide and set a static threat region in the terrain model, and initialize a start point coordinate and an end point coordinate of the UAV;

[0149] Respectively construct a global disturbance environment mathematical model and a local disturbance region mathematical model to quantitatively represent the influence of different types of environmental disturbances on the flight of the UAV;

[0150] Based on the global disturbance environment mathematical model and the local disturbance region mathematical model, the motion model of the UAV is adaptively corrected to reflect the actual motion state of the UAV in the disturbance environment;

[0151] A dual-scenario anti-disturbance dynamic window method DDR-DWA is designed: for the random influence of the global disturbance environment on the position of the UAV, a disturbance quality evaluation function is added in the dynamic window method, the consistency of the disturbance offset and the ideal sampling result is measured through the function, and then the stability of the UAV flying in the global disturbance environment is enhanced; for the regular influence of the local disturbance region on the position of the UAV, on the basis of the disturbance quality evaluation function, a dangerous distance evaluation function is introduced, the UAV close to the center of the local disturbance region is dynamically punished through the function, and the decision-making ability of the UAV to fly stably and autonomously escape from the disturbance region is considered.

[0152] Those skilled in the art should understand that the above-mentioned modules or steps of the present disclosure can be realized by a general computer device, and alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, or they can be respectively manufactured into individual integrated circuit modules, or a plurality of modules or steps among them can be manufactured into a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.

[0153] The above merely provides preferred embodiments of the present application, but not for limiting the present application. For those skilled in the field of the application, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical solutions of the present application shall fall into the protection scope of the present application.

[0154] Although the specific embodiments of the present disclosure are described above with reference to the drawings, the description is not intended to limit the protection scope of the present disclosure, and those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present disclosure without creative labor still fall within the protection scope of the present disclosure.

Claims

1. A method for UAV path planning resistant to environmental disturbances, characterized in that: The following steps are involved: Obtain a terrain model of the drone's flight area, divide and set static threat areas in the terrain model, and initialize the drone's starting and ending coordinates; The global disturbance environment mathematical model and the local disturbance area mathematical model are constructed respectively to quantitatively characterize the impact of different types of environmental disturbances on UAV flight; Based on the global disturbance environment mathematical model and the local disturbance area mathematical model, the motion model of the UAV is adaptively modified to reflect the actual motion state of the UAV in the disturbance environment; A dual-scenario disturbance rejection dynamic window method (DDR-DWA) is designed. To address the random impact of the global disturbance environment on the UAV's position, a disturbance quality evaluation function is added to the dynamic window method. This function measures the consistency between the disturbance offset and the ideal sampling result, thereby enhancing the UAV's flight stability in the global disturbance environment. To address the regular impact of local disturbance areas on the UAV's position, a danger distance evaluation function is introduced on the basis of the disturbance quality evaluation function. This function dynamically penalizes UAVs close to the local disturbance center, taking into account both the UAV's flight stability and its decision-making ability to autonomously escape the disturbance area.

2. The method for UAV path planning resistant to environmental disturbances according to claim 1, characterized in that: The LazyTheta* algorithm is introduced for global path planning. The path nodes generated during the planning process of the algorithm are extracted and used as path reference points of DDR-DWA to guide the local path planning direction and enhance the global guidance capability of the dynamic window method.

3. The method for UAV path planning resistant to environmental disturbances according to claim 1, characterized in that: The global disturbance environment mathematical model is based on the disturbance velocity v area (t) and the perturbation core angle θ area (t) Construction, expressed as: Among them, v max area is the global perturbation velocity peak; θ max area is the global perturbation core angle; Rand represents a random number uniformly distributed in the interval; w0 is π / 6; the sine function is added to add periodic modulation on the basis of random perturbation to simulate the periodic random changes in the perturbation environment.

4. The method for UAV path planning resistant to environmental disturbances according to claim 1, characterized in that: The process of constructing the mathematical model of the local disturbance area includes: Tangential velocity modeling: The tangential velocity v in the local disturbance area tang (t) follows the principle of nonlinear distribution, taking the distance r from the current position to the disturbance center as a variable, and the expression is: Among them, v max-a represents the maximum disturbance velocity in the local disturbance area, r represents the distance from the current position to the disturbance center, R peak R is the distance from the peak position of the disturbance velocity to the center of the disturbance, max Indicates the maximum radius of the local disturbance area; Radial velocity modeling: In order to reflect the centripetal contraction characteristics of the local disturbance area, the radial velocity v radial (t) is expressed as: v radial (t)=C·v tang (t) Where C is a constant proportional coefficient that determines the strength of the centripetal contraction; Two-dimensional velocity vector synthesis: The tangential velocity and radial velocity are synthesized into a two-dimensional velocity vector to obtain the disturbance velocity v in the local disturbance area. local (t) and direction angle θ local (t), as follows:

5. The method for UAV path planning resistant to environmental disturbances according to claim 1, characterized in that: The adaptive correction method for the UAV's motion model is: In a single sampling period, the position offset of the UAV caused by the global disturbance environment (Δx area (t),Δy area (t)) and the UAV position offset caused by the local disturbance area (Δx local (t),Δy local (t)), i.e., the perturbation offset, is as follows: The sampling position of the UAV under the influence of the global disturbance environment (x area (t),y area (t)) and the UAV sampling position under the influence of the local disturbance area (x local (t),y local (t)) is expressed as:

6. The method for UAV path planning resistant to environmental disturbances according to claim 1, characterized in that: The disturbance quality evaluation function is: Among them, (Δx(1), Δy(1)) represents the disturbance offset during the initial sampling, (x(1), y(1)) represents the ideal position of the first sampling drone, and (x(2), y(2)) represents the ideal position of the second sampling drone; In the global disturbance environment, the dynamic window method evaluation function is modified as follows: G1(v,ω)=σ(α·head(v,ω)+β·dist(v,ω)+γ·vel(v,ω)+ε·disturb(v,ω)) Among them, v is the linear velocity of the UAV, ω is the angular velocity of the UAV, head(v,ω) is the azimuth evaluation function, dist(v,ω) is the obstacle distance evaluation function, vel(v,ω) is the velocity evaluation function, α, β, γ, ε are the weight coefficients of head(v,ω), dist(v,ω), vel(v,ω), and disturbance(v,ω), respectively; σ is the normalization coefficient.

7. The method for UAV path planning resistant to environmental disturbances according to claim 6, characterized in that: The danger distance evaluation function is: Among them, K is the proportional coefficient, R max is the maximum radius of the local disturbance area, r is the distance from the current position of the UAV to the center of the local disturbance; In the local disturbance area, the dynamic window method evaluation function is modified as follows: Among them, ζ represents the weight of the danger distance evaluation function; combining it with the global anti-disturbance evaluation function to obtain the dual-scene anti-disturbance evaluation function, expressed as: Ω cyclone Indicates the influence range of the local disturbance area; by judging whether the real-time position (x, y) of the UAV is within the influence range of the local disturbance area Ω cyclone Automatically switch the evaluation logic.

8. A UAV path planning system resistant to environmental disturbances, characterized in that: include: An initialization module obtains a terrain model of the UAV's flight area, divides and sets static threat areas in the terrain model, and initializes the starting and ending coordinates of the UAV; The disturbance environment modeling module constructs a global disturbance environment mathematical model and a local disturbance area mathematical model to quantitatively characterize the impact of different types of environmental disturbances on UAV flight; Dynamic window method improvement module, including: A motion model correction unit, which adaptively corrects the motion model of the UAV based on the global disturbance environment mathematical model and the local disturbance area mathematical model to reflect the actual motion state of the UAV in the disturbance environment; The DDR-DWA design unit incorporates a disturbance quality evaluation function into the dynamic window method to address the random impact of the global disturbance environment on the drone's position. This function measures the consistency between the disturbance offset and the ideal sampling result, thereby enhancing the drone's flight stability in the global disturbance environment. To address the regular impact of local disturbance areas on the drone's position, a danger distance evaluation function is introduced on top of the disturbance quality evaluation function. This function dynamically penalizes drones close to the local disturbance center, balancing the drone's flight stability and its ability to autonomously escape the disturbance area.

9. An electronic device comprising a memory, a processor, and a computer program stored and running on the memory, characterized in that: When the processor executes the program, the method for UAV path planning resistant to environmental disturbances described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for planning a drone path that is resistant to environmental disturbances as described in any one of claims 1 to 7 is implemented.

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