Unmanned aerial vehicle path planning method based on improved artificial potential field algorithm and application thereof

Abstract

The application discloses a kind of unmanned plane path planning method based on improved artificial potential field algorithm and application thereof, the unmanned plane path planning method includes the following steps, S1: environment model in the flight process of unmanned plane is established;S2: using improved artificial potential field method, based on the environment model of step S1, the path of unmanned plane is planned;S3: the path of unmanned plane planned in step S2 is smoothed;By modifying repulsive potential field function in artificial potential field method, so that repulsive potential field is not simply determined by the position of unmanned plane and threat source, but is jointly determined by the distance between target point and unmanned plane and the distance between threat source and unmanned plane, a better route can be planned, to avoid threat with minimum cost, ensure the economy of task completion.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) route planning technology, and in particular to a UAV path planning method based on an improved artificial potential field algorithm and its application. Background Technology

[0002] Unmanned aerial vehicle (UAV) route planning refers to the process by which a UAV, while performing a mission, seeks the shortest path to its target point under controlled risks. This process considers environmental constraints, such as terrain between the takeoff location and the mission area, weather constraints like thunderstorms, and various enemy threats, such as anti-aircraft artillery, anti-aircraft missiles, and radar, within the mission space. UAV route planning is a crucial technology closely required by UAVs and is a guarantee and prerequisite for their safe and effective mission completion. Enabling UAVs to reasonably and promptly avoid various threats is paramount in route planning operations.

[0003] Unmanned aerial vehicle (UAV) route planning algorithms can be broadly categorized into two types based on their underlying principles: heuristic algorithms and evolutionary algorithms. Heuristic algorithms utilize points in space for search and computation, resulting in a complex process requiring a large amount of data. In contrast, evolutionary algorithms primarily employ intelligent algorithms.

[0004] Compared to heuristic algorithms, intelligent algorithms have the advantages of being simple and easy to understand, having a faster computation speed, and being easier to program. However, they suffer from problems such as the inability to escape local optima, and sometimes struggle to cope with complex environments, leading to navigation failures. Most currently used solutions are based on the traditional artificial potential field method. However, due to inherent limitations, this method can lead to the problem of target point unreachability during path planning. Specifically, when the UAV moves near the target point, the gravitational pull from the target point decreases, reaching zero upon arrival. However, if a threat exists near the target, the UAV will also experience repulsive forces from the threat as it approaches the target. The combined force of these forces can cause the UAV to deviate from its target flight path, preventing it from reaching the target point.

[0005] Besides the problem of unreachable target points, traditional artificial potential field methods may also encounter the problem of local minima. This occurs when a drone moves to a certain location in space, where the resultant force of attraction and repulsion is zero due to the relative positions of the threat source and the target point, preventing the drone from moving. However, this location is not the point of minimum potential energy in the entire potential field. This is a local extremum. When a drone reaches a local minimum, it may hover near this point or remain stationary; both states will lead to mission failure. Summary of the Invention

[0006] To address the aforementioned problems, this invention aims to provide a UAV path planning method and its application based on an improved artificial potential field algorithm. By modifying the repulsive potential field function in the artificial potential field method, the repulsive potential field is no longer solely determined by the positions of the UAV and the threat source, but is jointly determined by the distance between the target point and the UAV and the distance between the threat source and the UAV. This allows for the planning of a better flight path, achieving threat avoidance at the lowest cost and ensuring the economic efficiency of mission completion.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] The UAV path planning method based on the improved artificial potential field algorithm is characterized by the following steps:

[0009] S1: Establish an environmental model during the drone's flight process;

[0010] S2: Using the improved artificial potential field method, the path of the UAV is planned based on the environmental model established in step S1;

[0011] S3: Smooth the drone path planned in step S2.

[0012] Furthermore, the specific operation of step S1 includes the following steps:

[0013] S101: Let the threat source encountered by the drone during flight be threat source i, and the threat area of ​​threat source i be a circular region. Then, the degree of risk suffered by the drone is:

[0014] ;

[0015] In the formula, The probability of a threat to drones from a threatened area. For the safety factor of drones, The radius of the area damaged by the drone. The maximum threat radius of the threat area. m is the distance between the drone and threat source i; m is the number of threat sources near the drone.

[0016] S102: The threat probability that makes the drone acceptable is Then when the threat probability is greater than At that time, drones cannot fly in this area, and the corresponding threat area radius is [missing information]. ,but ;

[0017] S103: Threat sources are divided into permanent threat sources and temporary threat sources. The duration of temporary threat sources is... , For the moment when a temporary threat source appears, This marks the moment when a temporary threat source disappears; a permanent threat source always exists and remains in the same location.

[0018] Furthermore, the specific operation of step S2 includes the following steps:

[0019] S201: Establish a flight model for the unmanned aerial vehicle (UAV);

[0020] S202: Establish constraints for drone flight;

[0021] S203: Plan the path of the UAV using an improved artificial potential field method.

[0022] Furthermore, the specific operation of step S201 includes the following steps:

[0023] S2011: Let the safe cruising altitude of the UAV be H, and the total range of the UAV be l. Then l satisfies... Where k is the total number of route segments, l j Let l be the length of the j-th segment of the route, and l j satisfy That is, the length of the j-th segment of the route is greater than or equal to the length of the minimum segment of the route;

[0024] S2012: The maximum turning angle that the drone can withstand is... The turning angle between two adjacent flight paths satisfy .

[0025] Furthermore, the constraints for the UAV flight in step S202 include: the maximum length of the UAV flight path is less than or equal to the total flight distance, and the tilt angle of the UAV during flight is at its minimum.

[0026] Furthermore, the specific operation of step S203 includes the following steps:

[0027] S2031: Constructing a virtual potential field in the UAV path planning space;

[0028] S2032: Using an improved artificial potential field algorithm, calculate the gravitational force of the target point on the UAV and the repulsive force of the threat source on the UAV in the virtual potential field;

[0029] S2033: The gravitational force of the target point on the UAV and the repulsive force of the threat source on the UAV are superimposed to obtain the resultant external force function of the UAV, and the resultant external force function is solved and analyzed to obtain the force situation of the UAV under the resultant external force.

[0030] S2034: Search for collision-free directions based on the combined external forces acting on the UAV, and perform real-time path planning.

[0031] Furthermore, the method for calculating the gravitational potential field function of the target point for the UAV in step S2032 is as follows: In the formula, Let P be the gravitational potential constant, and P be the current position of the UAV. The location of the target point. The distance between the two;

[0032] The method for calculating the repulsive potential field function of the threat source on the drone is as follows: In the formula, The distance between the drone and the threat source. The minimum radius centered on the threat source from which the drone is completely unaffected by the threat source. The maximum radius of the absolute damage zone. Let n be the repulsive potential field constant, and n be any real number greater than zero.

[0033] Furthermore, the specific operation of step S2033 includes the following steps:

[0034] Step 1: Calculate the gravitational force acting on the drone ,

[0035] ;

[0036] Step 2: Calculate the repulsive force on the drone ,

[0037] In the formula, ;

[0038] Step 3: Superimpose the gravitational force of the target point on the drone and the repulsive force of the threat source on the drone to obtain the resultant external force function of the drone. ;

[0039] Step 4: Analyze the resultant external force function of the UAV by introducing escape force to obtain the resultant external force on the UAV.

[0040] Furthermore, the UAV path planning method based on the improved artificial potential field algorithm is applied in UAV automatic obstacle avoidance software.

[0041] Furthermore, the automatic obstacle avoidance software for UAV routes is developed using the MGIS framework.

[0042] The beneficial effects of this invention are:

[0043] 1. The UAV path planning method in this invention modifies the repulsive potential field function in the artificial potential field algorithm, so that the repulsive potential field is no longer solely determined by the positions of the UAV and the threat source, but is jointly determined by the target point, the position of the UAV, and the threat source. This allows for the planning of the shortest and safest route, achieving threat avoidance at the lowest cost. It can be applied to UAV route pre-planning and online real-time planning, and the planning results can guarantee the economy and safety of mission completion.

[0044] 2. The UAV path planning method of this invention is applied to UAV automatic obstacle avoidance software and developed using the MGIS framework. Based on real-time enemy threat information, it is faster than human reaction. Compared with human planning results, using this algorithm for task planning is faster and more efficient, and can complete the planned route more quickly, creating conditions for UAVs to respond to and avoid threats as early as possible. Attached Figure Description

[0045] Figure 1 This is a schematic diagram illustrating the force situation of a UAV using the improved artificial potential field method of this invention.

[0046] Figure 2 The flowchart illustrates the process of calculating and analyzing the resultant external force function of a UAV using the escape force method introduced in this invention.

[0047] Figure 3 This is a simulation flowchart for the simulation experiment of this invention.

[0048] Figure 4 The results are simulation results of the simulation experiment of this invention.

[0049] Figure 5 This is a flowchart of the automatic obstacle avoidance software for UAV routes in Embodiment 2 of the present invention.

[0050] Figure 6 This is a schematic diagram of the core code of the automatic obstacle avoidance software for UAV routes in Embodiment 2 of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] Example 1:

[0053] The UAV path planning method based on the improved artificial potential field algorithm includes the following steps:

[0054] S1: Establish an environmental model during the drone's flight process;

[0055] Specifically, S101: Let the threat source encountered by the UAV during flight be threat source i, and the threat area of ​​threat source i be a circular region. Considering that the UAV spends most of its time cruising at a fixed altitude and speed before reaching the mission target area, to reduce operational difficulty and save fuel, the altitude of the threat area can be ignored. The threat source faced by the UAV can be simplified into a series of concentric circles, and the threat level increases with approach to the center. When the UAV approaches the threat source to a certain radius... Within a certain range, the drone will be unable to avoid the threats it faces in time, or the threat posed by ground fire to the drone will be too great; in this case, the default probability of destruction is 1. The level of risk that the drone bears is...

[0056] ;

[0057] In the formula, The probability of a threat to drones from a threatened area. For the safety factor of drones, The radius of the area damaged by the drone. The maximum threat radius of the threat area. m is the distance between the drone and threat source i; m is the number of threat sources near the drone.

[0058] S102: The threat probability that makes the drone acceptable is Then when the threat probability is greater than At that time, drones cannot fly in this area, and the corresponding threat area radius is [missing information]. ,but When the threat probability is less than If so, it is believed that the drone can withstand this threat;

[0059] S103: Because the threats faced by drones are not static, threat sources are divided into permanent threat sources and temporary threat sources. The duration of temporary threat sources is... , For the moment when a temporary threat source appears, The moment a temporary threat source disappears is considered the point at which it is avoided. If the temporary threat source disappears while the drone is avoiding it, the drone will no longer fly along the original route but will recalculate a new path. Permanent threat sources, on the other hand, always exist, do not disappear, and remain in the same position; therefore, they can fly directly along the pre-planned route.

[0060] Further, step S2: Using the improved artificial potential field method, based on the environmental model established in step S1, the path of the UAV is planned.

[0061] Specifically, S201: Establish a flight model for the unmanned aerial vehicle (UAV);

[0062] Since drones spend most of their time cruising at a fixed altitude and speed, we will only consider the limitations of the drone's maximum flight distance and minimum turning angle here.

[0063] Let H be the safe cruising altitude of the drone, and l be the total range of the drone. Then l satisfies... Where k is the total number of route segments, l j Let l be the length of the j-th segment of the route, and l j satisfy That is, the length of the j-th segment of the route is greater than or equal to the length of the minimum segment of the route;

[0064] The maximum turning angle that the drone can withstand is The turning angle between two adjacent flight paths satisfy In other words, the planned flight path turning angle cannot exceed the maneuverability of the drone.

[0065] S202: Establish constraints for drone flight;

[0066] When performing threat evasion maneuvers, drones should seek the optimal flight path, minimizing the distance traveled. The maximum length of the flight path should not exceed the maximum range; that is, the maximum length of the drone's flight path should be less than or equal to the total range. Furthermore, the cost of evasion should be minimized, and the flight path should be relatively smooth, allowing the drone to maneuver at the smallest possible angle, i.e., minimizing the drone's tilt angle during flight. A planned flight path that is as smooth as possible ensures the drone's maneuverability.

[0067] S203: Plan the path of the UAV using an improved artificial potential field method.

[0068] The artificial potential field method defines the surrounding environment using a potential field. By determining the drone's position within this field, the forces acting on the drone are identified, thus controlling its movement path. This method is characterized by high real-time performance and strong adaptability to changes in the surrounding environment, making it well-suited for real-time planning. Specifically, the artificial potential field method constructs a global potential field environment by superimposing the gravitational potential field at the starting point with the repulsive potential field from the threat source. By calculating the resultant force acting on the unmanned platform, the method controls the mobile robot's movement path, finding a safe flight direction and thus completing real-time path planning.

[0069] S2031: Construct a virtual potential field in the UAV path planning space. This virtual potential field consists of two parts: the gravitational force at the target point and the repulsive force from the threat source. The UAV's potential energy should be at its lowest when it is at the target point.

[0070] S2032: Using an improved artificial potential field algorithm, calculate the gravitational force of the target point on the UAV and the repulsive force of the threat source on the UAV in the virtual potential field;

[0071] The method for calculating the gravitational potential field function of the target point for the UAV is as follows: In the formula, Let P be the gravitational potential constant, and P be the current position of the UAV. The location of the target point. The distance between the two;

[0072] The method for calculating the repulsive potential field function of the threat source on the drone is as follows: In the formula, The distance between the drone and the threat source. The minimum radius centered on the threat source from which the drone is completely unaffected by the threat source. The maximum radius of the absolute damage zone. Let n be the repulsive potential field constant, and n be any real number greater than zero; when the distance between the drone and the threat source... Greater than a certain value At that time, it was assumed that the drone was unaffected by the threat source, and the closer the distance got to the radius of the absolute damage zone... At this time, the repulsive force received by the drone from the threat source will increase accordingly. When the difference between the distance between the drone and the threat source and the absolute damage radius of the threat source is infinitely small, the repulsive force generated by the threat source will be infinitely large to ensure that the drone will not enter the absolute damage zone.

[0073] This invention improves the repulsive potential field function in the traditional artificial potential field method. The improved repulsive potential field is no longer solely determined by the positions of the UAV and the threat source, but is jointly determined by the distance between the target point and the UAV and the distance between the threat source and the UAV. This ensures that the potential energy of the target point is always at its minimum under any circumstances.

[0074] S2033: The gravitational force of the target point on the UAV and the repulsive force of the threat source on the UAV are superimposed to obtain the resultant external force function of the UAV. The resultant external force function is then solved and analyzed to obtain the force situation of the UAV under the resultant external forces. The specific operation steps include the following steps.

[0075] Step 1: Calculate the gravitational force acting on the drone The gravitational force acting on the drone can be obtained from the negative gradient of the gravitational potential field, that is...

[0076] ;

[0077] As the drone approaches the target, its gravitational pull gradually decreases, reaching zero upon reaching the target point.

[0078] Step 2: Calculate the repulsive force on the drone Similarly, by taking the negative gradient of the repulsive potential field, we obtain...

[0079] In the formula, ;

[0080] From the above formula, we can obtain that It consists of two components with different directions, which are divided according to their directions. and ,but

[0081] ,

[0082] The size is The direction is from the obstacle towards the drone;

[0083] ,

[0084] The size is The direction is from the drone pointing towards the target;

[0085] It can be seen that the magnitudes of the two force components are closely related to n. As the value of n changes, the magnitudes near the target point... The size of also changes accordingly. The relationship between its size and the value of n is as follows:

[0086]

[0087] Regardless of the value of n The size of the drone decreases as it approaches the target. Approaching zero means that even if a threat source exists near the target point, the repulsive force exerted by it on the drone, preventing it from moving towards the target point, is negligible. The force also decreases as the drone approaches the target point, thus improving upon the limitation of traditional artificial potential field methods where the target point is unreachable. The force diagram of the object at this point is shown in the attached figure. Figure 1 As shown.

[0088] Step 3: Superimpose the gravitational force of the target point on the drone and the repulsive force of the threat source on the drone to obtain the resultant external force function of the drone. ;

[0089] Step 4: Due to the local minima problem in the artificial potential field method, the net external force function of the UAV is solved and analyzed by introducing escape force, thus obtaining the net external force acting on the UAV. The workflow of the escape force method is shown in the attached figure. Figure 2 As shown.

[0090] when and At that time, increase the gravity of the drone clockwise. Deflection amount.

[0091] Recalculate the net external force F of the UAV using the minimum step size. total If the net external force F of the drone total If the value is zero, determine whether the drone has reached the target point. If it has reached the target point, end the search. If it has not reached the target point, recalculate the repulsive force on the drone and repeat steps S3 and S4.

[0092] S2034: Search for collision-free directions based on the combined external forces acting on the UAV, and perform real-time path planning.

[0093] Further, step S3: Smooth the UAV path planned in step S2. It should be noted here that since the artificial potential field algorithm simulates the motion of an object in a potential field, the avoidance path it obtains is relatively smooth. In most cases, path smoothing is not required and it can be used directly. If smoothing is necessary, existing smoothing methods can be used.

[0094] Simulation experiment:

[0095] The improved artificial potential field method in Example 1 was simulated using MATLAB. The specific steps include the following:

[0096] (1) Parameter establishment:

[0097] Let the coordinates of the UAV be... The coordinates of the target and the obstacle are (m, n are coordinate positions), the gravitational coefficient is k, the repulsive coefficient is m, and the threat radius of the threat zone is P. o Nine obstacles were set up, with J=300 iterations and h=0.1 step size. The coordinates of the nine obstacles were (1,1; 3,2.4; 4,4.5; 3,6; 6,2.5; 5.5, 6; 6,4.5; 9,9; 8.5,5), and the target's position was (10,10). The initial coordinates of the UAV were... (0, 0).

[0098] (2) Design concept

[0099] Calculate the resultant forces of attraction and repulsion in the X and Y directions respectively, and then iteratively calculate the next coordinates of the drone by combining the step size and the original coordinates of the drone until the target point is reached.

[0100] The specific implementation methods of each part are as follows:

[0101] 1) Angle Calculation Module

[0102] First, calculate the angles between the drone and the target point, as well as between the drone and each threat source, to prepare for further calculations. This is achieved using the coordinate differences between the drone and the target point and threat sources.

[0103] ;

[0104] 2) Gravity Calculation Module

[0105] By improving the formula for calculating the repulsive force of a potential field, and since the direction cannot be directly represented, the gravitational force is decomposed into two directions, X and Y. , r is the distance between the drone and the target point.

[0106] 3) Repulsion Calculation Module

[0107] The gravity calculation module first calculates the distance and angle between the drone and the threat source. Then, based on the distance between the drone and the threat source, it determines whether to calculate the repulsive force. If so, it performs the calculation and decomposes it into the X and Y directions.

[0108] 4) Loop Module

[0109] The overall cycle process is shown in the attached figure. Figure 3 As shown.

[0110] The simulation results of the simulation experiment of this invention are attached. Figure 4 As shown. Figure 4 As shown, the drone can find a threat avoidance path and complete the avoidance maneuver before entering the absolute destruction zone.

[0111] Example 2:

[0112] The UAV path planning method based on the improved artificial potential field algorithm in Example 1 is applied to the UAV automatic obstacle avoidance software, which is developed using the MGIS framework.

[0113] The MGIS framework infrastructure includes:

[0114] Service Layer: Provides basic services of the MGIS framework, including data services, communication services, error handling and battlefield mapping, and provides upper-layer applications with various services including data control, access proxy, data communication, error message sending and unified map mapping.

[0115] Basic layer: includes session management module and configuration management module.

[0116] (1) Session Management Module: This module is the core management module of the MGIS basic framework, responsible for managing the runtime sequence of all data objects and components within the route avoidance system. Among them, the packet collaboration engine module provides user data sharing to complete remote collaborative planning; the Undo / Redo module provides basic Undo / Redo functions.

[0117] (2) Configuration Management Module: Provides management of configuration information for each component.

[0118] Application layer: Includes main program, view manager, docking control, etc., providing services such as UI interface management.

[0119] General component layer: including trajectory engine, pre-simulation engine, VPS, 2 / 3D maps, etc., providing various components required by the program.

[0120] The compilation environment for the software in this invention is Qt4.8.5, and the compilation tool used is VS2010.

[0121] Qt is a cross-platform graphical user interface application development framework based on C++, developed by Qt Company in 1991. Compared with other software, Qt has the following main advantages:

[0122] 1) Good cross-platform compatibility

[0123] 2) It uses object-oriented design, has a high degree of modularity, and provides good human-computer interaction.

[0124] 3) Extensive API documentation provides convenient reference for users.

[0125] Qt includes over 250 C++ classes and provides template-based classes for collections, serialization, file, I / O device, directory management, and date / time. It even includes regular expression processing capabilities.

[0126] 4) Supports 2D / 3D graphics rendering and OpenGL.

[0127] 6) XML support

[0128] Visual Studio 2010 is a development environment launched by Microsoft. It is currently the most popular application development environment for the Windows platform. VS2010 is powerful, highly adaptable, and widely used C++ program development software.

[0129] The construction approach of the UAV automatic obstacle avoidance software is as follows: First, the location of the threat source is recorded. Then, an artificial potential field is established, and the UAV's position is continuously calculated iteratively and updated in real time. For new and disappearing threat sources, real-time updates are performed and incorporated into the calculation process. The current potential field is recalculated, and the UAV continues to be guided forward until it reaches the target. The coordinates of enemy threat sources are collected through functions and plotted on the current map.

[0130] The algorithm for the UAV automatic obstacle avoidance software is structured as follows: The gravitational and repulsive forces at each point are calculated, and these forces are decomposed based on the angles between UAVs originating from various threats, with directions determined by positive and negative values ​​(with the positive X-axis as positive). The resultant force is calculated, and the next coordinates of the UAV are calculated using the step size. These coordinates are then stored. A detailed flowchart is attached. Figure 5 As shown. The core code is attached. Figure 6 As shown.

[0131] The design and implementation method of the UAV automatic obstacle avoidance software is as follows:

[0132] Secondary development of the basic framework: The MGIS software provides a basic development interface, which enables the functionality of external code to be implemented in the MGIS system interface.

[0133] 1. Two-dimensional VPS basic plotting interface: MPFW_MarklnterfaceBase, MPFW_MarklnterfaceBase.

[0134] 2. Two-dimensional layer interface: MPFW_LayerInterface.

[0135] 3. Data service interface:

[0136] 1) Data object: MPFW_IDataObjectBase.

[0137] 2) Data Controller: MPFW_IDataControllerBase.

[0138] 3) Data Access Agent: MPFW_IDataAgentBase.

[0139] 4. Session Management: MPFW_ISession.

[0140] 5. Data Manager: MPFW_DataManager

[0141] 6. Component Manager: MPFW_ComponentManager.

[0142] Development process: The interface function is used to read the pre-set map information in MGIS, and the artificial potential field algorithm is used to plan the path points. Finally, the two-dimensional layer interface and the two-dimensional basic plotting interface are used to plot the route on the two-dimensional map to complete the task planning.

[0143] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for unmanned aerial vehicle path planning based on an improved artificial potential field algorithm, characterized in that, The method comprises the following steps, S1: establishing an environment model in the flight process of the unmanned aerial vehicle; S2: planning a path for the unmanned aerial vehicle based on the environment model established in step S1 by using an improved artificial potential field method; S3: smoothing the path for the unmanned aerial vehicle planned in step S2; The specific operation of step S2 comprises the following steps, S201: establishing a flight model of the unmanned aerial vehicle; S202: establishing a constraint condition for the flight of the unmanned aerial vehicle; S203: planning a path for the unmanned aerial vehicle by using the improved artificial potential field method; The specific operation of step S203 comprises the following steps, S2031: constructing a virtual potential field in the path planning space of the unmanned aerial vehicle; S2032: calculating the attractive force of a target point on the unmanned aerial vehicle and the repulsive force of a threat source on the unmanned aerial vehicle in the virtual potential field by using the improved artificial potential field algorithm; The target point attraction potential field function calculation method for the unmanned aerial vehicle is , wherein, is an attraction potential field constant, P is the current position of the unmanned aerial vehicle, is the position of the target point, is the distance between the two. The threat source repulsive potential field function calculation method for the unmanned aerial vehicle is ; wherein, is the distance between the unmanned aerial vehicle and the threat source, is the minimum radius of the unmanned aerial vehicle being completely free of the threat source with the threat source as the center, is the maximum radius of the absolute damage area, is a repulsive potential field constant, and n is an arbitrary real number greater than zero. S2033: superimposing the attractive force of the target point on the unmanned aerial vehicle and the repulsive force of the threat source on the unmanned aerial vehicle to obtain a resultant force function of the unmanned aerial vehicle, and analyzing the resultant force function to obtain the force bearing condition of the resultant force of the unmanned aerial vehicle; S2034: searching for a collision-free direction based on the force bearing condition of the resultant force of the unmanned aerial vehicle to perform real-time path planning; The specific operation of step S2033 comprises the following steps, Step 1: Calculate the gravitational force experienced by the drone , ； Step 2: Calculate the repulsive force experienced by the drone , , wherein ; Step 3: Superimpose the attractive force of the target point on the drone and the repulsive force of the threat source on the drone to obtain the resultant external force function of the drone ; Step 4: the total external force function of the UAV is calculated and analyzed by using the method of introducing escape force, and the total external force suffered by the UAV is obtained; when and , the gravitational force of the UAV is increased by a clockwise deflection amount.

2. The unmanned aerial vehicle path planning method based on the improved artificial potential field algorithm according to claim 1, characterized in that, The specific operation of step S1 comprises the following steps, S101: regarding a threat source encountered by the unmanned aerial vehicle in the flight process as a threat source i, regarding the threat area of the threat source i as a circular area, and regarding the risk degree borne by the unmanned aerial vehicle as ； wherein, is the threat probability of the threat area to the UAV, is the safety coefficient of the UAV, is the radius of the damage area of the UAV, is the maximum threat radius of the threat area, is the distance between the UAV and the threat source i; m is the number of threat sources near the UAV; S102: the threat probability acceptable by the UAV is When the threat probability is greater than , the UAV cannot fly in the area, and the corresponding threat area radius is , then ; S103: divide the threat source into permanent type threat source and temporary type threat source, the existing time of temporary type threat source is , the moment when temporary type threat source appears, the moment when temporary type threat source disappears; permanent type threat source always exists and the position is unchanged.

3. The unmanned aerial vehicle path planning method based on the improved artificial potential field algorithm according to claim 1, characterized in that, The specific operation of step S201 comprises the following steps, S2011: Let the safety height of the unmanned aerial vehicle cruise be H, and the total range of the unmanned aerial vehicle be l, then l satisfies , wherein k is the total number of route segments, l j is the length of the jth route segment, and l j satisfies , that is, the length of the jth route segment is greater than or equal to the length of the minimum route segment; S2012: The maximum steering angle that the UAV can withstand is The steering angle between the two adjacent segments of the flight path is satisfies .

4. The unmanned aerial vehicle path planning method based on the improved artificial potential field algorithm according to claim 3, characterized in that, The constraint condition for the flight of the unmanned aerial vehicle in step S202 comprises that the maximum length of the flight path of the unmanned aerial vehicle is less than or equal to the total flight range, and the inclination angle of the unmanned aerial vehicle in flight is minimum.

5. A computer program product comprising computer programs or instructions, characterized in that: The computer program or instructions are executed by a processor to perform the steps of the method according to any one of claims 1-4.

6. A computer program product according to claim 5, characterized in that: The program product is developed by using an MGIS framework.

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