An unmanned aerial vehicle path planning method based on a key obstacle surrounding point set method and an A* algorithm

Abstract

The application belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to an unmanned aerial vehicle path planning method based on a key obstacle surrounding point set method and an A* algorithm, which comprises the following steps: constructing a three-dimensional unmanned aerial vehicle environment model, and obtaining a starting point and a target point of the unmanned aerial vehicle; calculating a target path of the unmanned aerial vehicle according to the starting point, the target point and obstacle information of the unmanned aerial vehicle by using the key obstacle surrounding point set method and the A* algorithm; and deleting redundant nodes in the target path of the unmanned aerial vehicle by using a redundant node deletion algorithm to obtain a final path of the unmanned aerial vehicle; the safety of the unmanned aerial vehicle path can be ensured by the key obstacle surrounding point set method, the optimal path of the unmanned aerial vehicle can be quickly calculated by the A* algorithm, the redundant nodes in the target path of the unmanned aerial vehicle can be deleted by using the redundant node deletion algorithm, the flight time of the unmanned aerial vehicle is reduced, and the energy consumption of the unmanned aerial vehicle is reduced.

Description

Technical Field

[0001] This invention belongs to the field of UAV path planning technology, specifically relating to a UAV path planning method based on the key obstacle surrounding point set method and the A* algorithm. Background Technology

[0002] As a crucial industry for technological innovation, unmanned aerial vehicle (UAV) technology is experiencing explosive growth. With increasingly mature technology, the application areas of UAVs are constantly expanding, such as military reconnaissance, agricultural and forestry production, and logistics transportation. The mission environment for UAVs has expanded from the initial open and isolated airspace to complex airspace filled with irregular obstacles. Faced with various complex environments, UAV path planning technology is particularly important, as a good flight path directly affects the UAV's operational efficiency, its own safety, and its impact on ground safety.

[0003] Existing path planning methods for UAVs are mostly based on the A* algorithm and the obstacle surrounding point set method. The traditional A* algorithm is the most efficient direct search method for finding the shortest path in a static road network and is widely used in various types of search problems. The traditional A* algorithm is relatively mature in two-dimensional space, but in three-dimensional space, due to the increase in dimensionality, it is prone to problems such as slow computation speed and too many redundant nodes. To address these issues, some scholars have proposed a method of dynamically weighting to change the search neighborhood to improve the traditional A* algorithm, thereby reducing the number of search nodes and improving the search speed. At the same time, higher-order Bézier curves are used multiple times to smooth the route planned by the improved A* algorithm and reduce turning points. However, this method abandons the search in three directions when changing the search neighborhood, and may get stuck in local optima in complex cases, failing to find a path. The obstacle surrounding point set method is used to reduce unnecessary searches in three-dimensional space. This method is inspired by observation that the shortest path often bypasses obstacles, so it only focuses on the points around the obstacles and does not consider the entire three-dimensional space, thus reducing the pathfinding space. However, the generated path is too close to the obstacle, which cannot guarantee the safety of the UAV and is not suitable for UAV flight. Summary of the Invention

[0004] To address the problems existing in the background technology, this invention provides a UAV path planning method based on the key obstacle surrounding point set method and the A* algorithm. By setting a safe distance to improve the key obstacle surrounding point set method, the feasible points in the final UAV path maintain a safe distance from obstacles, thereby improving path safety and ensuring UAV safety. An adaptive weighting strategy improves the evaluation function of the +A* algorithm, enabling the algorithm to complete path planning faster. A redundant node removal algorithm optimizes redundant nodes in the path, reducing UAV flight time and thus reducing UAV energy consumption. This includes:

[0005] S1: Construct a 3D UAV environment model and obtain the UAV's starting point and target point;

[0006] S2: The target path of the UAV is calculated using the method based on the key obstacle surrounding point set and the A* algorithm, based on the starting point, target point and obstacle information of the UAV.

[0007] S3: Use the redundant node removal algorithm to remove redundant nodes in the UAV target path to obtain the UAV's final path.

[0008] Furthermore, the establishment of the three-dimensional UAV environment model includes:

[0009] S11: Select a suitable vertex in the 3D map as the origin A, establish a 3D rectangular coordinate system A-XYZ for the origin A, determine the maximum length AB, maximum width AD, and maximum height AE of the raster map, and construct a cube ABCD-EFGH to accommodate the 3D map.

[0010] S12: Divide the cube ABCD-EFGH into m equal parts along AB. Draw planes parallel to the ABEF plane through the division points to obtain m planes y. i (i = 1, 2, ..., m);

[0011] S13: For any plane y i Divide AD into n equal parts and AE into h equal parts. At this time, y i The cube ABCD-EFGH is discretized into n×h planar grids, that is, the cube is discretized into m×n×h three-dimensional grids. The path points to be extended by the UAV are represented as p(i,j,k), i=1,2,…,m,j=1,2,…,n,k=1,2,…,h.

[0012] Furthermore, the calculation of the UAV's target path using the key obstacle surrounding point set method and the A* algorithm includes:

[0013] S21: The grid containing the obstacle is defined as the obstacle grid, the grid that is less than the preset safe distance from any obstacle grid is defined as the danger grid, and the remaining grids are defined as free grids. The midpoint of each free grid is a feasible point that the UAV can expand.

[0014] S22: Connect the starting point S and the target point T of the UAV to form a line segment ST, and determine the critical obstacle CO and non-critical obstacle NCO based on obstacle information:

[0015] S23: Traverse key obstacles and determine CO. i and c i And the feasible point set V of the UAV is calculated using the point set method around key obstacles, where c iThis indicates that ST passes through the i-th critical obstacle o. i The dangerous grid cell that line segment ST passes through is closest to the starting point S; i This indicates that line segment ST passes through the i-th critical obstacle o. i At that time, the distance c among all the grids passed by line segment ST i The most recent free grid;

[0016] S24: Starting from the starting point S, search for expansion points and select an expansion point as the current expansion point. Calculate the distance between the starting point S and the current expansion point and the distance between the current expansion point and the target point T.

[0017] S25: Construct the evaluation function of the A* algorithm based on the distance between the starting point S and the current expansion point, the distance between the current expansion point and the target point T, and the neighbor grid and obstacle information of the current expansion point;

[0018] S26: The target path of the UAV is calculated using the A* algorithm based on the evaluation function of the A* algorithm and the feasible point set V of the UAV.

[0019] Furthermore, the critical obstacle CO and the non-critical obstacle NCO include:

[0020]

[0021] Where O is the set of obstacle grids; o i This represents the set of all obstacle grids belonging to the i-th obstacle; Indicates when o i and If there is an intersection, return 1; otherwise, return 0.

[0022] Furthermore, the steps for calculating the feasible point set V of the UAV include:

[0023] S231: s i Add to the tempset collection;

[0024] S232: Define the first element in the tempset set as the reference point. when When a grid in the grid intersects with a hazard grid around a critical obstacle, it will... Add to the SPS collection; and The free grid cells in the set are placed into the tempset collection; where, Indicates reference point The set of neighboring grid cells;

[0025] S233: Will Remove from the tempset collection;

[0026] S234: Determine if the tempset set is empty. If tempset is not empty, repeat steps S232 to S234. If tempset is empty, output the final set of feasible points V for the UAV.

[0027]

[0028] in, N represents a free grid set. ST This represents the set of grid cells along the straight line ST.

[0029] Furthermore, the evaluation function of the A* algorithm includes:

[0030] f(p n )=(2-p)g(p n )+ph(p n )

[0031]

[0032] Among them, dis(p n S) represents the current expansion point p. n Distance to the starting point S; dis(p) n (T) represents the current expansion point p. n Distance to target point T; nb(p n ) represents the current expansion point p n The set of neighboring grid cells, O represents the set of obstacle grid cells; g(p n () represents the distance from the starting point S to the current expansion point p. n The actual distance; h(p) n ) represents the current expansion point p n The Euclidean distance between the target point T and the target point T; f(p n ) represents the improved cost function.

[0033] Furthermore, the specific steps for calculating the target path of the UAV include:

[0034] S261: Initialize the OpenList and CloseList lists for the A* algorithm;

[0035] S262: Add the starting point S to the openlist, use the A* algorithm to calculate the point with the smallest evaluation function value in the openlist as the current point, and input the current point into the closelist;

[0036] S263: If the current point is the target point, then the planning ends, and the nodes of the closelist are sequentially arranged to form the target path of the UAV;

[0037] S264: Determine if there is an adjacent node of the current point in the openlist. If there is, take the adjacent node of the current point with the lowest g(n) value in the openlist as the new current point and calculate the evaluation function value of the new current node according to the evaluation function of the A* algorithm; where g(n) represents the actual distance from the starting point to the current point.

[0038] If it does not exist, iterate through all the adjacent nodes of the current point and move the adjacent nodes of the current point in the feasible point set V of the drone into the openlist list;

[0039] S265: Repeat steps S261-S265.

[0040] Furthermore, the process of using the redundant node removal algorithm to remove redundant nodes in the UAV target path includes:

[0041] S31: Take the starting point in the UAV target path as node P. cur The endpoint is node P. next ;

[0042] S32: Determine P cur and P next Check whether the line passing through obstacle grids or danger grids; if so, take the node preceding the endpoint as the new P. next The danger grid includes grids whose distance from any obstacle grid is less than a preset safety distance.

[0043] If not visited, delete node P. cur and node P next The intermediate node between;

[0044] S33: Determine node P next Is it equal to node P? cur If the value is equal to the value, output the final path of the drone; otherwise, repeat steps S31 to S33.

[0045] The present invention has at least the following beneficial effects

[0046] This invention improves the critical obstacle surrounding point set method by considering the safe distance of the UAV when creating a 3D UAV environment model and defining the danger grid in the 3D UAV environment model by setting a threshold. This makes the generated path safer and more reliable. The improved critical obstacle surrounding point set method generates a feasible point set V for the UAV, the distance between the starting point and the current expansion point, and the distance between the current expansion point and the target point. It involves the evaluation function of the A* algorithm with adaptive weight update, which makes the selection of UAV target path nodes more reasonable, reduces the number of traversed nodes, and further improves the calculation speed. The redundant node deletion algorithm removes redundant nodes in the UAV target path, which can remove a large number of redundant path nodes, reduce the number of path vertices, reduce the UAV's flight time, and thus reduce the UAV's energy consumption and improve the UAV's search efficiency.

[0047] Instruction manual illustrations

[0048] Figure 1 This is a flowchart of the method of the present invention;

[0049] Figure 2 This is a schematic diagram of the three-dimensional UAV environment model of the present invention;

[0050] Figure 3 This is a simulation comparison diagram of the present invention and a traditional UAV path planning algorithm;

[0051] Figure 4 This is a simulation comparison diagram of the present invention and traditional UAV path planning algorithms. Detailed Implementation

[0052] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0053] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0054] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0055] Please see Figure 1 This invention provides a UAV path planning method based on the key obstacle surrounding point set method and the A* algorithm, comprising:

[0056] S1: Construct a 3D UAV environment model and obtain the UAV's starting point and target point;

[0057] Please see Figure 2 The establishment of the three-dimensional UAV environment model includes:

[0058] S11: Select a suitable vertex in the 3D map as the origin A, establish a 3D rectangular coordinate system A-XYZ for the origin A, determine the maximum length AB, maximum width AD, and maximum height AE of the raster map, and construct a cube ABCD-EFGH to accommodate the 3D map.

[0059] S12: Divide the cube ABCD-EFGH into m equal parts along AB. Draw planes parallel to the ABEF plane through the division points to obtain m planes y. i (i = 1, 2, ..., m);

[0060] S13: For any plane y i Divide AD into n equal parts and AE into h equal parts. At this time, y i The cube ABCD-EFGH is discretized into n×h planar grids, that is, the cube is discretized into m×n×h three-dimensional grids. The path points to be extended by the UAV are represented as p(i,j,k), i=1,2,…,m,j=1,2,…,n,k=1,2,…,h.

[0061] Preferably, the three-dimensional UAV environment model includes: dividing the three-dimensional space into m×n×h three-dimensional grids with side length w to form the three-dimensional UAV environment model;

[0062] S2: The target path of the UAV is calculated using the method based on the key obstacle surrounding point set and the A* algorithm, based on the starting point, target point and obstacle information of the UAV.

[0063] Preferably, the calculation of the UAV's target path using the method based on the point set around key obstacles and the A* algorithm includes:

[0064] S21: The grid containing the obstacle is defined as the obstacle grid, the grid that is less than the preset safe distance from any obstacle grid is defined as the danger grid, and the remaining grids are defined as free grids. The midpoint of each free grid is a feasible point that the UAV can expand.

[0065] After outputting the point set around the critical obstacle, it means that the entire map does not need to be considered during subsequent path planning. This reduces the complexity caused by dimensionality increase and improves the speed of path planning. However, the grid points in the map filtered by the critical obstacle surrounding point set method are too close to the obstacle, which can affect the safety of the UAV during flight. Therefore, this method is improved. In the critical obstacle surrounding point set method, the grid has only two states: safe grid and obstacle grid. This paper adds dangerous grids and sets a safe distance ε. Grid points that are less than ε away from the obstacle are marked as dangerous grids. To ensure that the algorithm has a solution, it is stipulated that the start and end points are not dangerous grids.

[0066] The grid containing the obstacle includes: a grid where the entire grid space is filled with obstacles;

[0067] Preferably, the hazard grid comprises:

[0068] DO={p|dis(p,o)≤ε,p∈N f}

[0069] Where DO represents the set of hazardous grids, dis(p,o) represents the Euclidean distance between grid p and obstacle grid o, ε represents the preset safety distance, and N f This represents the feasible grid set, which is the set of grids in the 3D UAV environment model excluding obstacle grids, and o represents obstacle grids.

[0070] S22: Connect the starting point S and the target point T of the UAV to form a line segment ST, and determine the critical obstacle CO and non-critical obstacle NCO based on obstacle information:

[0071] Preferably, the critical obstacle CO and the non-critical obstacle NCO include:

[0072]

[0073]

[0074] Where O is the set of obstacle grids; o i This represents the set of all obstacle grids belonging to the i-th obstacle; Indicates when o i and If there is an intersection, return 1; otherwise, return 0.

[0075] S23: Traverse key obstacles and determine CO. i and c i And the feasible point set V of the UAV is calculated using the point set method around key obstacles, where c i This indicates that ST passes through the i-th critical obstacle o. i The dangerous grid cell that line segment ST passes through is closest to the starting point S; i This indicates that line segment ST passes through the i-th critical obstacle o. i At that time, the distance c among all the grids passed by line segment ST i A recent free grid.

[0076] Preferably, the s i and c i include:

[0077]

[0078] Among them, do i Represents the i-th critical obstacle o i The surrounding set of danger grids; IP i It is do i The intersection with line segment ST; It is an IP address i The k-th grid point; This indicates the distance from the starting point S to c on line segment ST. i The set of all free grids between.

[0079] Preferably, the calculation steps for the feasible point set V of the UAV include:

[0080] S231: s i Add to the tempset collection;

[0081] S232: Define the first element in the tempset set as the reference point. when When a grid in the grid intersects with a hazard grid around a critical obstacle, it will... Add to the SPS collection; and The free grid cells in the set are placed into the tempset collection; where, Indicates reference point The set of neighboring grid cells;

[0082] Preferably, the include:

[0083]

[0084] Where w is the side length of the grid;

[0085] S233: Will Remove from the tempset collection;

[0086] S234: Determine if the tempset set is empty. If tempset is not empty, repeat steps S232 to S234. If tempset is empty, output the final set of feasible points V for the UAV.

[0087]

[0088] in, N represents a free grid set. ST This represents the set of grid cells along the straight line ST.

[0089] S24: Starting from the starting point S, search for expansion points and select an expansion point as the current expansion point. Calculate the distance between the starting point S and the current expansion point and the distance between the current expansion point and the target point T.

[0090] S25: Construct the evaluation function of the A* algorithm based on the distance between the starting point S and the current expansion point, the distance between the current expansion point and the target point T, and the neighbor grid and obstacle information of the current expansion point;

[0091] Preferably, the evaluation function of the A* algorithm includes:

[0092] f(p n )=(2-p)g(p n )+ph(p n )

[0093] p = (1-β) α +1

[0094]

[0095]

[0096] Among them, dis(p n S) represents the current expansion point p. n Distance to the starting point S; dis(p) n (T) represents the current expansion point p. n Distance to target point T; nb(p n ) represents the current expansion point p n The set of neighboring grid cells, O represents the set of obstacle grid cells; g(p n () represents the distance from the starting point S to the current expansion point p.n The actual distance; h(p) n ) represents the current expansion point p n The Euclidean distance between the target point T and the target point T; f(p n ) represents the improved cost function;

[0097] From the above equation, it can be seen that when g(p) n When the weights are large, the algorithm tends to be breadth-first, which increases the number of nodes traversed and thus affects the solution time; when h(p n When the weights are large, the number of nodes traversed by the algorithm will decrease, but it may get stuck in a local optimum during the search process. Therefore, it is necessary to adaptively weight the heuristic function. The main idea of ​​the adaptive weighting function is that when p n When there are few infeasible points around, the node closer to the endpoint is preferred to reduce computation time. n When there are many infeasible points in the surrounding area, it tends to search the surrounding nodes to avoid getting trapped in local optima; when p n When p is close to the starting point, it means that the algorithm is in its early stages and needs to reach the destination as quickly as possible. n When the algorithm is close to the endpoint, it indicates that it is in the later stages of the algorithm and is biased towards the quality of the solution. By using an adaptive weighting function strategy, the number of nodes traversed by the algorithm is reduced, thereby improving search efficiency and reducing computation time.

[0098] S26: The target path of the UAV is calculated using the A* algorithm based on the evaluation function of the A* algorithm and the feasible point set V of the UAV.

[0099] Preferably, the steps for calculating the target path of the UAV specifically include:

[0100] S261: Initialize the OpenList and CloseList lists for the A* algorithm;

[0101] S262: Add the starting point S to the openlist, use the A* algorithm to calculate the point with the smallest evaluation function value in the openlist as the current point, and input the current point into the closelist;

[0102] S263: If the current point is the target point, then the planning ends, and the nodes of the closelist are sequentially arranged to form the target path of the UAV;

[0103] S264: Determine if there is an adjacent node of the current point in the openlist. If there is, take the adjacent node of the current point with the lowest g(n) value in the openlist as the new current point and calculate the evaluation function value of the new current node according to the evaluation function of the A* algorithm; where g(n) represents the actual distance from the starting point to the current point.

[0104] If it does not exist, iterate through all the adjacent nodes of the current point and move the adjacent nodes of the current point in the feasible point set V of the drone into the openlist list;

[0105] S265: Repeat steps S261-S265.

[0106] S3: Use the redundant node removal algorithm to remove redundant nodes in the UAV target path to obtain the UAV's final path.

[0107] Preferably, the step of using a redundant node removal algorithm to remove redundant nodes in the UAV target path includes:

[0108] S31: Take the starting point in the UAV target path as node P. cur The endpoint is node P. next ;

[0109] S32: Determine P cur and P next Check whether the line passing through obstacle grids or danger grids; if so, take the node preceding the endpoint as the new P. next The danger grid includes grids whose distance from any obstacle grid is less than a preset safety distance.

[0110] If not visited, delete node P. cur and node P next The intermediate node between;

[0111] S33: Determine node P next Is it equal to node P? cur If the value is equal to the value, output the final path of the drone; otherwise, repeat steps S31 to S33.

[0112] Experimental data

[0113] To verify that the method proposed in this invention can solve the problem of path planning for UAVs in a three-dimensional environment, a three-dimensional test environment was built using the simulation software MATLAB, and the path planning effects of the traditional A* algorithm and the point set method around key obstacles were compared.

[0114] Experimental environment

[0115] The simulation program runs on the MATLAB R2016b platform. The computer system environment is Windows 10, configured with an Intel(R) Core(TM) i7-11800H@2.30GHz CPU and 16GB RAM.

[0116] The settings parameters are shown in Table 1:

[0117] Table 1

[0118]

[0119] 3D environment setup

[0120] Before performing 3D path planning for a drone, a 3D spatial model needs to be constructed from the 3D map. This invention utilizes a grid method, selecting suitable vertices in the 3D map as the origin, establishing a 3D Cartesian coordinate system A-XYZ with point A as the origin, and determining the maximum length AB, maximum width AD, and maximum height AE of the grid map. A cube ABCD-EFGH is then constructed to accommodate the 3D map. Figure 2 As shown.

[0121] Divide ABCD-EFGH into m equal parts along AB. Construct planes parallel to the ABEF plane through the division points to obtain m-1 planes y. i (i = 1, 2, ..., m). For any of the above planes y i Divide AD into n equal parts and AE into h equal parts. At this point, y... i The path points to be extended by the UAV can be marked as p(i,j,k), i=1,2,…,m,j=1,2,…,n,k=1,2,…,h.

[0122] The performance metrics of path planning are generally expressed using the number of nodes traversed, the number of nodes in the path, the computation time, the path distance, the path hazard, and the path inflection points. The number of nodes traversed is the number of grid points traversed by the A* algorithm during planning. Dangerous grids and obstacle grids are both marked as unreachable nodes. However, if the surrounding grid nodes are dangerous grid nodes, the cost is increased by 1, and if the surrounding grid nodes are obstacle nodes, the cost is increased by 2.

[0123] To verify the performance of the UAV path planning method based on the key obstacle surrounding point set method and A* algorithm proposed in this invention, simulations were conducted on the same scene to verify the traditional path planning method based on A* algorithm, the path planning algorithm based on the key obstacle surrounding point set method, and the UAV path planning method based on the key obstacle surrounding point set method and A* algorithm proposed in this invention. The grid map size was 250X250X200. The test results are shown in Table 2.

[0124] Table 2

[0125]

[0126] In the key obstacle surrounding point set method, the 3D map is first optimized by considering only the grid points around the obstacle, and then the path is found by the A* algorithm, so that the planned path is superior to the traditional A* algorithm in terms of computation time and number of nodes traversed.

[0127] Statistical analysis of the performance parameters in the table shows that the UAV path planning method proposed in this invention, based on the key obstacle surrounding point set method and the A* algorithm, is superior to the traditional A* algorithm, mainly in the following aspects: the number of traversed nodes is reduced by 88.32%, the computation time is reduced by 95.06%, the number of path inflection points is reduced by 46.67%, and the path hazard is reduced by 40.88%. These data fully demonstrate that the improved A* algorithm based on obstacle avoidance proposed in this paper uses less time, traverses fewer nodes, and produces a smoother path, making it suitable for the daily flight of UAVs.

[0128] Please see Figure 3 and Figure 4 This invention provides a three-dimensional testing environment. The testing environment is used to analyze three algorithms in detail: the traditional A* algorithm, the key obstacle surrounding point set method, and others. Experimental results show that, compared with the traditional A* algorithm, the proposed algorithm significantly reduces the number of traversed nodes and the computation time. Compared with the key obstacle surrounding point set method, the proposed algorithm significantly reduces the path hazard and the number of vertices. When p... n When there are few obstacles around, the system tends to select the node closest to the endpoint to reduce computation time. n When there are many obstacles around, it tends to search for surrounding nodes to avoid getting trapped in local optima; when p n When p is close to the starting point, it means that the algorithm is in its early stages and needs to reach the destination as quickly as possible. n When you are close to the endpoint, it means you are in the later stages of the algorithm, which is biased towards the quality of the solution.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method based on point sets around key obstacles and A The algorithm's UAV path planning method is characterized by, include: S1: Construct a 3D UAV environment model and obtain the UAV's starting point and target point; S2: Based on the UAV's starting point, target point, and obstacle information, utilize the method based on the point set around key obstacles and A The algorithm calculates the target path of the drone; The method utilizes a point set based on key obstacles and A The algorithm calculates the target path for the drone, including: S21: The grid containing the obstacle is defined as the obstacle grid, the grid that is less than the preset safe distance from any obstacle grid is defined as the danger grid, and the remaining grids are defined as free grids. The midpoint of each free grid is a feasible point that the UAV can expand. S22: Connect the starting point S and the target point T of the UAV to form a line segment ST, and determine the critical obstacle CO and non-critical obstacle NCO based on obstacle information: S23: Traverse key obstacles and determine CO. and And the feasible point set V of the UAV is calculated using the point set method around key obstacles, where, Indicates that ST has passed the first Key Obstacles At that time, the dangerous grid cell that line segment ST passes through is the one closest to the starting point S among all the grid cells; Indicates that line segment ST passes through the first... Key Obstacles At that time, the distance among all the grids traversed by line segment ST is... The most recent free grid; S24: Starting from the starting point S, search for expansion points and select an expansion point as the current expansion point. Calculate the distance between the starting point S and the current expansion point and the distance between the current expansion point and the target point T. S25: Construct A based on the distance between the starting point S and the current expansion point, the distance between the current expansion point and the target point T, and the neighbor grid and obstacle information of the current expansion point. The algorithm's evaluation function; S26: According to A The algorithm's evaluation function and the feasible point set V of the UAV are used with A The algorithm calculates the target path of the drone; The specific steps for calculating the target path of a drone include: S261: Initialize A The algorithm's OpenList and CloseList; S262: Add the starting point S to the openlist and use A The algorithm's evaluation function calculates the point with the smallest evaluation function value in the openlist list as the current point, and inputs the current point into the closelist list; S263: If the current point is the target point, then the planning ends, and the nodes of the closelist are sequentially arranged to form the target path of the UAV; S264: Determine if there are adjacent nodes to the current node in the openlist. If so, add them to the openlist. The node adjacent to the current point with the lowest value is taken as the new current point, and then according to A... The algorithm's evaluation function calculates the new evaluation function value for the current node; where, This represents the actual distance from the starting point to the current point; If it does not exist, iterate through all the adjacent nodes of the current point and move the adjacent nodes of the current point in the feasible point set V of the drone into the openlist list; S265: Repeat steps S261-S265; S3: Use the redundant node removal algorithm to remove redundant nodes in the UAV target path to obtain the UAV's final path.

2. The method based on the point set around key obstacles and A as described in claim 1. The algorithm's UAV path planning method is characterized by, The establishment of the 3D UAV environment model includes: S11: Select a suitable vertex in the 3D map as the origin A, establish a 3D rectangular coordinate system A-XYZ for the origin A, determine the maximum length AB, maximum width AD, and maximum height AE of the raster map, and construct a cube ABCD-EFGH to accommodate the 3D map. S12: Perform the following operation along AB on the cube ABCD-EFGH. Divide the plane into equal parts, and draw a plane parallel to the ABEF plane through the points of division. a plane ; S13: For any plane Proceed along AD Divide into equal parts, proceed along AE Divide into equal parts, at this time Then it is separated into A planar grid, namely the cube ABCD-EFGH, is discretized into If there are 3D grids, then the path points to be expanded by the UAV are represented as follows: .

3. The method based on the point set around key obstacles and A as described in claim 1. The algorithm's UAV path planning method is characterized by, The critical obstacle CO and the non-critical obstacle NCO include: Where O is the set of obstacle grids; Representative belongs to the first The set of all obstacle grids for each obstacle; Indicates when and If there is an intersection, return 1; otherwise, return 0.

4. The method based on the point set around key obstacles and A as described in claim 1. The algorithm's UAV path planning method is characterized by, The steps for calculating the feasible point set V for the UAV include: S231: Will Add to the tempset collection; S232: Define the first element in the tempset set as the reference point. ;when When a grid in the grid intersects with a hazard grid around a critical obstacle, it will... Add to the SPS collection; and The free grid cells in the set are placed into the tempset collection; where, Indicates reference point The set of neighboring grid cells; S233: Will Remove from the tempset collection; S234: Determine if the tempset set is empty. If tempset is not empty, repeat steps S232-S234. If tempset is empty, output the final set of feasible points for the UAV. ; in, Represents a free grid set. This represents the set of grid cells along the straight line ST.

5. A method based on the point set around key obstacles and A as described in claim 1. The algorithm's UAV path planning method is characterized by, The A The algorithm's evaluation function includes: in, For the current expansion point The distance to the starting point S; Indicates the current extension point To the target point The distance between them; Indicates the current extension point The neighbor grid set, Represents a set of obstacle grids; This represents the distance from the starting point S to the current expansion point. The actual distance; Indicates the current extension point and target point The Euclidean distance between them; This is the improved cost function.

6. A method based on the point set around key obstacles and A as described in claim 1. The algorithm's UAV path planning method is characterized by, The method of using a redundant node removal algorithm to remove redundant nodes in the UAV target path includes: S31: Use the starting point in the UAV target path as a node. The endpoint is used as a node. ; S32: Judgment and Check whether the connecting line passes through obstacle grids or danger grids; if so, take the node before the endpoint as the new node. The danger grid includes grids whose distance from any obstacle grid is less than a preset safety distance. If not visited, delete the node. and nodes The intermediate node between; S33: Determine the node Is it equal to a node? If the value is equal to the value, output the final path of the drone; otherwise, repeat steps S31 to S33.

Images