A flight route planning method based on improved A* algorithm

Abstract

The application relates to a flight route planning method based on an improved A* algorithm, which comprises the following steps: establishing a suitable space grid model according to given map data and determining the size of the minimum grid; according to given threat data, assigning values to the grids covered by threats; the grids of terrain threats are assigned with a value of 1, the grids of radar or air defense weapon threats are assigned with a value of (0, 1), and the grids without threats are assigned with a value of 0; the grid where a starting point is located is determined as a starting node, and the grid where a target point is located is determined as a target node; an open table and a close table are established; the starting node is put into the open table, and searching is started; the following steps of expanding a node and searching a node are cyclically executed: the problems of the limitations of two-dimensional research, generation value and node expansion of the traditional A* algorithm in the application of flight route planning technology are solved, the threat factors in a three-dimensional space and the aircraft maneuvering performance are considered, a flight three-dimensional route meeting the conditions is planned, and balance between the shortest path and passing through a risk area is automatically achieved.

Description

Technical Field

[0001] This invention relates to a flight-assisted route planning technology, and more particularly to a route planning method based on an improved A* algorithm. Background Technology

[0002] With the development of computer technology and artificial intelligence, flight route planning has gradually shifted towards computer-based processes. Currently, AI-based flight route planning algorithms are mainly divided into two categories: static global route planning and real-time local route planning. Static global route planning designs the route offline based on deterministic information about the flight environment, then loads the pre-planned optimal path onto the aircraft, and the autopilot flies along the predetermined route. This type of planning is generally completed before takeoff and has low real-time requirements. Real-time local route planning, on the other hand, updates the route based on sensor feedback of environmental changes and designs the route within a corresponding time frame. This type of planning has high real-time requirements and is one of the most effective means to improve the probability of survival.

[0003] Despite the widespread application and attention garnered by AI-based flight path planning technology, several challenges and problems remain. The main difficulties currently lie in three areas: safety, optimal flight path planning, and flight path feasibility. Firstly, from a safety perspective, compared to high-altitude flight, low-altitude flight can utilize terrain shielding and reflected ground clutter to reduce the probability of detection. However, some detection signals will still be reflected back, making the design of a flight path with good stealth crucial. Common stealth methods include: keeping the planned route away from threat sources and utilizing terrain and other factors as much as possible to avoid detection. Optimal flight path planning refers to achieving global optimization, i.e., minimizing distance and time while ensuring safety. Limited by fuel and mission constraints, flight time must also be limited. Therefore, the planned path must minimize flight distance while meeting safety requirements. Finally, regarding flight path feasibility, the aircraft's physical limitations must be considered during flight path planning; otherwise, the aircraft will not be able to fly according to the generated trajectory. The physical limitations of an aircraft that constrain flight paths mainly include: maximum turning angle, maximum climb / dive angle, minimum flight segment length, and minimum flight altitude.

[0004] The A* algorithm is a heuristic search algorithm that selects the optimal path by evaluating the cost of the current node and estimating the heuristic function. It offers advantages such as high efficiency and optimization in both space and time. In flight path planning, the A* algorithm can be used to solve shortest path problems, path planning, and conflict avoidance problems.

[0005] Currently, route planning based on the A* algorithm has been widely applied and researched. For example, in aircraft route planning, the A* algorithm can be used to find the shortest or optimal path to reduce flight time and energy consumption. In the field of drone delivery, the A* algorithm can be used to plan the shortest or optimal delivery path to improve delivery efficiency. Furthermore, in the field of intelligent transportation, the A* algorithm can also be used for vehicle path planning, traffic signal control, and other problems. However, route planning based on the A* algorithm still faces some challenges and problems. For example, in complex environments, how to fully consider various constraints and design effective heuristic functions to guide the search direction is a key issue.

[0006] Currently, most flight path planning technologies are applied to two-dimensional scenarios, which presents numerous limitations in practical applications. Distance is often only one factor in path planning; other factors, such as obstacles and threats, must also be considered. Therefore, different types of costs must be used in the algorithm. Furthermore, the traditional A* algorithm expands nodes by extending one layer outwards from the current node, using the eight nearest neighbors as the expansion points. However, in actual flight, the aircraft's own motion constraints must be considered, and node expansion cannot be performed blindly. Summary of the Invention

[0007] To address the limitations of the traditional A* algorithm in flight route planning, namely its two-dimensional study, cost, and node expansion, this paper proposes a flight route planning method based on an improved A* algorithm. This method considers threat factors in three-dimensional space and aircraft maneuverability to plan a suitable three-dimensional flight route. The modified A* algorithm will automatically achieve a balance between the shortest path and passing through risk areas.

[0008] The technical solution of this invention is as follows:

[0009] A flight path planning method based on an improved A* algorithm includes the following steps:

[0010] Step 1: Establish a suitable spatial raster model based on the given map data and determine the minimum raster size;

[0011] Step 2: Assign values ​​to the grids of threat coverage based on the given threat data; assign a value of 1 to the grid of terrain threat, (0,1) to the grid of radar or air defense weapon threat, and assign a value of 0 to the grid of no threat;

[0012] Step 3: Determine the grid cell containing the starting point as the starting node and the grid cell containing the target point as the target node; create the open and close tables;

[0013] Step 4: Add the starting node to the open list and begin the search;

[0014] Step 5: Repeat the following steps for expanding nodes and finding nodes:

[0015] S5.1: Pop the first node in the open list. This node is also the node with the smallest cost function value. Record this node as node A.

[0016] S5.2: Add node A to the close list, indicating that node A is the node currently being searched;

[0017] S5.3: Expand the three neighboring nodes around node A; for each neighboring node, first determine if the grid cell containing the node is 1. If it is 1, it means the node is impassable and will not be expanded; if the grid cell containing the node is (0,1), it means the node is passable, that is, the node can be expanded; denote the expanded node as B, and its cost function is f(B); the operation on node B is divided into the following three cases:

[0018] Scenario 1: If node B already appears in the open list, it means that node B has been expanded to it via other nodes. In this case, it is necessary to determine which node is better for expanding node B, and then save the best expansion in the open list. The determination is as follows: Let f_new(B) and f_old(B) represent the cost function value of the newly expanded node B and the cost function value of the already expanded node B, respectively. If f_new(B) < f_old(B), it means that the current expansion of node B is better than the previous expansion of node B. Update the cost function value of the old node B in the open list with the new cost function value of node B, change the parent node of node B in the open list to node A, and reorder the open list. If f_new(B) > f_old(B), do not make any changes to node B in the open list.

[0019] Case 2: If node B already appears in the close list, it means that the node has not only been extended to by other nodes, but has also been found during the iteration process. In this case, it is necessary to determine whether the current way to reach node B is less costly or the previous way to find node B is less costly. The determination method is the same as in case 1, except that if f_new(B) < f_old(B), it is necessary not only to update the cost function value of node B and its corresponding parent node, but also to remove node B from the close list, add it back to the open list, and sort the open list.

[0020] Case 3: If node B appears in neither the open list nor the close list, it means that node B is a newly expanded node. Add the newly expanded node B to the open list and reorder the open list.

[0021] S5.4: If the node popped from the open list is the target node, it means that a safe path from the starting point to the target point has been found, and the loop in step 5 is terminated;

[0022] S5.5: Starting from the target node, backtrack upwards through the parent node of each node, directly back to the starting node, to obtain all nodes on the path from the starting node to the target node; during the backtracking process, if multiple nodes are on the same straight line, only the nodes at both ends of this straight line are saved, and finally a flight path containing the starting point, target point, and flight inflection points is generated; delete the open and close tables, and the algorithm ends.

[0023] S5.6: If the target node is never popped until the open table is empty, it means that there is no path from the starting node to the target node. Delete the open table and the close table, and the algorithm ends.

[0024] Furthermore, improving the A* algorithm requires gradually expanding nodes from the starting node, selecting the node with the lowest current cost each time for expansion; the expansion of the improved A* algorithm needs to consider the motion constraints of the aircraft, treating the aircraft as a point mass model with velocity, where P represents the current position of the aircraft, the short horizontal line with an arrow represents the current direction of the aircraft, θ represents the maximum turning angle of the aircraft, and v represents the current velocity of the aircraft; based on this, the aircraft can only appear on the arc segment with P as the center, v*dt as the radius, and an angle of 2θ in the next unit time dt.

[0025] Based on the aircraft's motion model, the improved A* algorithm uses a three-neighborhood node expansion. Each time a node is expanded, only the nodes in the forward-forward, forward-upward, and forward-downward directions of the aircraft's current velocity direction are expanded, while the nodes in the up-upward, down-downward, backward-backward, backward-upward, and backward-downward directions will no longer be expanded. The improved algorithm needs to save the direction of the node being expanded each time it expands a node, as the basis for the next node expansion.

[0026] During the node expansion process, the improved A* algorithm introduces two lists, called the open list and the close list. The open list is used to store nodes that have not yet been expanded and are yet to be expanded, while the close list is used to store nodes that have already been searched. The node expansion process involves taking the best node from the open list and putting it into the close list each time, and then using this node as the current node to expand new nodes.

[0027] Furthermore, improving the A* algorithm requires establishing an evaluation function:

[0028] f(n) = g(n) + h(n)

[0029] g(n) is the cost function, representing the cost incurred from the starting point to the current node n, where the actual cost at the starting point is 0. The cost function of the improved A* algorithm is shown in the following equation:

[0030]

[0031] Where g(0) = 0;

[0032] Taking a three-neighbor node diffusion graph as an example, node B is set as the starting point; when the diffusion nodes are D and E, they are adjacent to B on the same side; when the diffusion node is C, it is diagonally adjacent to B; the constants 10 and 14 are used because computers process integer data faster.

[0033] h(n) is a heuristic function, representing the cost that may be incurred from the current node to the target node, reflecting the heuristic information of the cost function; the heuristic function of the improved A* algorithm is shown in the following formula:

[0034]

[0035] In the formula (x n ,y n ) represents the position of the current node n, (x end ,y end ) represents the position of the target node, multiplied by 10 to offset the effect of the cost function g(n) being expanded by a factor of 10;

[0036] Th(n) represents the threat value of the current node n, k represents the weight of the distance cost, and (1-k) represents the weight of the threat cost. The importance of security and distance is determined by adjusting the value of k during the pathfinding process.

[0037] f(n) is the evaluation function, which represents the cost required to reach the target node from the starting node, through node n. The values ​​of f(n) are compared, and the node n with the smallest f(n) is selected as the valid node.

[0038] Furthermore, the basic principles of the improved A* algorithm are as follows:

[0039] The basic principle of the improved A* algorithm is consistent with that of the original A* algorithm; it is a heuristic graph search algorithm. The A* algorithm establishes two tables, open and close. For nodes in the discretized spatial region, node attributes are set. Nodes awaiting expansion are placed in the open table, while nodes that have already been expanded are placed in the close table. During automatic pathfinding, the open and close tables are dynamically changing, and the same node may cycle through both tables multiple times. During node expansion, the A* algorithm sets a heuristic cost function and uses this function to find the target node. After finding the target node, the final path is obtained through path backtracking.

[0040] Furthermore, adjusting the value of k determines the importance of safety and distance during the pathfinding process. When the value of k is relatively large, it means that the distance between the current node and the target node has a greater weight, which means that the current planning places more emphasis on the flight time required for the planned flight path. This is generally suitable for missions that require the aircraft to reach the battlefield quickly. When the value of k is relatively small, it means that the threat weight of the current node is greater, which means that the current planning places more emphasis on the safety of the planned flight path. This is generally suitable for reconnaissance and patrol missions.

[0041] The beneficial effects of this invention are as follows:

[0042] This invention fully considers the special constraints of flight path planning in three-dimensional space and proposes an improved A* algorithm. The improved A* algorithm combines threat factors in three-dimensional space and aircraft maneuverability, taking into account the actual flight conditions of the aircraft when expanding nodes, and plans a flight path trajectory that meets the conditions, automatically achieving a balance between the shortest path and passing through risk areas. Attached Figure Description

[0043] Figure 1 This is a model diagram of the aircraft mass of the present invention;

[0044] Figure 2 This is the extended graph of the three neighboring nodes of the present invention. Detailed Implementation

[0045] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0046] (1) Basic principle of the improved A* algorithm

[0047] The improved A* algorithm shares the same basic principle as the original A* algorithm, being a heuristic graph search algorithm. The A* algorithm establishes two tables, open and close. For nodes in the discretized spatial region, node attributes are set. Nodes awaiting expansion are placed in the open table, while nodes that have already expanded are placed in the close table. During automatic pathfinding, the open and close tables are dynamically updated, and the same node may cycle through both tables multiple times. During node expansion, the A* algorithm sets a heuristic cost function and uses this function to find the target node. After finding the target node, the final path is obtained through path backtracking.

[0048] (2) Node Expansion

[0049] Improving the A* algorithm requires starting from the initial node and progressively expanding the nodes, selecting the node with the lowest current cost at each step. The expansion process in the improved A* algorithm needs to consider the aircraft's motion constraints, treating the aircraft as a point mass model with velocity, such as... Figure 1 As shown.

[0050] P represents the aircraft's current position, the short horizontal line with an arrow indicates the aircraft's current direction of travel, θ represents the aircraft's maximum turning angle, and v represents the aircraft's current speed. Based on this, in the next unit of time dt, the aircraft can only appear on an arc segment with P as the center, v*dt as the radius, and an angle of 2θ.

[0051] Based on the aircraft's motion model, the improved A* algorithm employs a three-neighborhood node expansion approach. Each time a node is expanded, only nodes in the forward-forward, forward-upward, and forward-downward directions of the aircraft's current velocity direction are expanded; nodes in the up-upward, down-downward, backward-backward, backward-upward, and backward-downward directions are not expanded. The improved algorithm needs to save the direction of expansion for each node expansion as the basis for the next expansion.

[0052] Specifically, such as Figure 2 As shown in the diagram, assuming the aircraft starts flying from node A to node B, when expanding from node B, it will expand to node C along the forward-forward direction of the aircraft's current speed, to node D along the forward-upward direction of the aircraft's current speed, and to node E along the forward-downward direction of the aircraft's current speed. Expanding nodes D, C, and E in the same way will result in nodes L, K, J, F, G, H, and I, respectively.

[0053] In the process of expanding nodes, the improved A* algorithm introduces two lists, called the open list and the close list. The open list stores nodes that have not yet been expanded and are yet to be expanded, while the close list stores nodes that have already been searched. The node expansion process involves taking the best node from the open list and adding it to the close list, then using this node as the current node to expand the new node.

[0054] (3) Cost function

[0055] Improving the A* algorithm requires establishing an evaluation function:

[0056] f(n) = g(n) + h(n)

[0057] g(n) is the cost function, representing the cost incurred from the starting point to the current node n. The actual cost at the starting point is 0. The cost function of the improved A* algorithm is shown in the following equation:

[0058]

[0059] Where g(0) = 0.

[0060] Taking a three-neighbor node diffusion graph as an example, node B is set as the starting point. When the diffusion nodes are D and E, they are adjacent to B on the same side; when the diffusion node is C, it is diagonally adjacent to B. The constants 10 and 14 are used because computers process integer data faster.

[0061] h(n) is a heuristic function representing the cost that may be incurred from the current node to the target node, reflecting the heuristic information of the cost function. The heuristic function of the improved A* algorithm is shown in the following equation:

[0062]

[0063] In the formula (x n ,y n ) represents the position of the current node n, (x end ,y end ) represents the position of the target node, multiplied by 10 to offset the effect of the cost function g(n) being expanded by a factor of 10.

[0064] Th(n) represents the threat value of the current node n, k represents the weight of the distance cost, and (1-k) represents the weight of the threat cost. Adjusting the value of k determines the importance of safety and distance during the pathfinding process. When the value of k is relatively large, it means that the distance between the current node and the target node has a greater weight, that is, the current planning prioritizes the flight time required to plan the flight path, which is generally suitable for missions that require the aircraft to reach the battlefield quickly. When the value of k is relatively small, it means that the threat weight of the current node is greater, that is, the current planning prioritizes the safety of the planned flight path, which is generally suitable for missions such as reconnaissance and patrol.

[0065] f(n) is the evaluation function, which represents the cost required to reach the target node from the starting node, through node n. The values ​​of f(n) are compared, and the node n with the smallest f(n) is selected as the valid node.

[0066] (4) Algorithm steps

[0067] Step 1: Establish a suitable spatial raster model based on the given map data and determine the size of the minimum raster.

[0068] Step 2: Assign values ​​to the threat coverage grids based on the given threat data. Grids with terrain threats are assigned a value of 1, grids with radar or air defense weapon threats are assigned a value of (0,1), and grids with no threat are assigned a value of 0.

[0069] Step 3: Determine the starting point as the starting node and the target point as the target node. Create the open and close tables.

[0070] Step 4: Add the starting node to the open list and begin the search.

[0071] Step 5: Repeat the following steps for expanding nodes and finding nodes:

[0072] S5.1: Pop the first node in the open list. This node is also the node with the smallest cost function value. Record this node as node A.

[0073] S5.2: Add node A to the close list, indicating that node A is the node currently being searched.

[0074] S5.3: Expand the three neighboring nodes around node A. For each neighboring node, first check if the grid cell containing the node is 1. If it is 1, the node is impassable and will not be expanded. If the grid cell containing the node is (0,1), the node is passable and can be expanded. Let the expanded node be denoted as B, and its cost function be f(B). The operation on node B is divided into the following three cases:

[0075] ① If node B already appears in the open list, it means that node B has been expanded to it via other nodes. At this point, it's necessary to determine which node expansion was better and then save the best expansion in the open list. The determination is as follows: Let f_new(B) and f_old(B) represent the cost function value of the newly expanded node B and the cost function value of the already expanded node B, respectively. If f_new(B) < f_old(B), it means that the current expansion of node B is better than the previous expansion. Update the old cost function value of node B in the open list with the new cost function value of node B, change the parent node of node B in the open list to node A, and reorder the open list. If f_new(B) > f_old(B), do not make any changes to node B in the open list.

[0076] ② If node B already appears in the closed list, it means that the node has not only been extended to by other nodes, but has also been found during the iteration process. In this case, it is necessary to determine whether the current way to reach node B has a lower cost or the previous way to find node B has a lower cost. The determination method is the same as in ①, except that if f_new(B) < f_old(B), it is necessary not only to update the cost function value of node B and its corresponding parent node, but also to remove node B from the closed list, re-add it to the open list, and sort the open list.

[0077] ③ If node B appears in neither the open list nor the close list, it means that node B is a newly expanded node. Add the newly expanded node B to the open list and reorder the open list.

[0078] S5.4: If the node popped from the open table is the target node, it means that a safe path from the starting point to the target point has been found, and the loop in step 5 is terminated.

[0079] S5.5: Starting from the target node, backtrack upwards through the parent node of each node, directly back to the starting node, to obtain all nodes on the path from the starting node to the target node. During the backtracking process, if multiple nodes are on the same straight line, only the nodes at both ends of this line are saved, ultimately generating a flight path containing the starting point, target point, and flight inflection points. The open and close tables are then deleted, and the algorithm ends.

[0080] S5.6: If the target node is never popped until the open table is empty, it means that there is no path from the starting node to the target node. Delete the open table and the close table, and the algorithm ends.

[0081] The above-described embodiments are merely one implementation of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention should be determined by the appended claims.

Claims

1. A flight path planning method based on an improved A algorithm, characterized in that, The method comprises the following steps: Step 1: according to the given map data, a suitable spatial grid model is established, and the size of the minimum grid is determined; Step 2: according to the given threat data, the grid covered by the threat is valued; the grid value of the terrain threat is 1, the grid value of the radar or air defense weapon threat is (0, 1), and the grid value of the non-threat is 0; Step 3: the grid where the starting point is located is determined as the starting node, and the grid where the target point is located is determined as the target node; an open table and a close table are established; Step 4: the starting node is put into the open table, and the search is started; Step 5: the following node expansion and node search steps are executed in a loop: S5.1: the first node in the open table is popped out, which is also the node with the minimum cost function value, and the node is recorded as node A; S5.2: node A is put into the close table, indicating that node A is the node being currently searched; S5.3: 3 adjacent nodes around node A; for each adjacent node, first determine whether the grid where the node is located is 1, if it is 1, it means that the node cannot be passed through, and the node will not be expanded; if the grid where the node is located is (0, 1), it means that the node can be passed through, that is, the node can be expanded; mark the expanded node as B, and the cost function of the node is ; the operation on node B is divided into the following three cases: Case 1: If node B has already appeared in the open list, it means that node B has been expanded via other nodes, at this time, it is necessary to judge which node B is better expanded via which node, and then the best expansion of node B is saved in the open list; the judgment is as follows: respectively use and to represent the cost function value of the newly expanded node B and the cost function value of the already expanded node B; if , it means that the current expansion of node B is better than the previous expansion of node B, the old cost function value of node B in the open list is updated with the new cost function value of node B, the parent node of node B in the open list is changed to node A, and the open list is reordered; if , no change is made to node B in the open list; Case 2: If node B has already appeared in the close table, it means that the node has not only been expanded via other nodes, but also been found in the iteration process. At this time, it is necessary to determine whether the way of currently reaching node B is more cost-effective or the way of previously searching for node B is more cost-effective; the determination method is the same as that in case 1, except that if not only the cost function value of node B and the corresponding parent node need to be updated, but also node B needs to be removed from the close table, re-added to the open table, and the open table needs to be sorted; Case 3: if node B does not appear in the open table or the close table, it is indicated that node B is a newly expanded node, and node B is added to the open table, and the open table is reordered; S5.4: if the node popped out from the open table is the target node, it is indicated that a path from the starting point to the target point has been found, and the loop in step 5 is terminated; S5.5: starting from the target node, the parent node of each node is traced back upward, and the path from the starting node to the target node is directly traced back to the starting node; during the tracing back process, if multiple nodes are on the same straight line, only the two end nodes on the straight line are saved, and finally a flight path containing the starting point, the target point and the flight inflection point is generated; the open table and the close table are deleted, and the algorithm ends; S5.6: if the target node is not popped out until the open table is empty, it is indicated that there is no path from the starting node to the target node, and the open table and the close table are deleted, and the algorithm ends; Improvement A The algorithm needs to build an evaluation function: The cost function is given by The cost spent from the start point to the current node The cost function of the algorithm is given by wherein ; Taking a three-neighbor node diffusion graph as an example, node B is set as the starting point; When the diffusion node is D and E, B is a same-side adjacent relationship; When the diffusion node is C, B is a diagonal adjacent relationship; constants 10 and 14 are adopted because the computer processes integer data faster; As a heuristic function, it represents the cost value that can be spent from the current node to the target node, embodying the heuristic information of the cost function; the improved A The heuristic function of the algorithm is shown in the following formula: wherein , ) represents the position of the current node , ( , ) represents the position of the target node, and is multiplied by 10 to offset the effect of the cost function expansion by a factor of 10. represents a threat value of the current node represents a weight of the distance cost represents a weight of the threat cost, by adjusting the size of determines the importance of security and distance in the routing process;​ To evaluate the function, the generation cost required to reach the target node from the start node via the node is represented, and a comparison is made on the size of the generation cost to select the node with the smallest generation cost as the effective node. as the effective node.​ 2. The improved A Flight path planning method based on the algorithm, characterized in that, Improvement A The algorithm needs to spread the nodes step by step from the starting node, and each time a node with the minimum current cost is selected for expansion; Improvement A The spread of the algorithm needs to consider the motion constraint of the aircraft, regarding the aircraft as a particle model with speed, P represents the current position of the aircraft, the short horizontal line with an arrow represents the current direction of the aircraft, The maximum turning angle of the aircraft is represented by, and the current speed of the aircraft is represented by v; on this basis, the aircraft can only appear on the arc segment with P as the center, v dt as the radius, and the included angle as at the next unit time dt; According to the motion model of the aircraft, the improved A The algorithm adopts three-neighbor node expansion. Each time the node is expanded, only the nodes in the front-front direction, the front-up direction and the front-down direction of the current speed direction of the aircraft are expanded, and the nodes in the upper-up direction, the lower-down direction, the rear-rear direction, the rear-up direction and the rear-down direction will not be expanded. The improved algorithm needs to save the expanded direction of the node at each node expansion, as the basis for the next node expansion; In the process of expanding nodes, the improved A The algorithm introduces two lists, called open list and close list respectively, where the open list is used to store the nodes which have not been expanded and are to be expanded, and the close list is used to store the nodes which have been searched; The node expansion process is that a best node is taken out from the open table and put into the close table each time, and then the node is used as a current node to expand a new node.

3. The improved A Flight path planning method based on the algorithm, characterized in that, Improvement A Basic principle of the algorithm: Improvement A The basic principle of the algorithm is the same as A The algorithm is a heuristic graph search algorithm; A The algorithm sets up two tables, open and close, for the nodes in the discretized space region, sets node attributes, and puts the nodes to be expanded into the open table and the nodes that have been expanded into the close table; during the automatic pathfinding process, the open table and the close table are in dynamic change, and the same node may be circulated in the open table and the close table multiple times; during the node diffusion process, A The algorithm sets a heuristic cost function to find the target node using the cost function; After the target node is found, the final path is obtained through path backtracking.

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