Variable-step-size air route planning method and system based on unmanned aerial vehicle performance

Abstract

The invention provides a variable step route planning method and system based on unmanned aerial vehicle performance, and relates to the technical field of aeronautical engineering.The method comprises the following steps that flight information of an unmanned aerial vehicle is obtained, the position of a starting point of the unmanned aerial vehicle is initialized, and the course of the starting point serves as the initial flight direction of the unmanned aerial vehicle; the starting point position of the unmanned aerial vehicle is added to a preset to-be-explored node storage list, and a node with the minimum search cost is selected from the to-be-explored node storage list to serve as a current node; and moving the current node into a preset explored node storage list, expanding a neighborhood point of the current node by using a waypoint neighborhood search method, and updating the current node in combination with the search cost and the list position of the neighborhood point. According to the invention, route planning can be carried out on the premise of considering the kinetic energy of the fixed-wing unmanned aerial vehicle, and the traceability of the planned route is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation engineering. Specifically, it particularly relates to a variable-step path planning method and system based on the performance of an unmanned aerial vehicle (UAV). Background Art

[0002] The sparse A-star algorithm is a commonly used heuristic search method in UAV path planning. The design of the search step size affects the search efficiency and the final path. Existing works on the design of the step size include "UAV path planning based on variable-step sparse A-star algorithm" and "Variable-step A* algorithm in UAV path planning", etc. By controlling the expansion step size, different step size designs in different stages are realized to more flexibly avoid obstacles and complete path planning.

[0003] Due to its special aerodynamic characteristics, a fixed-wing UAV cannot fly arbitrarily in space, which limits the length of the flight segment during path planning. First, the minimum length of the flight segment in path planning is restricted by the upper limit of the UAV's maneuverability. The UAV cannot track a flight segment that does not meet the turning radius constraint. Second, the turning angle of the UAV affects the minimum flight segment size. The larger the turning angle, the longer the required flight segment length. When the UAV tracks a flight path that does not meet the segment constraints, a large deviation will occur, and even flight safety may be affected. Selecting a larger fixed flight segment step size to search for waypoints can meet the requirements of the UAV's maneuverability and turning angle constraints. However, due to the constraints of the flight mission area, the fixed-step search will affect the success rate of waypoint search and lead to planning failure.

[0004] Regarding the problems in the related technology, no effective solution has been proposed yet. Summary of the Invention

[0005] In view of this, the present invention provides a variable-step path planning method and system based on the performance of an unmanned aerial vehicle to solve the above-mentioned problems.

[0006] To solve the above problems, the specific technical solutions adopted by the present invention are as follows:

[0007] According to one aspect of the present invention, a variable-step path planning method based on the performance of an unmanned aerial vehicle is provided. The method includes the following steps:

[0008] S1. Obtain the flight information of the UAV, initialize the starting point position of the UAV, and take the heading of the starting point as the initial flight direction of the UAV;

[0009] S2. Add the starting point position of the UAV to a pre-set list for storing nodes to be explored, and select the node with the minimum search cost from the list for storing nodes to be explored as the current node;

[0010] S3. Determine whether the current node meets the stop condition. If it does, stop the search and trace back to find all the predecessor points to form a path. If not, execute step S4;

[0011] S4. Move the current node into the pre-set explored node storage list, use the waypoint neighborhood search method to expand the neighborhood points of the current node, update the current node in combination with the search cost and list position of the neighborhood points, and return to step S3 until the end point is found.

[0012] Preferably, the flight information of the drone includes: the starting point position and attitude, the target point position and attitude, the turning performance constraint, and the expansion angle.

[0013] Preferably, adding the starting point position of the drone to the pre-set to-be-explored node storage list and selecting the node with the smallest search cost from the to-be-explored node storage list as the current node includes the following steps:

[0014] S21. After adding the starting point position of the drone to the pre-set to-be-explored node storage list, regard all the nodes in the to-be-explored node storage list as candidate nodes;

[0015] S22. Calculate the actual cost value between the candidate node and the starting point and the distance cost value between the candidate node and the target end point respectively;

[0016] S23. Add the actual cost value and the distance cost value to obtain the search cost of the candidate node;

[0017] S24. Sort the candidate nodes according to the search cost of the candidate nodes and select the candidate node with the smallest search cost as the current node.

[0018] Preferably, the calculation formula for the actual cost value between the candidate node and the starting point is:

[0019]

[0020] In the formula, g(n) represents the actual cost value from candidate node n to the starting point, and d n-1,n represents the distance from node n - 1 to node n.

[0021] Preferably, the calculation formula for the distance cost value between the candidate node and the target end point is:

[0022] h(n) = k1γ + k2l;

[0023] In the formula, h(n) represents the distance cost value from candidate node n to the target end point, γ represents the deviation angle between the direction of candidate node n and the connection line between candidate node n and the target end point, and l represents the Euclidean distance between candidate node n and the target end point.

[0024] Preferably, the stop condition includes: the current point enters the end - point neighborhood and satisfies the termination condition.

[0025] Preferably, moving the current node into a pre - set explored node storage list, expanding the neighborhood points of the current node using the waypoint neighborhood search method, updating the current node by combining the search cost and list position of the neighborhood points, and returning to step S3 until the end - point is found includes the following steps:

[0026] S41. After moving the current node into a pre - set explored node storage list, determine the neighborhood search range of the current node based on the position and route of the current node and the pre - set turning direction;

[0027] S42. For each neighborhood point, calculate the distance from the current point to each neighborhood point within its neighborhood using the distance calculation formula, and calculate the direction of the neighborhood point according to the heading of the current point;

[0028] S43. Calculate the search cost of the neighborhood point, update the current node by combining the list position of the neighborhood point, and return to step S3 to continue the search.

[0029] Preferably, the distance calculation formula is:

[0030]

[0031] where d n,i represents the distance from the current point n to the neighborhood points within its neighborhood, R min represents the minimum available turning radius of the UAV, α n represents the turning angle of the current point n, α ni represents the turning angle of the i - th point within the neighborhood of the current point n.

[0032] Preferably, calculating the search cost of the neighborhood point, updating the current node by combining the list position of the neighborhood point, and returning to step S3 to continue the search includes:

[0033] If the neighborhood point is in the explored node storage list, return to step S3;

[0034] If the neighborhood point is in the to - be - explored node storage list, compare the current actual cost value of the neighborhood point with its original actual cost value. If the current actual cost value of the neighborhood point is less than its original actual cost value, update the actual cost value of the neighborhood point, and use the predecessor point of the neighborhood point as the current node. If the current actual cost value of the neighborhood point is not less than its original actual cost value, return to step S3.

[0035] According to another aspect of the present invention, there is provided a variable-step path planning system based on the performance of an unmanned aerial vehicle (UAV), which system includes: a data acquisition and initialization module, a current node selection module, a current node judgment module, and a current node update module;

[0036] The data acquisition and initialization module is configured to acquire the flight information of the UAV, initialize the starting point position of the UAV, and use the heading of the starting point as the initial flight direction of the UAV;

[0037] The current node selection module is configured to add the starting point position of the UAV to a pre-set list for storing nodes to be explored, and select the node with the minimum search cost from the list for storing nodes to be explored as the current node;

[0038] The current node judgment module is configured to judge whether the current node meets the stop condition. If it meets, stop the search and backtrack to find all the precursor points to form a path. If it does not meet, update the current node through the current node update module;

[0039] The current node update module is configured to move the current node into a pre-set list for storing explored nodes, expand the neighborhood points of the current node by using the waypoint neighborhood search method, update the current node in combination with the search cost and list position of the neighborhood points, and return to step S3 until the end point is found.

[0040] The beneficial effects of the present invention are as follows: The present invention can adaptively calculate the search step size according to the turning angle of the current waypoint and the turning angle of the next waypoint under the performance constraints of the UAV tracking waypoints, and can perform path planning on the premise of considering the maneuverability of a fixed-wing UAV, ensuring the traceability of the planned route. The algorithm uses the variable-step search method to make full use of the available flight area of the UAV, making the algorithm more adaptable to the path planning of fixed-wing aircraft in narrow and complex areas, and the planned route is more reasonable. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0042] Figure 1 is a flowchart of a variable-step path planning method based on the performance of an unmanned aerial vehicle according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of the UAV waypoint tracking trajectory in a variable-step path planning method based on the performance of an unmanned aerial vehicle according to an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of waypoint search in a variable-step path planning method based on UAV performance according to an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of distance cost estimation calculation in a variable-step path planning method based on UAV performance according to an embodiment of the present invention;

[0046] Figure 5 It is the path planning result of the algorithm of the present invention under fence constraint in a variable-step path planning method based on UAV performance according to an embodiment of the present invention;

[0047] Figure 6 It is a schematic diagram of the path planning result of the fixed-step A* algorithm without fence constraint in a variable-step path planning method based on UAV performance according to an embodiment of the present invention;

[0048] Figure 7 It is a schematic diagram of the path planning result of the algorithm of the present invention without fence constraint in a variable-step path planning method based on UAV performance according to an embodiment of the present invention;

[0049] Figure 8 It is a principle block diagram of a variable-step path planning system based on UAV performance according to an embodiment of the present invention.

[0050] In the figure:

[0051] 1. Data acquisition and initialization module; 2. Current node selection module; 3. Current node judgment module; 4. Current node update module. Detailed implementation manners

[0052] In order to enable those skilled in the art of the present technology to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0053] According to an embodiment of the present invention, a variable-step path planning method and system based on UAV performance are provided.

[0054] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation manners. As Figure 1 shown, according to an embodiment of the present invention, a variable-step path planning method based on UAV performance is provided, and the method includes the following steps:

[0055] S1. Obtain the flight information of the UAV, initialize the starting point position of the UAV, and use the heading of the starting point as the initial flight direction of the UAV;

[0056] As a preferred embodiment, the flight information of the UAV includes: starting point position and attitude, target point position and attitude, turning performance constraint, and expansion angle.

[0057] S2. Add the starting point position of the UAV to a preset list of nodes to be explored, and select the node with the minimum search cost from the list of nodes to be explored as the current node;

[0058] It should be noted that under the performance constraint conditions of a fixed-wing aircraft, the relationship between the flight trajectory of the aircraft and the planned waypoints is as Figure 2 . A0, A1, A2, and A3 are the planned waypoints. Taking the flight trajectory of the UAV from point A1 to A2 as an example, the UAV's flight of one flight segment can be divided into three stages: 1. The {A1, A 1-2} flight segment, where the UAV completes the previous heading turn in this segment; 2. The {A 1-2 , A 2-1} flight segment, where the UAV adjusts its attitude in this segment to prepare for the next turn; 3. The {A 2-1 , A2} flight segment, where the UAV enters the next heading turn. According to the Figure 1 geometric relationship, the length calculation formula of the flight segment {A1, A2} is expressed as:

[0059]

[0060] In the formula, R represents the turning radius of the UAV, a1 and a2 represent the angles of the UAV's two turns, and L represents the straight flight distance connecting the two process turns, which is usually selected as a fixed value. To ensure the flight safety of the UAV and make full use of the UAV's maneuverability, R selects the minimum available turning radius R min of the UAV. Thus, the flight segment length is related to the turning angles a1 and a2.

[0061] In addition, waypoint neighborhood search:

[0062] In the waypoint neighborhood search algorithm, each waypoint has two parameters, namely the turning angle a and the waypoint heading θ. a represents the turning direction and angle of this point, and the turning angle of the starting point is 0, that is, a0 = 0°. In the present invention, the turning direction is discretized into 7 cases: -90°, -60°, -30°, 0°, 30°, 60°, 90°. Starting from the initial point P0, there are 7 waypoints in the neighborhood of P0, as Figure 3 shown.

[0063] P 01 represents the straight flight waypoint, and the turning angle a01 = 0°, the direction of the waypoint is; θ 01 = θ0 + 0°;

[0064] P 02 represents a waypoint with a 30° right turn, and the turning angle a 02 = -30°, the direction of the waypoint is θ 02 = θ0 - 30°;

[0065] P 03 represents a waypoint with a 30° left turn, and the turning angle a 03 = 30°, the direction of the waypoint is θ 03 = θ0 + 30°;

[0066] P 04 represents a waypoint with a 60° right turn, and the turning angle a 04 = -60°, the direction of the waypoint is θ 04 = θ0 - 60°;

[0067] P 05 represents a waypoint with a 60° left turn, and the turning angle a 05 = 60°, the direction of the waypoint is θ 05 = θ0 + 60°;

[0068] P 06 represents a waypoint with a 90° right turn, and the turning angle a 06 = -90°, the direction of the waypoint is θ 06 = θ0 - 90°;

[0069] P 07 represents a waypoint with a 90° left turn, and the turning angle a 07 = 90°, the direction of the waypoint is θ 07 = θ0 + 90°;

[0070] a0 represents the direction of the initial waypoint.

[0071] Assume that P is selected during the second-step search 02 as the current point, then the waypoint direction θ 02 is 30°. At this time, mark P 02 as point P1, and mark θ 02 as θ1. Search for neighborhood points in the direction of θ1 at point P1, and each neighborhood point is distinguished by the turning angle.

[0072] P 11 represents a straight-ahead waypoint, and the turning angle a 11 = 0°, the direction of the waypoint is; θ 11 = θ1 + 0°;

[0073] P 12 represents a waypoint with a 30° right turn, and the turning angle a 12=-30°, the direction of the waypoint is θ 12 =θ1 - 30°;

[0074] P 13 represents a waypoint with a 30° left turn, the turning angle a 13 =30°, the direction of the waypoint is θ 13 =θ1 + 30°;

[0075] P 14 represents a waypoint with a 60° right turn, the turning angle a 14 =-60°, the direction of the waypoint is θ 14 =θ1 - 60°;

[0076] P 15 represents a waypoint with a 60° left turn, the turning angle a 15 =60°, the direction of the waypoint is θ 15 =θ1 + 60°;

[0077] P 16 represents a waypoint with a 90° right turn, the turning angle a 16 =-90°, the direction of the waypoint is θ 16 =θ1 - 90°;

[0078] P 17 represents a waypoint with a 90° left turn, the turning angle a 17 =90°, the direction of the waypoint is θ 17 =θ1 + 90°;

[0079] And so on until the nth search, when P n enters the neighborhood of the end point O, and determines whether the angle between the route {P n-1 , P n} and {P n , O} satisfies the aircraft maneuver constraint conditions. If it is satisfied, the search stops; otherwise, the search continues.

[0080] Based on the length calculation formula of the flight segment, the distance from the current point n to the points within its neighborhood can be calculated. The distance calculation formula is:

[0081]

[0082] In the formula, d n,i represents the distance from the current point n to the ith neighborhood point within its neighborhood, R min represents the minimum available turning radius of the UAV, a n represents the turning angle of the current point n, a niIt represents the turning angle of the i-th point within the neighborhood of the current point n. It can be seen that during the spatial search of this algorithm, the search step size adaptively changes according to the turning angle of the UAV at the waypoint and the turning angle of the next point in the future. Compared with the fixed-step search method, the variable-step search can effectively shorten the step distance, more easily find a feasible solution in a local narrow space, and improve the success rate of UAV route planning. At the same time, the variable step size satisfies the minimum turning radius constraint of the UAV and can ensure that the fixed-wing UAV flies along the connection line of the waypoints.

[0083] In addition, for the cost function calculation, the cost function during the algorithm search process is as follows:

[0084] f(n) = g(n) + h(n);

[0085] In the formula, g(n) is the actual cost from the starting point to the current point:

[0086]

[0087] h(n) is the estimated distance cost from the current point to the target end point. The expression of h(n) is:

[0088] h(n) = k1γ + k2l;

[0089] The distance cost consists of two parts. As Figure 4 shown, γ is the deviation angle between the direction of the current point n and the connection line between point n and the end point, and l is the Euclidean distance between point n and the end point; h(n) is used to guide the algorithm to search for waypoints in the direction of the end point, and k1 and k2 are weighting parameters, which are adjusted according to the actual usage situation.

[0090] As a preferred implementation manner, adding the starting point position of the UAV to a pre-set list of nodes to be explored and selecting the node with the minimum search cost from the list of nodes to be explored as the current node includes the following steps:

[0091] S21. After adding the starting point position of the UAV to the pre-set list of nodes to be explored, all the nodes in the list of nodes to be explored are used as candidate nodes;

[0092] S22. Calculate the actual cost value between the candidate node and the starting point and the distance cost value between the candidate node and the target end point respectively;

[0093] As a preferred implementation manner, the calculation formula for the actual cost value between the candidate node and the starting point is:

[0094]

[0095] In the formula, g(n) represents the actual cost value from the candidate node n to the starting point, and d n-1,nIndicates the distance between node n - 1 and node n.

[0096] As a preferred embodiment, the calculation formula for the distance cost value between the candidate node and the target end point is:

[0097] h(n) = k1γ + k2l;

[0098] In the formula, h(n) represents the distance cost value from the candidate node n to the target end point, γ represents the deviation angle between the direction of the candidate node n and the line connecting the candidate node n and the target end point, and l represents the Euclidean distance between the candidate node n and the target end point.

[0099] S23. Add the actual cost value and the distance cost value to obtain the search cost of the candidate node;

[0100] S24. Sort the candidate nodes according to the search cost of the candidate nodes, and select the candidate node with the smallest search cost as the current node.

[0101] S3. Determine whether the current node meets the stop condition. If it meets, stop the search and backtrack to find all the precursor points to form a path. If it does not meet, execute step S4;

[0102] As a preferred embodiment, the stop condition includes: the current point enters the end point neighborhood and meets the termination condition.

[0103] S4. Move the current node into the pre - set explored node storage list, use the waypoint neighborhood search method to expand the neighborhood points of the current node, update the current node by combining the search cost and the list position of the neighborhood points, and return to step S3 until the end point is found.

[0104] As a preferred embodiment, moving the current node into the pre - set explored node storage list, using the waypoint neighborhood search method to expand the neighborhood points of the current node, updating the current node by combining the search cost and the list position of the neighborhood points, and returning to step S3 until the end point is found includes the following steps:

[0105] S41. After moving the current node into the pre - set explored node storage list, determine the neighborhood search range of the current node based on the position and route of the current node, and the preset turning direction;

[0106] S42. For each neighborhood point, use the distance calculation formula to calculate the distance from the current point to each neighborhood point within its neighborhood, and calculate the direction of the neighborhood point according to the heading of the current point;

[0107] S43. Calculate the search cost of the neighborhood point, update the current node by combining the list position of the neighborhood point, and return to step S3 to continue the search.

[0108] As a preferred embodiment, calculating the search cost of neighboring points, updating the current node in combination with the list position of neighboring points, and returning to step S3 for continued search includes:

[0109] If the neighboring point is in the explored node storage list, return to step S3;

[0110] If the neighboring point is in the to-be-explored node storage list, compare the current actual cost value of the neighboring point with the original actual cost value of the neighboring point. If the current actual cost value of the neighboring point is less than the original actual cost value of the neighboring point, update the actual cost value of the neighboring point, and use the predecessor point of the neighboring point as the current node. If the current actual cost value of the neighboring point is not less than the original actual cost value of the neighboring point, return to step S3.

[0111] Specifically, the simulation objective is to plan the shortest route from the starting point to the ending point for a fixed-wing unmanned aerial vehicle within the air fence restriction. The starting point coordinates of the route are (0, 0), the heading is 0°, the ending point coordinates of the route are (-2400, 4260), the heading is 0°, and the coordinates of the four vertices of the rectangular air fence are (1000, -2000), (-3000, -2000), (-3000, 5000), and (1000, 5000) in sequence. The minimum turning radius of the fixed-wing unmanned aerial vehicle is 800 m. In the simulation, the fixed-step A* algorithm and the algorithm of the present invention are used for comparison.

[0112] The route planning algorithm of the present invention has found the flight path connecting the starting point and the ending point under the constraints of the fence and the turning radius. The results are shown in Figure 5 as follows. The fixed-step A* route planning algorithm fails in planning due to the fence restriction.

[0113] Based on the above simulation constraint conditions, the fence restriction is removed and the route planning simulation is carried out again. When the fence is removed, both the fixed-step A* algorithm and the algorithm of the present invention successfully complete the route planning. However, due to the relatively short distance between the starting point and the ending point, the fixed-step A* algorithm realizes the route planning from the starting point to the ending point through multiple route turns. The planned route contains 9 points, as shown in Figure 6 . While the algorithm of the present invention has better local optimization ability, and the route planning result only contains 4 points, as shown in Figure 7 . Comparing the two results, the route planned by the algorithm of the present invention is more reasonable.

[0114] As Figure 8 shown, according to another embodiment of the present invention, a variable-step route planning system based on the performance of an unmanned aerial vehicle is provided. The system includes: a data acquisition and initialization module 1, a current node selection module 2, a current node judgment module 3, and a current node update module 4;

[0115] The data acquisition and initialization module 1 is used to acquire the flight information of the UAV, initialize the starting point position of the UAV, and use the heading of the starting point as the initial flight direction of the UAV.

[0116] The current node selection module 2 is used to add the starting point position of the UAV to a preset list of unexplored node storage, and select the node with the minimum search cost from the list of unexplored node storage as the current node.

[0117] The current node judgment module 3 is used to judge whether the current node meets the stop condition. If it meets, the search is stopped and all predecessor points are found by backtracking to form a path. If it does not meet, the current node is updated by the current node update module 4.

[0118] The current node update module 4 is used to move the current node into a preset list of explored node storage, expand the neighborhood points of the current node by using the waypoint neighborhood search method, update the current node by combining the search cost and list position of the neighborhood points, and return to step S3 until the end point is found.

[0119] In summary, by means of the above technical solutions of the present invention, the present invention can adaptively calculate the search step size according to the current waypoint turning angle and the next waypoint turning angle under the performance constraints of the UAV tracking waypoints, and can perform route planning on the premise of considering the maneuverability of the fixed-wing UAV, ensuring the traceability of the planned route. The algorithm uses the variable step size search method to make full use of the available flight area of the UAV, making the algorithm more adaptable to the route planning of fixed-wing aircraft in narrow and complex areas, and the planned route is more reasonable.

[0120] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0121] The specific embodiments described above have further detailed the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A variable step length route planning method based on UAV performance, characterized in that: The method comprises the following steps: S1. Obtain the flight information of the drone, initialize the starting point of the drone, and use the heading of the starting point as the initial flight direction of the drone; S2, adding the starting point position of the drone to a preset storage list of nodes to be explored, and selecting a node with the smallest search cost from the storage list of nodes to be explored as the current node; S3, determine whether the current node meets the stop condition, if so, stop searching and reverse to find all the predecessor points to form the path, if not, execute step S4; S4, move the current node into the preset storage list of explored nodes, use the waypoint neighborhood search method to expand the neighborhood points of the current node, combine the search cost and list position of the domain point, update the current node, and return to step S3 until the end point is found.

2. The variable step length route planning method based on UAV performance according to claim 1 is characterized in that: The flight information of the UAV includes: starting point position and attitude, target point position and attitude, turning performance constraint and expansion angle.

3. The variable step length route planning method based on UAV performance according to claim 1 is characterized in that: The step of adding the starting point of the drone to a preset storage list of nodes to be explored, and selecting a node with the minimum search cost as the current node from the storage list of nodes to be explored includes the following steps: S21, after adding the starting point position of the drone to a preset storage list of nodes to be explored, all nodes in the storage list of nodes to be explored are taken as candidate nodes; S22, respectively calculating the actual cost value between the candidate node and the starting point and the distance cost value between the candidate node and the target end point; S23, adding the actual cost value to the distance cost value to obtain the search cost of the candidate node; S24. Sort the candidate nodes according to their search costs, and select the candidate node with the smallest search cost as the current node.

4. The variable step length route planning method based on UAV performance according to claim 3 is characterized in that: The calculation formula of the actual cost value between the candidate node and the starting point is: In the formula, g(n) represents the actual cost between candidate node n and the starting point, d n-1,n Represents the distance from node n-1 to node n.

5. The variable step length route planning method based on UAV performance according to claim 3 is characterized in that: The calculation formula of the distance cost value between the candidate node and the target end point is: h(n)=k1γ+k2l; Where h(n) represents the distance cost from candidate node n to the target endpoint, γ represents the deviation angle between the candidate node n and the line connecting the candidate node n and the target endpoint, and l represents the Euclidean distance between the candidate node n and the target endpoint.

6. The variable step length route planning method based on UAV performance according to claim 1 is characterized in that: The stopping condition includes: the current point enters the neighborhood of the end point and satisfies the termination condition.

7. The variable step length route planning method based on UAV performance according to claim 1 is characterized in that: The step of moving the current node into a preset storage list of explored nodes, expanding the neighborhood points of the current node using the waypoint neighborhood search method, updating the current node in combination with the search cost and list position of the neighborhood points, and returning to step S3 until the end point is found includes the following steps: S41, after the current node is moved into a preset explored node storage list, a neighborhood search range of the current node is determined based on the position and route of the current node and a preset turning direction; S42, for each neighborhood point, using a distance calculation formula to calculate the distance from the current point to each neighborhood point in its area, and calculating the direction of the area point according to the heading of the current point; S43, calculate the search cost of the neighborhood point, and update the current node in combination with the list position of the neighborhood point, and return to step S3 to continue searching.

8. The variable step length route planning method based on UAV performance according to claim 7 is characterized in that: The distance calculation formula is: Where, d n,i Represents the distance from the current point n to its neighboring points in the domain, R min Indicates the minimum available turning radius of the drone, a n represents the turning angle of the current point n, a ni Represents the turning angle of the i-th point in the neighborhood of the current point n.

9. The variable step length route planning method based on UAV performance according to claim 7 is characterized in that: The step of calculating the search cost of the neighboring points, updating the current node in combination with the list position of the neighboring points, and returning to step S3 to continue searching includes: If the domain point is in the explored node storage list, return to step S3; If the domain point is in the storage list of nodes to be explored, compare the current actual cost value of the neighboring point with the original actual cost value of the neighboring point. If the current actual cost value of the neighboring point is less than the original actual cost value of the neighboring point, update the actual cost value of the domain point and use the preceding point of the domain point as the current node. If the current actual cost value of the neighboring point is not less than the original actual cost value of the neighboring point, return to step S3.

10. A variable step length route planning system based on UAV performance, used to implement the variable step length route planning method based on UAV performance according to any one of claims 1 to 9, characterized in that: The system includes: a data acquisition and initialization module, a current node selection module, a current node judgment module and a current node update module; The data acquisition and initialization module is used to acquire the flight information of the UAV, initialize the starting point position of the UAV, and use the heading of the starting point as the initial flight direction of the UAV; The current node selection module is used to add the starting point position of the drone to a preset storage list of nodes to be explored, and select a node with the minimum search cost from the storage list of nodes to be explored as the current node; The current node judgment module is used to judge whether the current node meets the stop condition. If so, the search is stopped and all predecessor points are reversed to find the path. If not, the current node is updated through the current node update module; The current node update module is used to move the current node into a preset explored node storage list, expand the neighborhood points of the current node using the waypoint neighborhood search method, update the current node based on the search cost and list position of the domain point, and return to step S3 until the end point is found.

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