Fixed-wing aircraft route efficient planning method and device, medium and product

Abstract

The invention relates to the technical field of aircraft route planning, and particularly provides a fixed-wing aircraft route efficient planning method and device, a medium and a product, and the method comprises the steps: calculating the terrain complexity of a current node based on the terrain information of the current node; based on the fuzzy controller and the terrain complexity, calculating the distribution probability of a guiding strategy; generating a next extension node based on the search step size and the distribution probability by utilizing a guide strategy; and establishing a connection path between the current node and the next expansion node, determining that the connection path meets the dynamic constraint index and the kinematics constraint index, and taking the connection path as an optimal connection path. Fuzzy rule mapping between terrain complexity and distribution probability is designed, path planning and connection are accelerated, and improvement of route planning efficiency is facilitated. The optimal connection path meets the dynamic constraint index and the kinematics constraint index, the smooth connection path between the nodes is realized, and the traceability of the air route is ensured.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aircraft route planning, and in particular to a method, device, medium and product for efficient route planning of a fixed-wing aircraft. Background Art

[0002] The flight missions of future fixed-wing aircraft often involve dynamically changing environmental factors, such as complex environments, urban buildings, and bad weather, which increase the risk of collision between aircraft and obstacles. In addition, since fixed-wing aircraft cannot hover or climb vertically, their strict kinematic and dynamic constraints further increase the risk of collision. Therefore, the development of efficient route planning methods is crucial for fixed-wing aircraft to achieve collision-free flight.

[0003] For efficient route planning of fixed-wing aircraft, the kinodynamic RRT (Kinematic and Dynamic Rapidly-exploring Random Tree, kinodynamic RRT for short) algorithm is mostly used. However, the smooth connection constructed by this algorithm between two nodes is difficult to meet the kinematic and dynamic constraints of fixed-wing aircraft, and cannot guarantee the trackability of the planned route. Moreover, many invalid redundant nodes are prone to appear in the route planning process, resulting in low route planning efficiency, which is not suitable for scenarios that are sensitive to computational efficiency. Therefore, it is necessary to improve the kinodynamic RRT algorithm so that it can meet the application requirements of high efficiency and trackability. Summary of the invention

[0004] The present disclosure is proposed in view of the above problems, and provides a method, device, medium and product for efficient route planning of a fixed-wing aircraft.

[0005] According to one aspect of the present disclosure, there is provided a method for efficient route planning of a fixed-wing aircraft, comprising:

[0006] Calculating the terrain complexity of the current node based on the terrain information of the current node, where the current node is a node of a random tree generated with a preset route start point or end point as a starting node;

[0007] Based on the fuzzy controller and the terrain complexity of the current node, the allocation probability of the preset guidance strategy is calculated, and the allocation probability is used to determine the influence degree of different guidance strategies on the generation of the expansion node;

[0008] Using the guiding strategy, based on a preset search step length and the allocation probability, generating a next extended node of the current node, wherein the search step length is used to characterize a straight-line distance between any two adjacent nodes;

[0009] A connection path between the current node and the next extended node is established, and it is determined that the connection path satisfies a dynamic constraint index and a kinematic constraint index, and the connection path is used as an optimal connection path.

[0010] In addition, according to an efficient fixed-wing aircraft route planning method in one aspect of the present disclosure, the guidance strategy includes a potential field guidance strategy, an alternating guidance strategy, and a random guidance strategy;

[0011] Using the guiding strategy, based on a preset search step and the allocation probability, generating the next expansion node of the current node includes:

[0012] Determine the growth direction of the next extended node of the current node by using the potential field guidance strategy, determine the number of continuously generated extended nodes of the current node by using the alternating guidance strategy, and determine the random growth direction of the next extended node of the current node by using the random guidance strategy;

[0013] Determining the position of the next expansion node based on the allocation probability, the growth position, the continuous generation quantity and the random growth position;

[0014] Based on the preset search step size and the direction, a next expansion node of the current node is generated.

[0015] In addition, according to an efficient fixed-wing aircraft route planning method in one aspect of the present disclosure, the growth orientation of the next extended node of the current node is determined by using the potential field guidance strategy, including:

[0016] Adjusting the repulsive potential field of the potential field guidance strategy based on the distance correction factor of the current node and the target node, wherein the target node is the latest node of the random tree relative to the current node;

[0017] Based on the gravitational potential field of the potential field guidance strategy and the adjusted repulsive potential field, calculating the repulsive force and gravitational force generated by the potential field of the potential field guidance strategy;

[0018] Based on the repulsive force and the attractive force, determining a guiding direction of the potential field guiding strategy;

[0019] Based on the guiding direction, a growth direction of the next extended node of the current node is determined.

[0020] In addition, according to an efficient fixed-wing aircraft route planning method in one aspect of the present disclosure, the number of continuously generated extended nodes of the current node is determined by using the alternating guidance strategy, including:

[0021] Based on the terrain complexity of the current node, the alternating guidance strategy is used to calculate the number of continuous expansions of the current node;

[0022] Based on the number of consecutive expansions, the number of consecutively generated expansion nodes of the current node is determined.

[0023] In addition, according to an efficient fixed-wing aircraft route planning method in one aspect of the present disclosure, determining that the connection path satisfies a dynamic constraint index and a kinematic constraint index, and taking the connection path as an optimal connection path includes:

[0024] Based on the control input of the fixed-wing aircraft, the overload in each direction of the track coordinate system is calculated, and the overload is used as a dynamic constraint index that needs to be satisfied by the route planning, and the control input represents the acceleration of the aircraft in the earth coordinate system;

[0025] Based on the control input, minimizing a cost function, the cost function being used to characterize the cost of acceleration and transition time of the fixed-wing aircraft between the current node and the next extended node;

[0026] In the case where the connection path satisfies the dynamic constraint index and minimizes the cost function, it is determined that the connection path satisfies the kinematic constraint index, and the connection path is used as the optimal connection path.

[0027] In addition, according to an efficient fixed-wing aircraft route planning method in one aspect of the present disclosure, determining that the connection path satisfies the kinematic constraint index and taking the connection path as the optimal connection path includes:

[0028] Calculating a minimum turning radius based on the speed, maximum rolling angle and gravity acceleration of the fixed-wing aircraft, and calculating a maximum track angle based on the maximum thrust of the fixed-wing aircraft;

[0029] calculating curvature based on a parametric equation of the connection path with respect to time;

[0030] Based on a mathematical relationship between a turning radius and the curvature, confirming that the connection path satisfies a lateral kinematic constraint index using the minimum turning radius, and calculating a track angle based on the parametric equation, and confirming that the connection path satisfies a longitudinal kinematic constraint index based on the track angle and the maximum track angle;

[0031] The connection path is taken as the optimal connection path.

[0032] In addition, according to an aspect of the present disclosure, the method for efficient route planning of a fixed-wing aircraft further includes, after taking the connection path as the optimal connection path:

[0033] When the distance between the current node and the target node is less than a preset threshold value and the connection path between the current node and the target node does not collide with an obstacle, two random trees generated with a preset route start point and an end point as start nodes are connected through the connection path between the current node and the target node;

[0034] Taking the starting node on the random tree as a positioning node and taking the next node of the positioning node as a moving node;

[0035] Determining whether a connection path between the positioning node and the mobile node satisfies a dynamic constraint index and a kinematic constraint index;

[0036] If the result of the judgment is that the dynamic constraint index and the kinematic constraint index are satisfied, deleting the node between the positioning node and the mobile node;

[0037] The next node of the mobile node is updated to the mobile node, and the step of determining whether the connection path between the positioning node and the mobile node satisfies the dynamic constraint index and the kinematic constraint index is repeatedly performed until there is no next node for the mobile node, and the collision-free optimization path of the random tree is obtained.

[0038] According to another aspect of the present disclosure, there is provided a device for efficiently planning a route for a fixed-wing aircraft, comprising:

[0039] A first calculation module, configured to calculate the terrain complexity of a current node based on terrain information of the current node, wherein the current node is a node of a random tree generated with a preset route start point or end point as a starting node;

[0040] A second calculation module is used to calculate the allocation probability of the preset guidance strategy based on the fuzzy controller and the terrain complexity of the current node, and the allocation probability is used to determine the influence degree of different guidance strategies on the generation of the expansion node;

[0041] A node expansion module, used to generate the next expansion node of the current node based on a preset search step and the allocation probability by using the guidance strategy, wherein the search step is used to characterize the straight-line distance between any two adjacent nodes;

[0042] The path connection module is used to establish a connection path between the current node and the next extended node, determine whether the connection path satisfies the dynamic constraint index and the kinematic constraint index, and use the connection path as the optimal connection path.

[0043] According to another aspect of the present disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method of the above aspect.

[0044] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method of the above aspect is implemented.

[0045] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method of the above aspect is implemented.

[0046] As described in detail below, according to a method, device, medium and product for efficient route planning of a fixed-wing aircraft according to an embodiment of the present disclosure, the probability of allocation of the guidance strategy is calculated by terrain complexity, and a fuzzy rule mapping between terrain complexity and the probability of allocation of different guidance strategies is designed, thereby realizing a fuzzy self-allocation guidance strategy mechanism, accelerating path planning and connection, and being conducive to improving route planning efficiency. Moreover, the optimal connection path satisfies the dynamic constraint index and the kinematic constraint index, realizing a smooth connection path between adjacent nodes, and ensuring that the planned route is traceable.

[0047] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0049] Figure 1 The flowchart is a diagram illustrating the application of the method for efficient route planning of a fixed-wing aircraft according to an embodiment of the present disclosure.

[0050] Figure 2 is a schematic diagram illustrating the application of the potential field guidance strategy according to an embodiment of the present disclosure.

[0051] Figure 3 is a schematic diagram illustrating application of an alternating guiding strategy according to an embodiment of the present disclosure.

[0052] Figure 4 It is a schematic diagram illustrating the structure of an efficient route planning device for a fixed-wing aircraft according to an embodiment of the present disclosure.

[0053] Figure 5 It is a schematic diagram illustrating the structure of a computer device according to an embodiment of the present disclosure.

[0054] Figure 6 is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present disclosure more obvious, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described here.

[0056] The flight missions of future fixed-wing aircraft often involve dynamically changing environmental factors, such as complex environments, urban buildings, and bad weather, which increase the risk of collision between aircraft and obstacles. In addition, since fixed-wing aircraft cannot hover or climb vertically, their strict kinematic and dynamic constraints further increase the risk of collision. Therefore, the development of efficient route planning methods is crucial for fixed-wing aircraft to achieve collision-free flight.

[0057] For efficient route planning of fixed-wing aircraft, the kinodynamic RRT (Kinematic and Dynamic Rapidly-exploring Random Tree, kinodynamic RRT for short) algorithm is mostly used. However, the smooth connection constructed by this algorithm between two nodes is difficult to meet the kinematic and dynamic constraints of fixed-wing aircraft, and cannot guarantee the trackability of the planned route. Moreover, many invalid redundant nodes are prone to appear in the route planning process, resulting in low route planning efficiency, which is not suitable for scenarios that are sensitive to computational efficiency. Therefore, it is necessary to improve the kinodynamic RRT algorithm so that it can meet the application requirements of high efficiency and trackability.

[0058] In the above, with reference to the accompanying drawings, a method, device, medium and product for efficient route planning of a fixed-wing aircraft according to an embodiment of the present disclosure are described. The probability of allocation of the guidance strategy is calculated by terrain complexity, and a fuzzy rule mapping between terrain complexity and the allocation probability of different guidance strategies is designed, so as to realize a fuzzy self-allocation guidance strategy mechanism, accelerate path planning and connection, and help improve route planning efficiency. Moreover, the optimal connection path satisfies the dynamic constraint index and the kinematic constraint index, realizes a smooth connection path between adjacent nodes, and ensures that the planned route is traceable.

[0059] By introducing a distance correction factor to adjust the potential field function in the potential field guidance strategy, the directionality of route exploration can be improved and redundant nodes caused by blind sampling can be effectively reduced. The alternating guidance strategy is used to alternately explore between two independent random trees (tree 1 and tree 2). The frequency of alternation depends on the complexity of the terrain. In this process, the latest node generated by tree 1 is used as the target node for the next node generated by tree 2, and vice versa, which can accelerate the connection between the two random trees.

[0060] In order to ensure that the planned route meets the kinematic and dynamic constraints of the fixed-wing aircraft, not only the state is constrained during the sampling process, but also dynamic constraint indicators and kinematic constraint indicators are introduced when solving the optimal connection path (i.e., the optimal boundary value (OBVP) problem). If the established smooth connection path meets the constraint indicators, it will be retained. If it does not meet the constraint indicators, resampling will be performed.

[0061] By solving the optimal boundary value (OBVP) problem, the redundant nodes of the two random trees are optimized to obtain the minimum path nodes that meet the kinematic and dynamic constraints. Finally, a smooth connection between the two random trees is established based on the 3D Dubins smoothing method.

[0062] To facilitate understanding of the present embodiment, a method for efficiently planning a route for a fixed-wing aircraft disclosed in the embodiment of the present disclosure is first introduced in detail. The execution subject of the method for efficiently planning a route for a fixed-wing aircraft provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the method for efficiently planning a route for a fixed-wing aircraft may be implemented by a processor calling computer-readable instructions stored in a memory.

[0063] like Figure 1 As shown, it is a flow chart of a method for efficient route planning of a fixed-wing aircraft provided by an embodiment of the present disclosure, and the method includes S101-S104:

[0064] S101: Calculate the terrain complexity of the current node based on the terrain information of the current node.

[0065] The current node is a node of a random tree generated with the preset route start point or end point as the starting node. For example, tree 1 generated with the route start point as the starting node and tree 2 generated with the route end point as the starting node, the current node is the latest node of tree 1 or tree 2.

[0066] The terrain information includes the location of the obstacle at the current node and the equivalent geometric envelope. Specifically, the calculation formula for terrain complexity is as follows:

[0067]

[0068] Among them, δ represents the terrain complexity, α C represents the adjustable weight coefficient, Represents the position of the obstacle in the terrain information, that is, the distance between the kth obstacle and the current node n now The degree of proximity, The calculation formula is:

[0069]

[0070] Among them, B i (n now ) represents the equivalent geometric envelope of the obstacle in the terrain information, that is, the equivalent geometric envelope of the ith obstacle and the current node n now The positional relationship between them is similar to that between B k (n now ) represents the equivalent geometric envelope of the kth obstacle and the current node n now The positional relationship between them. i (n now ) is calculated as:

[0071]

[0072] Among them, a 0 , b 0 、c 0 , R a , p, q, r are parameters that determine the equivalent shape and envelope range of the obstacle, [x 0 ,y 0 ,z 0 ] is the equivalent geometric center of the obstacle.

[0073] S102: Calculating the allocation probability of a preset guidance strategy based on the fuzzy controller and the terrain complexity of the current node.

[0074] Among them, the guiding strategies include potential field guiding strategy, alternating guiding strategy and random guiding strategy, and the allocation probability is used to determine the influence of different guiding strategies on the generation of extended nodes.

[0075] Specifically, the terrain complexity is input into the fuzzy controller to obtain the allocation probabilities of the three guidance strategies.

[0076] The specific working principle of the fuzzy controller is as follows: Based on a large number of simulation tests, the fuzzy rules between the allocation probability and the terrain complexity are established in the fuzzy controller, as shown in Table 1 and Table 2.

[0077] Table 1 Fuzzy rules corresponding to terrain complexity and probability of alternating guidance strategy

[0078]

[0079] Table 2 Fuzzy rules corresponding to terrain complexity and potential field guidance strategy probability

[0080]

[0081] In order to prevent the distribution probability from changing suddenly due to excessive or small terrain complexity, an S-shaped membership function is used when the linguistic variables are NB and PB, and a triangular membership function is used for other linguistic variables. In order to guide the reasonable allocation of the guidance strategy, the fuzzy rules between terrain complexity and distribution probability need to follow the following trend:

[0082] When the terrain complexity is high, the allocation probability of the potential field guidance strategy is increased to avoid collisions between the generated extension nodes and obstacles. When the terrain complexity is low, the allocation probability of the alternating guidance strategy is increased to accelerate the connectivity between the two random trees. The allocation probabilities of these two strategies show opposite trends. At the same time, it is also necessary to consider that in order to avoid falling into the local optimum during the guidance process, which makes the extension nodes of the current node unable to be explored, a certain allocation probability of the random guidance strategy must be guaranteed. The probabilities of the three guidance strategies are specified as follows:

[0083]

[0084] Among them, P s represents the allocation probability of the potential field guided strategy, P j represents the allocation probability of the alternating bootstrap strategy, P r represents the allocation probability of the random bootstrap strategy.

[0085] Optionally, the probability distribution of the k-th node is most closely related to the current terrain complexity. In addition, it is also affected by the terrain complexity of the k-1-th node, which means that the k-th node has entered the current area and is affected by the terrain complexity trend of the area. Therefore, the terrain complexity of the k-th and k-1-th nodes can be used as the input of the fuzzy controller, and its domain range is [0,1]. The probability P of the potential field guided strategy s The probability P of the alternating bootstrap strategy jAs the output of the fuzzy controller, its domain range is [0.2, 0.6]. The probability of the two changes in opposite trends, and the sum of the probabilities does not exceed 1. Since the sum of the allocation probabilities of the three guidance strategies is 1, the allocation probability of the random guidance strategy can be obtained. The fuzzy subsets of terrain complexity and allocation probability are [NB, NM, NS, ZO, PS, PM, PB]. In order to avoid the sudden change of the allocation probability of the guidance strategy due to excessive or small terrain complexity, the S-shaped membership function is used when the language variables are NB and PB, and the triangular membership function is used for other language variables. After completing the fuzzy reasoning, the weighted average is used for defuzzification.

[0086] S103: Generate the next expansion node of the current node by using the guidance strategy based on the preset search step and allocation probability.

[0087] The search step length is used to represent the straight-line distance between any two adjacent nodes. S103 specifically includes the following steps 1-3:

[0088] Step 1: Use the potential field guidance strategy to determine the growth direction of the next extended node of the current node, use the alternating guidance strategy to determine the number of consecutively generated extended nodes of the current node, and use the random guidance strategy to determine the random growth direction of the next extended node of the current node.

[0089] First, this embodiment describes the potential field guidance strategy in detail, such as Figure 2 As shown, the process includes the following steps (1)-(4):

[0090] Step (1) adjusts the repulsive potential field of the potential field guidance strategy based on the distance correction factor of the current node and the target node.

[0091] Among them, the target node is the latest node of the random tree relative to the current node. For example, if the current node is the latest node of tree 1, then the target node is the latest node of tree 2. The target node is used to provide expansion direction when generating an expansion node for the current node, thereby accelerating the connection between tree 1 and tree 2.

[0092] Repulsive potential field U rep The expression is as follows:

[0093]

[0094] Among them, U rep represents the repulsive potential field function, n mow Indicates the position of the current node, n target represents the location of the target node, represents the distance correction factor, η and is the adjustment coefficient of the distance correction factor, dmin is the shortest distance from the current node to the obstacle, d safe is the threshold of the range of the repulsive potential field, β r is the action factor of the repulsive potential field.

[0095] When approaching the target node, the distance correction factor It will decrease rapidly, making the repulsion approach zero, to avoid the situation where the repulsion is too large and cannot reach the target node.

[0096] Step (2) calculates the repulsive force and attractive force generated by the potential field of the potential field guidance strategy based on the gravitational potential field of the potential field guidance strategy and the adjusted repulsive potential field.

[0097] Among them, the gravitational potential field U att The expression is as follows:

[0098]

[0099] Among them, U att represents the gravitational potential field function, n now Indicates the position of the current node, n target represents the location of the target node, β a is the action factor of the gravitational potential field, d(n now ,n target ) is the Euclidean distance function, d 0 is the critical distance between the current node and the target node, d 1 is the gravitational attenuation distance, d 1 In order to prevent collision with obstacles due to excessive gravity when approaching the target node, the gravitational potential field is attenuated when approaching the target node.

[0100] The calculation formulas for repulsion and attraction are:

[0101]

[0102] in, Repulsion, represents gravity, respectively for U rep and U att Take the derivative to obtain the repulsive and attractive forces.

[0103] Step (3) determines the guiding direction of the potential field guiding strategy based on repulsion and attraction.

[0104] The guidance direction can be determined by the combined force of repulsion and attraction. The guiding direction is

[0105] In addition, the guiding strategy designed by the present disclosure retains the random sampling strategy in the bidirectional RRT* algorithm, thereby avoiding falling into the local optimum during the node expansion process.

[0106] Step (4) determines the growth direction of the next extended node of the current node based on the guiding direction.

[0107] The improved artificial potential field guidance strategy of the present invention avoids blind sampling, reduces the generation of redundant nodes, and improves the sampling efficiency to a certain extent. In order to further improve the convergence speed, the present invention designs an alternating guidance strategy based on the bidirectional RRT* algorithm to accelerate the connection between two random trees, such as Figure 3 As shown, the alternating boot strategy is described in detail below:

[0108] Based on the terrain complexity of the current node, the alternating guidance strategy is used to calculate the number of continuous expansions of the current node; based on the number of continuous expansions, the number of continuous generation of expansion nodes of the current node is determined.

[0109] In the process of alternating expansion of tree 1 and tree 2, the alternation frequency is determined by the complexity of the terrain. The alternation frequency can be reflected by the number of consecutive expansions of the current node on each random tree. The number of consecutive expansions is the number of consecutively generated expansion nodes of the current node. Figure 3 As shown, assuming that the current node is node P of tree 1 i , the number of continuous expansions is 3, then when the expansion node of tree 1 is generated to P c Then, the latest node P of tree 2 j Update to the new current node, for the current node P of tree 2 j Expand, when the expansion node of tree 2 is generated to P c Then, the latest node P of tree 1 is c Update to the new current node and cycle in sequence.

[0110] It should be noted that P i -P a -P b -P c For example, P a -P b -P c All are current nodes P i The extended node, P a As P i The next expansion node needs to be based on P i The terrain complexity and guidance strategy generation of P b As P a The next expansion node needs to be based on P a The terrain complexity and guidance strategy generation. iAfter the continuous expansion times, for P a -P b -P c The number of consecutive extensions is no longer counted, for example, P c The next expansion node is P i+nc , then update the current node to P i+nc Then calculate the current node P i+nc The number of consecutive expansions.

[0111] Specifically, the calculation formula for the number of consecutive expansions is as follows:

[0112] C a =ε j RoundUp[ln(ε f δ)]

[0113] Among them, C a represents the number of consecutive expansions, ε j With ε f is the gain coefficient, and δ is the terrain complexity.

[0114] When the terrain complexity is low, the alternation frequency is reduced, the number of continuous expansions of the random tree is increased, and the growth rate of the random tree is improved. When the terrain complexity is high, the alternation frequency is increased, the number of continuous expansions of the random tree is reduced, and the possibility of invalid sampling is reduced. Optionally, when the distance between the latest nodes of tree 1 and tree 2 is less than the set threshold, and the line between the two latest nodes does not collide with obstacles, the two trees are connected.

[0115] Step 2: Based on the allocation probability, growth direction, continuous generation quantity and random growth direction, determine the direction of the next expansion node.

[0116] Step 3: Generate the next expansion node of the current node based on the preset search step size and direction.

[0117] S104: Establish a connection path between the current node and the next extended node, determine whether the connection path satisfies the dynamic constraint index and the kinematic constraint index, and use the connection path as the optimal connection path.

[0118] To establish the best connection path, it is necessary to solve the optimal boundary value problem (OBVP) to meet the dynamic constraint index and the kinematic constraint index. Specifically, S104 includes the following steps ①-③:

[0119] ① Based on the control input of the fixed-wing aircraft, calculate the overload in each direction of the track coordinate system, and use the overload as the dynamic constraint indicator that needs to be met in route planning. Specifically include:

[0120] According to the linearized model of the fixed-wing aircraft, the state space model is established as follows:

[0121]

[0122] Where p represents the coordinate position, p = [x, y, h], Indicates speed, represents acceleration, I 3×3 is the identity matrix, O 3×3 is a zero matrix, u i (i=1,2,3) is the new control input, which is used to characterize the acceleration of the aircraft in the geodetic coordinate system, χ represents the heading angle, and γ represents the track angle.

[0123] Control input u i The expression for (i=1,2,3) is as follows:

[0124]

[0125] Among them, a 1 =(T cos α-D) / m,a 1 is the tangential acceleration, α is the angle of attack, T is the thrust, D is the drag, and m is the mass; a 2 =(L+T sinα)cosφ / m,a 2 is the vertical component of normal acceleration, L is lift, φ is the roll angle; a 3 =(L+T sinα)sinφ / m,a 3 is the horizontal component of normal acceleration.

[0126] When studying aircraft guidance, the overload is projected into the track coordinate system. The overload of the track coordinate system determines how fast the aircraft speed changes and how fast the flight direction changes. The expressions of the overload in each direction of the track coordinate system are as follows:

[0127]

[0128] Among them, n x represents the tangential overload of the track coordinate system, n y and n Z Represents the normal overload of the track coordinate system.

[0129] Optionally, a relationship between the new control input and the overload of the fixed-wing aircraft in each direction in the track coordinate system can be established, and the relationship is as follows:

[0130]

[0131] Among them, u i(i=1,2,3) is the new control input, which represents the acceleration of the aircraft in the geodetic coordinate system; χ represents the heading angle, γ represents the track angle, and g represents the gravity acceleration. Overload is used as the dynamic constraint index that needs to be met in route planning. During the route planning process, the overload needs to fully meet the overload boundary, which is:

[0132]

[0133] Among them, n tx is the tangential overload n x The overload limit, n ty is the tangential overload n y The overload limit, n tz is the tangential overload n z overload boundary.

[0134] ② Based on the control input, minimize the cost function.

[0135] Among them, the cost function is used to characterize the cost of the acceleration and transition time of the fixed-wing aircraft between the current node and the next extended node. The expression of the cost function is as follows:

[0136]

[0137] Among them, J(u) is the cost function, R is the weight matrix, and u is the control input, which is equivalent to u T is the transposed matrix of u.

[0138] In the process of route planning, it is necessary to ensure that the cost function is minimized, that is, it is also necessary to solve the optimal boundary value problem (OBVP). The description of OBVP is as follows:

[0139]

[0140] Among them, u * is the optimal control input, Ax+Bu represents the system state, referring to the expression of the state space model, x is equivalent to Equivalent to A is equivalent to B is equivalent to x(0) and x(t f ) represents the boundary value (i.e. position) between the current node and the next expanded node, x a and x b is the boundary condition.

[0141] ③ When the connection path satisfies the dynamic constraint index and minimizes the cost function, it is determined that the connection path satisfies the kinematic constraint index and the connection path is taken as the optimal connection path.

[0142] Through the above steps ① and ②, a smooth connection path is established between the current node and the next extended node. This connection path minimizes the cost function while satisfying the dynamic constraint index. In addition, it is also necessary to determine whether the above connection path satisfies the kinematic constraint index of the fixed-wing aircraft. The kinematic constraint index mainly includes the speed range, the minimum turning radius and the maximum track angle.

[0143] Specifically, ③ includes the following steps 1)-4):

[0144] Step 1) Calculate the minimum turning radius based on the speed, maximum rolling angle and gravity acceleration of the fixed-wing aircraft, and calculate the maximum track angle based on the maximum thrust of the fixed-wing aircraft.

[0145] The minimum turning radius reflects the lateral maneuverability of a fixed-wing aircraft, which is usually limited by the flight speed and the maximum roll angle. The calculation formula is as follows:

[0146]

[0147] Among them, r tmin represents the minimum turning radius, v is the speed, φ max is the maximum roll angle, and g is the acceleration due to gravity.

[0148] The maximum track angle reflects the longitudinal maneuverability of a fixed-wing aircraft, which is usually constrained by the thrust and gravity of the aircraft. The calculation formula is as follows:

[0149]

[0150] in, is the maximum track angle, T max is the maximum thrust, m is the mass of the vehicle, and g is the acceleration due to gravity.

[0151] Step 2) Calculate the curvature based on the parametric equation of the connection path with respect to time.

[0152] The expression of the parametric equation is:

[0153]

[0154] Among them, Γ x , Γ y , Γ z are the three coordinates of the connection path, and t represents time.

[0155] The calculation formula of curvature κ is:

[0156]

[0157] Step 3) Based on the mathematical relationship between the turning radius and the curvature, the minimum turning radius is used to confirm that the connection path meets the lateral kinematic constraint index, and the track angle is calculated based on the parameter equation, and based on the track angle and the maximum track angle, it is confirmed that the connection path meets the longitudinal kinematic constraint index.

[0158] The mathematical relationship between turning radius and curvature is: According to the above curvature κ calculation formula, we can calculate the curvature in the interval t∈[0,t f ], if the turning radius calculated by the maximum curvature meets the minimum turning radius, the connection path meets the lateral kinematic constraint index.

[0159] We can also calculate the range of the interval t∈[0,t f ] to determine whether the connection path meets the longitudinal kinematic constraints, that is, whether it meets the maximum track angle. The calculation formula of the track angle is:

[0160]

[0161] Where γ represents the track angle. If the track angle calculated according to the above parameter equation satisfies the maximum track angle It means that the connection path satisfies the longitudinal kinematic constraint index.

[0162] Step 4) taking the connection path as the best connection path.

[0163] If the connection path satisfies both the lateral kinematic constraint index and the longitudinal kinematic constraint index, the path can be retained as the optimal connection path between the current node and the next extended node.

[0164] Optionally, after S104, this embodiment further includes S105-S107:

[0165] S105: When the distance between the current node and the target node is less than a preset threshold and the connection path between the current node and the target node does not collide with an obstacle, two random trees generated with the preset route starting point and end point as starting nodes are connected through the connection path between the current node and the target node.

[0166] That is, by connecting the current node and the target node, tree 1 and tree 2 are connected.

[0167] S106: Taking the starting node on the random tree as the positioning node, and taking the next node of the positioning node as the moving node.

[0168] Taking tree 1 as an example, the nodes of tree 1 are marked as node 1, node 2, ... node n in order, and node n is the last node of tree 1. During the first route optimization, node 1 is used as the positioning node and node 2 is used as the mobile node.

[0169] S107: Determine whether the connection path between the positioning node and the mobile node satisfies the dynamic constraint index and the kinematic constraint index.

[0170] Specifically, by solving the optimal boundary value (OBVP) problem, it is determined whether the connection path between the positioning node and the mobile node can achieve collision-free flight, that is, whether the dynamic constraint index and the kinematic constraint index are satisfied.

[0171] 1) When the result of the judgment is that the dynamic constraint index and the kinematic constraint index are satisfied, the nodes between the positioning node and the mobile node are deleted.

[0172] It should be noted that if there is no other node between the positioning node and the mobile node (for example, there is no other node between node 1 and node 2), the deletion operation is not performed and the process directly proceeds to step 2).

[0173] 2) The next node of the mobile node is updated to the mobile node, and the steps of determining whether the connection path between the positioning node and the mobile node satisfies the dynamic constraint index and the kinematic constraint index are repeatedly performed until there is no next node for the mobile node, and the collision-free optimization path of the random tree is obtained.

[0174] For example, the next node of node 2 is node 3. Node 3 is used as the updated mobile node to determine whether the connection path between node 1 and node 3 meets the steps of dynamic constraint index and kinematic constraint index. If satisfied, node 2 located between node 1 and node 3 is deleted. If not satisfied, node 4 is updated as a new mobile node until node n becomes the new mobile node and node n does not have a next node. After the connection path between node 1 and node n is judged, the collision-free optimization path of tree 1 is obtained.

[0175] It should be noted that steps S106-S107 are performed for both tree 1 and tree 2 to obtain two collision-free optimization paths. Optionally, in order to avoid an uneven connection between the two collision-free optimization paths at the connection, the present disclosure introduces a 3D Dubins smoothing method to smooth the path at the connection so that it can meet the kinematic constraints of the fixed-wing aircraft.

[0176] The present disclosure is simulated and calculated in MTALAB R2021b, and the test platform is configured as follows: the processor is AMD4800H, the main frequency is 2.90GHz, the memory is 16GB, and the operating system is 64-bit Windows 10. In order to verify the effectiveness of the present disclosure, a comparative analysis was carried out with existing algorithms in different scenarios, including Informed-RRT*, A*, and Kinodynamic RRT* algorithms. The four algorithms were compared and analyzed in 60 simulations with different starting points and different obstacle distributions. The search step size was 200m, the map size was 2000*2000m, and the distance threshold was 20m. The simulation results of different algorithms are shown in Table 3:

[0177] Table 3 Comparison of planning performance of different algorithms

[0178] algorithm Average planning time / s Average path length / m Simulation times Success rate Informed-RRT* 3.58 2783.45 60 93.33％ Kinodynamic-RRT* 7.88 3365.18 60 93.33％ A* with filtering 13.78 2557.41 60 96.67％ Algorithms of the present disclosure 0.63 2613.27 60 96.67％

[0179] According to Table 3, compared with Informed-RRT*, Kinodynamic-RRT*, and A* algorithms with filtering, the planning time of the algorithm proposed in the present disclosure is reduced by 81.8%, 91.8%, and 95.3%, respectively. In addition, compared with Informed-RRT* and Kinodynamic-RRT* algorithms, the path length is reduced by 6.5% and 22.7%, respectively. In terms of success rate, the proposed algorithm is the same as the A* algorithm with filtering, and is slightly better than other algorithms in 60 simulation scenarios, which proves the effectiveness of the improved Kinodynamic RRT* route planning algorithm disclosed in the present disclosure, which can improve the path planning efficiency while meeting the dynamic and kinematic constraints of fixed-wing aircraft.

[0180] According to another aspect of the embodiments of the present disclosure, a device for efficiently planning a route for a fixed-wing aircraft is provided. Figure 4 As shown, the device comprises:

[0181] A first calculation module 101 is used to calculate the terrain complexity of the current node based on the terrain information of the current node, where the current node is a node of a random tree generated with a preset route start point or end point as a starting node;

[0182] A second calculation module 102 is used to calculate the allocation probability of the preset guidance strategy based on the fuzzy controller and the terrain complexity of the current node, and the allocation probability is used to determine the influence degree of different guidance strategies on the generation of the expansion node;

[0183] A node expansion module 103, used to generate the next expansion node of the current node by using the guiding strategy based on a preset search step and the allocation probability, wherein the search step is used to characterize the straight-line distance between any two adjacent nodes;

[0184] The path connection module 104 is used to establish a connection path between the current node and the next extended node, determine whether the connection path satisfies the dynamic constraint index and the kinematic constraint index, and use the connection path as the optimal connection path.

[0185] In one or more embodiments, the node expansion module 103 is used to:

[0186] Determine the growth direction of the next extended node of the current node by using the potential field guidance strategy, determine the number of continuously generated extended nodes of the current node by using the alternating guidance strategy, and determine the random growth direction of the next extended node of the current node by using the random guidance strategy;

[0187] Determining the position of the next expansion node based on the allocation probability, the growth position, the continuous generation quantity and the random growth position;

[0188] Based on the preset search step size and the direction, a next expansion node of the current node is generated.

[0189] In one or more embodiments, the node expansion module 103 is further configured to:

[0190] Adjusting the repulsive potential field of the potential field guidance strategy based on the distance correction factor of the current node and the target node, wherein the target node is the latest node of the random tree relative to the current node;

[0191] Based on the gravitational potential field of the potential field guidance strategy and the adjusted repulsive potential field, calculating the repulsive force and gravitational force generated by the potential field of the potential field guidance strategy;

[0192] Based on the repulsive force and the attractive force, determining a guiding direction of the potential field guiding strategy;

[0193] Based on the guiding direction, a growth direction of the next extended node of the current node is determined.

[0194] In one or more embodiments, the node expansion module 103 is further configured to:

[0195] Based on the terrain complexity of the current node, the alternating guidance strategy is used to calculate the number of continuous expansions of the current node;

[0196] Based on the number of consecutive expansions, the number of consecutively generated expansion nodes of the current node is determined.

[0197] In one or more embodiments, the path connection module 104 is used to:

[0198] Based on the control input of the fixed-wing aircraft, the overload in each direction of the track coordinate system is calculated, and the overload is used as a dynamic constraint index that needs to be satisfied by the route planning, and the control input represents the acceleration of the aircraft in the earth coordinate system;

[0199] Based on the control input, minimizing a cost function, the cost function being used to characterize the cost of acceleration and transition time of the fixed-wing aircraft between the current node and the next extended node;

[0200] In the case where the connection path satisfies the dynamic constraint index and minimizes the cost function, it is determined that the connection path satisfies the kinematic constraint index, and the connection path is used as the optimal connection path.

[0201] In one or more embodiments, the path connection module 104 is further configured to:

[0202] Calculating a minimum turning radius based on the speed, maximum rolling angle and gravity acceleration of the fixed-wing aircraft, and calculating a maximum track angle based on the maximum thrust of the fixed-wing aircraft;

[0203] calculating curvature based on a parametric equation of the connection path with respect to time;

[0204] Based on a mathematical relationship between a turning radius and the curvature, confirming that the connection path satisfies a lateral kinematic constraint index using the minimum turning radius, and calculating a track angle based on the parametric equation, and confirming that the connection path satisfies a longitudinal kinematic constraint index based on the track angle and the maximum track angle;

[0205] The connection path is taken as the optimal connection path.

[0206] The fixed-wing aircraft route efficient planning device is further used for: after taking the connection path as the optimal connection path, when the distance between the current node and the target node is less than a preset threshold value and the connection path between the current node and the target node does not collide with an obstacle, connecting two random trees generated with a preset route starting point and an end point as starting nodes through the connection path between the current node and the target node;

[0207] Taking the starting node on the random tree as a positioning node and taking the next node of the positioning node as a moving node;

[0208] Determining whether a connection path between the positioning node and the mobile node satisfies a dynamic constraint index and a kinematic constraint index;

[0209] If the result of the judgment is that the dynamic constraint index and the kinematic constraint index are satisfied, deleting the node between the positioning node and the mobile node;

[0210] The next node of the mobile node is updated to the mobile node, and the step of determining whether the connection path between the positioning node and the mobile node satisfies the dynamic constraint index and the kinematic constraint index is repeatedly performed until there is no next node for the mobile node, and the collision-free optimization path of the random tree is obtained.

[0211] The device for efficiently planning the route of a fixed-wing aircraft provided in the embodiment of the present disclosure and the method for efficiently planning the route of a fixed-wing aircraft provided in the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0212] The present disclosure also provides a computer device to execute the above-mentioned method for efficient route planning of fixed-wing aircraft. Figure 5 It shows a schematic diagram of a computer device provided by some embodiments of the present disclosure. Figure 5 As shown, the computer device 8 includes: a processor 800, a memory 801, a bus 802 and a communication interface 803, wherein the processor 800, the communication interface 803 and the memory 801 are connected via the bus 802; the memory 801 stores a computer program that can be run on the processor 800, and when the processor 800 runs the computer program, the efficient fixed-wing aircraft route planning method provided in any of the aforementioned embodiments of the present disclosure is executed.

[0213] The memory 801 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 803 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0214] The bus 802 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store a program, and the processor 800 executes the program after receiving an execution instruction. The method for efficient route planning of a fixed-wing aircraft disclosed in any implementation of the aforementioned embodiment of the present disclosure may be applied to the processor 800, or implemented by the processor 800.

[0215] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 800. The above processor 800 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-made programmable gate array (FPTA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and completes the steps of the above method in combination with its hardware.

[0216] The computer device provided in the embodiment of the present disclosure and the method for efficient fixed-wing aircraft route planning provided in the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0217] The embodiments of the present disclosure also provide a computer-readable storage medium corresponding to the method for efficient fixed-wing aircraft route planning provided in the aforementioned embodiments. The computer-readable storage medium is a CD on which a computer program (i.e., a computer program product) is stored. When the computer program is run by a processor, it will execute the method for efficient fixed-wing aircraft route planning provided in any of the aforementioned embodiments.

[0218] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0219] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the method for efficient fixed-wing aircraft route planning provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0220] The present disclosure also provides a computer program product. Figure 6 The computer program product 600 carries a program code, namely a computer program 601. The instructions included in the computer program 601 can be used to execute the steps of the efficient fixed-wing aircraft route planning method described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0221] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0222] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0223] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0224] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0225] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0226] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0227] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0228] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for efficient route planning of a fixed-wing aircraft, characterized in that: include: Calculating the terrain complexity of the current node based on the terrain information of the current node, where the current node is a node of a random tree generated with a preset route start point or end point as a starting node; Based on the fuzzy controller and the terrain complexity of the current node, the allocation probability of the preset guidance strategy is calculated, and the allocation probability is used to determine the influence degree of different guidance strategies on the generation of the expansion node; Using the guiding strategy, based on a preset search step length and the allocation probability, generating a next extended node of the current node, wherein the search step length is used to characterize a straight-line distance between any two adjacent nodes; A connection path between the current node and the next extended node is established, and it is determined that the connection path satisfies a dynamic constraint index and a kinematic constraint index, and the connection path is used as an optimal connection path.

2. The method for efficient fixed-wing aircraft route planning according to claim 1, characterized in that: The guiding strategies include potential field guiding strategy, alternating guiding strategy and random guiding strategy; Using the guiding strategy, based on a preset search step and the allocation probability, generating the next expansion node of the current node includes: Determine the growth direction of the next extended node of the current node by using the potential field guidance strategy, determine the number of continuously generated extended nodes of the current node by using the alternating guidance strategy, and determine the random growth direction of the next extended node of the current node by using the random guidance strategy; Determining the position of the next expansion node based on the allocation probability, the growth position, the continuous generation quantity and the random growth position; Based on the preset search step size and the direction, a next expansion node of the current node is generated.

3. The method for efficient fixed-wing aircraft route planning according to claim 2, characterized in that: Determining the growth direction of the next extended node of the current node by using the potential field guidance strategy includes: Adjusting the repulsive potential field of the potential field guidance strategy based on the distance correction factor of the current node and the target node, wherein the target node is the latest node of the random tree relative to the current node; Based on the gravitational potential field of the potential field guidance strategy and the adjusted repulsive potential field, calculating the repulsive force and gravitational force generated by the potential field of the potential field guidance strategy; Based on the repulsive force and the attractive force, determining a guiding direction of the potential field guiding strategy; Based on the guiding direction, a growth direction of the next extended node of the current node is determined.

4. The method for efficient fixed-wing aircraft route planning as claimed in claim 2, characterized in that: Determining the number of continuously generated extension nodes of the current node by using the alternating boot strategy includes: Based on the terrain complexity of the current node, the alternating guidance strategy is used to calculate the number of continuous expansions of the current node; Based on the number of consecutive expansions, the number of consecutively generated expansion nodes of the current node is determined.

5. The method for efficient fixed-wing aircraft route planning according to claim 1, characterized in that: Determining that the connection path satisfies a dynamic constraint index and a kinematic constraint index, and taking the connection path as an optimal connection path, comprises: Based on the control input of the fixed-wing aircraft, the overload in each direction of the track coordinate system is calculated, and the overload is used as a dynamic constraint index that needs to be satisfied by the route planning, and the control input represents the acceleration of the aircraft in the earth coordinate system; Based on the control input, minimizing a cost function, the cost function being used to characterize the cost of acceleration and transition time of the fixed-wing aircraft between the current node and the next extended node; In the case where the connection path satisfies the dynamic constraint index and minimizes the cost function, it is determined that the connection path satisfies the kinematic constraint index, and the connection path is used as the optimal connection path.

6. The method for efficient fixed-wing aircraft route planning according to claim 5, characterized in that: Determining that the connection path satisfies a kinematic constraint index and taking the connection path as an optimal connection path includes: Calculating a minimum turning radius based on the speed, maximum rolling angle and gravity acceleration of the fixed-wing aircraft, and calculating a maximum track angle based on the maximum thrust of the fixed-wing aircraft; calculating curvature based on a parametric equation of the connection path with respect to time; Based on a mathematical relationship between a turning radius and the curvature, confirming that the connection path satisfies a lateral kinematic constraint index using the minimum turning radius, and calculating a track angle based on the parametric equation, and confirming that the connection path satisfies a longitudinal kinematic constraint index based on the track angle and the maximum track angle; The connection path is taken as the optimal connection path.

7. The method for efficient fixed-wing aircraft route planning according to claim 6, characterized in that: After taking the connection path as the optimal connection path, the method further includes: When the distance between the current node and the target node is less than a preset threshold value and the connection path between the current node and the target node does not collide with an obstacle, two random trees generated with a preset route start point and an end point as starting nodes are connected through the connection path between the current node and the target node; Taking the starting node on the random tree as a positioning node and taking the next node of the positioning node as a moving node; Determining whether a connection path between the positioning node and the mobile node satisfies a dynamic constraint index and a kinematic constraint index; If the result of the judgment is that the dynamic constraint index and the kinematic constraint index are satisfied, deleting the node between the positioning node and the mobile node; The next node of the mobile node is updated to the mobile node, and the step of determining whether the connection path between the positioning node and the mobile node satisfies the dynamic constraint index and the kinematic constraint index is repeatedly performed until there is no next node for the mobile node, and the collision-free optimization path of the random tree is obtained.

8. An efficient route planning device for a fixed-wing aircraft, characterized in that: include: A first calculation module, configured to calculate the terrain complexity of a current node based on terrain information of the current node, wherein the current node is a node of a random tree generated with a preset route start point or end point as a starting node; A second calculation module is used to calculate the allocation probability of the preset guidance strategy based on the fuzzy controller and the terrain complexity of the current node, and the allocation probability is used to determine the influence degree of different guidance strategies on the generation of the expansion node; A node expansion module, used to generate the next expansion node of the current node based on a preset search step length and the allocation probability by using the guidance strategy, wherein the search step length is used to characterize the straight-line distance between any two adjacent nodes; The path connection module is used to establish a connection path between the current node and the next extended node, determine whether the connection path satisfies the dynamic constraint index and the kinematic constraint index, and use the connection path as the optimal connection path.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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