A sampling-based global path planning method for multi-UAV formations

Abstract

A sampling-based global path planning method for a multi-UAV formation includes the following steps: S1, constructing a global grid map, given a formation starting state and a formation target state; S2, adding the formation starting state to a first random tree as a root node, and adding the formation target state to a second random tree as a root node; S3, sampling to obtain a new sampling node and incrementing the number of iterations by 1; S4, bidirectionally expanding two random trees from the new sampling node; S5, determining whether the two random trees are connected after expansion; if so, optimizing the cost values ​​of the nodes in the two random trees using a region pruning method; S6, determining whether a termination condition is met; if so, proceeding to S7; otherwise, executing S3; S7, if the two random trees are connected, the path is successfully solved and a path planning result is obtained; otherwise, the path is failed. The present invention effectively improves the solution speed and solution quality of sampling-based global path planning for a multi-UAV formation in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-UAV path planning, and in particular to a sampling-based global path planning method for a multi-UAV formation. Background Art

[0002] In recent years, multi-UAV formation technology has rapidly developed. With advantages such as a wide field of view, large payload, and strong adaptability, multi-UAV formations can accomplish tasks unattainable by a single aircraft. However, current formation technology is mostly designed for ideal open-air environments, which greatly limits its scope of application.

[0003] When multiple drones fly in complex environments, global path planning is essential to guide subsequent local path planning, otherwise they are prone to falling into local optima. However, global path planning at the formation level is more complex than that of a single drone, making it difficult to achieve a fast solution rate and high solution quality, which significantly reduces the efficiency of multi-drone formations. Given these challenges, designing an efficient global path planning method for multi-drone formations is crucial to improving their efficiency in complex environments. Summary of the Invention

[0004] The present invention provides a sampling-based global path planning method for a multi-UAV formation to solve the technical problems mentioned in the background technology.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] The present invention provides a sampling-based global path planning method for a multi-UAV formation, comprising the following steps:

[0007] S1. Build a global grid map, given the formation starting state S and formation target state G, and construct the desired formation of multiple UAVs;

[0008] S2. Add the formation starting state S to the first random tree T1 as the root node, add the formation target state G to the second random tree T2 as the root node, and set the number of iterations k to 0;

[0009] S3, obtain the new sampling node qne by sampling based on the Gaussian sampling method guided by obstacles w , and increase the number of iterations k by 1;

[0010] S4, according to the global grid map and the expected formation, resample the node q new Bidirectionally expand the first random tree T1 and the second random tree T2;

[0011] S5. Determine whether the two random trees are connected after expansion. If they are connected, optimize the cost of the nodes in the two random trees by using a region pruning method.

[0012] S6, determine whether the termination condition is met, if yes, go to S7, otherwise go to S3;

[0013] S7. If the first random tree T1 and the second random tree T2 are connected, the path solving is successful and the final path planning result is obtained; otherwise, the path solving fails.

[0014] Furthermore, the S1 specifically includes the following steps:

[0015] S11, build a global grid map, given the formation starting state S = [x s ,y s , z s , 1.0] T , formation target state G=[x g ,y g , z g , 1.0] T , maximum number of trials M, neighbor radius R, maximum formation size s max , Minimum fleet size S min , maximum number of iterations N and maximum running time T max , where [x s ,y s , z s ] T ∈ Indicates the center position of the formation at the initial state of the formation, The center position of the formation representing the formation target state; T is the transpose of the matrix;

[0016] S12, construct the desired formation of n drones [p des，1 T , p des，2 T ,...,p des，n T ] T , where p des，i ∈ represents the expected position of the i-th UAV in the expected formation.

[0017] Furthermore, the S3 specifically includes the following steps:

[0018] S31. Obtain random sampling node q′ through random sampling new , randomly sample node q′ new The formation center position is sampled in the global grid map, and the random sampling node q′ new The size of the formation is [smin , s max ] range of sampling;

[0019] S32, check the random sampling node q′ new Is it valid? If not, set the number of trials τ to 0 and execute S33; otherwise, execute S35;

[0020] S33, sampling by Gaussian sampling method to obtain Gaussian sampling node q″ new , Gaussian sampling node q″ new The formation center is randomly sampled at the node q′ within the global grid map. new The formation center is the mean, and σ 2 Gaussian sampling is performed to obtain the variance, q″ new The size of the formation is [s min , s max ] is obtained by random sampling within the range, and the number of trials τ is increased by 1;

[0021] S34, determine Gaussian sampling node q″ new Is it valid? If it is invalid and the number of trials τ does not exceed the maximum number of trials M, then jump to S33 to execute. If it is invalid and the number of trials τ exceeds the maximum number of trials M, then jump to S31 to execute. Otherwise, the Gaussian sampling node q″ new Update to a randomly sampled node q′ new , and execute S35;

[0022] S35, randomly sample node q′ new As a new sampling node q new , and increase the number of iterations k by 1.

[0023] Furthermore, the S4 specifically includes the following steps:

[0024] S41, respectively obtain new sampling nodes q new In the first random tree T1 and the second random tree T2, the first neighbor node set Ψ1 and the second neighbor node set Ψ2, each neighbor node q in the two neighbor node sets neigh Satisfy neighbor node q neigh The formation center to the new sampling node q new The formation center distance is less than the neighbor radius R;

[0025] If the first neighbor node set Ψ1 and / or the second neighbor node set Ψ2 is empty, the corresponding random tree with the new sample node q new The node with the smallest distance to the formation center is added to the first neighbor node set Ψ1 and / or the second neighbor node set Ψ2 to ensure that the set is not empty;

[0026] S42, searching for node q in the first neighbor node set Ψ1. u，1 , satisfying node q u，1 To the new sampling node q new The connection is valid and passes through node q u，1 To the new sampling node q new The formation level cost δ1 is the smallest; similarly, find node q in the second neighbor node set Ψ2 u，2 , satisfying node q u，2 To the new sampling node q new The connection is valid and passed u2 to q new The formation level cost δ2 is the smallest; the connection is effective, that is, the connection between the two nodes does not collide with obstacles;

[0027] S43. If node q is not found u，1 and q u，2 , it means that the growth fails, end S4 and jump to S3; if only node q is found u，1 , then jump to S44, if only node q is found u，2 , then jump to S45, otherwise jump to S46;

[0028] S44, the new sampling node q new Add to the node set V1 of the first random tree T1, and connect the node relationship (q u，1 ,q new ) is added to the node connection relationship set E1 of the first random tree T1, and the new sampling node q new The cost η(q new )=δ1, then jump to S48;

[0029] S45, the new sampling node q new Add to the node set V2 of the second random tree T2, and connect the node relationship (q u，2 ,q new ) is added to the node connection relationship set E2 of the second random tree T2, and the new sampling node q new The cost η(q new )=δ2, then jump to S48;

[0030] S46, compare the formation level costs δ1 and δ2, if δ1≤δ2, then the new sampling node q new Add to the node set V1 of the first random tree T1, and connect the node relationship (q u，1 ,q new ) is added to the node connection relationship set E1 of the first random tree T1, and the new sampling node q new The cost η(q new )=δ1, and the node q u，2Marked as new sampling node q new Otherwise, a new sampling node q new Add to the node set V2 of the second random tree T2, and connect the node relationship (q u，2 ,q new ) is added to the node connection relationship set E2 of the second random tree T2, and the new sampling node q new The cost η(q new )=δ2, and the node q u，1 Marked as new sampling node q new connection points;

[0031] S47, the new sampling node q new Add to the intersection point set Π, which means that the two random trees have been connected;

[0032] S48, at the new sampling node q new The reconnection operation is performed in the random tree where the new sampling node q is located. new Other neighbor nodes q except its parent node neigh , judge η(q new )+c(q new ,q neigh )<η(q neigh ) is established, if so, then at the new sampling node q new Delete the node connection relationship in the node connection relationship set of the random tree (q p ,q neigh ), increase the node connection relationship (q new ,q neigh ), where q p q neigh The original parent node of .

[0033] Furthermore, the calculation formula of the formation level cost δ1 in S42 is:

[0034] δ1=η(q u，1 )+c(q u1 ,q new )

[0035] Where η(.) represents the first cost function; c(.) represents the state transition cost function; and η(q u，1 ) and c(q u1 ,q new ) satisfies the following relationship:

[0036]

[0037] Among them, p u1，i 、p new，i Represents node q respectivelyu1 and new sampling node q new The corresponding position of UAV i; p u1，i 、p new，i They are calculated by the following formulas:

[0038]

[0039]

[0040] Among them, s u1 、s new Node q u1 and new sampling node q new The size of the formation, X u1 、X new Node q u1 and new sampling node q new The center position of the formation.

[0041] Furthermore, the calculation formula of the formation level cost δ2 in S42 is:

[0042] δ2=η(q u，2 )+c(q u2 ,q new )

[0043] In the formula, c(q u2 ,q new ) satisfies the following relationship:

[0044]

[0045] Among them, p u2，i Represents node q u2 The corresponding position of UAV i; p u2，i Calculated by the following formula:

[0046]

[0047] Among them, s u2 For node q u2 The size of the formation, X u2 For node q u2 The center position of the formation.

[0048] Furthermore, the S5 specifically includes the following steps:

[0049] S51, determine whether the two random trees are connected after growth. If not, end S5 and jump to S6;

[0050] S52, define the update set Q, and add the new sampling node q new Add to the update set Q;

[0051] S53. Take out node q from the update set Q cur ;

[0052] S54, detection node q cur Is there a grandparent node q g If not, execute S56, otherwise determine the grandparent node q g To node q cur Whether the connection is valid, if not valid, execute S56; if valid, execute S55;

[0053] S55. Delete q from the node connection relationship set E1 or the node connection relationship set E2. cur and its parent node q p The connection relationship (q p ,q cur ), add connection relationship (q g ,q cur ); at this time q g Updated to q cur The parent node of ; then jump to S54;

[0054] S56, the new sampling node q new All child nodes of are added to the update set Q;

[0055] S57. If the update set Q is not empty, jump to S53; otherwise, end S5 and execute S6.

[0056] Furthermore, the termination condition in S6 is whether the number of iterations k exceeds the maximum number of iterations N or the algorithm reaches the maximum running time T. max .

[0057] Furthermore, the S7 specifically includes the following steps:

[0058] S71, determining whether the first random tree T1 and the second random tree T2 are connected. If the connection is not completed, it means that the path solving fails, and exit S7; if the connection is successful, enter S72;

[0059] S72. Find node q in the intersection point set Π. f , so that through node q f The path cost L f smallest;

[0060] S73, from node q f Go to node q respectively f The parent node and its connection point are traced back to obtain a formation global path connecting the formation starting state and the formation target state as the solution result.

[0061] Furthermore, the node q in S72 f and path cost L f The calculation formula is:

[0062]

[0063] L f =η(q f )+η(q f ′)+c(q f ,q f ′)

[0064] Among them, q σ ′ is node q σ The connection point, in addition, node q σ It is also the function η(q σ )+η(q σ ′)+c(q σ ,q σ ′) of the independent variable. f ′ is node q f connection point.

[0065] Beneficial effects of the present invention:

[0066] 1. The present invention defines the node states and cost functions of the random tree, so that the sampling-based method can be applied to the global path planning problem of multiple UAV formations.

[0067] 2. The present invention adopts a bidirectional sampling method and a Gaussian sampling method based on obstacle guidance, so that the sampling-based method can still maintain fast convergence characteristics in complex environments, greatly reducing the solution time of path planning.

[0068] 3. The present invention proposes a region pruning method that can reduce redundant connections of nodes in a random tree, reduce the cost of nodes, optimize the structure of the random tree, and improve the solution quality of path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of the present invention;

[0070] Figure 2 is the result of global formation path planning for multiple UAVs in an embodiment of the present invention;

[0071] Figure 3 This is a comparison chart of the global formation path planning results of multiple UAVs in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate preferred embodiments of the present invention. However, the present invention may be implemented in many other forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present disclosure.

[0073] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0075] Reference Figure 1 , the embodiment of the present application provides a sampling-based global path planning method for a multi-UAV formation, comprising the following steps:

[0076] S1. Build a global grid map, given the formation starting state S and formation target state G, and construct the desired formation of multiple UAVs;

[0077] S2. Add the formation starting state S to the first random tree T1 as the root node, add the formation target state G to the second random tree T2 as the root node, and set the number of iterations k to 0;

[0078] S3. Obtain a new sampling node q by sampling using the Gaussian sampling method guided by obstacles new , and increase the number of iterations k by 1;

[0079] S4, according to the global grid map and the expected formation, resample the node q new Bidirectionally expand the first random tree T1 and the second random tree T2;

[0080] S5. Determine whether the two random trees are connected after expansion. If they are connected, optimize the cost of the nodes in the two random trees by using a region pruning method.

[0081] S6, determine whether the termination condition is met, if yes, go to S7, otherwise go to S3;

[0082] S7. If the first random tree T1 and the second random tree T2 are connected, the path solving is successful and the final path planning result is obtained; otherwise, the path solving fails.

[0083] In some embodiments, the step S1 specifically includes the following steps:

[0084] S11, build a global grid map, given the formation starting state S = [x s ,y s , z s , 1.0] T , formation target state G=[x g ,y g , z g , 1.0] T , maximum number of trials M, neighbor radius R, maximum formation size s max , minimum fleet size min , maximum number of iterations N and maximum running time T max ,in Indicates the center position of the formation at the initial state of the formation, The center position of the formation representing the formation target state; T is the transpose of the matrix;

[0085] S12, construct the desired formation of n drones [p des，1 T , p des，2 T ,...,p des，n T ] T ,in represents the expected position of the i-th UAV in the expected formation (1≤i≤n).

[0086] In some embodiments, the random tree T in S2 (the first random tree T1 and the second random tree T2 are collectively referred to as T) is defined as follows:

[0087] The random tree T is defined as T = (V, E), where V is the node set, is a set of node connection relationships. Node q in V = [X T ,s] T Can represent formation information, where Indicates the center position of the formation, Represents the size of the formation. The effective definition of node q is: for any UAV i in the formation, it satisfies where X free Indicates the collision-free area, obtained through the global grid map, p q，i is the position of UAV i corresponding to node q, which is calculated by the following formula

[0088]

[0089] For the node connection relationship (u, v)∈E, it means that node u∈V is connected to node v∈V, and u is the parent node of v, and v is the child node of u. Except for the root node, each node in the random tree has only one parent node. The root node cost is 0, and the cost of other nodes can be calculated by the cost function Calculated and defined as follows

[0090] η(v)=η(u)+c(u,v)

[0091]

[0092] in is the state transition cost function, p u，i 、p v，i are the positions of UAV i corresponding to u and v respectively.

[0093] The node connection relationship (u, v) in the random tree is effectively defined as: for any UAV i, p in the formation u，i and p v，i The lines between them do not collide with obstacles.

[0094] In some embodiments, S3 specifically includes the following steps:

[0095] S31. Obtain random sampling node q′ through random sampling new , randomly sample node q′ new The formation center position is sampled in the global grid map, and the random sampling node q′ new The size of the formation is [s min , s max ] range of sampling;

[0096] S32, check the random sampling node q′ new Is it valid? If not, set the number of trials τ to 0 and execute S33; otherwise, execute S35;

[0097] S33, obtain Gaussian sampling node q″ by sampling using Gaussian sampling method new , Gaussian sampling node q″ new The formation center is randomly sampled at the node q′ within the global grid map. new The formation center is the mean, and σ 2 Gaussian sampling is performed to obtain the variance, q″ new The size of the formation is [s min , s max ] is obtained by random sampling within the range, and the number of trials τ is increased by 1;

[0098] S34, determine Gaussian sampling node q″ new Is it valid? If it is invalid and the number of trials τ does not exceed the maximum number of trials M, then jump to S33 to execute. If it is invalid and the number of trials τ exceeds the maximum number of trials M, then jump to S31 to execute. Otherwise, the Gaussian sampling node q″ new Update to a randomly sampled node q′ new , and execute S35;

[0099] S35, randomly sample node q′ new As a new sampling node q new , and increase the number of iterations k by 1.

[0100] In some embodiments, the S4 specifically includes the following steps:

[0101] S41, respectively obtain new sampling nodes q new In the first random tree T1 and the second random tree T2, the first neighbor node set Ψ1 and the second neighbor node set Ψ2, each neighbor node q in the two neighbor node sets neigh Satisfy neighbor node q neigh The formation center to the new sampling node q new The formation center distance is less than the neighbor radius R;

[0102] If the first neighbor node set Ψ1 and / or the second neighbor node set Ψ2 is empty, the corresponding random tree with the new sample node q new The node with the smallest distance to the formation center is added to the first neighbor node set Ψ1 and / or the second neighbor node set Ψ2 to ensure that the set is not empty;

[0103] S42, searching for node q in the first neighbor node set Ψ1. u，1 , satisfying node q u，1 To the new sampling node q new The connection is valid and passes through node q u，1 To the new sampling node q new The formation level cost δ1 is the smallest; similarly, find node q in the second neighbor node set Ψ2 u，2 , satisfying node q u，2 To the new sampling node q new The connection is valid and passed u2 to q new The formation level cost δ2 is the smallest; the connection is effective, that is, the connection between the two nodes does not collide with obstacles;

[0104] S43. If node q is not found u，1 and q u，2, it means that the growth fails, end S4 and jump to S3; if only node q is found u，1 , then jump to S44, if only node q is found u，2 , then jump to S45, otherwise jump to S46;

[0105] S44, the new sampling node q new Add to the node set V1 of the first random tree T1, and connect the node relationship (q u，1 ,q new ) is added to the node connection relationship set E1 of the first random tree T1, and the new sampling node q new The cost η(q new )=δ1, then jump to S48;

[0106] S45, the new sampling node q new Add to the node set V2 of the second random tree T2, and connect the node relationship (q u，2 ,q new ) is added to the node connection relationship set E2 of the second random tree T2, and the new sampling node q new The cost η(q new )=δ2, then jump to S48;

[0107] S46, compare the formation level costs δ1 and δ2, if δ1≤δ2, then the new sampling node q new Add to the node set V1 of the first random tree T1, and connect the node relationship (q u，1 ,q new ) is added to the node connection relationship set E1 of the first random tree T1, and the new sampling node q new The cost η(q new )=δ1, and the node q u，2 Marked as new sampling node q new Otherwise, a new sampling node q new Add to the node set V2 of the second random tree T2, and connect the node relationship (q u，2 ,q new ) is added to the node connection relationship set E2 of the second random tree T2, and the new sampling node q new The cost η(q new )=δ2, and the node q u，1 Marked as new sampling node q new connection points;

[0108] S47, the new sampling node q new Add to the intersection point set Π, which means that the two random trees have been connected;

[0109] S48, at the new sampling node qnew The reconnection operation is performed in the random tree where the new sampling node q is located. new Other neighbor nodes q except its parent node neigh , judge η(q new )+c(q new ,q neigh )<η(q neigh ) is established, if so, then at the new sampling node q new Delete the node connection relationship in the node connection relationship set of the random tree (q p ,q neigh ), increase the node connection relationship (q new ,q neigh ), where q p q neigh The original parent node of .

[0110] In some embodiments, the calculation formula of the formation level cost δ1 in S42 is:

[0111] δ1=η(q u，1 )+c(q u1 ,q new )

[0112] Where η(.) represents the first cost function; c(.) represents the state transition cost function; and η(q u，1 ) and c(q u1 ,q new ) satisfies the following relationship:

[0113]

[0114] Among them, p u1，i 、p new，i Represents node q respectively u1 and new sampling node q new The corresponding position of UAV i; p u1，i 、p new，i They are calculated by the following formulas:

[0115]

[0116] Among them, s u1 、s new Node q u1 and new sampling node q new The size of the formation, X u1 、X new Node q u1 and new sampling node q new The center position of the formation.

[0117] In some embodiments, the calculation formula of the formation level cost δ2 in S42 is:

[0118] δ2=η(q u，2 )+c(q u2 ,q new )

[0119] In the formula, c(q u2 ,q new ) satisfies the following relationship:

[0120]

[0121] Among them, p u2，i Represents node q u2 The corresponding position of UAV i; p u2，i Calculated by the following formula:

[0122]

[0123] Among them, s u2 For node q u2 The size of the formation, X u2 For node q u2 The center position of the formation.

[0124] In some embodiments, the step S5 specifically includes the following steps:

[0125] S51, determine whether the two random trees are connected after growth. If not, end S5 and jump to S6;

[0126] S52, define the update set Q, and add the new sampling node q new Add to the update set Q;

[0127] S53. Take out node q from the update set Q cur ;

[0128] S54, detection node q cur Is there a grandparent node q g If not, execute S56, otherwise determine the grandparent node q g To node q cur Whether the connection is valid, if not valid, execute S56; if valid, execute S55;

[0129] S55. Delete q from the node connection relationship set E1 or the node connection relationship set E2. cur and its parent node q p The connection relationship (q p ,q cur ), add connection relationship (q g ,qcur ); at this time q g Updated to q cur The parent node of ; then jump to S54;

[0130] S56, the new sampling node q new All child nodes of are added to the update set Q;

[0131] S57. If the update set Q is not empty, jump to S53; otherwise, end S5 and execute S6.

[0132] In some embodiments, the termination condition in S6 is whether the number of iterations k exceeds the maximum number of iterations N or the algorithm reaches the maximum running time T max .

[0133] In some embodiments, the step S7 specifically includes the following steps:

[0134] S71, determining whether the first random tree T1 and the second random tree T2 are connected. If the connection is not completed, it means that the path solving fails, and exit S7; if the connection is successful, enter S72;

[0135] S72. Find node q in the intersection point set Π. f , so that through node q f The path cost L f smallest;

[0136] S73, from node q f Go to node q respectively f The parent node and its connection point are traced back to obtain a formation global path connecting the formation starting state and the formation target state as the solution result.

[0137] In some embodiments, the node q in S72 f and path cost L f The calculation formula is:

[0138]

[0139] L f =η(q f )+η(q f ′)+c(q f ,q f ′)

[0140] Among them, q σ ′ is node q σ The connection point, in addition, node q σ It is also the function η(q σ )+η(q σ ′)+c(q σ,q σ ′) of the independent variable. f ′ is node q f connection point.

[0141] The present invention completes the global path planning of multiple UAV formations on a complex map. The planned path results are as follows: Figure 2 To fully demonstrate the advantages of the Gaussian sampling method and the region pruning method in the present invention, a method based on bidirectional sampling only (BRRT*), a method based on bidirectional sampling and Gaussian sampling (BRRT*-OGS), a method based on bidirectional sampling and region pruning (BRRT*-RP), and a method based on bidirectional sampling, Gaussian sampling, and region pruning (BRRT*-OGS-RP) are compared. The curves of the changes in path length over time are shown in Fig. Figure 3 As shown, it can be seen that the method proposed in the present invention effectively improves the solution speed and solution quality of global path planning of multi-UAV formation based on sampling in complex environments.

[0142] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A sampling-based global path planning method for multi-UAV formations, characterized by: The steps include: S1. Build a global grid map and give the formation starting state , formation target status , and construct the desired formation of multiple UAVs; S2, the formation starts Add to the first random tree As the root node, the formation target state Add to the second random tree As the root node, the number of iterations Set to 0; S3. Obtain new sampling nodes by sampling using the Gaussian sampling method guided by obstacles , and the number of iterations Add 1; S4, resample nodes based on the global grid map and the expected formation Bidirectionally extended first random tree and the second random tree ; S5. Determine whether the two random trees are connected after expansion. If they are connected, optimize the cost of the nodes in the two random trees by using a region pruning method. S6, determine whether the termination condition is met, if yes, go to S7, otherwise go to S3; S7, if the first random tree , the second random tree If the connection is completed, the path solving is successful and the final path planning result is obtained; otherwise, the path solving fails; The S5 specifically includes the following steps: S51, determine whether the two random trees are connected after growth. If not, end S5 and jump to S6; S52, define the update set Q, and add the new sampling node Add to the update set Q; S53. Take out the node from the update set Q ; S54, detection node Is there a grandparent node? If not, execute S56, otherwise determine the grandparent node To Node Whether the connection is valid, if not valid, execute S56; if valid, execute S55; S55, in the node connection relationship set or node connection relationship set Delete and its parent node Connection relationship , increase the connection relationship ;at this time Updated to The parent node of ; then jump to S54; S56, the new sampling node All child nodes of are added to the update set Q; S57. If the update set Q is not empty, jump to S53; otherwise, end S5 and execute S6.

2. The global path planning method for a multi-UAV formation according to claim 1, characterized in that: The S1 specifically includes the following steps: S11. Build a global grid map and give the formation starting state , formation target status , Maximum number of attempts , Neighbor Radius , maximum formation size , minimum fleet size , maximum number of iterations and maximum run time ,in Indicates the center position of the formation at the initial state of the formation, The center position of the formation indicating the formation's target state; T is the transpose of the matrix; represents the set of real numbers; S12. Construct the desired formation of n drones ,in Indicates the The desired positions of the UAVs in the desired formation.

3. The global path planning method for a multi-UAV formation according to claim 2, characterized in that: The S3 specifically includes the following steps: S31. Obtain random sampling nodes through random sampling , randomly sample nodes The formation center position is obtained by sampling in the global grid map, and the nodes are randomly sampled. The size of the formation is Sampling is obtained within the range; S32, check random sampling nodes Is it valid? If not, try again. Set to 0 and execute S33, otherwise execute S35; S33, obtain Gaussian sampling nodes by sampling using Gaussian sampling method , Gaussian sampling node The formation center is randomly sampled within the global grid map. The formation center is the mean, Gaussian sampling is performed to obtain the variance, The size of the formation is The range is obtained by random sampling, and the number of trials is Add 1; S34, determine the Gaussian sampling node Is it valid? If not, and the number of attempts The maximum number of attempts has not been exceeded , then jump to S33 to execute, if invalid and the number of attempts Exceeded the maximum number of attempts , then jump to S31 to execute, otherwise, the Gaussian sampling node Update to random sampling node , and execute S35; S35, randomly sampling nodes As a new sampling node , and the number of iterations Add 1.

4. The global path planning method for a multi-UAV formation according to claim 3, characterized in that: The S4 specifically includes the following steps: S41, respectively obtain new sampling nodes In the first random tree and the second random tree The first neighbor node set and the second neighbor node set , where each neighbor node in the two neighbor node sets Satisfy neighbor nodes The formation center to the new sampling node The formation center distance is less than the neighbor radius ; If the first neighbor node set and / or the second neighbor node set If it is empty, the corresponding random tree will be The node with the smallest distance to the formation center is added to the first neighbor node set and / or the second neighbor node set In the above example, it is guaranteed that the set is not empty; S42: In the first neighbor node set Find nodes in , satisfying the node To the new sampling node The connection is valid and passes the node To the new sampling node The formation level cost Minimum; similarly in the second neighbor node set Find nodes in , satisfying the node To the new sampling node The connection is valid and passed arrive The formation level cost Minimum; the connection is valid, that is, the connection between the two nodes does not collide with obstacles; S43. If the node is not found and , it means that the growth fails, end S4 and jump to S3; If only nodes are found , then jump to S44, if only node , then jump to S45, otherwise jump to S46; S44, the new sampling node Add to the first random tree A collection of nodes In the example, the node connection relationship Add to the first random tree The node connection relationship set And let the new sampling node The price , then jump to S48; S45, the new sampling node Add to the second random tree A collection of nodes In the example, the node connection relationship Add to the second random tree The node connection relationship set And let the new sampling node The price , then jump to S48; S46. Comparing formation level costs and ,if , then the new sampling node Add to the first random tree A collection of nodes In the example, the node connection relationship ( ) added to the first random tree The node connection relationship set And let the new sampling node The price , and put the node Mark as new sampling node Otherwise, a new sampling node will be Add to the second random tree A collection of nodes In the example, the node connection relationship Add to the second random tree The node connection relationship set And let the new sampling node The price , and put the node Mark as new sampling node connection points; S47, the new sampling node Add to Junction Collection , this indicates that the two random trees have been connected; S48, at the new sampling node Perform reconnection operations in the random tree where the new sampling node is located. Other neighbor nodes except its parent node ,judge Is it true? If so, then at the new sampling node Delete the node connection relationship in the node connection relationship set of the random tree , increase node connection relationship ,in for The original parent node of .

5. The global path planning method for a multi-UAV formation according to claim 4, characterized in that: The formation level cost in S42 The calculation formula is: ； Where, represents the first cost function; represents the state transition cost function; and and The following relationship is satisfied: ； in, 、 Represents nodes respectively and new sampling nodes Corresponding drone location; 、 They are calculated by the following formulas: ； ； in, 、 Node and new sampling nodes The size of the formation, 、 Node and new sampling nodes The center position of the formation.

6. The global path planning method for a multi-UAV formation according to claim 5, characterized in that: The formation level cost in S42 The calculation formula is: ； Where, The following relationship is satisfied: ； in, Representation node Corresponding drone location; Calculated by the following formula: ； in, For nodes The size of the formation, For nodes The center position of the formation.

7. The global path planning method for a multi-UAV formation according to claim 5, characterized in that: The termination condition in S6 is the number of iterations Whether the maximum number of iterations is exceeded or the algorithm reaches its maximum running time .

8. The global path planning method for a multi-UAV formation according to claim 5, characterized in that: The S7 specifically includes the following steps: S71, judging the first random tree , the second random tree Whether the connection is completed. If the connection is not completed, it means that the path solution fails and exits S7; if the connection is successful, enter S72; S72, meet at the intersection Find the node , so that through the node The path cost smallest; S73, slave node Go to the nodes respectively The parent node and its connection point are traced back to obtain a formation global path connecting the formation starting state and the formation target state as the solution result.

9. The global path planning method for a multi-UAV formation according to claim 8, characterized in that: The node in S72 and path cost The calculation formula is: ； ； in, For nodes The connection point, For nodes connection point.

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