Cluster time-varying formation control method for air-ground cooperation in complex environment

Abstract

The present application relates to a kind of complex environment under air-ground cooperation's cluster time-varying formation control method, belong to unmanned aerial vehicle cluster planning and control technical field, the technical problem that cluster formation cannot simultaneously consider trajectory smoothness, obstacle avoidance safety and formation stability is solved.The cluster time-varying formation control method of the present application considers that unmanned aerial vehicle cluster safely and reliably flies in complex environment, based on leader-follower architecture, when solving leader unmanned system safety trajectory, the safety constraint of follower unmanned vehicle elliptical formation shape is considered simultaneously, the method of convex optimization is used to obtain the continuous trajectory of leader unmanned system and the safety time-varying formation shape of each follower unmanned vehicle simultaneously, air-ground cooperation cluster time-varying elliptical formation is realized, time-varying formation control protocol is obtained, and the efficient obstacle avoidance and cooperative flight of unmanned aerial vehicle cluster in complex environment are realized.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm planning and control technology, specifically to a time-varying formation control method for air-ground collaborative swarms in complex environments. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are highly maneuverable and have good maneuverability, but their payload capacity is limited, individual UAVs have low mission efficiency, and they are poorly adaptable to unstructured and complex environments. UAV swarm technology, due to its high mission efficiency and strong environmental adaptability, has shown great potential in disaster relief, military reconnaissance, and logistics transportation. However, the control of UAV swarms in complex environments still faces serious challenges: limited perception capabilities of individual UAVs, difficulties in dynamically adjusting formation patterns, and real-time conflicts between obstacle avoidance and path planning urgently need to be addressed. For example, Chinese patent announcement number CN114924588B and Chinese patent applications publication numbers CN119396182A and CN116909319A illustrate this.

[0003] Especially in unstructured scenarios, such as urban canyons, forests, or areas with dense dynamic obstacles, existing formation control methods often struggle to balance trajectory smoothness, obstacle avoidance safety, and formation stability. Therefore, cooperative formation control in complex environments has become a core research topic. Time-varying formation technology needs to achieve obstacle avoidance while maintaining formation integrity, which places stringent demands on the real-time performance, robustness, and adaptability of swarm systems.

[0004] Existing cluster formation obstacle avoidance methods mostly use artificial potential fields to achieve local obstacle avoidance, but there is a problem of local minima caused by potential field superposition. The potential field parameters need to be manually adjusted based on experience, and cannot adapt to complex environments. Summary of the Invention

[0005] In view of the above problems, this invention proposes a time-varying formation control method for air-ground cooperative swarms in complex environments. This invention considers the complex environment of safe and reliable flight of UAV swarms. Based on a leader-follower architecture, it simultaneously considers the safety constraints of the elliptical formation of the follower UAVs when solving for the safe trajectory of the leader UAV system. Using convex optimization methods, it simultaneously obtains the continuous trajectory of the leader UAV system and the safe time-varying formation of each follower UAV, realizing a time-varying elliptical formation for air-ground cooperative swarms and obtaining a time-varying formation control protocol. It also balances trajectory smoothness, obstacle avoidance safety, and formation stability, achieving efficient obstacle avoidance and cooperative flight of UAV swarms in complex environments. This invention provides a time-varying formation control method for air-ground cooperative swarms in complex environments, including:

[0006] Step S1, let t =1, when t =1 indicates the initial time.

[0007] Step S2, based on time t The leader of the unmanned system uses the starting trajectory point to perform global path planning and obtain the time. t The safe path;

[0008] Step S3, set the time t The safe path is divided into multiple straight lines, and time intervals are set. t Safety corridors in each straight line Get the time t Update the security path; i Indicates the number of line segments;

[0009] Step S4: Set the time t Constraints on the elliptical formation of each follower drone;

[0010] Step S5, based on time t Update security path construction time t The objective function is determined and solved to obtain the time step. t The optimal safety trajectory for unmanned leader systems;

[0011] Time t The optimal trajectory point of the last straight segment of the leader unmanned system's optimal safe trajectory is taken as the moment. t +1 The starting trajectory point of the leader unmanned system;

[0012] Step S6, based on time t Optimal safety trajectory and timing for leader-controlled unmanned systems t The constraints of each follower drone in elliptical formation, at any given time. t The various follower drones formed a formation and obtained the time. t The optimal safe trajectory for each follower drone;

[0013] Step S7, Judgment t Is it greater than or equal to? T′ , T′ Represents the total number of moments; if so, we get... T′ The optimal safety trajectory of the leader unmanned system at any given moment and T′ The optimal safe trajectory for each follower drone at any given moment is determined to complete the time-varying formation of the swarm; otherwise, [then...] t = t +1, return to step S2.

[0014] Optionally, the leader unmanned system is an unmanned vehicle or a drone.

[0015] Optionally, step S2 also includes obtaining the time. t The specific steps involved in creating a grid map for a leader-centric unmanned system include:

[0016] Obtain the time in the radar coordinate system t The coordinates of various obstacles in the surrounding environment of the leader unmanned system are obtained and converted into coordinates in the world coordinate system to obtain the time. t Updated coordinate information of each obstacle, establishment time t Global map;

[0017] For time t The global map is updated to obtain the time. t Update the global map;

[0018] Based on time t Update the global map, construct the corresponding octree map, and mark obstacles.

[0019] At any moment t Within the leader's unmanned system's field of vision, the corresponding octree map is rasterized to obtain the time-sharing data. t A grid map of the leader-unmanned system.

[0020] Optionally, obtain the time. t The specific steps for updating the global map include:

[0021] The time t The leader unmanned system receives the time via inter-machine communication t The location information of each follower drone at any time t Marked on the global map;

[0022] Deletion and Time t The position information of each follower drone and the coordinate information of the obstacles are used to obtain the time. t Update the global map.

[0023] Optionally, obtain the time. t The specific steps for updating the security path include:

[0024] Step S31: Set the time t No. i straight line segment The original enclosing ellipse And expand it until the corresponding original enclosing ellipse is reached. Its edge comes into contact with multiple obstacles;

[0025] Step S32: Set the original enclosing ellipse at each contact point. Tangent hyperplanes, obtaining time t No. i Multiple hyperplanes of a line segment;

[0026] Step S33, based on timet No. i Multiple hyperplanes of a straight line segment form a safe convex polyhedron, which serves as the corresponding safe corridor. ;

[0027] Step S34, Traverse Time t Obtain the time from each straight line segment. t Safety corridors along each straight section;

[0028] Step S35, based on time t Safety corridors in each straight line , obtain the moment t Update the security path.

[0029] Optionally, set the time. t The specific steps for each follower drone to operate within the constraints of an elliptical formation include:

[0030] Step S41: Set the initial constraints for each follower drone in the elliptical formation.

[0031] Step S42: The initial constraints of each follower UAV in the elliptical formation are simplified into convex constraints using the radius approximation method, resulting in the simplified constraints.

[0032] Step S43: Convert the simplified constraints into matrix form to obtain the constraints of each follower UAV in the elliptical formation.

[0033] Optionally, the constraint conditions for each follower drone in the elliptical formation are expressed as follows:

[0034]

[0035] in, Indicates the first j The matrix form of the orientation of a follower drone in an elliptical formation. Indicates time t The location of the leader's unmanned system, Indicates time t The major axis of the elliptical formation, Indicates time t The minor axis of the elliptical formation T Indicates transpose. Indicates the first j A matrix form of the safe distance threshold for each follower drone. Represents the result of rotating the rotation matrix. x Axial direction vector, Represents the result of rotating the rotation matrix. y Axial direction vector.

[0036] Optionally, obtain the time. t The specific steps of the optimal safety trajectory for a leader-controlled unmanned system include:

[0037] Time t The updated safe path uses polynomial trajectory fitting to obtain the time step. t Three-dimensional trajectory equation;

[0038] Based on time t Three-dimensional trajectory equation construction time t The objective function;

[0039] Based on the time t The objective function is used to obtain the time step. t The leader unmanned system finds the optimal trajectory points on each straight line segment and constructs the timeline. t The optimal safety trajectory for leader-controlled unmanned systems.

[0040] Optionally, the expression for the objective function is:

[0041]

[0042] in, This represents the total cost of minimizing the spatial location of the unmanned leader system. Indicates time t The spatial location of the leader's unmanned system Indicates time t The i The fourth derivative of the spatial position of a straight line segment; Indicates the first i At the end of the time interval of the straight line segment, Indicates time t The i The spatial position of a straight line segment i =1,2,3… I , I This represents the total number of segments of the straight line.

[0043] Compared with the prior art, the present invention has at least the following beneficial effects:

[0044] (1) The cluster time-varying formation control method of the present invention is based on the leader-follower control architecture. When solving the safe trajectory of the leader unmanned system, the safety constraints of the elliptical formation of the follower UAVs are considered at the same time. The convex optimization method is used to obtain the continuous trajectory of the leader unmanned system and the safe time-varying formation of each follower UAV at the same time, realizing the air-ground cooperative cluster time-varying elliptical formation.

[0045] (2) The cluster time-varying formation control method of the present invention enables safe flight of multi-aircraft formations with continuous formation changes in obstacle environment, and the formation transition is natural without obvious abrupt changes. Attached Figure Description

[0046] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0047] Figure 1 This is a schematic diagram of the time-varying formation planning and control process in an embodiment of the time-varying formation control method of the present invention;

[0048] Figure 2 This is a schematic diagram of the communication topology between air-to-ground cooperative formations in an embodiment of the cluster time-varying formation control method of the present invention. Detailed Implementation

[0049] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0050] A specific embodiment of the present invention, such as Figure 1-Figure 2 A time-varying formation control method for air-ground cooperative clustering in complex environments is disclosed. The specific implementation steps are as follows:

[0051] Step S1, let t =1, when t =1 indicates the initial time.

[0052] Step S2, based on time t The leader unmanned system's starting trajectory point and acquisition time. t A grid map of the leader-unmanned system;

[0053] According to time t The leader of the unmanned system uses a grid map to perform global path planning and obtain time points. t The safe path;

[0054] It is understood that the complex environment referred to is an unstructured complex environment, which refers to a physical environment that lacks fixed rules, is dynamically changing, and is difficult to predict, including urban canyons, forest / vegetation-covered areas, and areas with dense dynamic obstacles.

[0055] Optionally, the leader unmanned system is an unmanned vehicle or a drone;

[0056] Optionally, obtain timet The specific steps involved in creating a grid map for a leader-centric unmanned system include:

[0057] Based on the scanning time of the radar sensors carried by the leader unmanned system t The surrounding environment is used to obtain the time in the radar coordinate system. t Coordinate information of each obstacle;

[0058] Time in the lidar coordinate system t The coordinate information of each obstacle is converted into coordinate information in the world coordinate system to obtain the time. t Updated coordinate information of each obstacle, establishment time t Global map;

[0059] The time t The leader unmanned system receives the time via inter-machine communication t The location information of each follower drone at any time t Marked on the global map;

[0060] Deletion and Time t The position information of each follower drone and the coordinate information of the obstacles are used to obtain the time. t Update the global map;

[0061] Based on time t Update the global map, construct the corresponding octree map, and mark obstacles.

[0062] At any moment t Within the leader's unmanned system's field of vision, the corresponding octree map is rasterized to obtain the time-sharing data. t A grid map of the leader-unmanned system.

[0063] It is understandable that the positions of each follower drone are obtained through inter-drone communication, and then deleted from the corresponding global map along with their timestamps. t The location information of each follower drone corresponds to the coordinate information of the obstacle, which prevents the leader drone system from misdetecting followers within its field of vision as obstacles.

[0064] Optionally, the obstacles include static obstacles, dynamic obstacles, natural obstacles, artificial obstacles, and special environmental interference factors.

[0065] Furthermore, the expression for updating the coordinate information of each obstacle is as follows:

[0066]

[0067] in, This refers to the coordinates of the obstacle in the world coordinate system. , For obstacles in the world coordinate system x Axis coordinates Let y be the y-coordinate of the obstacle in the world coordinate system. Let z be the z-axis coordinate of the obstacle in the world coordinate system. R wb This is the rotation matrix from the radar coordinate system to the world coordinate system. This indicates the coordinate position of the obstacle in the radar coordinate system. , For obstacles in radar coordinate system x Axis coordinates For obstacles in radar coordinate system y Axis coordinates For obstacles in radar coordinate system z Axis coordinates T This represents the translation matrix.

[0068] Optionally, global path planning can be performed based on the A* algorithm.

[0069] Step S3, set the time t The safe path is segmented to obtain the time. t Multiple straight lines ;

[0070] Set time t Each straight line safe corridor ;

[0071] Based on time t Safety corridors in each straight line , obtain the moment t Update the security path;

[0072] It is understood that the safe path is a path that avoids obstacles;

[0073] Optionally, obtain the time. t The specific steps for updating the security path include:

[0074] Step S31: Set the time t No. i straight line segment The original enclosing ellipse ;

[0075] It is understood that the original enclosing ellipse is the enclosing ellipse with the smallest area that includes the corresponding segmented straight lines and does not contain any obstacles;

[0076] Step S32, Extend the time frame t No. i straight line segment The original enclosing ellipse The major axis, minor axis, and angle, until the corresponding original bounding ellipse. The edge contacts multiple obstacles, and at each contact point, the original surrounding ellipse is set. The tangent hyperplane, as the dividing plane, divides the corresponding original enclosing ellipse. Separated from obstacles, obtain time. t No. i Multiple hyperplanes of a line segment;

[0077] Step S33, based on time t No. i Multiple hyperplanes of a straight line segment form a safe convex polyhedron, which serves as the corresponding safe corridor. ;

[0078] Understandably, a safe corridor serves as a secure passageway to ensure the safety of the leader's drone or unmanned vehicle in complex environments;

[0079] The safety convex polyhedron is a safety boundary defined based on convex geometric properties.

[0080] Step S34, Traverse Time t Obtain the time from each straight line segment. t Safety corridors along each straight section;

[0081] Step S35, based on time t Safety corridors in each straight line , obtain the moment t Update the security path.

[0082] Optionally, it also includes time. t No. i straight line segment safe corridor The constraint condition is expressed as:

[0083]

[0084] in, For a moment t No. i The safety corridor of a straight line represents the geometric region that the trajectory points must satisfy; For a moment t No. i The position of the leader of the unmanned system in the straight line segment. For a moment t No. i The normal vector of the leader of the unmanned system on the straight line segment. For a moment t No. iThe offset of the leader unmanned system on the straight line segment.

[0085] Step S4: Set the time t Constraints on each follower drone in an elliptical formation;

[0086] Optionally, the time t The elliptical formation is centered on the leader unmanned system;

[0087] Optionally, set the time. t The specific steps for defining the constraints on each follower drone in an elliptical formation include:

[0088] Step S41: Set the initial constraints for each follower drone in the elliptical formation, expressed as:

[0089]

[0090]

[0091] in, Indicates the first j The orientation of a follower drone within an elliptical formation. Indicates time t The location of the leader's unmanned system, Indicates time t The rotation matrix is ​​used to describe the overall rotation of the elliptical formation. z The rotation of the axis adjusts the overall posture of the formation. Indicates time t The major axis of the elliptical formation, Indicates time t The minor axis of the elliptical formation The parameters representing the follower drones in an elliptical formation. Indicates the first j The safe distance threshold for a follower drone Indicates time t rotation angle, T This indicates transpose.

[0092] Step S42: Simplify the initial constraints of each follower UAV in the elliptical formation into convex constraints using the radius approximation method, resulting in the simplified constraint conditions, expressed as:

[0093]

[0094] in, Represents the result of rotating the rotation matrix. x Axial direction vector, ; Represents the result of rotating the rotation matrix. y Axial direction vector, .

[0095] Step S43: Convert the simplified constraints into matrix form to obtain the constraints of each follower UAV in the elliptical formation, expressed as:

[0096]

[0097] in, Indicates the first j The matrix form of the orientation of a follower drone in an elliptical formation. Indicates the first j The matrix form of the safe distance threshold for each follower drone.

[0098] Step S5: Set the time t The updated safe path uses polynomial trajectory fitting to obtain the time step. t Three-dimensional trajectory equation;

[0099] Based on time t Three-dimensional trajectory equation construction time t The objective function;

[0100] Based on the time t The objective function is used to obtain the time step. t The leader unmanned system finds the optimal trajectory points on each straight line segment and constructs the timeline. t The optimal safety trajectory for unmanned leader systems;

[0101] Time t The optimal trajectory point of the last straight segment of the leader unmanned system's optimal safe trajectory is taken as the moment. t +1 The starting trajectory point of the leader unmanned system;

[0102] Step S6, based on time t The optimal trajectory points and times of the leader unmanned system on each straight line segment t The constraints of each follower drone in elliptical formation, at any given time. t The various follower drones formed a formation and obtained the time. t Each follower drone is positioned at the optimal trajectory point in the elliptical formation, and the moment is constructed. t The optimal safe trajectory for each follower drone;

[0103] Optionally, the expression for the three-dimensional trajectory equation is:

[0104]

[0105] in, Indicates time t The spatial location of the leader unmanned system, including x axis, y axis, z Position of the axis in three directions; Indicates time t The i The spatial position of a straight line segment n Let be the order of the polynomial. For the first i The first segment of the straight line n Polynomial coefficients, For the first i The time point of the straight line segment For the first i- The time points of a straight line segment.

[0106] The expression for the objective function is:

[0107]

[0108] in, This represents the total cost of minimizing the spatial location of the unmanned leader system. Indicates time t The i The fourth derivative of the spatial position of a straight line segment; Indicates the first i At the end of the time interval of the straight line segment, , i =1,2,3… I , I This represents the total number of line segments.

[0109] Optionally, it also includes: constraints on the objective function, expressed as:

[0110]

[0111] in, Indicates the first i The straight line segment at the end of the time interval Δt i The n First derivative, Indicates the first i + A straight line at the starting point of the time interval n First derivative, n =0: Indicates the first i The position of the straight line segment at the end of time; n =1: Indicates the first i The straight line segment at the end of the time interval Δt i speed; n =2: Indicates the first i The straight line segment at the end of the time interval Δt i Δt i The acceleration.

[0112] Step S7, Judgment t Is it greater than or equal to? T′ , T′ Represents the total number of moments; if so, we get... T′ The optimal safety trajectory of the leader unmanned system at any given moment and T′ The optimal safe trajectory for each follower drone at any given moment;

[0113] based on T′ The optimal safety trajectory of the leader unmanned system at any given moment and T′ The optimal safe trajectory for each follower drone at any given moment is determined to complete the time-varying formation of the swarm; otherwise, [then...] t = t +1, return to step S2;

[0114] The present invention also includes step S8, based on T′ The optimal safety trajectory of the leader unmanned system at any given moment and T′ The optimal safe trajectory of each follower drone at each moment is obtained to obtain the cluster time-varying formation control protocol;

[0115] Based on the cluster time-varying formation control protocol, the leader unmanned system and each follower UAV are formed into an elliptical formation.

[0116] Example 1

[0117] Taking 6 drones and 1 unmanned vehicle as an example, the unmanned vehicle, as the leader, is located in the center of the elliptical formation, while the 6 drones, as followers, are located on the elliptical formation.

[0118] Let G be an undirected graph to represent the communication topology between the leader drone and the follower drones. Each follower can communicate with the leader, and adjacent followers can communicate with each other.

[0119] Based on the time-varying formation control protocol and non-singular matrices, design a formation tracking controller;

[0120] Gaining Leaders' Autonomous Vehicle Moments t The actual location and the k A follower drone moment t The actual position is input to the formation tracking controller, and the output is the first... k A follower drone momentt The desired position;

[0121] Traversal F Each follower drone receives its own time data. t The desired position is to maintain an elliptical formation centered on the leader autonomous vehicle.

[0122] The expression for the formation tracking controller is:

[0123] K w k,1 (g) k (t)- h k ( t )- g 1 (t)+

[0124] K [(g k (t)- h k ( t ))-( g j (t)- h j ( t ))]+ (t)

[0125] (t)= (A h k ( t )- (t) )

[0126] in, u k ( t ) indicates the first k A follower drone moment t The control input, K For the control gain matrix, w k,1 Indicating that the leader of the autonomous vehicle and the first k Communication information of a follower drone g k (t) Indicates the first k A follower drone moment t The expected position h k ( t ) indicates the firstk A follower drone moment t The actual location, g 1 (t) Indicating the leader's autonomous vehicle moment t The expected position w kj Indicates the first k The follower drone and the first j Communication information of a follower drone (t) Indicates the first k A follower drone moment t Speed ​​estimation error, g j (t) Indicates the first j A follower drone moment t The expected position h j ( t ) indicates the first j A follower drone moment t The actual location, Represents the system's dynamic characteristic matrix. (t) Indicates the first k A follower drone moment t The rate of change of position, A Represents the system matrix, describing the dynamic relationships between positions. j =2,3,4… F , k =2,3,4… F , F Indicates the total number of follower drones, when j When =1, it indicates a leader unmanned system, including leader drones or leader unmanned vehicles.

[0127] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A time-varying formation control method for air-ground coordinated flight in complex environments, characterized in that, include: Step S1, let t =1, when t =1 indicates the initial time. Step S2, based on time t The leader of the unmanned system uses the starting trajectory point to perform global path planning and obtain the time. t The safe path; Step S3, set the time t The safe path is divided into multiple straight lines, and time intervals are set. t The safety corridors of each straight segment are obtained at specific times. t The steps to update the security path include: Step S31: Set the time t No. i The original enclosing ellipse of the straight line segment is expanded until the edge of the corresponding original enclosing ellipse contacts multiple obstacles. Step S32: Set a hyperplane tangent to the original enclosing ellipse at each contact point to obtain the time. t No. i Multiple hyperplanes of a line segment; Step S33, based on time t No. i Multiple hyperplanes of a straight line segment form a safe convex polyhedron, which serves as the corresponding safe corridor; Step S34, Traversing Time t Obtain the time from each straight line segment. t Safety corridors along each straight section; Step S35, based on time t The safe corridors of each straight line are obtained at specific times. t Update the security path; Step S4: Set the time t The constraints on each follower drone in an elliptical formation, and the specific steps include: Step S41: Set the initial constraints for each follower drone in the elliptical formation. Step S42: Use the radius approximation method to simplify the initial constraint conditions into convex constraint conditions, and obtain the simplified constraint conditions; Step S43: Convert the simplified constraints into matrix form to obtain the constraints of each follower UAV in the elliptical formation. Step S5, based on time t Update the safe path construction time t The objective function is used to obtain the time step. t The optimal safety trajectory for unmanned leader systems; Time t The optimal trajectory point of the last straight segment of the leader unmanned system's optimal safe trajectory is taken as the moment. t +1 The starting trajectory point of the leader unmanned system; Step S6, based on time t Optimal safety trajectory and timing for leader-controlled unmanned systems t The constraints of each follower drone in elliptical formation, at any given time. t The various follower drones formed a formation and obtained the time. t The optimal safe trajectory for each follower drone; Step S7, Judgment t Is it greater than or equal to? T′ , T′ Represents the total number of moments; if so, we get... T′ The optimal safety trajectory of the leader unmanned system at any given moment and T′ The optimal safe trajectory for each follower drone at any given moment is determined to complete the time-varying formation of the swarm; otherwise, [then...] t = t +1, return to step S2.

2. The method for time-varying formation control of air-ground coordinated clusters in complex environments according to claim 1, characterized in that, The leader unmanned system is an unmanned vehicle or drone.

3. The method for time-varying formation control of air-ground coordinated clusters in complex environments according to claim 1, characterized in that, Step S2 also includes obtaining the time. t The grid map of the leader-controlled unmanned system includes the following steps: Obtain the time in the radar coordinate system t The coordinates of various obstacles in the surrounding environment of the leader unmanned system are obtained and converted into coordinates in the world coordinate system to obtain the time. t Updated coordinate information of each obstacle, establishment time t Global map; For time t The global map is updated to obtain the time. t Update the global map; Based on time t Update the global map, construct the corresponding octree map, and mark obstacles. At any moment t Within the leader's unmanned system's field of vision, the corresponding octree map is rasterized to obtain the time-sharing data. t A grid map of the leader-unmanned system.

4. The method for time-varying formation control of air-ground coordinated clusters in complex environments according to claim 3, characterized in that, Get the moment t The specific steps for updating the global map include: The time t The leader unmanned system receives the time via inter-machine communication t The location information of each follower drone at any time t Marked on the global map; Deletion and Time t The position information of each follower drone and the coordinate information of the obstacles are used to obtain the time. t Update the global map.

5. The method for time-varying formation control of air-ground coordinated clusters in complex environments according to claim 1, characterized in that, The expression for the constraint conditions of each follower drone in the elliptical formation is as follows: in, Indicates the first j The matrix form of the orientation of a follower drone in an elliptical formation. Indicates time t The location of the leader's unmanned system, Indicates time t The major axis of the elliptical formation Indicates time t The minor axis of the elliptical formation T Indicates transpose. Indicates the first i A matrix form of the safe distance threshold for each follower drone. Represents the result of rotating the rotation matrix. x Axial direction vector, Represents the result of rotating the rotation matrix. y Axial direction vector.

6. The method for time-varying formation control of air-ground coordinated clusters in complex environments according to claim 1, characterized in that, Gaining Time t The specific steps of the optimal safety trajectory for a leader-controlled unmanned system include: Time t The updated safe path uses polynomial trajectory fitting to obtain the time step. t Three-dimensional trajectory equation; Based on time t Three-dimensional trajectory equation construction time t The objective function; Based on the time t The objective function is used to obtain the time step. t The leader unmanned system finds the optimal trajectory points on each straight line segment and constructs the timeline. t The optimal safety trajectory for leader-controlled unmanned systems.

7. The method for time-varying formation control of air-ground coordinated clusters in complex environments according to claim 6, characterized in that, The time t The expression for the objective function is: in, This represents the total cost of minimizing the spatial location of the unmanned leader system. Indicates time t The spatial location of the leader's unmanned system Indicates time t The i The fourth derivative of the spatial position of a straight line segment; Indicates the first i At the end of the time interval of the straight line segment, Indicates time t The i The spatial position of a straight line segment i =1,2,3… I , I This represents the total number of segments on the straight line.

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