A cluster unmanned aerial vehicle distributed flight control method fusing pipeline vector field

Abstract

This invention discloses a distributed flight control method for swarmed UAVs that integrates a pipeline vector field. The method involves constructing a mathematical model of the UAVs; establishing pipeline boundary collision avoidance vectors based on the pipeline boundaries and guidance curves; establishing UAV collision avoidance vectors between UAVs based on their positional deviations from adjacent UAVs; and establishing a UAV swarm vector to prevent individual UAVs from dispersing. The pipeline boundary collision avoidance vectors, UAV collision avoidance vectors, and UAV swarm vectors of all UAVs in the swarm are fused to obtain a comprehensive vector field. A distributed controller is constructed based on the comprehensive vector field and the input constraint model. The UAVs are then controlled by this distributed controller. By introducing a comprehensive vector field into the UAV's distributed controller, each UAV can independently adjust its position and orientation based on local vector field information without maintaining a fixed formation, thereby enabling obstacle avoidance, swarm flight, and global safe passage for swarmed UAVs in complex constraint environments.

Description

Technical Field

[0001] This invention relates to flight safety control of unmanned aerial vehicles (UAVs), specifically to a distributed flight control method for swarm UAVs that integrates pipeline vector fields. Background Technology

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, multi-UAV collaborative systems have become an important research direction for intelligent autonomous flight. Swarm UAVs, through the distributed cooperation of multiple UAVs, can demonstrate high efficiency, flexibility, and redundancy in tasks such as disaster search and rescue, environmental monitoring, and urban low-altitude logistics. However, when performing tasks in complex environments, the flight control of swarm UAVs must simultaneously meet multiple constraints, including dynamic constraints, input constraints, safe distance constraints, and communication constraints. How to achieve efficient collaboration and stable flight of the swarm while ensuring safety is a significant technical challenge in the field of intelligent control of UAV swarms.

[0003] Current swarm drone control methods mainly include two types: centralized control and distributed control. Centralized control relies on a central node for global information processing and path planning, enabling precise control. However, when there are many drones or communication is limited, it is prone to latency, failure, or system bottlenecks. In contrast, distributed control allows each drone to make autonomous decisions based on local perception and neighborhood communication, exhibiting good scalability and fault tolerance. However, in complex dynamic environments, traditional distributed control still suffers from many problems, such as discontinuous obstacle avoidance paths, flight stagnation due to local extrema, poor formation stability, and insufficient handling of input constraints and external disturbances, making it difficult to achieve highly robust and globally safe flight control.

[0004] To address these issues, existing technologies have proposed artificial potential field methods and pipe flight methods. These methods can achieve obstacle avoidance and cooperation to a certain extent, but they still suffer from the problem of not satisfying dynamic constraints. When UAV swarms fly in complex obstacle environments, traditional pipe flight methods often struggle to maintain path smoothness due to the lack of dynamic constraints. Summary of the Invention

[0005] Purpose of the invention: To address the above shortcomings, this invention provides a distributed flight control method for swarm UAVs that integrates a pipeline vector field for flexible obstacle avoidance, swarm maintenance, and global safe passage.

[0006] Technical Solution: To solve the above problems, this invention adopts a distributed flight control method for swarm UAVs that integrates pipeline vector fields, including the following steps:

[0007] (1) Construct a mathematical model of the UAV that considers unknown external disturbances and control input constraints;

[0008] (2) Determine the conduit restricting the flight of the UAV swarm, and obtain the center curve and boundary of the conduit; based on the center curve of the conduit, and combined with the offset vector of the UAV relative to the center curve, obtain the guide curve of the UAV's desired position;

[0009] (3) Based on the position deviation vector between the real-time position and the desired position of the UAV, establish the pipeline boundary collision avoidance vector of the UAV relative to the pipeline boundary; based on the relative position deviation vector between the real-time position of the UAV and the real-time position of the adjacent UAV, establish the UAV collision avoidance vector between the UAVs; based on the relative position deviation vector and the minimum distance of the swarm effect, establish the UAV swarm vector to avoid the dispersion of individual UAVs.

[0010] (4) The pipe boundary collision avoidance vector, UAV collision avoidance vector and UAV cluster vector of all UAVs in the cluster are fused to obtain the comprehensive vector field;

[0011] (5) Construct a distributed controller based on the integrated vector field and input constraint model;

[0012] (6) Control the drone through the constructed distributed controller.

[0013] Furthermore, the mathematical model of the drone is as follows:

[0014] ;

[0015] in, For the first The position vector of the drone, Represents the position vector Find the first derivative. For the first The velocity vector of the drone Represents the velocity vector Find the first derivative. For the first Damping term of the drone Represents the gain matrix. For the first The system control input vector after the UAV has been subjected to control constraints. For the first Unknown external interference with the drone.

[0016] Furthermore, an input constraint model is constructed to control the input constraints. The input constraint model for the drone is as follows:

[0017] ;

[0018] ;

[0019] in, For the first An unconstrained control input For the first The control input after a constraint For the first The maximum value of a control input. It is a symbolic function.

[0020] Furthermore, the guidance curve for the desired location of the drone. for:

[0021] ;

[0022] in, The center curve of the pipeline, For the first The relative position offset vector between the expected operating location of the drone and the center curve.

[0023] Furthermore, the pipeline boundary collision avoidance vector Using a Lyapunov function, the expression is:

[0024] ;

[0025] in, , It is a positive coefficient. For the first The position deviation vector of the drone's real-time position relative to the desired position. For the first The position deviation vector of the drone's real-time position relative to the center curve. For the pipeline in the first The maximum length of the drone in the direction it is positioned. For the first The safe radius of a drone For the first The obstacle avoidance distance of the drone in the pipeline. It is an exponential function.

[0026] Furthermore, the drone collision avoidance vector Using a Lyapunov function, the expression is:

[0027] ;

[0028] in, , It is a positive coefficient. For the first The real-time location of the drone and the first The relative position deviation vector of the drone's real-time position. This refers to the collision avoidance distance between drones.

[0029] Furthermore, the drone swarm vector uses a Lyapunov-like function, expressed as:

[0030] ;

[0031] in, , It is a positive coefficient. The minimum distance for cluster action.

[0032] Furthermore, the pipeline boundary collision avoidance vector, the UAV collision avoidance vector, and the UAV swarm vector are fused to obtain a comprehensive vector field, which is:

[0033] ;

[0034] in, The total number of all drones in the cluster. For the cluster except the first A collection of other drones besides the main drone. For the first The serial number of the drone.

[0035] Furthermore, the distributed controller is:

[0036] ;

[0037] ;

[0038] ;

[0039] in, The input is not saturated. The gain matrix of the swarm drones. Let be the damping matrix for all drones in the cluster. As an auxiliary variable, The coefficient matrix of the interference. For estimating unknown external interference for all drones in the cluster, To find the second derivative of the drone's guidance curve, It is a positive coefficient. Let be the partial derivative of the combined vector field of all UAVs in the cluster with respect to the position vector. The diagonal coefficient matrix of the auxiliary variables. It is a diagonal matrix. To saturate unsaturated inputs, This is the actual input for the drone after saturation processing. The speed error of all drones in the cluster, Signals generated by the auxiliary system Indicates the signal generated by the auxiliary system Find the first derivative.

[0040] Furthermore, the partial derivative of the combined vector field of all UAVs in the cluster with respect to the position vector is:

[0041] ;

[0042] ;

[0043] in, To find the partial derivative of the composite vector field with respect to the position vector of the first UAV, For the partial derivative of the composite vector field with respect to the position vector of the second UAV, For the comprehensive vector field about the first The partial derivative of the position vector of the UAV. For the comprehensive vector field about the first The partial derivative of the position vector of the UAV. Represents the transpose of a matrix. For the first The partial derivative of the collision avoidance vector of the UAV at the pipe boundary with respect to the position vector. For the first The partial derivative of the drone's collision avoidance vector with respect to its position vector. For the first The partial derivative of the drone swarm vector with respect to the position vector.

[0044] Beneficial effects: Compared with the prior art, the significant advantage of this invention is that it introduces a comprehensive vector field into the distributed controller of the UAV, enabling each UAV to independently adjust its position and orientation based on local vector field information without maintaining a fixed formation. This enables the swarm of UAVs to avoid obstacles, fly in swarms, and pass safely around the entire network in complex and constrained environments. Attached Figure Description

[0045] Figure 1 This is the composition of the distributed controller of the present invention.

[0046] Figure 2 The relationship between variables related to collision avoidance at pipeline boundaries. Detailed Implementation

[0047] This embodiment presents a distributed flight control method for swarm UAVs that integrates pipeline vector fields, comprising the following steps:

[0048] Step 1: Based on the dynamic characteristics of the UAV, establish a mathematical model of the UAV that considers unknown external disturbances and control input constraints, and determine the range of values ​​for the state variables and input variables of the UAV using simulation, flight experiments and theoretical calculations.

[0049] Step 2: Determine the pipeline that restricts the flight of the drone swarm, obtain the center curve and boundary of the pipeline; analyze the flight characteristics of the drone swarm within the pipeline, and construct four directional vectors including curve guidance, pipeline boundary collision avoidance, drone collision avoidance, and drone swarm.

[0050] Step 3: Based on the pipeline boundary collision avoidance vector, the UAV collision avoidance vector, and the UAV swarm vector, the vectors are fused to form a comprehensive vector field with attraction and repulsion properties;

[0051] Step 4: Design a distributed controller that incorporates integrated vectors, enabling each UAV to independently adjust its position and orientation based on local vector field information without maintaining a fixed formation. This allows the swarm of UAVs to avoid obstacles, fly in swarms, and pass safely across the entire network in complex constrained environments.

[0052] Specifically, the steps for establishing a mathematical model of a drone are as follows:

[0053] A mathematical model reflecting the actual dynamic characteristics and control input limitations of an aircraft is established, and the range of control variables in the model is determined through simulation and flight testing. The swarm of UAVs consists of... The system consists of several unmanned aerial vehicles (UAVs), and their flight state in three-dimensional space can be represented by position and velocity state variables. For the first... A drone is deployed, and the following mathematical model is established:

[0054] (1)

[0055] in, Indicates the first The position vector of the drone, Represents the position vector Find the first derivative. Indicates the first The velocity vector of the drone Represents the velocity vector Find the first derivative. Indicates the first The system control input vector after the UAV has been subjected to control constraints. Indicates the first Damping term of the drone , The damping coefficient is... These are the velocity components along the three axes. Indicates the first The quality of the drone Represents gravitational acceleration. Represents the gain matrix. This indicates unknown external interference. These are the interference components in the three-axis directions.

[0056] To ensure that the control input is within the physical range, for the first Input of drones The input constraint model is set as follows:

[0057] (2)

[0058] (3)

[0059] in, For the first An unconstrained control input For the first The control input after a constraint For the first The maximum value of a control input. It is a symbolic function.

[0060] Specifically, the steps for constructing the direction vector within the pipe are as follows:

[0061] Analyze the kinematic behavior and swarm interaction characteristics of UAVs in the pipe space, and design directional vectors to guide and constrain the flight of swarm UAVs in the pipe space: pipe boundary collision avoidance vector, UAV collision avoidance vector, and UAV swarm vector.

[0062] The pipeline restricting the flight of the drone swarm is determined, and the center curve and boundary of the pipeline are obtained. Based on the center curve of the pipeline, combined with the offset vector of the drone relative to the center curve, the guidance curve of the drone's desired position is obtained.

[0063] The guide curve for the desired position of the drone provides the drone with pointing force along its trajectory within the pipe, guiding it to move along the curve within the pipe. For the first... The guidance curve of the drone Defined as:

[0064] (4)

[0065] in, The center curve of the pipeline, For the first The relative position offset vector between the theoretical operating position of the drone and the center curve.

[0066] Specifically, based on the position deviation vector between the UAV's real-time position and its desired position, a pipeline boundary avoidance vector is established for the UAV relative to the pipeline boundary. This vector enables the UAV to exert a repulsive force when approaching the pipeline boundary, preventing it from crossing the boundary and keeping it within the safe pipeline. For the first... Real-time location of the drone Lyapunov-like functions for pipe boundary collision avoidance as follows:

[0067] (5)

[0068] in, It is a positive coefficient. Let be the position deviation vector of the UAV relative to the guide curve. This is the position deviation vector of the UAV relative to the center curve. This represents the maximum length of the pipeline in the direction the drone is located. For the safe radius of the drone, The obstacle avoidance distance for drones in pipelines. It is an exponential function. The relationship between the variables related to pipeline boundary collision avoidance is as follows: Figure 2 As shown, an example is given with the centerline of the pipeline as the straight line.

[0069] Specifically, based on the relative position deviation vector between the real-time position of the UAV and that of adjacent UAVs, a collision avoidance vector is established between the UAVs. This collision avoidance vector generates a repulsive force when adjacent UAVs approach each other, ensuring a minimum safe distance and preventing collisions. For the first... Real-time location of the drone , No. Real-time location of the drone Lyapunov-like functions for drone collision avoidance as follows:

[0070] (6)

[0071] in, It is a positive coefficient. Let be the vector of relative positional deviation between the two drones. This refers to the collision avoidance distance between drones.

[0072] Specifically, based on the relative position deviation vector and the minimum swarm effect distance, a drone swarm vector is established to prevent individual drones from dispersing; this swarm vector enables the drones to fly in swarms, preventing individual drones from dispersing and causing mission failure. For the first... Real-time location of the drone Lyapunov-like functions for drone swarms as follows:

[0073] (7)

[0074] in, It is a positive coefficient. The minimum distance from which a cluster item begins to have an effect.

[0075] Specifically, based on the pipeline boundary collision avoidance vector, the UAV collision avoidance vector, and the UAV swarm vector, these vectors are fused to form a comprehensive vector field that simultaneously possesses attractive and repulsive properties. The Lyapunov-like function of the comprehensive vector field is then described. Defined as:

[0076] (8)

[0077] in, For the first Lyapunov-like functions of the composite vector field of an unmanned aerial vehicle (UAV). For the cluster except the first A collection of drones other than the drone itself.

[0078] Specifically, a distributed controller is constructed based on a comprehensive vector field and input constraint model; to enable the swarm of drones to fly safely within the pipeline, auxiliary variables are first designed. as follows:

[0079] (9)

[0080] in, , For the first Auxiliary variables of the drone The error between the actual speed and the expected speed of the drone. For the first The speed error of the drone It is a positive coefficient. The signals generated for subsequent auxiliary systems design For the whole class of Lyapunov functions with respect to The partial derivatives, Indicates the location of the swarm of drones. For Lyapunov functions with respect to The partial derivatives of .

[0081] beg about From the partial derivatives, we can obtain:

[0082] (10)

[0083] To address interference in the drone model, an adaptive law is designed. as follows:

[0084] (11)

[0085] in, , For the first Estimation of interference from drones, To estimate the components of the disturbance in the three axes, The coefficient matrix of the interference. For the first The coefficient matrix of interference from drones. This represents a diagonal matrix.

[0086] To address input constraints, an auxiliary system was designed. as follows:

[0087] (12)

[0088] in, Indicates the signal generated by the auxiliary system Find the first derivative. , It is a diagonal matrix. , For the first Auxiliary systems for drones, The gain matrix of the swarm drones. For the first The gain matrix of the drone To saturate unsaturated inputs, The input is not saturated. For the first Unsaturated input of the drone The components of the unsaturated input in the three-axis directions, and and Correspondingly.

[0089] Saturation function As shown below:

[0090] (13)

[0091] in, It is the hyperbolic tangent function. This represents the maximum value of the unsaturated input.

[0092] By integrating the integrated vector field, auxiliary variables, adaptive law, and auxiliary system into the controller, a distributed controller for swarm UAVs that incorporates the pipeline vector field is designed as follows:

[0093] (14)

[0094] in, The damping matrix of the swarm drones, For the first The damping matrix of the drone The diagonal coefficient matrix of the auxiliary variables. For the first The diagonal system matrix of auxiliary variables of the drone. The input constraint model constrains the actual input of the UAV after saturation processing.

[0095] This distributed controller enables each UAV to independently adjust its position and orientation based on vector field information without maintaining a fixed formation, thereby achieving obstacle avoidance, cluster maintenance, and safe passage of swarm UAVs in complex environments.

Claims

1. A distributed flight control method for swarmed UAVs that integrates pipeline vector fields, characterized in that, Includes the following steps: (1) Construct a mathematical model of the UAV that considers unknown external disturbances and control input constraints; (2) Determine the conduit restricting the flight of the UAV swarm, and obtain the center curve and boundary of the conduit; based on the center curve of the conduit, and combined with the offset vector of the UAV relative to the center curve, obtain the guide curve of the UAV's desired position; (3) Based on the position deviation vector between the real-time position and the desired position of the UAV, establish the pipeline boundary collision avoidance vector of the UAV relative to the pipeline boundary; based on the relative position deviation vector between the real-time position of the UAV and the real-time position of the adjacent UAV, establish the UAV collision avoidance vector between the UAVs; based on the relative position deviation vector and the minimum distance of the swarm effect, establish the UAV swarm vector to avoid the dispersion of individual UAVs. (4) The pipe boundary collision avoidance vector, UAV collision avoidance vector and UAV cluster vector of all UAVs in the cluster are fused to obtain the comprehensive vector field; (5) Construct a distributed controller based on the integrated vector field and input constraint model; (6) Control the drone through the constructed distributed controller.

2. The distributed flight control method for swarm UAVs based on fused pipeline vector fields according to claim 1, characterized in that, The mathematical model of the drone is: ； in, For the first The position vector of the drone, Represents the position vector Find the first derivative. For the first The velocity vector of the drone Represents the velocity vector Find the first derivative. For the first Damping term of the drone Represents the gain matrix. For the first The system control input vector after the UAV has been subjected to control constraints. For the first Unknown external interference with the drone.

3. The swarm UAV distributed flight control method based on fused pipeline vector fields according to claim 2, characterized in that, Construct an input constraint model to control input constraints, the first The input constraint model for the drone is as follows: ； ； in, For the first An unconstrained control input For the first The control input after a constraint For the first The maximum value of a control input. It is a symbolic function.

4. The distributed flight control method for swarm UAVs based on fused pipeline vector fields according to claim 3, characterized in that, The guide curve for the desired location of the drone for: ； in, The center curve of the pipeline, For the first The relative position offset vector between the expected operating location of the drone and the center curve.

5. The swarm UAV distributed flight control method based on fused pipeline vector fields according to claim 4, characterized in that, The pipeline boundary collision avoidance vector Using a Lyapunov function, the expression is: ； in, , It is a positive coefficient. For the first The position deviation vector of the drone's real-time position relative to the desired position. For the first The position deviation vector of the drone's real-time position relative to the center curve. For the pipeline in the first The maximum length of the drone in the direction it is positioned. For the first The safe radius of a drone For the first The obstacle avoidance distance of the drone in the pipeline. It is an exponential function.

6. The distributed flight control method for swarm UAVs based on fused pipeline vector fields according to claim 5, characterized in that, The drone collision avoidance vector Using a Lyapunov function, the expression is: ； in, , It is a positive coefficient. For the first The real-time location of the drone and the first The relative position deviation vector of the drone's real-time position. This refers to the collision avoidance distance between drones.

7. The swarm UAV distributed flight control method based on fused pipeline vector fields according to claim 6, characterized in that, The drone swarm vector uses a Lyapunov-like function, expressed as follows: ； in, , It is a positive coefficient. The minimum distance for cluster action.

8. The swarm UAV distributed flight control method based on fused pipeline vector fields according to claim 7, characterized in that, The pipeline boundary collision avoidance vector, the UAV collision avoidance vector, and the UAV swarm vector are fused to obtain a comprehensive vector field, which is: ； in, The total number of all drones in the cluster. For the cluster except the first A collection of other drones besides the main drone. For the first The serial number of the drone.

9. The swarm UAV distributed flight control method based on fused pipeline vector fields according to claim 8, characterized in that, The distributed controller is: ； ； ； in, The input is not saturated. The gain matrix of the swarm drones. Let be the damping matrix for all drones in the cluster. As an auxiliary variable, The coefficient matrix of the interference. For estimating unknown external interference for all drones in the cluster, To find the second derivative of the drone's guidance curve, It is a positive coefficient. Let be the partial derivative of the combined vector field of all UAVs in the cluster with respect to the position vector. The diagonal coefficient matrix of the auxiliary variables. It is a diagonal matrix. To saturate unsaturated inputs, This is the actual input for the drone after saturation processing. The speed error of all drones in the cluster, Signals generated by the auxiliary system Indicates the signal generated by the auxiliary system Find the first derivative.

10. The swarm UAV distributed flight control method based on fused pipeline vector fields according to claim 9, characterized in that, The partial derivative of the combined vector field of all UAVs in the cluster with respect to the position vector is: ； ； in, To find the partial derivative of the composite vector field with respect to the position vector of the first UAV, For the partial derivative of the composite vector field with respect to the position vector of the second UAV, For the comprehensive vector field about the first The partial derivative of the position vector of the UAV. For the comprehensive vector field about the first The partial derivative of the position vector of the UAV. To represent the transpose of a matrix, For the first The partial derivative of the collision avoidance vector of the UAV at the pipe boundary with respect to the position vector. For the first The partial derivative of the drone's collision avoidance vector with respect to its position vector. For the first The partial derivative of the drone swarm vector with respect to the position vector.

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