Multi-unmanned aerial vehicle high-speed dynamic flight formation control method, system and equipment

Abstract

The invention discloses a multi-unmanned-aerial-vehicle high-speed dynamic flight formation control method, system and device, and relates to the technical field of multi-unmanned-aerial-vehicle cooperative formation control, and the method comprises the steps: analyzing a multi-unmanned-aerial-vehicle formation, setting a reasonable flight safety distance, and constructing a formation topological structure through an adjacent matrix and an offset; a distributed control model combining leader target driving and follower cooperation is designed, and global navigation and local formation keeping are achieved; setting a kinematics constraint function and a dynamic formation transformation mechanism to ensure that the unmanned aerial vehicle formation stably and safely advances and has an adaptive formation adjustment capability; according to the improved artificial potential field method, various resultant forces are considered, and virtual edge traction force is introduced, so that the local minimum problem of the traditional artificial potential field method is effectively solved, and the obstacle avoidance reliability of the system is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of multi-aircraft cooperative formation control, and in particular to a method, system and equipment for controlling a multi-UAV high-speed dynamic flight formation. Background Art

[0002] With the rapid development of the low-altitude economy, fixed-wing UAVs, with their advantages of long flight time, high speed, and long range, have shown broad application prospects in reconnaissance, mapping, search and rescue, and logistics. However, when multiple fixed-wing UAVs simultaneously perform missions in complex environments, their high speed and strong maneuverability constraints place higher demands on multi-aircraft coordinated control, making this task extremely challenging.

[0003] Traditional UAV cooperative control technologies primarily rely on centralized formation control methods and simple obstacle avoidance algorithms. While these methods can provide effective formation flight paths for low-speed rotorcraft and single-obstacle environments, they often fail to address the potential risks posed by unknown terrain in high-speed, complex, and diverse environments. Furthermore, these traditional methods exhibit significant deficiencies in formation stability and dynamic adaptability, particularly in scenarios requiring long-duration coordinated flight and diverse environments.

[0004] In recent years, although formation control methods have made some progress in some applications, multiple challenges still exist when facing the formation control problem of fixed-wing UAVs in complex environments. For example, in complex scenarios such as complex obstacle areas or mountainous flight, traditional formations are often single and fixed, and control methods find it difficult to balance the stability and dynamic adaptability of the formation. In addition, existing UAV formation control algorithms usually use simple particle models and fail to fully consider the constraints of the UAV's dynamic characteristics and the dynamic constraints of flight safety. In terms of obstacle avoidance, traditional artificial potential field methods usually fail to fully consider the superposition effect of multi-source potential fields and are prone to falling into local minimum problems and overshoot phenomena in high-speed scenarios. Summary of the Invention

[0005] The main purpose of the present invention is to provide a multi-UAV high-speed dynamic flight formation control method, aiming to solve the problems of formation instability, poor obstacle avoidance and lack of flexibility in the existing technology when fixed-wing UAV formations travel at high speed in complex environments.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a method for controlling a multi-UAV high-speed dynamic flight formation, the method comprising:

[0007] The system obtains information about all UAVs participating in the formation flight, sets up three formations for the UAV formation, selects one UAV as the leader from among all the UAVs participating in the formation flight, and uses an adjacency matrix to describe the information exchange relationship between the UAVs in the formation. The spatial offset relative to the leader is pre-set for each follower to form one of the three formations.

[0008] A leader target-driven model and a follower error compensation control model are constructed. The leader's target-driven model is used to achieve leader navigation based on the destination point information. The follower error compensation control model is used to guide the followers to the desired position in the UAV formation based on the relative position error between each follower and other UAVs in the UAV formation and the spatial offset of each follower pre-set relative to the leader.

[0009] During the UAV formation flight, the acceleration increment limit is introduced to constrain the motion changes of the UAVs in the UAV formation. The formation switching mechanism is adaptively triggered according to the environmental state to switch the UAV formation from the current formation to another formation.

[0010] During the UAV formation flight, the resultant force acting on the UAVs is calculated based on the repulsive force of multiple obstacles, the repulsive force between multiple UAVs, and the virtual edge traction force. The UAV formation is then controlled to avoid obstacles based on the resultant force.

[0011] Among them, the present invention comprehensively designs distributed control methods, flight constraints and formation dynamic transformation mechanisms, and comprehensively improves the traditional artificial potential field method, including fully considering the superposition effect of multi-source potential fields and introducing virtual edge traction, so as to more accurately and efficiently handle the obstacle avoidance problem of multi-aircraft formations in complex environments and improve the flight stability and dynamic adaptability.

[0012] This method first analyzes the multi-UAV formation and sets a reasonable flight safety distance, and constructs the formation topology structure through the adjacency matrix and offset; designs a distributed control model that combines leader goal drive and follower collaboration to achieve global navigation and local formation maintenance; sets kinematic constraint functions and a formation dynamic transformation mechanism to ensure that the UAV formation flies smoothly and safely and has the ability to dynamically adapt to the formation; the improved artificial potential field method considers the superposition effect of multi-source potential fields and introduces virtual edge traction to effectively deal with the local minimum problem of the traditional artificial potential field method and the overshoot phenomenon in high-speed scenarios, thereby enhancing the reliability of the system's obstacle avoidance.

[0013] The technical solution in this invention comprehensively designs a distributed control method, flight constraints, dynamic formation adjustment strategy and improved artificial potential field method, realizing safe, efficient, coordinated flight and dynamic obstacle avoidance of multiple UAVs in complex environments, providing a solution with high robustness and practicality for UAV formation applications.

[0014] Preferably, the adjacency matrix is:

[0015] ;

[0016] ;

[0017] in, is the adjacency matrix, represents the control influence of the j-th UAV on the i-th UAV, N is the number of UAVs, is the jth UAV, is the i-th drone;

[0018] Each follower is pre-set to have a spatial offset relative to the leader:

[0019] ;

[0020] Among them, the three formations are: triangle formation, V formation and line formation. 、 and They represent the spatial offset of each drone relative to the leader in the triangle formation, V formation and line formation, respectively. and are the x-coordinate offset and y-coordinate offset of the i-th UAV from the leader in the triangle formation, and are the x-coordinate offset and y-coordinate offset of the j-th UAV from the leader in the V-formation, and are the x-coordinate offset and y-coordinate offset of the kth UAV from the leader in the line formation, respectively.

[0021] Preferably, a leader target-driven model and a follower error compensation control model are constructed, and leader navigation is achieved using the leader target-driven model based on the destination point information, specifically including:

[0022] Based on the destination information, the leader's target-driven model is used to calculate the distance between the destination and the leader, the leader's heading angle, and the leader's velocity components in the x and y directions.

[0023] in:

[0024] ;

[0025] ;

[0026] ;

[0027] Among them, the position of the leader drone is , is the Euclidean distance between the target point and the leader, is the horizontal coordinate of the target point, is the ordinate of the target point, The heading angle of the leader's movement, is the velocity component of the leader in the x direction, is the velocity component of the leader in the y direction, is the leader control coefficient, It is the maximum distance limit in the guidance speed.

[0028] Preferably, based on the relative position error between each follower and other drones in the drone formation and the preset spatial offset of each follower relative to the leader, a follower error compensation control model is used to guide the follower to a desired position in the drone formation, specifically including:

[0029] Based on the relative position error between each follower and other UAVs in the UAV formation and the pre-set spatial offset of each follower relative to the leader, the relative position error amplitude and relative position error direction are calculated by superimposing the relative position errors. The speed of the follower is updated based on the relative position error amplitude and relative position error direction, and the follower is guided to reach the desired position in the UAV formation.

[0030] Preferably, the relative position error between the follower and other drones in the drone formation is calculated as follows:

[0031] ;

[0032] in, is the relative position error between the i-th UAV and the j-th UAV in the x direction, is the relative position error between the i-th UAV and the j-th UAV in the y direction, and Indicates the current coordinates of the i-th follower, and represents the formation bias expected by the i-th follower, and Indicates the current coordinates of the j-th follower, and represents the formation bias expected by the j-th follower;

[0033] ;

[0034] ;

[0035] in, and represents the relative position error component accumulated by the i-th follower in the x and y directions, is the control influence of the j-th UAV on the i-th UAV, and N is the number of UAVs;

[0036] ;

[0037] ;

[0038] in, represents the comprehensive position error of the i-th follower, represents the error direction of the i-th follower;

[0039] ;

[0040] in, and Represents the control speed of the i-th follower in the x and y directions, Represents the following control coefficient.

[0041] Preferably, an acceleration increment limit is introduced to constrain the motion changes of the drones in the drone formation, specifically:

[0042] Construct a constraint function based on acceleration increment;

[0043] Constraining the motion changes of the UAVs in the UAV formation based on the constraint function to obtain an updated speed;

[0044] The constraint function is:

[0045] ;

[0046] ;

[0047] The updated speed calculation method is:

[0048] ;

[0049] in, The updated speed of the drone. Indicates the current speed of the drone. and Represents the new velocity increment of the drone in the x and y directions, and Represents the velocity increment of the drone in the x and y directions, and Respectively represent the maximum acceleration of the drone in the x and y directions, For time;

[0050] The method further includes constraining the absolute value of the velocity, and the constraint formula is:

[0051] ;

[0052] ;

[0053] ;

[0054] in, and Respectively represent the upper limit of the absolute value of the speed of the drone in the x and y directions, is the maximum velocity in the x direction, is the updated x-direction velocity, is the maximum velocity in the y direction, is the updated velocity in the y direction, is the final velocity after constraint.

[0055] Preferably, the formation switching mechanism is adaptively triggered according to the environmental state to switch the UAV formation from the current formation to another formation, specifically including:

[0056] Each drone in the drone formation is equipped with a sensor module for positioning and navigation;

[0057] Based on the leader's sensor module detection, the width of the terrain ahead is obtained;

[0058] Compare the width of the terrain ahead with the maximum width threshold of each formation to obtain a comparison result;

[0059] Based on the comparison results, it is determined whether the formation switching mechanism is triggered. If triggered, the drone formation is switched from the current formation to the formation that meets the comparison results.

[0060] Preferably, the calculation method of the formation switching mechanism is:

[0061] ;

[0062] in, For the specific selected UAV formation, 、 and They represent triangle formation, V-formation and line formation respectively. Indicates the width of the terrain ahead, Indicates the maximum width threshold of the triangle formation, Indicates the maximum width threshold of the V-shaped formation.

[0063] Preferably, in the transition phase when the UAV formation switches from the current formation to another formation, the real-time position of each follower in the transition phase is calculated using linear interpolation method, and the calculation method is:

[0064] ;

[0065] in, Indicates the real-time relative offset of the i-th follower relative to the leader at the current moment, Indicates the relative offset of the starting formation of the i-th follower, Indicates the relative offset of the target formation of the i-th follower, Represents the interpolation factor.

[0066] Preferably, the calculation method of the repulsive force of multiple obstacles on the drone is:

[0067] ;

[0068] ;

[0069] in, represents the repulsion coefficient of the obstacle on the drone, Indicates the relative distance between the drone and the obstacle center, Indicates the safe distance threshold between the drone and the obstacle center. is the repulsive potential field generated by the obstacle, is the position of the obstacle center, is the position of the drone, is the repulsive force of the obstacle on the drone;

[0070] The repulsive force between multiple drones is calculated as follows:

[0071] ;

[0072] ;

[0073] in, represents the repulsion coefficient between UAVs, represents the relative distance between the i-th UAV and the j-th UAV, Indicates the safety distance threshold between drones, is the repulsive potential field generated by the drone, is the position of the i-th UAV, is the position of the jth UAV, is the repulsive force of the i-th UAV on the j-th UAV;

[0074] Virtual edge traction:

[0075] ;

[0076] in, is the virtual edgewise traction, represents the traction coefficient, represents the Euclidean distance between the drone and the obstacle center, represents the radius of the circular obstacle, represents the distance attenuation factor, Indicates the azimuth of the obstacle center relative to the drone, Indicates the current heading angle of the drone. Indicates the tangential direction of the drone;

[0077] The calculation method of the resultant force acting on the drone is:

[0078] ;

[0079] in, is the net force acting on the UAV, n is the number of obstacles, and N is the number of UAVs.

[0080] The present invention also provides a multi-UAV high-speed dynamic flight formation control system, the system comprising:

[0081] The formation unit is used to obtain information about all UAVs participating in the formation flight, set up three formations for the UAV formation, select one UAV from all the UAVs participating in the formation flight as the leader, and the rest of the UAVs as followers. The adjacency matrix is ​​used to describe the information exchange relationship between the UAVs in the UAV formation, and the spatial offset relative to the leader is pre-set for each follower to form one of the three formations.

[0082] A model unit is used to construct a leader target-driven model and a follower error compensation control model. The leader's target-driven model is used to achieve leader navigation based on the destination point information. The follower error compensation control model is used to guide the follower to the desired position in the UAV formation based on the relative position error between each follower and other UAVs in the UAV formation and the spatial offset of each follower preset relative to the leader.

[0083] The constraint unit is used to introduce acceleration increment limits to constrain the motion changes of UAVs in the UAV formation during UAV formation flight, and adaptively trigger the formation switching mechanism according to the environmental state to switch the UAV formation from the current formation to another formation;

[0084] The force control unit is used to calculate the resultant force acting on the drones based on the repulsive forces of multiple obstacles, the repulsive forces between multiple drones, and the virtual edge traction force during the formation flight of the drones, and to perform obstacle avoidance control on the drone formation based on the resultant forces acting on the drones.

[0085] The present invention also provides a multi-UAV high-speed dynamic flight formation control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0086] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0087] The present invention comprehensively utilizes distributed control methods, flight constraints and dynamic formation adjustment strategies to comprehensively improve the traditional artificial potential field method, including fully considering multiple resultant forces and introducing virtual edge traction. It can more accurately handle the obstacle avoidance problem of multi-aircraft formations in complex environments and improve flight stability and dynamic adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention;

[0089] Figure 1 The figure is a schematic diagram of the overall process of a multi-UAV high-speed dynamic flight formation control method;

[0090] Figure 2 Schematic diagram of the force acting on the UAV;

[0091] Figure 3 This is the result diagram of the obstacle avoidance flight trajectory of the UAV formation;

[0092] Figure 4 The result graph of drone formation passing through a narrow area;

[0093] Figure 5 This is the result of a drone formation passing through an extremely narrow area. DETAILED DESCRIPTION

[0094] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein can be combined with each other without conflict.

[0095] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also 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.

[0096] Embodiment 1;

[0097] Please refer to Figure 1 , Figure 1 The figure is a schematic diagram of the overall process of a method for controlling a multi-UAV high-speed dynamic flight formation. The present invention provides a method for controlling a multi-UAV high-speed dynamic flight formation, the method comprising:

[0098] Constructing a formation topology: Obtain information about all participating drones, set up three formations for the drone formation, select one drone as the leader from among all participating drones, and the rest as followers. Use an adjacency matrix to describe the information exchange relationship between drones in the formation, and pre-set a spatial offset for each follower relative to the leader to form one of the three formations.

[0099] Design a distributed control model: Construct a leader goal-driven model and a follower error compensation control model. Use the leader goal-driven model to achieve leader navigation based on destination information. Use the follower error compensation control model to guide followers to their desired positions within the formation, based on the relative position error between each follower and other UAVs in the formation and each follower's preset spatial offset relative to the leader.

[0100] Setting constraint functions and formation change mechanisms: During the UAV formation flight process, acceleration increment limits are introduced to constrain the motion changes of UAVs in the formation. The formation switching mechanism is adaptively triggered based on the environmental state to switch the UAV formation from the current formation to another formation.

[0101] Constructing a dynamic obstacle avoidance system for formations: During the UAV formation flight, the resultant force on each UAV is calculated based on the repulsive force of multiple obstacles, the repulsive force between multiple UAVs, and the virtual edge traction force. The UAV formation is then controlled to avoid obstacles based on the resultant force. For details, please refer to Figure 2 The schematic diagram given, and the reference Figure 3Realize the dynamic adaptability of the formation when encountering obstacles, change the formation when avoiding obstacles, and restore the formation after the obstacle avoidance is completed.

[0102] Execute the control algorithm and generate the final trajectory for each drone. Specifically, this can include executing the flight formation control method of the present invention, enabling the drone formation to adaptively avoid obstacles, dynamically change formations, and safely and smoothly reach the destination from the starting point, ultimately generating the final trajectory for each drone. Other flight control methods, algorithms, equipment, or devices used by the drones can be implemented using existing methods in the art and are not detailed in this embodiment of the present invention.

[0103] The following is a detailed introduction to the specific steps of this method:

[0104] Build the formation topology:

[0105] The present invention analyzes the formation of multiple drones and sets reasonable flight safety distances, constructing three formations: triangle, V-shaped, and straight. One drone is selected as the leader, and the rest are followers. The adjacency matrix in graph theory is used to describe the information exchange relationship between the drones, and a spatial offset relative to the leader is pre-set for each follower to form a flight formation. The formation's adjacency matrix and offset are defined by the following formula:

[0106] Adjacency Matrix:

[0107] ;

[0108] ;

[0109] Three types of formation offsets:

[0110] ;

[0111] in, represents the control influence of the j-th UAV on the i-th UAV, N is the number of UAVs, is the jth UAV, is the i-th drone; 、 and They represent the spatial offset of each drone relative to the leader in the triangle formation, V formation and line formation, respectively. and are the x-coordinate offset and y-coordinate offset of the i-th UAV from the leader in the triangle formation, and are the x-coordinate offset and y-coordinate offset of the j-th UAV from the leader in the V-formation, and are the x-coordinate offset and y-coordinate offset of the kth UAV from the leader in the line formation, respectively.

[0112] Design a distributed control model:

[0113] A leader goal-driven and follower error compensation control model is designed to achieve leader global navigation and follower local collaboration to maintain relative formation.

[0114] Leader Navigation:

[0115] Define the position of the leader drone as , calculate the distance between the target point and the leader, the heading angle and velocity components of the leader's movement. The specific formula is as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] in, is the Euclidean distance between the target point and the leader, is the horizontal coordinate of the target point, is the ordinate of the target point, The heading angle of the leader's movement, is the velocity component of the leader in the x direction, is the velocity component of the leader in the y direction, is the leader control coefficient, It is the maximum distance limit in the guidance speed.

[0120] Follower collaborative control:

[0121] Based on the relative position error between each follower and other drones in the drone formation and the spatial offset of each follower preset relative to the leader, the relative position error amplitude and relative position error direction are calculated by superimposing the relative position errors. The speed of the follower is updated based on the relative position error amplitude and relative position error direction to guide the follower to the desired position in the drone formation. The specific formula is as follows:

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] in, is the relative position error between the i-th UAV and the j-th UAV in the x direction, is the relative position error between the i-th UAV and the j-th UAV in the y direction, and Indicates the current coordinates of the i-th follower, and represents the formation bias expected by the i-th follower, and Indicates the current coordinates of the j-th follower, and represents the formation bias expected by the j-th follower; and represents the relative position error component accumulated by the i-th follower in the x and y directions, is the control influence of the j-th UAV on the i-th UAV, and N is the number of UAVs; represents the comprehensive position error of the i-th follower, represents the error direction of the i-th follower; and Represents the control speed of the i-th follower in the x and y directions, Represents the following control coefficient.

[0129] Set up constraint functions and dynamic formation transformation mechanisms:

[0130] During the flight, in order to ensure that the UAV formation movement is stable, smooth and complies with kinematic constraints, acceleration increment limits are introduced to constrain the movement changes of the UAVs, and the formation switching mechanism is adaptively triggered according to the environmental conditions, such as switching to a V-shape or a straight line formation.

[0131] Constraint function:

[0132] Limiting the acceleration increment:

[0133] ;

[0134] ;

[0135] Updated speed:

[0136] ;

[0137] in, The updated speed of the drone. Indicates the current speed of the drone. and Represents the new velocity increment of the drone in the x and y directions, and Represents the velocity increment of the drone in the x and y directions, and Respectively represent the maximum acceleration of the drone in the x and y directions, For time.

[0138] In order to further ensure that the speed does not exceed the limit, the absolute value of the speed is constrained after the limit to ensure that the drone speed meets the maximum capacity and safety requirements. The specific formula is as follows:

[0139] ;

[0140] ;

[0141] ;

[0142] in, and Respectively represent the upper limit of the absolute value of the speed of the drone in the x and y directions, is the maximum velocity in the x direction, is the updated x-direction velocity, is the maximum velocity in the y direction, is the updated velocity in the y direction, is the final velocity after constraint.

[0143] Dynamic formation transformation mechanism:

[0144] Each drone is equipped with a sensor module for positioning and navigation, such as a lidar, GPS, or inertial measurement unit. When the leader's drone's onboard sensor module detects a narrow area ahead, it dynamically changes its formation based on the width of the narrow area and the maximum width threshold of each formation. After the drone formation passes through the narrow area, it returns to its initial formation, using the following formula:

[0145] ;

[0146] in, For the specific selected UAV formation, 、 and They represent triangle formation, V-formation and line formation respectively. Indicates the width of the terrain ahead. Here, it indicates the width of the narrow area. Indicates the maximum width threshold of the triangle formation, Indicates the maximum width threshold of the V-shaped formation.

[0147] In order to allow the UAV formation to transition safely and smoothly from one formation to another, the present invention uses linear interpolation to calculate the real-time position of each follower UAV during the transition phase. The formula is as follows:

[0148] ;

[0149] in, It represents the real-time relative offset of the ith follower relative to the leader at the current moment, which can be obtained by using the distance calculation formula between two points. Indicates the relative offset of the starting formation of the i-th follower, obtained by setting the formation; Indicates the relative offset of the target formation of the i-th follower, obtained by setting the formation; Represents the interpolation factor.

[0150] Drone formation obstacle avoidance:

[0151] This invention improves the artificial potential field method by considering the superposition of forces generated by multiple potential fields, including the repulsive forces of multiple obstacles and between drones. It designs a virtual edgewise traction force based on the direction of the obstacles relative to the boundary and the dynamic attenuation of the repulsive potential field. This solves the problem of drone formations effectively avoiding local minima in complex multi-obstacle environments, avoids overshoot in high-speed scenarios and collisions within the formation, and enhances the system's collision avoidance reliability. The specific formula is as follows:

[0152] The drone is subject to repulsive forces from multiple obstacles:

[0153] ;

[0154] ;

[0155] in: represents the repulsion coefficient of the obstacle on the drone, Indicates the relative distance between the drone and the obstacle center, Indicates the safe distance threshold between the drone and the obstacle center. is the repulsive potential field generated by the obstacle, is the position of the obstacle center, is the position of the drone, is the repulsive force of obstacles on the drone.

[0156] Repulsion between multiple drones:

[0157] ;

[0158] ;

[0159] in: represents the repulsion coefficient between UAVs, represents the relative distance between the i-th UAV and the j-th UAV, Indicates the safety distance threshold between drones, is the repulsive potential field generated by the drone, is the position of the i-th UAV, is the position of the jth UAV, is the repulsive force of the i-th UAV on the j-th UAV.

[0160] When a UAV is subjected to the combined force of multiple potential fields during flight, resulting in local minima and overshoot in high-speed scenarios, a virtual edge traction force is designed based on the relative direction of the obstacle to the boundary and the dynamic attenuation of the repulsive potential field to solve the local minima problem and overshoot in high-speed scenarios of the traditional artificial potential field method.

[0161] Virtual edge traction:

[0162] ;

[0163] in, is the virtual edgewise traction, represents the traction coefficient, represents the Euclidean distance between the drone and the obstacle center, represents the radius of the circular obstacle, represents the distance attenuation factor, Indicates the azimuth of the obstacle center relative to the drone, Indicates the current heading angle of the drone. Indicates the tangential direction of the drone.

[0164] The total force on the kth drone:

[0165]

[0166] in, is the net force acting on the UAV, n is the number of obstacles, and N is the number of UAVs.

[0167] The force diagram of the UAV under the improved artificial potential field method of the present invention is shown in FIG. Figure 2 , Figure 2 middle is the repulsive force of UAV uav1 on the target UAV, is the repulsive force of UAV2 on the target UAV, is the repulsive force of obstacle 1 on the target drone, The repulsive force of obstacle 2 on the target drone.

[0168] Embodiment 2:

[0169] Based on the first embodiment, the present method is described in detail below with reference to specific examples:

[0170] The experimental scenario of the present invention is set in a 60km×40km map. There are 5 drones, and the starting point and end point of the leader drone are set to (5, 20) and (55, 20) respectively. Several circular obstacles or a narrow passage are scattered between the starting point and the end point. The adjacency matrix is ​​used to preset three drone formations: triangle, V-shape and straight line.

[0171] Build the formation topology:

[0172] The formation of multiple drones is analyzed and a reasonable flight safety distance is set. The adjacency matrix and offset are used to set three drone formations: triangle, V-shape, and straight line. One leader is selected and the rest are followers. The adjacency matrix and offset of the formation are defined by the following formula:

[0173] Adjacency Matrix:

[0174] ;

[0175] The three formation offsets are as follows:

[0176] ;

[0177] ;

[0178] ;

[0179] Design a distributed control model:

[0180] A leader goal-driven and follower error compensation control model is designed to achieve leader global navigation and follower local collaboration to maintain relative formation.

[0181] Leader Navigation:

[0182] Define the position of the leader drone as The maximum distance limit in the guidance speed is 1.5 km, the leader control coefficient is 0.4, and the distance between the target point and the leader, the heading angle and velocity components of the leader's movement are calculated as follows:

[0183] ;

[0184] ;

[0185] ;

[0186] .

[0187] Follower collaborative control:

[0188] Each follower i is guided to the desired position in the formation by superimposing the relative distance errors with other UAVs and the spatial offset of each follower relative to the leader. The distance error magnitude and error direction are calculated, and the follower's speed is finally updated. The specific formula is as follows:

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] ;

[0195] ;

[0196] ,

[0197] in, Set to 0.6.

[0198] Constraint function and dynamic formation transformation mechanism:

[0199] During the flight, in order to meet the actual application scenario and ensure that the UAV formation movement is stable, smooth and in compliance with kinematic constraints, the acceleration increment limit and the speed absolute value limit are introduced to constrain the UAV's movement changes, limiting the UAV's maximum acceleration and speed to 10m / s respectively. 2 and 120m / s, and adaptively trigger the formation switching mechanism according to the environmental status, such as switching to a V-formation or a straight-line formation.

[0200] Constraint function:

[0201] Limiting the acceleration increment:

[0202] ;

[0203] ;

[0204] Updated speed:

[0205] .

[0206] In order to further ensure that the speed does not exceed the limit, the absolute value of the speed is constrained after the limit to ensure that the drone speed meets the maximum capacity and safety requirements. The specific formula is as follows:

[0207] ;

[0208] ;

[0209] ;

[0210] in, and Indicates the absolute upper limit of the drone's speed in the x and y directions.

[0211] Dynamic formation transformation mechanism:

[0212] When the leader drone's onboard radar or visual sensor detects a narrow area ahead, it dynamically changes its formation based on the comparison of the narrow area's width with the maximum width threshold of each formation. After passing through the narrow area, it returns to the initial formation. The maximum width thresholds for triangle and V-shaped formations are calculated as 8 and 4, respectively. The specific formulas are as follows:

[0213] ;

[0214] in, Indicates the width of the narrow area.

[0215] In order to allow the UAV formation to transition safely and smoothly from one formation to another, the present invention uses linear interpolation to calculate the real-time position of each follower UAV during the transition phase. The formula is as follows:

[0216] ;

[0217] in, Represents the interpolation factor, ranging from 0 to 1, indicating the degree of transition completion.

[0218] Drone formation obstacle avoidance:

[0219] In the improved artificial potential field method of the present invention, the repulsion coefficient of the obstacle on the drone is set to 9, the repulsion coefficient between the drones is set to 5, and the coefficient of the virtual edge traction is set to 4. The specific formula is as follows:

[0220] The drone is repelled by the center of mass of the obstacle:

[0221] ;

[0222] ;

[0223] in: Set to 5km.

[0224] Repulsion between drones:

[0225] ;

[0226] ;

[0227] in: Set to 1km.

[0228] Virtual traction:

[0229] ;

[0230] in, Set to 0.8.

[0231] The total force on the kth drone:

[0232] ;

[0233] The present invention integrates the design of distributed control strategy, flight constraints, dynamic transformation mechanism of formation and improved artificial potential field method to achieve high-speed coordinated obstacle avoidance flight of multiple UAV formations. The UAV formation control algorithm is executed to generate the trajectory results of the UAV formation adaptive obstacle avoidance as shown below. Figure 3 , Figure 3-5 The Leader is the leader, and Followers 1 to 4 are followers 1 to 4, which reflects both the stability of the formation and the dynamic adaptability of the formation. Figure 4 This is the formation transformation result of the drone formation passing through a narrow area. Figure 5 The result of the formation transformation of a drone formation passing through an extremely narrow area.

[0234] Embodiment 3;

[0235] The third embodiment of the present invention further provides a multi-UAV high-speed dynamic flight formation control system, the system comprising:

[0236] The formation unit is used to obtain information about all UAVs participating in the formation flight, set up three formations for the UAV formation, select one UAV from all the UAVs participating in the formation flight as the leader, and the rest of the UAVs as followers. The adjacency matrix is ​​used to describe the information exchange relationship between the UAVs in the UAV formation, and the spatial offset relative to the leader is pre-set for each follower to form one of the three formations.

[0237] A model unit is used to construct a leader target-driven model and a follower error compensation control model. The leader's target-driven model is used to achieve leader navigation based on the destination point information. The follower error compensation control model is used to guide the follower to the desired position in the UAV formation based on the relative position error between each follower and other UAVs in the UAV formation and the spatial offset of each follower preset relative to the leader.

[0238] The constraint unit is used to introduce acceleration increment limits to constrain the motion changes of UAVs in the UAV formation during UAV formation flight, and adaptively trigger the formation switching mechanism according to the environmental state to switch the UAV formation from the current formation to another formation;

[0239] The force control unit is used to calculate the resultant force acting on the drones based on the repulsive forces of multiple obstacles, the repulsive forces between multiple drones, and the virtual edge traction force during the formation flight of the drones, and to perform obstacle avoidance control on the drone formation based on the resultant forces acting on the drones.

[0240] Embodiment 3 of the present invention also provides a multi-UAV high-speed dynamic flight formation control device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0241] The embodiment of a multi-UAV high-speed dynamic flight formation control device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as: a multi-UAV high-speed dynamic flight formation control program. When the processor executes the computer program, the steps of each of the above-mentioned multi-UAV high-speed dynamic flight formation control method embodiments are implemented, such as Figure 1 The steps shown.

[0242] The multi-UAV high-speed dynamic flight formation control device can be a computing device such as a desktop computer, laptop, PDA, or cloud server. The multi-UAV high-speed dynamic flight formation control device can include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the multi-UAV high-speed dynamic flight formation control device can also include input / output devices, network access devices, buses, and the like.

[0243] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor serves as the control center of a multi-UAV high-speed dynamic flight formation control device, utilizing various interfaces and circuits to connect various components of the multi-UAV high-speed dynamic flight formation control device.

[0244] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of a multi-UAV high-speed dynamic flight formation control device by running or executing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback, image playback, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0245] If the module / unit integrated in the control system for high-speed dynamic flight formation of multiple UAVs is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be stored in a computer-readable storage medium through a computer program. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, point carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0246] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0247] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for controlling a multi-UAV high-speed dynamic flight formation, characterized in that: The method comprises: The system obtains information about all UAVs participating in the formation flight, sets up three formations for the UAV formation, selects one UAV as the leader from among all the UAVs participating in the formation flight, and uses an adjacency matrix to describe the information exchange relationship between the UAVs in the formation. The spatial offset relative to the leader is pre-set for each follower to form one of the three formations. A leader goal-driven model and a follower error compensation control model are constructed. The leader's goal-driven model is used to achieve leader navigation based on the destination point information. The follower error compensation control model is used to guide the follower to the desired position in the UAV formation based on the relative position of each follower and the other UAVs in the UAV formation and the spatial offset of each follower preset relative to the leader. During the UAV formation flight, the acceleration increment limit is introduced to constrain the motion changes of the UAVs in the UAV formation. The formation switching mechanism is adaptively triggered according to the environmental state to switch the UAV formation from the current formation to another formation. During the UAV formation flight, the resultant force acting on the UAVs is calculated based on the repulsive force of multiple obstacles, the repulsive force between multiple UAVs, and the virtual edge traction force. The UAV formation is then controlled to avoid obstacles based on the resultant force.

2. A multi-UAV high-speed dynamic flight formation control method according to claim 1, characterized in that: The adjacency matrix is: ； ； in, is the adjacency matrix, represents the control influence of the j-th UAV on the i-th UAV, N is the number of UAVs, is the jth UAV, is the i-th drone; Each follower is pre-set to have a spatial offset relative to the leader: ； Among them, the three formations are: triangle formation, V formation and line formation. 、 and They represent the spatial offset of each drone relative to the leader in the triangle formation, V formation and line formation, respectively. and are the x-coordinate offset and y-coordinate offset of the i-th UAV from the leader in the triangle formation, and are the x-coordinate offset and y-coordinate offset of the j-th UAV from the leader in the V-formation, and are the x-coordinate offset and y-coordinate offset of the kth UAV from the leader in the line formation.

3. The method for controlling a multi-UAV high-speed dynamic flight formation according to claim 1, characterized in that: Construct a leader goal-driven model and a follower error compensation control model. Based on the destination information, the leader goal-driven model is used to implement leader navigation. Specifically, it includes: Based on the destination information, the leader's target-driven model is used to calculate the distance between the destination and the leader, the leader's heading angle, and the leader's velocity components in the x and y directions. in: ； ； ； Among them, the position of the leader drone is , is the Euclidean distance between the target point and the leader, is the horizontal coordinate of the target point, is the ordinate of the target point, The heading angle of the leader's movement, is the velocity component of the leader in the x direction, is the velocity component of the leader in the y direction, is the leader control coefficient, It is the maximum distance limit in the guidance speed.

4. The method for controlling a multi-UAV high-speed dynamic flight formation according to claim 1, characterized in that: Based on the relative position error between each follower and other UAVs in the UAV formation and the preset spatial offset of each follower relative to the leader, the follower error compensation control model is used to guide the follower to the desired position in the UAV formation. Specifically, it includes: Based on the relative position error between each follower and other drones in the drone formation and the preset spatial offset of each follower relative to the leader, the comprehensive position error and position error direction are calculated by superimposing the relative position errors. The speed of the follower is updated based on the comprehensive position error and position error direction to guide the follower to reach the desired position in the drone formation; The relative position error between the follower and other drones in the drone formation is calculated as follows: ； in, is the relative position error between the i-th UAV and the j-th UAV in the x direction, is the relative position error between the i-th UAV and the j-th UAV in the y direction, and Indicates the current coordinates of the i-th follower, and represents the formation bias expected by the i-th follower, and Indicates the current coordinates of the j-th follower, and represents the formation bias expected by the j-th follower; ； ； in, and represents the relative position error component accumulated by the i-th follower in the x and y directions, is the control influence of the j-th UAV on the i-th UAV, and N is the number of UAVs; ； ； in, represents the comprehensive position error of the i-th follower, represents the position error direction of the i-th follower; ； in, and Represents the control speed of the i-th follower in the x and y directions, Represents the following control coefficient.

5. The method for controlling a multi-UAV high-speed dynamic flight formation according to claim 1, characterized in that: The acceleration increment limit is introduced to constrain the motion changes of UAVs in the UAV formation. Specifically: Construct a constraint function based on acceleration increment; Constraining the motion changes of the UAVs in the UAV formation based on the constraint function to obtain an updated speed; The constraint function is: ； ； The updated speed calculation method is: ； in, The updated speed of the drone. Indicates the current speed of the drone. and Represents the new velocity increment of the drone in the x and y directions, and Represents the velocity increment of the drone in the x and y directions, and Respectively represent the maximum acceleration of the drone in the x and y directions, For time; The method further includes constraining the absolute value of the velocity, and the constraint formula is: ； ； ； in, and Respectively represent the upper limit of the absolute value of the speed of the drone in the x and y directions, is the maximum velocity in the x direction, is the updated x-direction velocity, is the maximum velocity in the y direction, is the updated velocity in the y direction, is the final velocity after constraint.

6. The method for controlling a multi-UAV high-speed dynamic flight formation according to claim 1, characterized in that: The formation switching mechanism is triggered adaptively according to the environmental status to switch the UAV formation from the current formation to another formation, including: Each drone in the drone formation is equipped with a sensor module for positioning and navigation; Based on the leader's sensor module detection, the width of the terrain ahead is obtained; Compare the width of the terrain ahead with the maximum width threshold of each formation to obtain a comparison result; Based on the comparison results, it is determined whether to trigger the formation switching mechanism. If triggered, the UAV formation is switched from the current formation to the formation that meets the comparison results; The calculation method of the formation switching mechanism is: ； in, For the specific selected UAV formation, 、 and They represent triangle formation, V-formation and line formation respectively. Indicates the width of the terrain ahead, Indicates the maximum width threshold of the triangle formation, Indicates the maximum width threshold of the V-shaped formation.

7. The method for controlling a multi-UAV high-speed dynamic flight formation according to claim 1, characterized in that: During the transition phase when the UAV formation switches from the current formation to another formation, the linear interpolation method is used to calculate the real-time position of each follower in the transition phase. The calculation method is: ； in, Indicates the real-time relative offset of the i-th follower relative to the leader at the current moment, Indicates the relative offset of the starting formation of the i-th follower, Indicates the relative offset of the target formation of the i-th follower, Represents the interpolation factor.

8. The method for controlling a multi-UAV high-speed dynamic flight formation according to claim 1, characterized in that: The calculation method of the repulsive force of multiple obstacles on the drone is: ； ； in, represents the repulsion coefficient of the obstacle on the drone, Indicates the relative distance between the drone and the obstacle center, Indicates the safe distance threshold between the drone and the obstacle center. is the repulsive potential field generated by the obstacle, is the position of the obstacle center, is the position of the drone, is the repulsive force of the obstacle on the drone; The repulsive force between multiple drones is calculated as follows: ； ； in, represents the repulsion coefficient between drones, represents the relative distance between the i-th UAV and the j-th UAV, Indicates the safety distance threshold between drones, is the repulsive potential field generated by the drone, is the position of the i-th UAV, is the position of the jth UAV, is the repulsive force of the i-th UAV on the j-th UAV; Virtual edge traction: ； in, is the virtual edgewise traction, represents the traction coefficient, represents the Euclidean distance between the drone and the obstacle center, represents the radius of the circular obstacle, represents the distance attenuation factor, Indicates the azimuth of the obstacle center relative to the drone, Indicates the current heading angle of the drone. Indicates the tangential direction of the drone; The calculation method of the resultant force acting on the drone is: ； in, is the net force acting on the UAV, n is the number of obstacles, and N is the number of UAVs.

9. A multi-UAV high-speed dynamic flight formation control system, characterized in that: The system comprises: The formation unit is used to obtain information about all UAVs participating in the formation flight, set up three formations for the UAV formation, select one UAV from all the UAVs participating in the formation flight as the leader, and the rest of the UAVs as followers. The adjacency matrix is ​​used to describe the information exchange relationship between the UAVs in the UAV formation, and the spatial offset relative to the leader is pre-set for each follower to form one of the three formations. A model unit is used to construct a leader target-driven model and a follower error compensation control model. The leader's target-driven model is used to achieve leader navigation based on the destination point information. The follower error compensation control model is used to guide the follower to the desired position in the UAV formation based on the relative position error between each follower and other UAVs in the UAV formation and the spatial offset of each follower preset relative to the leader. The constraint unit is used to introduce acceleration increment limits to constrain the motion changes of UAVs in the UAV formation during UAV formation flight, and adaptively trigger the formation switching mechanism according to the environmental state to switch the UAV formation from the current formation to another formation; The force control unit is used to calculate the resultant force acting on the drones based on the repulsive forces of multiple obstacles, the repulsive forces between multiple drones, and the virtual edge traction force during the formation flight of the drones, and to perform obstacle avoidance control on the drone formation based on the resultant forces acting on the drones.

10. A multi-UAV high-speed dynamic flight formation control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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