Unmanned aerial vehicle cluster cooperative control method and system and unmanned aerial vehicle system

Abstract

The invention discloses an unmanned aerial vehicle cluster cooperative control method and system and an unmanned aerial vehicle system. The method comprises the steps of obtaining an initial position of each unmanned aerial vehicle in an unmanned aerial vehicle cluster; determining a formation member set, a first target position and an initial formation according to a received task instruction; performing path planning based on the initial position, the first target position and the initial formation, and determining a second target position and a target formation corresponding to each unmanned aerial vehicle; determining a first moving speed of the formation member set, determining a second moving speed of each unmanned aerial vehicle for keeping a target formation based on the first moving speed, and determining an artificial potential field repulsive force suffered by each unmanned aerial vehicle in the formation member set based on the relative distance, and determining a target moving speed corresponding to each unmanned aerial vehicle based on the first moving speed, the second moving speed and the artificial potential field repulsive force, and controlling each unmanned aerial vehicle in the formation member set to move to a corresponding second target position according to the corresponding target moving speed. According to the invention, the stability and safety of task execution can be improved.

Description

Technical Field

[0001] The present application relates to the field of drone control, and in particular to a drone cluster collaborative control method, system, and drone system. Background Art

[0002] With the continuous advancement of intelligent control technology, the complexity and diversity of drone missions have increased significantly. In complex environments, drone swarms optimize task allocation and flight paths through collaborative mechanisms, effectively adapting to environmental changes while ensuring safe distances between drones to avoid collisions, thereby improving the stability and safety of mission execution.

[0003] Current UAV swarm collaborative control technologies achieve swarm collaborative operations through preset behavioral rules, virtual piloting, and PID control-based methods. However, these methods all have their drawbacks. Preset behavioral rules can easily change the topology of a swarm due to obstacle avoidance in confined spaces or environments with multiple obstacles, leading to communication interruptions. Furthermore, these methods are less adaptable to large-scale swarms. Virtual piloting, on the other hand, places extremely high demands on the stability of the navigation points. Once calculations or communications deviate, the coordination of the entire formation will be severely impacted. Furthermore, in complex environments, virtual piloting struggles to adapt to rapidly changing dynamic obstacles, making mission execution safety difficult to ensure. PID control-based methods are highly dependent on the UAV dynamics model. When the model changes or uncertainties exist, the control effect decreases significantly. Furthermore, parameter adjustment is difficult, resulting in poor adaptability in dynamic environments. Summary of the Invention

[0004] The present application provides a drone cluster collaborative control method, system and drone system to adapt to rapidly changing dynamic environments and improve the stability and safety of mission execution.

[0005] In a first aspect, the present application provides a method for cooperative control of a drone cluster, comprising:

[0006] Get the initial position of each drone in the drone cluster;

[0007] According to the received mission instructions, the formation members of the UAV swarm, the first target position of each UAV and the initial formation are determined;

[0008] Performing path planning based on each of the initial positions, each of the first target positions, and the initial formation to determine a second target position and a target formation corresponding to each of the UAVs;

[0009] Determine a first moving speed of the formation member set, determine a second moving speed of each UAV to maintain the target formation based on the first moving speed, determine an artificial potential field repulsion applied to each UAV in the formation member set based on a relative distance, and determine a target moving speed corresponding to each UAV based on the second moving speed and the artificial potential field repulsion, and control each UAV in the formation member set to move to a corresponding second target position at the corresponding target moving speed, wherein the relative position is the distance between each UAV and other UAVs or obstacles.

[0010] By obtaining the initial position of each drone in the drone cluster, the embodiment of the present application can timely understand the current position of the drones, providing data support for subsequent dynamic adjustments. By responding to the task instruction, the theoretical and preliminary first target position and initial formation can be quickly determined, providing an initial formation reference for path planning, and ensuring that the formation members have a clear direction at the beginning of the task. By determining the second target position and target formation corresponding to each drone through path planning, a second target position and target formation that better meet the actual requirements of the task can be generated after considering the actual environment, ensuring that each drone in the formation member set can stably move to the second target position, avoiding collisions or other safety issues caused by improper path planning. By determining the first moving speed of the formation member set, it is possible to ensure that the formation members as a whole move toward the second target position. By detecting the relative distance between each drone and obstacles or other drones in real time, the drones can form a formation while maintaining the travel speed, and obtain a second moving speed. Based on the first moving speed, the second moving speed, and the repulsive force of the artificial potential field, the target moving speed can be obtained, ensuring that the drone cluster can move to the second target position safely and efficiently in a complex environment while maintaining the target formation and avoiding collisions. Compared with the existing technology, this application can adapt to rapidly changing dynamic environments and improve the stability and security of task execution.

[0011] Furthermore, the path planning is performed based on each of the initial positions, each of the first target positions, and the initial formation to determine the second target position and target formation corresponding to each UAV, specifically:

[0012] Calculating a first distance between the initial position corresponding to each UAV in the formation member set and the first target position, and constructing a distance matrix corresponding to the formation member set based on the first distance;

[0013] Solving the distance matrix with the goal of minimizing the total moving distance corresponding to the formation member set, and obtaining a target assignment result between each UAV and the first target position;

[0014] Based on the target allocation result, a second target position corresponding to each UAV is determined, and based on the second target position, the formation parameter sequence of each UAV in the formation member set is adjusted to obtain a target formation.

[0015] In this way, by determining the second target position and target formation corresponding to each UAV through path planning, a second target position and target formation that better meets the actual needs of the mission can be generated after considering the actual environment, ensuring that every UAV in the formation can move stably to the second target position, avoiding collisions or other safety issues caused by improper path planning.

[0016] Furthermore, the objective function and constraints of the distance matrix are specifically:

[0017]

[0018] Where A is the total moving distance, n is the number of drones, and d ik is the first distance from UAV i to the first target position k, x ik is a binary decision variable, i is the index of the UAV, and k is the index of the first target position.

[0019] Furthermore, the determining of the first moving speed of the formation member set is specifically:

[0020] Obtaining a second distance between a position vector corresponding to a virtual leader and the second target position, wherein the virtual leader is the center point of the formation member set;

[0021] A first moving speed of the formation member set is determined based on the second distance using proportional control.

[0022] In this way, by determining the first moving speed of the formation member set, it can be ensured that the formation members move toward the second target position as a whole.

[0023] Furthermore, the second moving speed of each UAV for maintaining the target formation is determined based on the first moving speed, specifically:

[0024] Establishing an adjacency matrix according to the number of drones in the formation member set and preset rules;

[0025] Calculating the coordination variables of each UAV based on the position vector of the virtual leader and the target formation;

[0026] Based on the collaborative variables and the adjacency matrix, a second moving speed of each UAV for maintaining the target formation is generated through a preset consistency control algorithm.

[0027] In this way, by detecting the relative distance between each drone and obstacles or other drones in real time, the drones can form a formation while maintaining the travel speed, thereby obtaining a second moving speed.

[0028] Furthermore, the relevant calculation formula of the synergistic variable is specifically:

[0029] ρ i (t+1)=ρ i (t)+v i (t+1)dt;

[0030]

[0031] Where, ρ i (t) and ρ i (t+1) represents the position vector of drone i at the current moment and the next moment respectively; N is the number of drones, and the Nth drone is the virtual leader; v i (t+1) is the control input of the i-th UAV at the next moment; a ij Represents the communication topology relationship between the i-th UAV and the j-th UAV, 0 represents no communication topology, and 1 represents communication topology; Represents the position vector pointing from the virtual leader to the i-th UAV in the target formation, that is, the formation parameter.

[0032] Furthermore, the relevant formula for generating the second moving speed of each UAV to maintain the target formation through the preset consistency control algorithm is specifically:

[0033]

[0034] Where u i (t) represents the control input of UAV i at time t, which is used to directly control the second moving speed of the UAV; N is the number of UAVs; x i (t) and x j (t) are the position vectors of UAV i and UAV j at time t; a ij is the (i, j)th item in the adjacency matrix, which is used to describe the communication relationship between UAV i and UAV j. If there is a communication connection between UAV i and UAV j, then a ij =1; otherwise a ij =0; i and j are the indexes of the drone.

[0035] In a second aspect, the present application provides a drone cluster collaborative control system, comprising: an acquisition module, a first determination module, a second determination module, and a movement module;

[0036] The acquisition module is used to obtain the initial position of each drone in the drone cluster;

[0037] The first determination module is used to determine the formation members participating in the formation in the drone cluster, the first target position corresponding to each drone, and the initial formation according to the received task instruction;

[0038] The second determination module is configured to perform path planning based on each of the initial positions, each of the first target positions, and the initial formation, and determine a second target position and a target formation corresponding to each of the UAVs;

[0039] The mobile module is used to determine a first moving speed of the formation member set, determine a second moving speed of each drone to maintain the target formation based on the first moving speed, determine the artificial potential field repulsion of each drone in the formation member set based on the relative distance, and determine the target moving speed corresponding to each drone based on the second moving speed and the artificial potential field repulsion, and control each drone in the formation member set to move to the corresponding second target position according to the corresponding target moving speed, wherein the relative position is the distance between each drone and other drones or obstacles.

[0040] By obtaining the initial position of each drone in the drone cluster, the embodiment of the present application can timely understand the current position of the drones, providing data support for subsequent dynamic adjustments. By responding to the task instruction, the theoretical and preliminary first target position and initial formation can be quickly determined, providing an initial formation reference for path planning, and ensuring that the formation members have a clear direction at the beginning of the task. By determining the second target position and target formation corresponding to each drone through path planning, a second target position and target formation that better meet the actual requirements of the task can be generated after considering the actual environment, ensuring that each drone in the formation member set can stably move to the second target position, avoiding collisions or other safety issues caused by improper path planning. By determining the first moving speed of the formation member set, it is possible to ensure that the formation members as a whole move toward the second target position. By detecting the relative distance between each drone and obstacles or other drones in real time, the drones can form a formation while maintaining the travel speed, and obtain a second moving speed. Based on the first moving speed, the second moving speed, and the repulsive force of the artificial potential field, the target moving speed can be obtained, ensuring that the drone cluster can move to the second target position safely and efficiently in a complex environment while maintaining the target formation and avoiding collisions. Compared with the existing technology, this application can adapt to rapidly changing dynamic environments and improve the stability and security of task execution.

[0041] On the third aspect, the present application also provides a drone system, comprising: a control console, at least one drone, and each of the drones is connected via a network, wherein the drone system can execute the drone cluster collaborative control method as described in the present application.

[0042] Furthermore, each of the drones includes: a flight controller, an operating system, an ad hoc network module, at least one gimbal camera and at least one laser radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of an embodiment of the UAV cluster collaborative control method provided by the present application;

[0044] Figure 2 This application provides Figure 1 Flow chart of step S103 in FIG.

[0045] Figure 3 This is a schematic diagram of a triangular formation of three drones provided by this application;

[0046] Figure 4 This is a schematic diagram of a three-drone swarm in a line formation provided by this application;

[0047] Figure 5 This is a schematic structural diagram of an embodiment of the UAV cluster collaborative control system provided by the present application;

[0048] Figure 6 It is a structural schematic diagram of an embodiment of the drone system provided by this application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0051] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0052] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0053] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0054] With the advancement of intelligent control technology, drone missions have become more complex and diverse. Drone swarms optimize task allocation and routing through collaborative mechanisms, maintaining safe distances in complex environments and improving stability and safety. Existing collaborative control technologies include preset behavioral rules, virtual piloting, and PID control, but each has its own limitations. Preset behavioral rules can cause communication interruptions in narrow or obstacle-filled environments and are not suitable for large swarms. Virtual piloting requires high stability of the waypoints, making it difficult to adapt to dynamic obstacles, impacting mission safety. PID control is highly dependent on the drone model. Changes to the model or difficulty adjusting parameters can reduce control effectiveness and adaptability.

[0055] Next, the nouns involved in this application are analyzed:

[0056] Preset behavioral rules are an important technical means in drone control systems. Through predefined behavioral logic, drones can make autonomous decisions and take actions in specific tasks.

[0057] Virtual piloting is a method for controlling drone formations. Its core concept is to introduce a virtual pilot, which can be a physical drone or a virtual reference point. Other drones (followers) adjust their flight attitude and position based on the virtual pilot's position, speed, and other information, thereby achieving coordinated flight within the entire formation.

[0058] PID control is a classic control algorithm widely used for attitude and position control in drones. The PID controller uses proportional, integral, and differential control to adjust the drone's flight attitude, ensuring stability and accuracy.

[0059] The Hungarian algorithm is an algorithm for solving the assignment problem, which aims to find the optimal assignment solution that minimizes the total cost (or distance, time, etc.).

[0060] The consensus algorithm is a distributed control algorithm for multi-agent systems. It enables agents to achieve global consistency through local interactions. The key to using the consensus algorithm for designated formation control lies in the definition of collaborative variables. Each formation configuration corresponds to a collaborative variable. During the collaborative variable design process, the relative position of each drone in the desired formation needs to be determined, so a target allocation algorithm is required to determine the relative position. For the formation reconstruction problem, the key to planning and controlling formation reconstruction based on consistency theory lies in the switching of collaborative variables. On the basis of establishing a connection between collaborative variables and formation configurations, that is, each formation configuration corresponds to a collaborative variable, the collaborative variables can be switched according to the corresponding configuration transformation rules, thereby switching the formation configuration.

[0061] Artificial potential fields are an algorithm used for robot path planning and obstacle avoidance. They guide the robot's motion by introducing a virtual potential field between the robot and the target or obstacle. In this method, agents are treated as elastic spheres that attract each other. When the distance between two agents is large, they attract each other. However, when the two agents approach, the closer the distance between the sphere centers, the stronger the repulsion, causing them to rapidly move away until they reach equilibrium.

[0062] Based on this, the embodiments of the present application provide a drone cluster collaborative control method, system and drone system, which can adapt to rapidly changing dynamic environments and improve the stability and safety of task execution.

[0063] The embodiments of the present application provide a method, system, and system for collaborative control of a drone cluster, which are specifically described through the following embodiments. First, the method for collaborative control of a drone cluster in the embodiments of the present application is described.

[0064] The drone cluster collaborative control method provided in the embodiment of the present application relates to the field of drone control. The drone cluster collaborative control method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a drone cluster collaborative control method, etc., but is not limited to the above forms.

[0065] The present application can also be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0066] Example 1

[0067] Please refer to Figure 1 , Figure 1 This is a flow chart of an embodiment of the UAV cluster collaborative control method provided by the present application, including steps S101 to S104;

[0068] Step S101, obtaining the initial position of each drone in the drone cluster;

[0069] In some embodiments, the current position of each drone in the drone cluster is obtained through the drone's sensors (such as GPS, IMU).

[0070] It should be noted that a drone swarm is a collaborative system consisting of multiple drones that communicate and collaborate to complete complex missions. The subsequent formation member set refers to the collection of drones participating in formation flight during a specific mission. It is a subset of a drone swarm and is typically used to perform specific formation missions. In other words, a drone swarm consists of at least one or more formation member sets.

[0071] It's important to note that before obtaining each drone's initial position, each drone's basic parameters must be initialized. The specific process is as follows: Upon receiving the initialization command, each drone's basic parameters are initialized. These parameters include the drone cluster member list, formation member set, formation parameters, and the drone's initial position. After initialization is complete, the drone awaits mission instructions.

[0072] Step S102: determining the formation members of the UAV cluster, the first target position of each UAV, and the initial formation according to the received mission instruction;

[0073] In some embodiments, a mission instruction is received through a communication network. When the mission instruction is received, the mission instruction is parsed, which drones are used to perform the current mission are identified, and it is determined how to divide the drone cluster to determine which drones in the drone cluster participate in the formation (formation member set), and the target position (first target position) of each drone is preliminarily determined, as well as the desired formation (initial formation).

[0074] It should be noted that the information contained in the mission instruction also includes but is not limited to the cluster number, subcluster number, target location, target formation, and formation spacing of the drone.

[0075] It should be noted that the formation member list of each drone must be updated according to the mission instructions to ensure that each drone is aware of its relationship with other drones in the current mission. In addition, the drone cluster can be divided into several subclusters based on the subcluster number. Each subcluster can perform tasks independently or work together, and can form a formation or execute tasks. This application does not impose any restrictions.

[0076] It should be noted that the first and second do not indicate order, but should be understood as nouns. The first target position is the target position assigned to each UAV participating in the formation, and the subsequent second target position is the target position corresponding to each UAV after path planning optimization.

[0077] It should be noted that the initial formation is the expected formation of the UAV cluster preliminarily determined in the mission instructions. It is usually a theoretical and preliminary formation design, which can provide an initial formation reference for path planning (at this time it is not clear where each UAV is in the formation); and the subsequent second target formation is the final formation of the UAV cluster determined after path planning and optimization. It is an optimized formation (the specific position of each UAV in the formation is clear), taking into account various constraints in the actual environment, and more in line with the actual needs of mission execution.

[0078] Step S103, performing path planning based on the initial positions, the first target positions, and the initial formation, and determining the second target position and target formation corresponding to each UAV;

[0079] Please refer to Figure 2 , Figure 2 This is a flowchart of step S103 provided in this application, where step S103 includes but is not limited to steps S201 to S203;

[0080] Step S201, calculating a first distance between the initial position corresponding to each UAV in the formation member set and the first target position, and constructing a distance matrix corresponding to the formation member set based on the first distance;

[0081] In some embodiments, for each drone in the formation member set, a first distance between its initial position and the first target position is calculated using a Euclidean distance formula or a Euclidian distance formula, which is not limited in this application. Once the first distance corresponding to each drone in the formation member set is calculated, the first distances between all drones in the formation member set and the first target position are organized into a distance matrix.

[0082] In some embodiments, the correlation formula calculated using the Euclidean distance formula is:

[0083] d ik =‖diff_init[i]-(formation_list[k]+goal)‖;

[0084] Where, d ik represents the distance from UAV i to target position k, diff_init represents the initial position of the UAV, formation_list represents the formation displacement of the UAV, and goal represents the first target position.

[0085] Step S202, solving the distance matrix with the goal of minimizing the total moving distance corresponding to the formation member set, and obtaining a target allocation result between each UAV and the first target position;

[0086] In some embodiments, after the distance matrix is constructed, it is necessary to establish an objective function that minimizes the total moving distance based on the distance matrix and determine the constraints. Then, the Hungarian algorithm is used to solve the problem and find the optimal target allocation solution. By minimizing the distance between each pair of drones and the target position (that is, the total moving distance is minimized), the target allocation is optimized to ensure the efficiency of task execution.

[0087] In some embodiments, the objective function and constraints of the distance matrix are specifically:

[0088]

[0089] Where A is the total moving distance, n is the number of drones, and d ik is the first distance from UAV i to the first target position k, x ik is a binary decision variable, i is the index of the UAV, and k is the index of the first target position.

[0090] In some embodiments, the relevant process of using the Hungarian algorithm to solve the objective function (that is, calculating the distance matrix as a coefficient matrix) is as follows: Step 1: Perform row and column reduction on the coefficient matrix so that each row and column of the matrix has 0; ① Subtract the minimum value in each row of the coefficient matrix; ② Subtract the minimum value in each column of the matrix. Step 2: Perform trial assignment to find the optimal solution. ① After the processing of step 1, each row and column of the coefficient matrix will have a value of 0, but it is necessary to find n independent 0s. If they can be found, the optimal assignment ends; if not, proceed to the following steps. ② Find a row (column) with only one 0 in the matrix, and mark this 0 as Then mark it with H ③Mark the 0 value of the column (row) with only one 0 as Then mark it with H ④ Repeat ② and ③ until all 0s are marked. ⑤ If there are unmarked 0s in the matrix, and there are at least 2 0s in the same row (column), select the row (column) with the least 0 values, compare the number of 0s in the column (row) where the 0 value is located, and select the column with the least 0s, and use Mark the 0 in the column and mark the other 0 values in the same row and column with H. Repeat until all 0 elements are crossed out. The number of 0s is equal to the order n of the matrix C, and the optimal assignment is completed. Otherwise, go to the next step. Step 3: Draw the least straight line to cover all 0 values and determine the maximum number of independent 0s in the matrix. ① Use × to mark the values that do not have ② Mark the columns containing zeros in the rows marked with × with an ×. ③ Mark the rows containing zeros in the columns marked with × with an ×. ④ Repeat steps ② and ③ until no new × marks are generated. ⑤ Draw horizontal lines to the rows not marked with ×, and vertical lines to the columns marked with ×. The sum of the number of horizontal and vertical lines is the maximum number of independent zeros. Count the number of straight lines. If the sum is less than the order n of matrix C, the current coefficient matrix needs to be transformed to obtain n independent zero elements and proceed to the next step. If the sum is equal to the order n of matrix C, go back to step ⑤ of step 2 and try again. Step 4: Reprocess the coefficient matrix. The purpose of reprocessing the coefficient matrix is to increase the number of zeros. First, find the minimum value in the area not covered by the straight line, then add the minimum value to all columns marked with ×, and subtract the minimum value from all rows marked with ×, so as not to change the position of the previous zeros. At this point, the reprocessing of the coefficient matrix is completed. If n independent 0 elements can be obtained, the optimal assignment is completed, otherwise go to step 2.

[0091] Step S203: determining a second target position corresponding to each UAV based on the target allocation result, and adjusting the formation parameter sequence of each UAV in the formation member set based on the second target position to obtain a target formation.

[0092] In some embodiments, the second target position corresponding to each UAV is determined based on the target allocation result. Specifically, since the obtained target allocation result has clearly defined the pairing relationship between each UAV in the formation member set and the nearest first target position, the second target position corresponding to each UAV can be directly determined based on the target allocation result.

[0093] In some embodiments, the formation parameter sequence of each drone in the formation member set is adjusted based on the second target position to obtain a target formation. Specifically, after determining the second target position corresponding to each drone, the formation parameter sequence of each drone needs to be adjusted according to the second target position. The formation parameter sequence refers to the arrangement order of the drones in the target formation, which determines the specific position and role of each drone in the formation. After the adjustment is completed, the target formation can be determined. For example, assuming that there are three drones in the formation member set, the initial formation is a triangle formation, and the initial formation parameter sequence is: drone 1 at the vertex, drone 2 at the lower left corner, and drone 3 at the lower right corner; if the mission instruction requires adjustment to a straight line formation, the formation parameter sequence may be adjusted to: drone 1 at the far left, drone 2 in the middle, and drone 3 at the far right.

[0094] It should be noted that when adjusting the order of formation parameters, it is also necessary to plan a path from the initial position to the second target position for each drone to avoid collision; among them, the path planning algorithm can be but is not limited to the A* algorithm, Dijkstra algorithm and other path planning algorithms. The specific path planning algorithm is not the focus of this application, so it will not be expanded here.

[0095] In this way, by adjusting the order of formation parameters, the drone cluster can quickly adapt to new mission requirements or environmental changes, and at the same time optimize task allocation, path planning and formation reconstruction to ensure that the drone cluster can complete the task efficiently and safely in a complex environment.

[0096] In this way, by determining the second target position and target formation corresponding to each UAV through path planning, a second target position and target formation that better meets the actual needs of the mission can be generated after considering the actual environment, ensuring that every UAV in the formation can move stably to the second target position, avoiding collisions or other safety issues caused by improper path planning.

[0097] Step S104: determine a first moving speed of the formation member set, determine a second moving speed of each UAV to maintain the target formation based on the first moving speed, determine the artificial potential field repulsion of each UAV in the formation member set based on the relative distance, and determine the target moving speed corresponding to each UAV based on the second moving speed and the artificial potential field repulsion, and control each UAV in the formation member set to move to the corresponding second target position at the corresponding target moving speed, wherein the relative position is the distance between each UAV and other UAVs or obstacles.

[0098] In some embodiments, determining the first moving speed of the formation member set includes: obtaining a second distance between a position vector corresponding to a virtual leader and a second target position, wherein the virtual leader is the center point of the formation member set; and using proportional control to generate the first moving speed of the formation member set based on the second distance. Specifically, first, since the position vector of the virtual leader is the center point of the formation member set, it can be obtained by calculating the average value of the positions of all drones; second, after obtaining the position vector corresponding to the virtual leader, it is necessary to use the Euclidean distance formula to calculate the second distance between the virtual leader position vector and the second target position; then, based on the calculated second distance, using proportional control to generate the virtual leader's travel speed v leader ; and introduce the obstacle repulsion F obstacle and the velocity v at the previous moment prev , to update the travel speed v new , the relevant formula can be: v new =v leader +F obstacle + α v prev , where α is the velocity attenuation coefficient, which can be set as needed; finally, the updated velocity v new Normalization is performed to ensure that the speed does not exceed the maximum speed limit, thereby ensuring stable movement and correct direction, so as to ensure that subsequent formation members move towards the second target position as a whole according to the speed.

[0099] It should be noted that the moving speed of the virtual leader is represented as the first moving speed of the entire formation member set.

[0100] It should be noted that if the velocity is close to zero and is far away from the second target position, random disturbances will be introduced to avoid falling into the local optimum.

[0101] In this way, by determining the first moving speed of the formation member set, it can be ensured that the formation members move toward the second target position as a whole.

[0102] In some embodiments, the method of determining the second movement speed of each drone to maintain the target formation based on the first movement speed includes: establishing an adjacency matrix based on the number of drones in the formation member set and preset rules; calculating the coordination variables of each drone based on the position vector of the virtual leader and the target formation; and generating the second movement speed of each drone to maintain the target formation through a preset consistency control algorithm based on the coordination variables and the adjacency matrix. Specifically, first, an adjacency matrix is established based on the number of drones and preset rules (such as communication topology) to characterize the communication relationship between drones; second, the coordination variables of each drone are calculated based on the formation parameters and the position vector of the virtual leader; thereafter, the consistency control algorithm determines the second movement speed of each drone to maintain the target formation based on the coordination variables and the adjacency matrix.

[0103] It should be noted that the control input generated by the consistency controller will make the cooperative variables of the UAVs in the system tend to be consistent. When the cooperative variables of all UAVs are consistent, the target formation is formed.

[0104] In some embodiments, the relevant calculation formula of the synergistic variable is specifically:

[0105] ρ i (t+1)=ρ i (t)+v i (t+1)dt;

[0106]

[0107] Where, ρ i (t) and ρ i (t+1) represents the position vector of drone i at the current moment and the next moment respectively; N is the number of drones, and the Nth drone is the virtual leader; v i (t+1) is the control input of the i-th UAV at the next moment; a ij Represents the communication topology relationship between the i-th UAV and the j-th UAV, 0 represents no communication topology, and 1 represents communication topology; represents the cooperative variable directed from the virtual leader to the i-th UAV in the target formation.

[0108] It should be noted that, assuming that the UAV's dynamic model is a first-order model, its mathematical expression is: in, Represents the position vector of drone i, u i (t) represents the control input of UAV i at time t. i (t) is set to directly control the speed of the controlled object, and the specific control input u i (t) can be calculated by the following formula: Where u i (t) represents the control input of UAV i at time t, which is used to directly control the second moving speed of the UAV; N is the number of UAVs; x i (t) and x j (t) are the position vectors of UAV i and UAV j at time t; a ij is the (i, j)th item in the adjacency matrix, which is used to describe the communication relationship between UAV i and UAV j. If there is a communication connection between UAV i and UAV j, then a ij =1; otherwise a ij = 0; i and j are both indexes of drones. If the communication network topology graph between multiple drones is an undirected graph, and the undirected graph is connected, then under the action of formula (2), the first-order drone motion model can achieve consistency. That is, when t→∞, there is x i →x j Substituting formula (1) into formula (2), we can get the global controller:

[0109]

[0110] (3) where D is a diagonal matrix and L = DA is a Laplace matrix, which describes the communication topology of the UAV cluster. From formula (3), it can be seen that the closed-loop dynamic characteristics of the multi-UAV formation depend on the Laplace matrix of the formation. In discrete time, the state parameter update method of each UAV in the formation is:

[0111] x i (t+1)=x i (t)+u i (t)*dt,

[0112] Among them, dt represents the sampling time interval. Based on the above theoretical basis, the formation switching problem can be transformed into the problem of collaborative variable switching, that is, a group of collaborative variables corresponds to a formation, and a certain collaborative variable switching mechanism is designed to realize the formation switching process.

[0113] It should be noted that the consistency control algorithm of this application adopts a multi-agent consistency algorithm based on graph theory, by modeling the agents as nodes in the graph and using the graph structure to represent the interactions between them. Through local rules and communication between agents, the interactions in the system are adjusted to achieve the consistency goals of the entire system in certain aspects. By using tools such as the graph Laplacian matrix to describe the relationship graph structure, these algorithms are usually distributed, and each agent adjusts based on local information and communication with neighbors so that the system eventually reaches a consistent state and remains stable. This method enables the multi-agent system to self-organize and achieve consistency without central control.

[0114] In this way, by detecting the relative distance between each drone and obstacles or other drones in real time, the drones can form a formation while maintaining the travel speed, thereby obtaining a second moving speed.

[0115] It should be noted that the first moving speed, the second moving speed and the target moving speed can all be understood as nouns, among which the first moving speed is the speed of the virtual leader, that is, the moving speed of the entire cluster, which can ensure that the cluster moves toward the target point as a whole; the second moving speed is the speed generated by the consistency control algorithm on the basis of the first moving speed, which can ensure that the drone cluster moves toward the target point while maintaining the target formation; the target moving speed is the speed generated by the consistency control algorithm on the basis of the second moving speed, which ensures that the drone cluster moves toward the target point while maintaining the target formation and avoiding collisions.

[0116] In some embodiments, the artificial potential field repulsion force on each UAV in the formation member set is determined based on the relative distance. Specifically, the distance between each UAV and other UAVs and the distance to the obstacle is detected in real time to obtain the relative distance. Then, the repulsive force of each UAV relative to the obstacle, i.e., the artificial potential field repulsion force, is calculated using the artificial potential field method. The calculation formula of the artificial potential field repulsion force is specifically:

[0117]

[0118] Where, is the artificial potential field repulsion between UAV i and UAV j; is the gradient of the repulsive force field with respect to position; K rep is the gain coefficient of the artificial potential field repulsion, used to adjust the strength of the artificial potential field repulsion; d ij is the relative distance between UAV i and UAV j; d min is the minimum safe distance between drones; p i is the location of drone i.

[0119] In some embodiments, the target moving speed corresponding to each UAV is determined based on the second moving speed and the artificial potential field repulsion. Specifically, after determining the second moving speed (maintaining formation) and the artificial potential field repulsion (taking obstacles into account), the two are added together to obtain the target moving speed corresponding to each UAV. That is, it can be understood that the second moving speed is adjusted in combination with the artificial potential field repulsion to obtain the target moving speed.

[0120] In some embodiments, the formation members are controlled to concentrate each drone to move to the corresponding second target position according to the corresponding target moving speed. Specifically, according to the target moving speed, the motor speed and flight attitude of each drone are controlled, and the position and speed of the drone are monitored in real time, and dynamic adjustments are made as needed (if an obstacle suddenly appears) to ensure that the drone can successfully reach the target position.

[0121] For ease of understanding, two examples are provided to explain the solution of this application:

[0122] Example 1: Assume that there are three drones in the formation. The three drones complete the tasks of takeoff, target point navigation, formation adjustment and return in sequence. First, the ground station sends the initialization command through wireless communication, and sends a confirmation signal back to the ground station after the initialization is successful. Then, the ground station instructs the drone cluster to go to the first target point. During the flight, according to the mission requirements, the ground station sends a triangle formation (the schematic diagram of the triangle formation of the three drone cluster is as follows Figure 3 After reaching the first target point, the ground station sends the coordinates of the second target point and adjusts the formation to a straight line again (the schematic diagram of the straight line formation of three drones is shown in the figure). Figure 4 As shown in the figure, the UAV cluster completes the formation change according to the command and continues to fly. After reaching the second target point, the ground station issues a return command and the cluster begins to return.

[0123] Example 2: Assume a formation consists of eight drones, each of which takes off, executes its mission, and returns to the air. At the start of the experiment, the ground station sends initialization instructions to all drones, including tasks such as attitude adjustment and sensor calibration. After completing initialization, the drones transmit confirmation signals via wireless communication confirming successful initialization. Next, the ground station sends the coordinates of the first target point to the drone swarm and instructs the swarm to maintain its initial formation. During flight, the ground station sends formation adjustment instructions based on mission requirements, instructing the swarm to change to a triangular formation mid-flight. Then, the ground station issues a mission instruction of equal priority, which includes a second mission target. Following this instruction, the swarm splits into two sub-clusters: one sub-cluster (e.g., five drones) heads for the first target point, and the other sub-cluster (e.g., three drones) heads for the second target point. Each sub-cluster executes its mission independently, reaching its target points according to its own planned paths. After completing the target point mission, the ground station sends a return instruction, directing all drones in the swarm to return to the air, thus concluding the experiment.

[0124] By obtaining the initial position of each drone in the drone cluster, the embodiment of the present application can timely understand the current position of the drones, providing data support for subsequent dynamic adjustments. By responding to the task instruction, the theoretical and preliminary first target position and initial formation can be quickly determined, providing an initial formation reference for path planning, and ensuring that the formation members have a clear direction at the beginning of the task. By determining the second target position and target formation corresponding to each drone through path planning, a second target position and target formation that better meet the actual requirements of the task can be generated after considering the actual environment, ensuring that each drone in the formation member set can stably move to the second target position, avoiding collisions or other safety issues caused by improper path planning. By determining the first moving speed of the formation member set, it is possible to ensure that the formation members as a whole move toward the second target position. By detecting the relative distance between each drone and obstacles or other drones in real time, the drones can form a formation while maintaining the travel speed, and obtain a second moving speed. Based on the first moving speed, the second moving speed, and the repulsive force of the artificial potential field, the target moving speed can be obtained, ensuring that the drone cluster can move to the second target position safely and efficiently in a complex environment while maintaining the target formation and avoiding collisions. Compared with the existing technology, this application can adapt to rapidly changing dynamic environments and improve the stability and security of task execution.

[0125] Example 2

[0126] Please refer to Figure 5 , Figure 51 is a schematic structural diagram of an embodiment of the UAV swarm collaborative control system provided by the present application, comprising: an acquisition module 100, a first determination module 200, a second determination module 300, and a movement module 400;

[0127] The acquisition module 100 is used to obtain the initial position of each drone in the drone cluster;

[0128] The first determination module 200 is used to determine the formation members participating in the formation in the drone cluster, the first target position corresponding to each drone, and the initial formation according to the received task instruction;

[0129] The second determination module 300 is configured to perform path planning based on the initial positions, the first target positions, and the initial formation, and determine the second target position and target formation corresponding to each UAV;

[0130] The mobile module 400 is used to determine a first moving speed of the formation member set, determine a second moving speed of each UAV to maintain the target formation based on the first moving speed, determine the artificial potential field repulsion of each UAV in the formation member set based on the relative distance, and determine the target moving speed corresponding to each UAV based on the second moving speed and the artificial potential field repulsion, and control each UAV in the formation member set to move to the corresponding second target position according to the corresponding target moving speed, wherein the relative position is the distance between each UAV and other UAVs or obstacles.

[0131] The information interaction, execution process, etc. between the modules within the above-mentioned drone cluster collaborative control system are based on the same concept as the embodiment of the drone cluster collaborative control method of the first aspect of the present invention, and the technical effects achieved are basically the same. For specific contents, please refer to the description in Example 1 of the method of the present invention, and will not be repeated here.

[0132] Example 3

[0133] Please refer to Figure 6 , Figure 6 This is a structural diagram of an embodiment of the drone system provided by the present application, including: a control console, at least one drone, and each of the drones is connected via a network, wherein the drone system can execute the drone cluster collaborative control method as described in Example 1 of the method of the present invention.

[0134] It's important to note that in the collaborative work of a drone swarm, communication between different drones relies on self-organizing networking technology (that is, communication between drones and between drones and ground control stations is carried out through a mesh network). Each drone can independently establish network connections with other drones and dynamically adjust the network topology. A custom communication protocol based on UDP multicast technology is designed to ensure that every drone in the swarm can maintain communication with other members.

[0135] It should be noted that the console is used to send mission instructions, monitor the status of drones and receive data sent back by drones, and communicate with the drone cluster through the Mesh network; while drones exchange attitude and speed information through self-organizing network modules to achieve collaborative control and formation flight.

[0136] In some embodiments, each of the drones includes: a flight controller, an operating system, an ad hoc network module, at least one gimbal camera, and at least one lidar.

[0137] In some embodiments, the flight controller includes: IMU (measurement unit), GPS navigation and altitude determination module, wherein the flight controller is responsible for controlling the flight status of the UAV, including attitude, speed and position control; IMU (measurement unit) is used to measure the acceleration and angular velocity of the UAV and provide attitude information; GPS navigation is used to provide global positioning information of the UAV for navigation and positioning; the altitude determination module is used to keep the flight altitude of the UAV stable.

[0138] In some embodiments, the operating system uses Robot Operating System version 2, which is used for communication and data exchange between modules within the drone. The Orange Pi 5 Plus is the drone's main control board or computing unit, responsible for running flight control software and processing sensor data.

[0139] In some embodiments, the ad hoc network module is used to communicate with other drones and a ground control console, forming a mesh ad hoc network. The gimbal camera is used to capture and transmit image data, potentially for reconnaissance or surveillance missions. The lidar is used to measure the distance between the drone and its surroundings, providing obstacle avoidance and environmental awareness capabilities.

[0140] It's important to note that within a single drone, the various software modules within the onboard computer communicate via ROS2 (Robot Operating System 2). ROS2 provides an efficient, stable, and flexible message-passing mechanism that supports distributed, real-time, and multi-threaded communication. Each software module publishes and subscribes to messages via ROS2, enabling data exchange and command transmission between modules within the drone.

[0141] The apparatus embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network elements. Some or all of these elements may be selected based on actual needs to achieve the objectives of the methods of this embodiment.

[0142] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-monitorable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0143] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application.

[0144] It is particularly pointed out that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A method for cooperative control of a drone cluster, characterized in that: include: Get the initial position of each drone in the drone cluster; According to the received mission instructions, the formation members of the UAV swarm, the first target position of each UAV and the initial formation are determined; Performing path planning based on each of the initial positions, each of the first target positions, and the initial formation to determine a second target position and a target formation corresponding to each of the UAVs; Determine a first moving speed of the formation member set, determine a second moving speed of each UAV to maintain the target formation based on the first moving speed, determine an artificial potential field repulsion applied to each UAV in the formation member set based on a relative distance, and determine a target moving speed corresponding to each UAV based on the second moving speed and the artificial potential field repulsion, and control each UAV in the formation member set to move to a corresponding second target position at the corresponding target moving speed, wherein the relative position is the distance between each UAV and other UAVs or obstacles.

2. The UAV cluster collaborative control method according to claim 1, characterized in that: The path planning is performed based on each of the initial positions, each of the first target positions, and the initial formation to determine the second target position and target formation corresponding to each of the UAVs, specifically: Calculating a first distance between the initial position corresponding to each UAV in the formation member set and the first target position, and constructing a distance matrix corresponding to the formation member set based on the first distance; Solving the distance matrix with the goal of minimizing the total moving distance corresponding to the formation member set, and obtaining a target assignment result between each UAV and the first target position; A second target position corresponding to each UAV is determined based on the target allocation result, and a formation parameter sequence of each UAV in the formation member set is adjusted based on the second target position to obtain a target formation.

3. The UAV cluster collaborative control method according to claim 2, characterized in that: The objective function and constraints of the distance matrix are specifically: Where A is the total moving distance, n is the number of drones, and d ik is the first distance from UAV i to the first target position k, x ik is a binary decision variable, i is the index of the UAV, and k is the index of the first target position.

4. The UAV cluster collaborative control method according to claim 1, characterized in that: The determining of the first moving speed of the formation member set is specifically: Obtaining a second distance between a position vector corresponding to a virtual leader and the second target position, wherein the virtual leader is the center point of the formation member set; A first moving speed of the formation member set is determined based on the second distance using proportional control.

5. The UAV cluster collaborative control method according to claim 4, characterized in that: The determining of the second moving speed of each UAV for maintaining the target formation based on the first moving speed is specifically: Establishing an adjacency matrix according to the number of drones in the formation member set and preset rules; Calculating the coordination variables of each UAV based on the position vector of the virtual leader and the target formation; Based on the collaborative variables and the adjacency matrix, a second moving speed of each UAV for maintaining the target formation is generated through a preset consistency control algorithm.

6. The UAV cluster collaborative control method according to claim 5, characterized in that: The relevant calculation formula of the synergistic variable is specifically: ρ i (t+1)=ρ i (t)+v i (t+1)dt; Where, ρ i (t) and ρ i (t+1) represents the position vector of drone i at the current moment and the next moment respectively; N is the number of drones, and the Nth drone is the virtual leader; v i (t+1) is the control input of the i-th UAV at the next moment; a ij Represents the communication topology relationship between the i-th UAV and the j-th UAV, 0 represents no communication topology, and 1 represents communication topology; Represents the position vector pointing from the virtual leader to the i-th UAV in the target formation, that is, the formation parameter.

7. The UAV cluster collaborative control method according to claim 5, characterized in that: The formula for generating the second moving speed of each UAV to maintain the target formation by the preset consistency control algorithm is specifically: Where u i (t) represents the control input of UAV i at time t, which is used to directly control the second moving speed of the UAV; N is the number of UAVs; x i (t) and x j (t) are the position vectors of UAV i and UAV j at time t; a ij is the (i, j)th item in the adjacency matrix, which is used to describe the communication relationship between UAV i and UAV j. If there is a communication connection between UAV i and UAV j, then a ij =1; otherwise a ij =0; i and j are the indexes of the drone.

8. A UAV cluster collaborative control system, characterized in that: include: an acquisition module, a first determination module, a second determination module, and a movement module; The acquisition module is used to obtain the initial position of each drone in the drone cluster; The first determination module is used to determine the formation members participating in the formation in the drone cluster, the first target position corresponding to each drone, and the initial formation according to the received task instruction; The second determination module is configured to perform path planning based on each of the initial positions, each of the first target positions, and the initial formation, and determine a second target position and a target formation corresponding to each of the UAVs; The mobile module is used to determine a first moving speed of the formation member set, determine a second moving speed of each drone to maintain the target formation based on the first moving speed, determine the artificial potential field repulsion of each drone in the formation member set based on the relative distance, and determine the target moving speed corresponding to each drone based on the second moving speed and the artificial potential field repulsion, and control each drone in the formation member set to move to the corresponding second target position according to the corresponding target moving speed, wherein the relative position is the distance between each drone and other drones or obstacles.

9. A drone system, characterized in that: include: A control console and at least one drone, wherein the drones are connected via a network, wherein the drone system can execute the drone cluster collaborative control method according to any one of claims 1 to 8.

10. The UAV system according to claim 9, characterized in that: Each of the drones includes: a flight controller, an operating system, an ad hoc network module, at least one gimbal camera and at least one laser radar.

Images