Unmanned aerial vehicle cluster formation cooperative combined strike method and device

Abstract

The invention relates to an unmanned aerial vehicle (UAV) cluster formation cooperative combined click method and device, and relates to the technical field of UAVs. The method comprises the following steps: acquiring a task demand, a space environment and a state vector of an unmanned aerial vehicle in real time; obtaining a consistency control law, a combined control law and a split control law according to task requirements, space environments and state vectors; and constructing an optimization model with the optimal task execution efficiency as a target, and solving the optimization model to obtain the formation and combination click strategy of the unmanned aerial vehicle cluster. An unmanned aerial vehicle cluster can keep a formation through a consistency control rhythm, the unmanned aerial vehicle cluster can be efficiently gathered near a target point for attacking through a combination control rhythm, the unmanned aerial vehicle cluster can be rapidly dispersed to avoid attacks through a split attack strategy, and finally an optimization model is constructed by taking the overall task execution efficiency of the unmanned aerial vehicle as a target. Therefore, it is ensured that the unmanned aerial vehicle cluster efficiently executes the combination and division strategy, and the task response speed of the unmanned aerial vehicle cluster in a complex environment is increased.

Description

Technical Field

[0001] The present application relates to the field of UAV technology, and in particular to a method and device for coordinated attack by a cluster of UAVs. Background Art

[0002] With the rapid development of drone technology, drone swarm formations are widely used in military, logistics, emergency rescue and other fields. When drone swarms perform complex tasks (such as combat, search and rescue or logistics distribution), they usually need to flexibly adjust the formation according to changes in the mission scenario. This dynamic formation requires the drone swarm to be able to reorganize its form in real time based on information such as the environment, target point location, and threat situation, so as to achieve efficient target approach and attack (merge) or rapid dispersion after attack (split attack), thereby achieving efficient execution of the mission and self-defense.

[0003] However, most existing UAV formation control methods are based on fixed formation structures and preset mission planning, and formation adjustments are often difficult to cope with complex and changing battlefield environments. When faced with changes in targets, changes in threat environments, or the need for multi-task coordination, existing fixed formations cannot dynamically adapt to changes in the environment, resulting in slow cluster response, easy exposure of weaknesses, or loss of collaborative combat advantages. Summary of the invention

[0004] Based on this, it is necessary to provide a method and device for coordinated and combined attack of drone cluster formations that can achieve flexible and efficient coordination in order to address the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for coordinated attack by a drone swarm formation. The method comprises:

[0006] Obtain mission requirements, space environment and drone state vector in real time; mission requirements include target relative position and target point position, and space environment includes obstacle position;

[0007] Obtain consistent control law based on the state vector and the target relative position;

[0008] Based on the state vector, a convergence attraction force is generated around the target point position, and a convergence repulsion force is generated around the obstacle position and the adjacent UAV. The convergence control law is obtained according to the convergence attraction and convergence repulsion forces.

[0009] Based on the state vector, a split repulsive force is generated around the center position of the drone cluster, and the split repulsive force and the combined repulsive force are used to obtain the split control law.

[0010] According to the consistency control law, the merging control law and the split-attack control law, an optimization model with the goal of optimizing the task execution efficiency is constructed. The formation and merging-split-attack strategies of the UAV cluster are obtained by solving the optimization model.

[0011] In one embodiment, the state space includes spatial positions;

[0012] Based on the state vector and the target relative position, the consistent control law is obtained including:

[0013] For each drone in the drone cluster, the neighboring drones are determined according to their spatial position, and the spatial position is adjusted to maintain the target relative position with the neighboring drones, and a consistent control law for achieving spatial position adjustment is obtained.

[0014] In one embodiment, according to the consistency control law, the merging control law and the split control law, constructing an optimization model with the goal of optimizing task execution efficiency includes:

[0015] According to the consistency control law, the merging control law and the split control law, the total task completion time and the total consumed energy for evaluating the task execution efficiency are obtained;

[0016] An optimization model is constructed based on the total task completion time and total consumed energy.

[0017] In one embodiment, each drone in the drone cluster exchanges data including mission requirements, space environment and state vectors through a 6G communication module.

[0018] In one embodiment, a distributed computing and edge computing collaborative control strategy is used to obtain a formation and combined attack strategy;

[0019] Among them, the collaborative control strategy of distributed computing and edge computing allocates computing power with the goal of minimizing the time cost of completing computing tasks.

[0020] In one embodiment, a multi-level collaborative decision-making mechanism is used to perform collaborative control of a drone cluster;

[0021] Among them, the multi-level collaborative decision-making mechanism includes local decision-making with a single drone as the decision-making object, cluster decision-making with a sub-cluster composed of several drones as the decision-making object, and global decision-making with a drone swarm as the decision-making object.

[0022] In the second aspect, the present application also provides a drone cluster formation coordinated attack device. The device includes:

[0023] Real-time perception module, used to obtain mission requirements, spatial environment and state vector of the UAV in real time; mission requirements include target relative position and target point position, and spatial environment includes obstacle position;

[0024] A consistency control module, used for obtaining a consistency control law based on a state vector and a target relative position;

[0025] A merging control module is used to generate a merging attraction force around the target point position and a merging repulsion force around the obstacle position and the adjacent UAV based on the state vector, and obtain a merging control law according to the merging attraction force and the merging repulsion force;

[0026] The split-strike control module is used to generate split-strike repulsive forces around the center position of the drone cluster based on the state vector, and obtain the split-strike control law according to the split-strike repulsive forces and the combined repulsive forces;

[0027] The decision-making module is used to construct an optimization model with the goal of optimizing task execution efficiency based on the consistency control law, the merging control law and the split-attack control law, and obtain the formation and merging-split-attack strategies of the drone cluster by solving the optimization model.

[0028] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the above-mentioned drone cluster formation coordinated attack method are implemented.

[0029] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned drone cluster formation coordinated combined attack method.

[0030] In a fifth aspect, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned drone cluster formation coordinated attack method are implemented.

[0031] The above-mentioned drone cluster formation collaborative combined advance and split attack method and device include: real-time acquisition of task requirements, spatial environment and state vectors of drones; the task requirements include target relative position and target point position, and the spatial environment includes obstacle position; based on the state vector and target relative position, a consistency control law is acquired; based on the state vector, a combined advance attraction force is generated around the target point position, and a combined advance repulsion force is generated around the obstacle position and adjacent drones, and a combined advance control law is acquired according to the combined advance attraction and combined advance repulsion force; based on the state vector, a split attack repulsion force is generated around the center position of the drone cluster, and a split attack control law is acquired according to the split attack repulsion force and the combined advance repulsion force; based on the consistency control law, the combined advance control law and the split attack control law, an optimization model with the goal of optimizing task execution efficiency is constructed, and the formation and combined advance and split attack strategy of the drone cluster is acquired by solving the optimization model. Through the consistency control law, the drone cluster can maintain its formation; through the convergence control law, the drone cluster can efficiently gather near the target point for attack; through the split-attack strategy, the drone cluster can quickly disperse to avoid attack. Finally, an optimization model is constructed with the overall mission execution efficiency of the drones as the goal, thereby ensuring that the drone cluster efficiently executes the convergence and split-attack strategy, improving the mission response speed of the drone cluster in complex environments, and has significant practicality and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the process of a method for coordinated attack by a swarm of drones in one embodiment;

[0033] Figure 2 It is a structural block diagram of a drone cluster formation coordinated attack device in one embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] The present application embodiment provides a method for coordinated attack by a drone swarm formation, such as Figure 1 As shown, the following steps are included:

[0036] Step 102, acquiring the mission requirements, the space environment and the state vector of the UAV in real time; the mission requirements include the relative position of the target and the position of the target point, and the space environment includes the position of obstacles.

[0037] Mission requirements include mission objectives, attacks, formation changes, etc. The spatial environment is collected by the sensor array configured on the drone, including obstacles in the surrounding environment of the drone cluster, mission target objects, surrounding drones, etc., to fully perceive the environment. The state vector of the drone includes the spatial position, speed, acceleration, etc. of the drone, which can be perceived by the sensor array, or the state vector can be shared with other drones in the drone cluster through wireless communication.

[0038] Step 104: Obtain a consistency control law based on the state vector and the target relative position.

[0039] Among them, the target relative position refers to the relative position distance between drones.

[0040] The consistency control law is the key to the coordinated control of UAV swarms. It aims to achieve the convergence of the flight status of each UAV through information sharing and interaction, and improve the overall efficiency and performance.

[0041] Step 106: Based on the state vector, generate a merging attraction force around the target point position, generate a merging repulsion force around the obstacle position and the adjacent UAV, and obtain a merging control law according to the merging attraction force and the merging repulsion force.

[0042] The goal of the convergence control law is to gather the drone cluster near the mission target during the attack phase and form a siege situation to achieve concentrated firepower or mission execution. The convergence strategy generates an attractive potential field for the target and a repulsive potential field for threats or obstacles to guide the drone cluster to converge towards the target.

[0043] Step 108, based on the state vector, generate a separate repulsive force around the center position of the drone cluster, and obtain the separate repulsive force and the combined repulsive force according to the separate repulsive force.

[0044] The split-attack control law is to quickly disperse the drone cluster after the combined attack is completed to prevent the enemy's counterattack or interference. The split-attack control law requires that the drone cluster can leave the battlefield as soon as possible after completing the attack mission and enter a dispersed defense posture. The split-attack control law can be implemented through a potential field method similar to the combined attack strategy, but unlike the combined attack, the drone cluster is no longer attracted by the target during the split-attack, but is affected by the repulsive potential field to maintain the dispersion and maneuverability of the cluster.

[0045] Step 110, based on the consistency control law, the merging control law and the split attack control law, an optimization model with the goal of optimizing the task execution efficiency is constructed, and the formation and merging and split attack strategies of the UAV cluster are obtained by solving the optimization model.

[0046] The execution of the formation and combined attack strategy needs to be dynamically optimized according to real-time mission requirements and environmental changes. This embodiment builds an optimization model with the goal of optimizing mission execution efficiency, and formulates the formation and combined attack strategy of the drone cluster by solving the model.

[0047] This embodiment defines consistency control law, convergence control law and split attack control law, so that the UAV can dynamically adjust the formation form and attack and defense posture according to real-time mission requirements and environmental changes. At the same time, the optimization model is used to optimize the formation and convergence and split attack strategy of the UAV, thereby improving the flexibility and efficiency of mission execution.

[0048] In one embodiment, the state space includes spatial position; based on the state vector and the target relative position, obtaining the consistency control law includes: for each drone in the drone cluster, determining the adjacent drones according to the spatial position, adjusting the spatial position with the purpose of maintaining the target relative position with the adjacent drones, and obtaining the consistency control law for realizing the spatial position adjustment.

[0049] The formation problem of a drone swarm can be abstracted as a formation maintenance and adjustment problem of a multi-agent system. Let the number of drones in the swarm be N, and the state vector of each drone be where p i (t), v i (t) and a i (t) represent the spatial position, velocity and acceleration of UAV i at time t, respectively.

[0050] The dynamic formation control goal of the UAV swarm can be expressed as: at any time t, the relative positions between the UAVs must satisfy the constraints of the formation. Define the target relative position between UAVs i and j as d ij (t), then the control objective of the dynamic formation can be expressed as:

[0051]

[0052] where d ij (t) is the relative position of the dynamic target that changes with time, which is used to adapt to different mission scenarios. The dynamic formation goal of the UAV cluster is to enable each UAV to adjust its position and speed according to the state of the neighboring UAVs, so as to ensure that the formation of the entire cluster is maintained.

[0053] The consistency control law can be expressed as:

[0054]

[0055] Where: u i(t) is the control input of UAV i (control methods include acceleration or speed adjustment); K is the control gain matrix used to adjust the stability of the formation; N i is the neighbor set of UAV i, which refers to the UAVs that have direct communication with UAV i.

[0056] Through the consistency control law, the UAVs can maintain the formation by adjusting the relative position with the neighboring UAVs, and at the same time, the relative position d ij (t) changes to achieve dynamic adjustment of the formation. For example, during the target approach phase, the drone cluster can gradually aggregate from a dispersed formation to an attack formation, and then quickly disperse after completing the attack.

[0057] In one embodiment, the purpose of the convergence control law is to ensure that the drones can gradually converge to form a siege situation when approaching the target, and maintain a certain distance to avoid collision, so as to achieve coordinated attack.

[0058] Define the target point position as p target (t), the combined attraction force on UAV i can be expressed as:

[0059]

[0060] Among them: U att,i (t) is the attractive potential of UAV i; k att is the gain coefficient of the attractive potential; p i (t is the spatial position of UAV i; p target (t) is the target point position.

[0061] To avoid collisions between drones or with other obstacles, the repulsive potential field needs to be considered during the merging process. The position of the obstacle is defined as p obs (t), then the combined repulsive force on UAV i is:

[0062]

[0063] Where: k rep is the gain coefficient of the repulsive potential; ||p i (t)-p j (t)|| is the distance between drones i and j, p j (t) also thinks p obs (t).

[0064] The total control input of UAV i during merging, i.e., the merging control law, can be expressed as the resultant force of the merging attraction and repulsion:

[0065] u i (t) = F att,i (t)+F rep,i(t).

[0066] In one embodiment, the purpose of the split-attack control law is to ensure that the drones remain dispersed during the split-attack process while avoiding collisions with each other.

[0067] Define the center position p center (t) is the center of mass of the drone cluster. The repulsive force on drone i during the attack process can be expressed as:

[0068]

[0069] Where: k split is the gain coefficient of the scattered potential field; It is the center of the drone cluster.

[0070] In this way, the drone cluster will quickly leave the central area after completing the mission and maintain a safe distance from neighboring drones to avoid being counterattacked by the enemy. The overall attack control law can be expressed as:

[0071]

[0072] In one embodiment, based on the consistency control law, the merging control law and the split control law, an optimization model is constructed with the goal of optimizing task execution efficiency, including: according to the consistency control law, the merging control law and the split control law, the total task completion time and total consumed energy used to evaluate the task execution efficiency are obtained; and the optimization model is constructed based on the total task completion time and total consumed energy.

[0073] The overall task execution efficiency E(t) of the drone cluster can be expressed as:

[0074]

[0075] Where: T complete,i (t) is the time it takes for UAV i to complete the task; E energy,i (t) is the energy consumption of UAV i; α i It is the weight coefficient of energy consumption, which is used to balance the relationship between task completion time and energy consumption.

[0076] The optimization goal is to minimize the total task completion time and reduce energy consumption. This problem can be solved through real-time dynamic programming or reinforcement learning algorithms to ensure that the drone cluster achieves optimal efficiency when executing the combined attack and divided attack strategy.

[0077] In one embodiment, each drone in the drone cluster exchanges data including mission requirements, space environment and state vectors through a 6G communication module.

[0078] With the support of the real-time communication capabilities of the 6G communication network, drones can obtain information about neighboring drones and the environment through low-latency communication networks, and then make dynamic formation adjustments and optimize the combined attack and split attack strategy based on this information, thereby improving the dynamic response capabilities of drone clusters and ensuring the efficient execution and autonomous defense of the cluster in complex tasks. It has significant practicality and application prospects.

[0079] 6G communication technology has achieved higher communication bandwidth and lower latency. Compared with traditional 4G / 5G networks, 6G communication provides Tbps-level data transmission rates and millisecond-level ultra-low latency, which can effectively support the real-time communication needs of large-scale drone clusters. When drone clusters are working together or performing complex tasks, each drone can share status information, mission instructions, and perception data in real time, greatly improving the efficiency and reliability of information transmission, and ensuring that all drones in the cluster can complete task coordination and response within millisecond-level delays. This ultra-low latency communication ensures the real-time and accuracy of dynamic formation adjustment, path planning, and target tracking, and improves the efficiency of cluster collaboration and combat capabilities.

[0080] In one embodiment, a distributed computing and edge computing collaborative control strategy is used to obtain a formation and combined attack strategy; wherein the distributed computing and edge computing collaborative control strategy allocates computing power with the goal of minimizing the time cost of completing the computing task.

[0081] In the coordinated control of large-scale drone swarms, each drone in the swarm not only needs to maintain communication with other drones, but also needs to calculate its flight trajectory, path planning, obstacle avoidance strategy, etc. in real time according to mission requirements. Traditional centralized computing architectures have problems such as data processing delays and excessive computing load when facing large-scale tasks, and cannot meet the real-time requirements of drone swarms.

[0082] The introduction of distributed computing and edge computing provides new ideas for solving these problems. Distributed computing distributes computing tasks to each drone, so that each drone not only acts as a task executor, but also as a computing node, sharing part of the computing tasks. Edge computing relies on edge devices close to drones, such as edge servers on 6G base stations, which can effectively balance the communication delay and computing burden of drones, improving the real-time and accuracy of collaborative control.

[0083] In the distributed computing model, considering that each drone in the drone cluster is both a computing node and a task executor, the total computing burden of each drone performing a task consists of two parts: local computing and network collaborative computing.

[0084] Let the N drones in the drone cluster be represented as U1, U2, ..., U N , each drone Ui The computational requirements of the local computing task can be expressed as:

[0085] C local,i =f(x i (t),u i (t))

[0086] Where: C local,i is the local computing task of UAV i; x i (t) is the state vector of UAV i at time t, including its position, velocity, acceleration, etc.; u i (t) is the control input of UAV i at time t.

[0087] In order to reduce the local computing burden, distributed computing introduces a collaborative mechanism, namely, UAV i Part of the computing tasks can be distributed to other UAVs for computing, and the total burden of collaborative computing is defined as C coop,i In an ideal situation, the UAV i The total computational burden can be expressed by the following formula:

[0088]

[0089] Where: C total,i is the total computational burden of UAV i; It's a drone i The neighbor set of C (i.e., the set of drones that can perform collaborative computing through the 6G network); coop,ij It is the task that UAV i delegates to UAV j for calculation.

[0090] For each drone U i For example, the computational burden C total,i It is necessary to optimize between local computing and collaborative computing. The effect of collaborative computing depends on the communication bandwidth and delay between drones. Therefore, considering the communication delay τ ij (t) and bandwidth B ij The transmission time of the collaborative computing task can be expressed as:

[0091]

[0092] Where: D coop,ij is the amount of task data that UAV i assigns to UAV j for calculation; B ij is the communication bandwidth between UAVs i and j; τ ij (t) is the communication delay between UAVs i and j at time t. i The total task execution time T total,i It can be expressed as the sum of local computation time and collaborative computation time:

[0093]

[0094] Where: f local,i is the local computing capacity of UAV i; f coop,j is the collaborative computing capability of UAV j.

[0095] In order to minimize the task execution time of the entire cluster, it is necessary to allocate the computing tasks of each drone through an optimization algorithm. The optimization goal can be expressed as:

[0096]

[0097] This optimization problem can be solved by distributed optimization algorithms, such as gradient descent or Lagrange multiplier method, to dynamically adjust the task allocation between drones to achieve optimal collaborative computing performance.

[0098] Although distributed computing can effectively disperse computing tasks, when the task complexity is high, collaborative computing between drones alone may still not meet real-time requirements. For this reason, edge computing is introduced, which offloads part of the computing tasks to edge computing nodes close to drones, such as edge servers of 6G base stations. This architecture can significantly improve computing power while maintaining low communication latency.

[0099] For UAV i For example, the task offloading model of edge computing can be expressed as:

[0100]

[0101] Where: D offload,i It's a drone i The amount of task data that needs to be offloaded to the edge computing node; R edge,i It's a drone i Communication rate with edge nodes; C edge,i is the amount of computing tasks that the edge computing node needs to process; f edge It is the computing power of the edge computing node. i The total task execution time can be optimized as follows:

[0102]

[0103] The above shows that the UAV can choose the optimal strategy of local computing, collaborative computing or task offloading according to the current network status, task complexity and load of edge computing nodes to ensure that the task is completed in the shortest time.

[0104] Collaborative control based on distributed computing and edge computing is mainly reflected in the overall task execution and decision optimization of the UAV cluster. Each UAV in the cluster must make autonomous decisions based on its own local information, and optimize the action strategy of the entire cluster through communication and collaboration with other UAVs.

[0105] The cooperative control problem of UAV swarm can be modeled as the optimal control problem of a multi-agent system, where each UAV U i The control goal is to minimize its local cost function J i (x i (t),u i (t)), the cost function is related to factors such as the UAV’s energy consumption, mission completion, and path planning.

[0106] The global collaborative control objective can be expressed as the overall cost function J of the entire cluster: total :

[0107]

[0108] Where: ij is the coordination weight between neighboring UAVs, which controls the coordination degree between each UAV. The coordination control law can be derived from the overall cost function J by gradient descent method. total It is derived from the above to obtain the optimal control input u for each UAV. i (t):

[0109]

[0110] The control law shows that each drone must not only perform local optimization based on its own mission status, but also exchange information with neighboring drones to make collaborative decisions and adjust paths, thereby optimizing the overall action strategy of the cluster. By combining distributed computing with edge computing, the collaborative control of drone clusters has been greatly optimized. Distributed computing improves the autonomous decision-making ability of each drone and reduces the delay problem caused by centralized computing by distributing computing tasks to drone clusters; the introduction of edge computing provides a guarantee for real-time processing of complex tasks through powerful computing power and low-latency communication support.

[0111] The innovativeness of this collaborative control method is reflected in its combination of the high bandwidth and low latency characteristics of the 6G communication network, ensuring efficient communication and task allocation between drones. At the same time, the combination of distributed computing and edge computing has greatly improved the overall computing power of the cluster, reduced the delay in task execution, and improved the efficiency and accuracy of task completion. In the multi-task scenarios of future drone clusters, this method has broad application prospects, especially in complex mission environments such as military, logistics distribution, and disaster relief, and can significantly improve the combat and collaborative efficiency of drone clusters.

[0112] This embodiment effectively distributes computing tasks to different drones and edge computing nodes in the cluster by combining distributed computing and edge computing. This not only reduces the computing pressure of a single drone, but also significantly improves the overall computing efficiency of large-scale task processing. In particular, for complex path planning, obstacle avoidance decisions, and multi-target collaborative tasks, edge computing can provide powerful computing support close to the drone, achieving more efficient task execution.

[0113] In one embodiment, a multi-level collaborative decision-making mechanism is used to perform collaborative control of a drone cluster;

[0114] Among them, the multi-level collaborative decision-making mechanism includes local decision-making with a single drone as the decision-making object, cluster decision-making with a sub-cluster composed of several drones as the decision-making object, and global decision-making with a drone swarm as the decision-making object.

[0115] In the collaborative control and task execution process of large-scale drone swarms, a single-level control and decision-making mechanism is often unable to cope with the multi-task requirements in a complex dynamic environment. Drone swarms need to be able to make decisions at multiple levels, including autonomous decisions of individual drones, small-scale collaborative decisions of neighboring drones, and global decisions of the entire swarm. In the multi-level collaborative decision-making mechanism, decision-making tasks are divided into local decisions (local layer), cluster collaborative decisions (middle layer) and global decisions (top layer), and efficient operation of the swarm is achieved through collaboration at different levels.

[0116] The high-speed, low-latency communication and edge computing capabilities of 6G networks provide the basis for multi-level collaborative decision-making, enabling drone swarms to quickly share information and optimize decisions at different levels, ensuring efficient execution of tasks in complex and dynamic environments.

[0117] In order to effectively coordinate the decisions at different levels, a hierarchical optimization framework is introduced, which performs collaborative decision-making at three levels: local, regional, and global.

[0118] At the local level, each drone makes independent decisions based on its own perception information, mainly dealing with the drone's path planning, obstacle avoidance, and local task execution. The local decision-making goal of drone i can be expressed as a local optimization problem:

[0119]

[0120] in: is the local objective function of UAV i, which may include multiple factors such as path planning, obstacle avoidance, and energy consumption; u i (t) is the control input of UAV i at time t; x i (t) is the state vector (including position, speed, etc.) of UAV i at time t. With the support of 6G network, local decision-making can obtain the perception information of UAVs and the status information of neighboring UAVs in real time to ensure the real-time performance of path planning and obstacle avoidance.

[0121] At the cluster level, a sub-cluster consisting of several drones makes collaborative decisions based on regional mission objectives to ensure efficient collaboration between drones within the cluster. The goal of this level is to optimize the execution of local collaborative tasks of the entire cluster, such as collective advance, target siege, joint detection, etc.

[0122] The cluster collaborative decision-making problem can be expressed as a distributed optimization problem:

[0123]

[0124] Among them: J cluster is the objective function of the cluster, which may include tasks such as target capture, task allocation, and joint detection; X C (t)=[x1(t),x2(t),…,x k (t)] is the state matrix of the UAVs in the cluster; U C (t)=[u1(t),u2(t),…,u k (t)] is the control input of the drones in the cluster; k represents the number of drones in the cluster. The collaborative decision-making of the cluster can be achieved through the "consensus control" mechanism. This mechanism enables each drone in the cluster to gradually tend to the shared decision results through communication between adjacent drones, that is, the control input of each drone depends not only on its own state, but also on the state of neighboring drones. The collaborative control law of the cluster can be expressed as:

[0125]

[0126] Where: K is the control gain matrix; is the neighbor set of drone i; g i(t) is the local task-driven control input of UAV i. This method ensures that the UAVs in the cluster maintain relative consistency when performing tasks, while being able to make individual adjustments based on local needs.

[0127] At the global level, the top-level command center or the global controller of the drone cluster is responsible for the strategic decisions of the entire cluster, such as task allocation, target prioritization, resource allocation, etc. The goal of global decision-making is to coordinate all drones and sub-clusters to ensure the optimal completion of the entire cluster's tasks.

[0128] The global decision can be modeled as a multi-objective optimization problem:

[0129]

[0130] Among them: J global is the global objective function of the cluster, which may include task completion time, energy consumption, target capture rate, etc.; X(t) = [x1(t), x2(t), …, x N (t)] is the state matrix of the entire cluster; U(t) = [u1(t),u2(t),…,u N (t)] is the control input for the entire cluster.

[0131] Global decisions can be optimized through methods such as reinforcement learning or dynamic programming. At the global level, the edge computing nodes of the 6G network can provide powerful computing power to process the information flow from all drones in real time and make the best decision based on the global situation.

[0132] In order to achieve high efficiency of multi-level collaborative decision-making, a hierarchical optimization framework is adopted to ensure that decisions at all levels can cooperate and optimize each other through a top-down coordination mechanism and a bottom-up feedback mechanism. The global level determines the task allocation and priority of each sub-cluster according to the overall requirements of the task, and generates a preliminary task allocation matrix T through the global task planner. assign :

[0133]

[0134] in: Represents the set of all subclusters, the task allocation matrix T assign It is generated by optimizing the global goal of the cluster and the synergy of the sub-clusters. The cluster level is based on the task allocation matrix T of the global decision assign , combined with the local goals of the cluster, optimize its internal task execution and collaborative operations. Individual drones at the local level make autonomous adjustments based on the collaborative decision-making of the cluster and the information they perceive locally. Each drone will perform real-time path optimization and local goal correction in its local environment based on the decisions of neighboring drones and its independently perceived tasks.

[0135] The effective implementation of multi-level collaborative decision-making mechanisms depends on efficient communication and computing resource allocation within the drone cluster. The ultra-high-speed communication and edge computing capabilities of 6G networks provide support for multi-level collaborative decision-making, especially at the cluster collaboration and global decision-making levels where data transmission and computing requirements are high.

[0136] For communication resource allocation, the network slicing technology of 6G networks can dynamically adjust the communication bandwidth and latency according to the decision-making requirements at different levels. The global decision-making level requires higher bandwidth and lower latency to obtain global situation information, while the cluster level needs to ensure low latency and high reliability of communication to achieve efficient collaborative control. The optimization goal of communication resource allocation can be expressed as:

[0137]

[0138] Where: B(t) is the communication bandwidth allocation matrix of each level of decision; τ l (t) is the communication delay at different levels; ω l are the delay weight coefficients of different levels.

[0139] For computing resource allocation, the edge computing nodes of the 6G network dynamically allocate computing resources according to the computing needs of each level. Decisions at the global level require strong computing power to handle complex task planning and optimization problems, while drones at the local level mainly rely on their own computing resources for real-time path planning and control. Therefore, the optimization of computing resource allocation can be expressed by the following formula:

[0140]

[0141] Where: f edge (t) is the allocation matrix of edge computing resources; T compute，l (t) is the computation time of each level; γ l is the weight coefficient for computing resource allocation.

[0142] The multi-level collaborative decision-making mechanism combines global strategic decision-making, cluster collaborative decision-making and individual autonomous decision-making through a hierarchical optimization framework, greatly improving the execution efficiency of drone clusters in complex tasks. The novelty and innovation of this mechanism are reflected in its realization of rapid response and efficient collaboration of drone clusters in different mission scenarios through hierarchical collaboration and optimization. In addition, the high-speed communication and edge computing capabilities of the 6G network provide strong technical support for multi-level collaborative decision-making, ensuring real-time and robustness in complex dynamic environments. This collaborative decision-making mechanism has broad application prospects, especially in the fields of military, disaster relief and logistics distribution, and can effectively improve the task execution efficiency and resource utilization of drone clusters.

[0143] This embodiment ensures that the UAV cluster achieves a high degree of coordination and cooperation between local autonomous decision-making, cluster collaborative decision-making and global mission planning through a multi-level collaborative decision-making mechanism. This multi-level decision-making model not only improves the autonomy of the UAVs in the cluster, but also enhances the cluster's adaptability to complex and dynamic environments, and can flexibly execute multi-task scenarios in complex combat environments, achieving more efficient and reliable combat results.

[0144] The present invention defines control laws and optimization models to enable drone clusters to dynamically adjust formation morphology and task allocation according to real-time mission requirements and environmental changes. Compared with the traditional fixed formation method, the dynamic formation control algorithm flexibly adjusts the cluster morphology according to changes in mission complexity, threat environment, and target location. Whether it is an attack formation in the convergence phase or a dispersed defense posture in the attack phase, it can quickly adapt to environmental changes and maximize task execution efficiency. In addition, the task allocation mechanism based on distributed computing and edge computing makes full use of the information sharing capabilities inside and outside the cluster, combined with a multi-level collaborative decision-making mechanism, to improve the flexibility and efficiency of task completion.

[0145] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0146] Based on the same inventive concept, the embodiment of the present application also provides a drone swarm formation coordinated combined advance and separate attack device for implementing the drone swarm formation coordinated combined advance and separate attack method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more drone swarm formation coordinated combined advance and separate attack device embodiments provided below can refer to the limitations of the drone swarm formation coordinated combined advance and separate attack method above, and will not be repeated here.

[0147] In one embodiment, Figure 2 As shown, a drone cluster formation coordinated combined attack device is provided, including: a real-time perception module 202, a consistency control module 204, a combined control module 206, a separate attack control module 208 and a decision module 210, wherein:

[0148] The real-time perception module 202 is used to obtain the task requirements, the spatial environment and the state vector of the UAV in real time; the task requirements include the relative position of the target and the position of the target point, and the spatial environment includes the position of obstacles;

[0149] A consistency control module 204, used to obtain a consistency control law based on the state vector and the target relative position;

[0150] A merging control module 206 is used to generate a merging attraction force around the target point position and a merging repulsion force around the obstacle position and the adjacent UAV based on the state vector, and obtain a merging control law according to the merging attraction force and the merging repulsion force;

[0151] The split-strike control module 208 is used to generate a split-strike repulsive force around the center position of the drone cluster based on the state vector, and obtain a split-strike control law according to the split-strike repulsive force and the combined repulsive force;

[0152] The decision module 210 is used to construct an optimization model with the goal of optimizing task execution efficiency according to the consistency control law, the merging control law and the split-attack control law, and obtain the formation and merging-split-attack strategy of the drone cluster by solving the optimization model.

[0153] Each module in the above-mentioned drone cluster formation coordinated attack device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0154] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in all the above method embodiments when executing the computer program.

[0155] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in all the above method embodiments are implemented.

[0156] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in all the above method embodiments when executed by a processor.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0158] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0159] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0160] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for coordinated attack by drone swarm formation, characterized in that: The method comprises: Acquire mission requirements, space environment and state vector of the UAV in real time; the mission requirements include target relative position and target point position, and the space environment includes obstacle position; Acquire a consistency control law based on the state vector and the target relative position; Based on the state vector, a merging attraction force is generated around the target point position, a merging repulsion force is generated around the obstacle position and the adjacent UAV, and a merging control law is obtained according to the merging attraction force and the merging repulsion force; Based on the state vector, a split repulsive force is generated around the center position of the drone cluster, and a split repulsive force control law is obtained according to the split repulsive force and the combined repulsive force; According to the consistency control law, the merging control law and the split-attack control law, an optimization model with the goal of optimizing task execution efficiency is constructed, and the formation and merging-split-attack strategy of the drone cluster are obtained by solving the optimization model.

2. The method according to claim 1, characterized in that The state space includes spatial positions; The obtaining of a consistency control law based on the state vector and the target relative position includes: For each of the drones in the drone cluster, a neighboring drone is determined according to the spatial position, the spatial position is adjusted for the purpose of maintaining the target relative position with the neighboring drone, and the consistency control law for realizing the spatial position adjustment is obtained.

3. The method according to claim 1, characterized in that The step of constructing an optimization model with the goal of optimizing task execution efficiency according to the consistency control law, the merging control law and the split control law includes: According to the consistency control law, the merging control law and the split control law, the total task completion time and total consumed energy for evaluating the task execution efficiency are obtained; The optimization model is constructed according to the total task completion time and the total consumed energy.

4. The method according to claim 1, characterized in that: The drones in the drone cluster interact with each other through a 6G communication module to perform data exchange including the mission requirements, the space environment and the state vector.

5. The method according to claim 1, characterized in that: The method further includes: using a distributed computing and edge computing collaborative control strategy to obtain the formation and combined attack strategy; Among them, the distributed computing and edge computing collaborative control strategy allocates computing power with the goal of minimizing the time cost of completing the computing task.

6. The method according to claim 1, characterized in that The method further comprises: adopting a multi-level collaborative decision-making mechanism to perform collaborative control of the drone cluster; Among them, the multi-level collaborative decision-making mechanism includes local decision-making with a single drone as the decision-making object, cluster decision-making with a subcluster composed of several drones as the decision-making object, and global decision-making with the drone group as the decision-making object.

7. A drone cluster formation coordinated attack device, characterized in that: The device comprises: A real-time perception module, used to obtain task requirements, spatial environment and state vector of the UAV in real time; the task requirements include the relative position of the target and the position of the target point, and the spatial environment includes the position of obstacles; A consistency control module, used for obtaining a consistency control law based on the state vector and the target relative position; a merging control module, configured to generate a merging attraction force around the target point position, generate a merging repulsion force around the obstacle position and the adjacent UAV based on the state vector, and obtain a merging control law according to the merging attraction force and the merging repulsion force; A split-strike control module is used to generate a split-strike repulsive force around the center position of the drone cluster based on the state vector, and obtain a split-strike control law according to the split-strike repulsive force and the combined repulsive force; The decision module is used to construct an optimization model with the goal of optimizing task execution efficiency according to the consistency control law, the merging control law and the split-attack control law, and obtain the formation and merging-split-attack strategy of the drone cluster by solving the optimization model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images