Unmanned aerial vehicle cluster cooperative control method for target search in dynamic obstacle environment

Abstract

The invention relates to an unmanned aerial vehicle cluster cooperative control method for target search in a dynamic obstacle environment. Compared with the prior art, the unmanned aerial vehicle cluster cooperative control method solves the defects that an unmanned aerial vehicle cluster is low in target search efficiency and poor in obstacle avoidance performance in a complex and dynamic environment. The method comprises the following steps: initializing an unmanned aerial vehicle cluster; sensing a dynamic environment and acquiring obstacle information; searching a multi-subgroup collaborative optimization target of the unmanned aerial vehicle; unmanned aerial vehicle cluster path planning and obstacle avoidance processing; and updating the dispatching position of the unmanned aerial vehicle. According to the method, real-time environment perception, a data fusion technology and an obstacle avoidance mechanism based on a force field are combined, so that the safety and stability of the unmanned aerial vehicle cluster in a complex environment are ensured, and possible collision and path planning errors of the unmanned aerial vehicles in a dynamic obstacle environment are avoided; according to the path optimization method based on the cost matrix, the total time and the flight distance of task completion are effectively reduced, and the task execution efficiency is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle cluster control, and in particular to a method for cooperative control of an unmanned aerial vehicle cluster for target search in a dynamic obstacle environment. Background Art

[0002] With the rapid development of drone technology, the application of drone swarms in complex environments has gradually become a hot topic of research. Especially in dynamic obstacle environments, how drone swarms can efficiently and collaboratively complete target search tasks has become an urgent problem to be solved. In practical applications, dynamic obstacles (such as wind-driven objects, moving vehicles or people) can interfere with the flight trajectory of drones.

[0003] Existing drone target search technologies mostly focus on static environments or assume that obstacles in the environment do not change. However, in the dynamic environment of the real world, obstacles and targets usually change over time, which brings great challenges to drone target search and path planning. Traditional path planning methods often rely on static maps or preset obstacle positions. Therefore, when faced with unknown and constantly changing dynamic obstacles, effective adjustments cannot be made in real time, resulting in low search efficiency and poor task completion quality.

[0004] In order to meet this challenge, in recent years, researchers have proposed some collaborative control methods based on swarm intelligence, trying to improve the efficiency and accuracy of target search through the collaborative cooperation of multiple drones. Although these methods have solved the problem of low efficiency of single drones to a certain extent, the existing technologies still have many shortcomings in dynamic obstacle environments. Specifically, the existing drone swarm control strategies often ignore the impact of dynamic changes in the environment and cannot make fast and flexible path adjustments and decision updates based on real-time perceived obstacle information and target position changes.

[0005] In order to solve the problem of target search of drone swarm in dynamic obstacle environment, Phung et al. proposed a particle swarm algorithm based on motion coding to solve the problem of searching moving targets. Wang et al. proposed a drone swarm algorithm based on bat algorithm to detect dynamic intrusion targets in oil fields. Duan et al. proposed a dynamic discrete pigeon swarm optimization method based on pigeon swarm algorithm to solve the planning problem of drone swarm when performing search and attack tasks. Zheng et al. proposed a human-machine collaborative algorithm for the problem of fugitive capture.

[0006] Although a variety of collaborative control strategies have been proposed, how to balance the needs of target search and obstacle avoidance and maintain efficient collaboration between clusters in a dynamic environment remains a technical challenge. Existing methods often rely too much on static search patterns or focus too much on local areas, easily falling into local optimal solutions, and cannot effectively cope with the dynamic changes of targets and obstacles in complex environments.

[0007] Therefore, how to optimize the target search efficiency of drone swarms and ensure the coordination and cooperation between swarm members through flexible collaborative control strategies in complex dynamic obstacle environments is a key problem that needs to be solved in the current drone swarm target search technology. The existing technology lacks efficient and practical solutions for the collaborative control method of drone swarms in dynamic obstacle environments. Summary of the invention

[0008] The purpose of the present invention is to solve the defects of low target search efficiency and poor obstacle avoidance performance of drone clusters in complex and dynamic environments in the prior art, and to provide a drone cluster collaborative control method for target search in a dynamic obstacle environment to solve the above problems.

[0009] In order to achieve the above object, the technical solution of the present invention is as follows:

[0010] A method for cooperative control of a drone cluster for target search in a dynamic obstacle environment comprises the following steps:

[0011] Initialization of the UAV cluster: Initialize the search area according to the mission requirements, set the initial flight parameters of the UAV cluster, and establish a cluster communication network;

[0012] Dynamic environment perception and obstacle information acquisition: Use drone sensors to collect surrounding environment information in real time, generate real-time environment maps through data fusion technology, and feed back environmental data to each drone in a timely manner;

[0013] UAV multi-subgroup collaborative optimization of target search: The UAV cluster is divided into multiple subgroups, each of which is responsible for target search tasks in different areas, and the flight mission and path planning of the subgroup are dynamically adjusted according to the target distribution and environmental information;

[0014] UAV cluster path planning and obstacle avoidance: Calculate the optimal movement path for each drone, use a real-time obstacle avoidance mechanism, and adjust the path according to the information fed back by the sensors during flight;

[0015] Update of UAV dispatch position: The dispatch system performs dynamic dispatch according to the current status of the cluster. After the UAV is dispatched to the new position, the UAV cluster collaborative target search step continues until the target search task is successfully completed.

[0016] The initialization of the drone cluster includes the following steps:

[0017] According to the relative positions between the drone clusters, an initial plane coordinate system is established in the target working area. When the initial coordinate system C is set in the target working area, it is assumed that the target area R is:

[0018] R = [x min ,x max ]×[y min ,y max ],

[0019] Among them, x max 、x min and represent the upper and lower boundaries of the x-axis, respectively, max ,y min and represent the upper and lower boundaries of the y-axis respectively;

[0020] Set the initial flight parameters for each drone, including: the number of sub-drone clusters N s represents the number of sub-UAV clusters in the target search of UAV multi-sub-swarm collaborative optimization, and the historical archive size A h Indicates the size of the archive storing historical information about the environment, and the initial speed v0 of the drone;

[0021] The setting formula for initial speed and flight mode is:

[0022] v0=MinSpeed+(MaxSpeed-MinSpeed)·β(β∈[0,1]),

[0023] Among them, MinSpeed ​​and MaxSpeed ​​are the minimum and maximum speeds of the drone respectively, and β is a random factor between [0,1], which is used to simulate the randomness of speed;

[0024] It is assumed that the drone cluster communication network is implemented through the wireless communication protocol Wi-Fi, and its topology is represented by graph theory:

[0025] Each drone is considered as a node in the graph. If the distance d between two drones ij Less than the communication threshold d th , then there is an edge between the two drones, and the construction formula of the topological structure is as follows:

[0026] G=(V,E),V={1,2,…,N},E={(i,j)∣∣d ij ≤d th}

[0027] Among them, G is the graph structure of the communication network, V is all the nodes in the drone cluster, E is the connected edge set, and d ijis the distance between UAV i and UAV j, and d th is the set communication distance threshold;

[0028] According to the target search task of the drone cluster in a dynamic obstacle environment, a fitness value function f(x) is specified to represent the signal strength of the search target:

[0029] Assuming that the target signal strength is calculated based on the target position x and environmental conditions ∈, the fitness function is expressed as:

[0030]

[0031] Where S(x) is the signal strength of the target at position x, and d(x,x goal ) is from the current position x to the target position x goal ∈(x) represents the degree of environmental obstacles or interference at that location, α is the attenuation coefficient, which determines the degree of environmental impact, and x represents the regional coordinate position.

[0032] The fitness value f(x) is used to guide the adjustment of the search strategy. The stronger the target signal strength, the greater the fitness, indicating that the search priority of the area is higher;

[0033] Initialize the location set of the drone cluster:

[0034] Set the initial position set P init When the location is selected according to the regional division and the distribution of the target, it is assumed that the drone cluster is in the target area R = [x min ,x max ]×[y min ,y max ] distribution, the initialization position of each drone P i It is set by the following formula:

[0035] P init = {P1,P2,…,P N},P i =(x i ,y i ),x i ∈[x min ,x max ],y i ∈[y min ,y max ]

[0036] Where N is the number of drones in the swarm, P i is the initial position of the i-th UAV, P initThe position set is the initial state of the UAV cluster. During the task execution, the cluster dynamically adjusts its position according to the target distribution, environmental changes and task requirements;

[0037] Initialize history file A:

[0038] Put the position of a random drone cluster in the initialization drone cluster into the historical file A:

[0039] A=A∪x i ,i∈[1,N]

[0040] Historical archive A is used to save the individual positions of drones that are eliminated due to failure in target strength comparison during the iterative position update process during the execution of the drone cluster target search mission.

[0041] The dynamic environment perception and obstacle information acquisition includes the following steps:

[0042] Collect sensor data:

[0043] The sensors equipped by the drones are used to collect real-time information about changes in the surrounding environment, including other drones, static and dynamic obstacles. The sensor perception range of each drone is set to r. sensor , then the perception area of ​​each UAV i is expressed as:

[0044]

[0045] Among them, S i represents the area perceived by drone i, (x j ,y j ) is the position of the jth object, and r sensor is the maximum sensing radius of the sensor;

[0046] Classification and identification of sensor data:

[0047] According to the sensed data, different types of obstacles and target objects are identified and classified. The drone uses infrared sensors and visual sensors to identify static obstacles and targets. static and dynamic obstacles O dynamic , the target object is represented by G, and the boundary between each obstacle and the target in the environment is approximated by a circular model. The detection formula for static and dynamic obstacles is:

[0048]

[0049] If d i,j ≤r static , d i,j ≤r dynamic , it is considered that UAV i detects static or dynamic obstacle j, where rstatic and r dynamic are the safety distances of static obstacles and dynamic obstacles respectively, d i,j is the distance between the drone and the obstacle;

[0050] Data fusion technology is used to integrate the input data of multiple sensors. UAV i is equipped with K sensors for data collection. The output of sensor K is D k , then the environmental information D after sensor data fusion fusion , calculated by weighted average method,

[0051]

[0052] Among them, w k is the weight of sensor k;

[0053] Build a real-time environment map:

[0054] The fused data is used to build a real-time environment map. Each UAV i is based on its current position P i =(x i ,y i ) and the perceived obstacle information D fusion Updated environment map M env , the environment map is represented as a matrix, where each element M i,j Indicates the position (x i ,y i ) is the environmental state at which the update rules are as follows:

[0055]

[0056] Among them, M env (x i ,y i )=0 means there is no object at this location, M env (x i ,y i )=1 indicates a static obstacle, M env (x i ,y i )=2 means there is a dynamic obstacle, M env (x i ,y i )=3 means that there is a drone individual in the drone cluster at this location, M env (x i ,y i )=4 means that the position is the target object position.

[0057] The multi-subgroup collaborative optimization target search of UAVs includes the following steps:

[0058] The entire drone cluster is divided into three drone sub-groups:

[0059] Initially, the cluster is divided into three sub-clusters: exploration drone sub-cluster P1, development drone sub-cluster P2, and archive drone sub-cluster P3.

[0060] The size of the initial drone sub-cluster satisfies the following conditions: N1+N2+N3=N. The initial division of drones within the sub-cluster is completed by random selection:

[0061]

[0062] Among them, N1, N2, and N3 represent the number of drones in subgroups P1, P2, and P3, respectively. N1, N2, and N3 are positive integers. In the later stage of target search, the size of a single sub-drone cluster is allowed to be 0. k Represents the subgroup P k The drone collection included;

[0063] Set the subgroup target search strategy:

[0064] Explore UAV sub-cluster P1: Explore UAV sub-cluster for global search, generate the next generation of positions near the current UAV cluster random UAV positions, Indicates the j-th dimension coordinate value of the i-th drone in the drone sub-cluster P1 in the current t-th iteration time slice, and explores the position of each drone in the drone sub-cluster P1 The update strategy is:

[0065]

[0066] Randomly select the j-th dimension coordinate value p of the position of an individual drone in the drone cluster in the current t-th iteration time slice rj As the reference individual position, the current position is updated based on its position and random disturbance, rand is a random number in [0,1], and r is a dynamically decreasing random factor;

[0067] Develop drone sub-cluster P2: Develop drone sub-cluster responsible for local search, generating the next generation of positions around the current drone cluster's optimal signal strength position, Indicates the j-th dimension coordinate value of the i-th drone in the current t-th iteration time slice of the development drone subcluster P2, and the position of each drone The update formula is:

[0068]

[0069]

[0070] v rj=2 rand-1

[0071] Among them, d rj Indicates the j-th dimension coordinate value of the current UAV individual position and the j-th dimension coordinate value of the global optimal position g j distance, add random direction v rj The perturbation is used to enhance diversity and the updated position is in g j Randomly generate nearby and gradually converge to the optimal solution area;

[0072] Archive drone sub-cluster P3: The archive drone sub-cluster uses the historical optimal solution to search and generates the next generation of positions near the positions of individual drones that were eliminated in the previous search process. It represents the j-th dimension coordinate value of the i-th drone in the drone subcluster P3 of the current t-th iteration time slice archive. Its position update formula is:

[0073]

[0074] Among them, a rj The historical optimal solution is randomly selected from the historical archive A. The historical archive provides a stable and diverse reference by storing solutions with improved fitness over generations;

[0075] Information sharing and dynamic adjustment of subgroup size:

[0076] After obtaining the individual positions of the next generation of drones based on different strategies of the three subgroups, the drones exchange information with each other. Each drone compares its current position information with the position information of the previous time slice to determine whether it is closer to the target. If successful, it will be included in the winner drone cluster of this generation. If unsuccessful, the current position will be stored in the position history archive.

[0077] For the location set X of all drone clusters at the tth time slice t ={x1 t ,x2 t ,…,x N t}, Updated winner drone cluster S t The relationship with location history file A is expressed as:

[0078] S t ={x i t ∈X t |f(x i t )≥f(x i t-1 )}

[0079] A=A∪{x i t |xi t ∈X t and f(x i t ) <f(x i t-1 )}

[0080] Among them, f(x i t ) represents the target signal strength value of the i-th UAV in the t-th time slice;

[0081] When placing individual positions into historical archive A, if the number of individual positions exceeds the historical archive size A h , first randomly delete an individual position, and then perform the add operation:

[0082] x remove ∈A

[0083] A=(A\{x remove})∪{x new}

[0084] Among them, x remove represents a random individual position in the historical archive A, x new Indicates the individual to be placed in historical archive A.

[0085] Then, the size of the sub-UAV cluster is adjusted according to the optimization performance of the sub-UAV cluster, that is, the proportion of successful optimization of individual drones in the sub-UAV cluster:

[0086]

[0087] in, For subgroup P k The number of successfully optimized drones, R k is the optimization performance of the kth UAV subgroup in this iteration. According to the optimization performance R k , dynamically adjust the t+1 generation of sub-drone cluster P k t+1 The number of drone individuals N k t+1 , the total number of drones N remains unchanged, To dynamically adjust the t-th generation sub-UAV cluster P k t The number of drone individuals in R i is the optimization performance of the kth drone subgroup in this iteration.

[0088] The UAV cluster path planning and obstacle avoidance process includes the following steps:

[0089] After determining the next generation of detection locations of the drone cluster, dispatch the drones to the corresponding locations and determine the movement path of each drone:

[0090] The goal planning is to minimize the moving distance of the UAV and the time to complete the task. The multiple cost functions in the path planning are combined and optimized in a weighted manner to optimize the objective function F. d as follows:

[0091] F d =min{a·γ+(1-a)·Δ}

[0092] Among them, a represents the weighting coefficient, γ represents the total moving distance of the drone cluster after completing the entire task, and Δ represents the maximum moving time of the drone cluster after completing the task. The specific variables are defined as follows:

[0093]

[0094]

[0095] in, represents the moving distance of the i-th UAV in the t-th generation, Z(t) represents the total moving distance of all UAVs in the t-th generation, represents the moving time of the i-th UAV in the t-th generation, T span (t) represents the maximum movement time of all drones in the tth generation, and N is the number of drones;

[0096] In the path planning process, the cost matrix F(i,j) is used to represent the i-th UAV from its current position S i (t) to the target position X j The comprehensive cost of (t+1) is calculated as follows:

[0097] F(i,j)=a·μ ij +(1-a)·θ ij

[0098]

[0099] Among them, μ ij is the normalized distance, indicating the length of the path, γ max represents the maximum moving distance of the previous generation of drones, θ ij is the normalized time cost, indicating the time required to complete the path, Δ max represents the maximum moving time of the previous generation of drones, v i represents the flight speed of the i-th UAV, D(S i (t),X j(t+1 represents the distance from the current position Sit to the target position Xjt+1 of the i-th UAV in the t-th generation;

[0100] Based on the generated cost matrix F(i,j), the optimal moving path of each drone in the drone cluster in this time slice is obtained;

[0101] During the movement of the drone, a real-time obstacle avoidance processing mechanism based on the force field model is used to adjust the path according to the information fed back by the sensors during the flight:

[0102] When the distance between a drone and other drones or obstacles is less than the set safety radius d, a virtual repulsion field is generated to make the drone deviate from the dangerous area. The repulsion model U rep,ij for:

[0103]

[0104] Among them, d ij is the distance between the i-th UAV and the j-th obstacle or the j-th obstacle,

[0105] The total repulsion of a drone is the vector sum of its repulsions with all other drones and obstacles:

[0106]

[0107] Among them, F i (t) is the total repulsion of the ith UAV in the tth generation, r ij and r io are the unit vectors between the UAV and other UAVs and obstacles,

[0108] When considering collision avoidance between drones, a fitness value priority mechanism is adopted, and the formula is expressed as:

[0109]

[0110] Among them, f(x i ) represents the signal strength of the individual position of the i-th UAV, and the fraction represents the degree of avoidance of the UAV with poor fitness to the UAV with good fitness, U rep,ij It is a repulsion model that adopts the fitness value priority mechanism.

[0111] The updating of the UAV dispatching position includes the following steps:

[0112] Dynamic scheduling based on the current status of the cluster:

[0113] Assume that the current position of the i-th drone is P i (t), the task completion degree is C i(t), the obstacle information is O(t), and the scheduling system dynamically calculates the next position through the following process;

[0114] Calculate the scheduling weight W of each drone i (t), the expression is:

[0115] W i (t) = α·f pos (p i (t))+β·f task (C i (t))+γ·f obs (O(t))

[0116] Among them, α, β, γ are weight coefficients, f pos 、f task , and f obs They represent the influence functions of position, task completion and obstacle information on UAV scheduling respectively;

[0117] f pos (P i (t))=∥P i (t)-P target (t)∥,f task (C i (t)) = 1-C i (t),

[0118]

[0119] Among them, P target (t) represents the current target position, P o Represents the position of obstacles. Through this weight function, the scheduling system comprehensively considers different task requirements and dynamically adjusts the position of the UAV so that it always moves in the optimal direction for the task.

[0120] Continue the UAV cluster collaborative target search steps until the search mission is successfully completed:

[0121] Assuming that the current time is t, the state of target search is S(t), the target search process of the drone cluster is expressed as:

[0122]

[0123] Among them, v i (t) is the velocity vector of the i-th UAV at time t. When the mission reaches the predetermined target state S goal When the target search task is completed, S(t)→S goal .

[0124] A computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for cooperative control of a drone cluster for target search in a dynamic obstacle environment as described in any one of claims 1 to 6 is implemented.

[0125] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes, a drone cluster collaborative control method for target search in a dynamic obstacle environment as described in any one of claims 1 to 6 is implemented.

[0126] Beneficial Effects

[0127] Compared with the prior art, the collaborative control method of a drone cluster for target search in a dynamic obstacle environment of the present invention combines real-time environmental perception, data fusion technology and an obstacle avoidance mechanism based on force field, thereby ensuring the safety and stability of the drone cluster in a complex environment and avoiding possible collisions and path planning errors of drones in a dynamic obstacle environment; the path optimization method based on the cost matrix effectively reduces the total time and flight distance for task completion, further improving the efficiency of task execution.

[0128] The present invention fully considers the changes of obstacles and targets in dynamic environments through a multi-subgroup collaborative optimization strategy. It can optimize the target search efficiency of drone clusters based on real-time perception and flexible decision-making, avoiding the limitations of existing methods that rely on static maps and preset obstacle positions.

[0129] The present invention uses dynamic subgroup division and collaborative control mechanisms to ensure that the drone cluster can efficiently divide the work and cooperate in the face of complex environments, and timely adjust the scale and task allocation of each subgroup through the information sharing mechanism, thereby achieving collaborative cooperation and global optimization within the cluster. Compared with traditional path planning methods, the present invention can flexibly respond to the impact of dynamic obstacles, and quickly update the flight path and mission strategy, thereby improving the success rate and efficiency of the search mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Figure 1 is a method sequence diagram of the present invention;

[0131] Figure 2 Schematic diagram of dynamic obstacles for forest fire rescue target search mission;

[0132] Figure 3 Schematic diagram of the environment model for forest fire rescue target search mission;

[0133] Figure 4 Provides real-time environment map for target search missions;

[0134] Figure 5 Schematic diagram of the UAV movement path planning;

[0135] Figure 6 This is a schematic diagram of the force field model for collision and obstacle avoidance of UAV swarms;

[0136] Figure 7a A distribution map of the first generation positions of the drone cluster using the method of the present invention;

[0137] Figure 7b A distribution map of the fifth generation positions of the drone cluster using the method of the present invention;

[0138] Figure 7c This is a distribution map of the tenth generation positions of a drone cluster using the method of the present invention. DETAILED DESCRIPTION

[0139] In order to have a further understanding and recognition of the structural features and the effects achieved by the present invention, a preferred embodiment and accompanying drawings are used for detailed description as follows:

[0140] like Figure 1 As shown, the UAV cluster collaborative control method for target search in a dynamic obstacle environment described in the present invention includes the following steps:

[0141] The first step is to initialize the drone cluster: initialize the search area according to the mission requirements, set the initial flight parameters of the drone cluster, and establish a cluster communication network. The initialization of the drone cluster is the basis for mission execution. It is necessary to model the search area according to the complex environment, set flight parameters, and build a dynamic communication network to ensure that the cluster can collaborate efficiently, adapt to the dynamic environment, and complete the mission safely and reliably.

[0142] (1) According to the relative positions between the UAV clusters, an initial plane coordinate system is established in the target working area. When the initial coordinate system C is set in the target working area, it is assumed that the target area R is:

[0143] R = [x min ,x max ]×[y min ,y max ],

[0144] Among them, x max 、x min and represent the upper and lower boundaries of the x-axis, y max ,y min and represent the upper and lower boundaries of the y-axis respectively.

[0145] (2) Set the initial flight parameters of each drone, including: the number of sub-drone clusters N srepresents the number of sub-UAV clusters in the target search of UAV multi-sub-swarm collaborative optimization, and the historical archive size A h Indicates the size of the archive storing historical environmental information and the initial speed v0 of the drone. These parameters will affect the flight strategy, path planning, and cluster collaborative operation of the drone.

[0146] When setting the initial speed v0 of the drone, the area of ​​the target area, environmental conditions, and the urgency of the cluster mission are considered. The setting formula for the initial speed and flight mode is:

[0147] v0=MinSpeed+(MaxSpeed-MinSpeed)·β(β∈[0,1]),

[0148] Among them, MinSpeed ​​and MaxSpeed ​​are the minimum and maximum speeds of the drone respectively, and β is a random factor between [0,1] used to simulate the randomness of speed.

[0149] (3) It is assumed that the drone cluster communication network is implemented through the wireless communication protocol Wi-Fi, and its topological structure is represented by graph theory:

[0150] The construction of cluster communication network is the basis for ensuring the coordinated operation of drone clusters. In order to ensure that drones can coordinate with each other and exchange information, the network topology needs to be dynamically adjusted according to the relative position of each drone. Each drone is regarded as a node in the graph. If the distance d between two drones is ij Less than the communication threshold d th , then there is an edge between the two drones, and the construction formula of the topological structure is as follows:

[0151] G=(V,E),V={1,2,…,N},E={(i,j)∣∣d ij ≤d th}

[0152] Among them, G is the graph structure of the communication network, v is all the nodes in the drone cluster, E is the connected edge set, and d ij is the distance between UAV i and UAV j, and d th is the communication distance threshold.

[0153] (4) According to the target search task of the drone cluster in a dynamic obstacle environment, a fitness value function f(x) is specified to represent the signal strength of the search target.

[0154] like Figure 2 This is a schematic diagram of dynamic obstacles in the forest fire rescue target search mission. Figure 3The figure is a schematic diagram of the forest fire rescue target search task environment model. According to the forest fire rescue target search environment model, a fitness value function f(x) is specified.

[0155] x represents the coordinate position of the region. This function is evaluated based on the target signal strength. The stronger the target signal strength, the greater the fitness value of the region. Assuming that the target signal strength is calculated based on the target position x and the environmental condition ∈, the fitness function is expressed as:

[0156]

[0157] Where S(x) is the signal strength of the target at position x, and d(x,x goal ) is from the current position x to the target position x goal ∈(x) represents the degree of environmental obstacles or interference at that location, α is the attenuation coefficient, which determines the degree of environmental impact, and x represents the regional coordinate position.

[0158] The fitness value f(x) is used to guide the adjustment of the search strategy. The stronger the target signal strength, the greater the fitness, indicating that the search priority of the area is higher.

[0159] (5) Initialize the location set of the drone cluster:

[0160] Initialize the position set of the drone cluster. The position initialization of the drone cluster is allocated according to the mission area and target location. Set the initial position set P init When the location is selected according to the regional division and the distribution of the target, it is assumed that the drone cluster is in the target area R = [x min ,x max ]×[y min ,y max ] distribution, the initialization position of each drone P i It is set by the following formula:

[0161] P init = {P1,P2,…,P N},P i =(x i ,y i ),x i ∈[x min ,x max ],y i ∈[y min ,y max ]

[0162] Where N is the number of drones in the swarm, P i is the initial position of the i-th UAV, P initThe position set is the initial state of the UAV cluster. During the mission execution, the cluster dynamically adjusts its position according to target distribution, environmental changes and mission requirements.

[0163] (6) Initialize historical file A:

[0164] Put the position of a random drone cluster in the initialization drone cluster in the historical file A:

[0165] A=A∪x i ,i∈[1,N]

[0166] Historical archive A is used to save the individual positions of drones that are eliminated due to failure in target strength comparison during the iterative position update process during the execution of the drone cluster target search mission.

[0167] The second step is dynamic environment perception and obstacle information acquisition: use drone sensors to collect surrounding environment information in real time, generate real-time environment maps through data fusion technology, and promptly feed back environmental data to each drone. Dynamic environment perception and obstacle information acquisition are the core links of autonomous flight of drone clusters, which can ensure the safe flight of drones in complex and changing environments, improve the accuracy of path planning and task execution, and provide high-quality environmental data support for cluster collaboration. It is necessary to solve the real-time and accuracy problems of sensor data, handle the fusion and error correction of multiple sensor data, and efficiently generate dynamic environment maps with limited computing resources.

[0168] (1) Collect sensor data:

[0169] The sensors equipped by the drones are used to collect real-time information about changes in the surrounding environment, including other drones, static and dynamic obstacles. The sensor perception range of each drone is set to r. sensor , then the perception area of ​​each UAV i is expressed as:

[0170]

[0171] Among them, S i represents the area perceived by drone i, (x j ,y j ) is the position of the jth object, and r sensor is the maximum sensing radius of the sensor.

[0172] (2) Classification and identification of sensor data:

[0173] According to the perceived data, different types of obstacles and target objects are identified and classified. The drone uses infrared sensors and visual sensors to identify static obstacles and targets. static and dynamic obstacles O dynamic, the target object is represented by G, and the boundary between each obstacle and the target in the environment is approximated by a circular model. The detection formula for static and dynamic obstacles is:

[0174]

[0175] If d i,j ≤r static , d i,j ≤r dynamic , it is considered that UAV i detects static or dynamic obstacle j, where r static and r dynamic are the safety distances of static obstacles and dynamic obstacles respectively, d i,j is the distance between the drone and the obstacle.

[0176] (3) Data fusion technology is used to integrate the input data of multiple sensors. UAV i is equipped with K sensors for data collection. The output of sensor K is D k , then the environmental information after sensor data fusion D fusion , calculated by weighted average method,

[0177]

[0178] Among them, w k is the weight of sensor k.

[0179] (4) Build a real-time environment map:

[0180] The fused data is used to build a real-time environment map. Each UAV i is based on its current position P i =(x i ,y i ) and the perceived obstacle information D fusion Updated environment map M env , the environment map is represented as a matrix, where each element M i,j Indicates the position (x i ,y i ) is the environmental state at which the update rules are as follows:

[0181]

[0182] Among them, M env (x i ,y i )=0 means there is no object at this location, M env (x i ,y i )=1 indicates a static obstacle, M env (x i ,y i)=2 means there is a dynamic obstacle, M env (x i ,y i )=3 means that there is a drone individual in the drone cluster at this location, M env (x i ,y i )=4 means that the position is the target object position, Figure 4 It is a real-time environment schematic map for target search tasks.

[0183] The third step is to coordinate and optimize target search among multiple subgroups of drones: divide the drone cluster into multiple subgroups, each of which is responsible for target search tasks in different areas. Dynamically adjust the flight mission and path planning of the subgroup according to the target distribution and environmental information. Coordinated optimization of multiple subgroups of drones can improve the efficiency of task execution, achieve comprehensive coverage of the target area through task division and dynamic adjustment, reduce resource waste, and enhance the flexibility and adaptability of drone clusters in complex environments. It is necessary to solve the global optimization problem of subgroup division and task allocation, balance the task load among subgroups, ensure real-time adjustment in a dynamic environment, and achieve efficient collaboration.

[0184] (1) Divide the entire drone cluster into three drone sub-groups:

[0185] Initially, the cluster is divided into three sub-clusters: exploration drone sub-cluster P1, development drone sub-cluster P2, and archive drone sub-cluster P3.

[0186] The size of the initial drone sub-cluster satisfies the following conditions: N1+N2+N3=N. The initial division of drones within the sub-cluster is completed by random selection:

[0187]

[0188] Among them, N1, N2, and N3 represent the number of drones in subgroups P1, P2, and P3, respectively. N1, N2, and N3 are positive integers. In the later stage of target search, the size of a single sub-drone cluster is allowed to be 0. k Represents the subgroup P k A collection of drones included.

[0189] (2) Setting subgroup target search strategy:

[0190] The flight mission and path planning of each sub-group will be dynamically adjusted according to the target distribution and environmental information to ensure the optimality of the overall search strategy of the cluster.

[0191] Explore UAV sub-cluster P1: Explore UAV sub-cluster for global search, generate the next generation of positions near the current UAV cluster random UAV positions, Indicates the j-th dimension coordinate value of the i-th drone in the drone sub-cluster P1 in the current t-th iteration time slice, and explores the position of each drone in the drone sub-cluster P1 The update strategy is:

[0192]

[0193] Randomly select the j-th dimension coordinate value p of the position of an individual drone in the drone cluster in the current t-th iteration time slice rj As the reference individual position, the current position is updated based on its position and random disturbance, rand is a random number in [0,1], and r is a dynamically decreasing random factor;

[0194] Develop drone sub-cluster P2: Develop drone sub-cluster responsible for local search, generating the next generation of positions around the current drone cluster's optimal signal strength position, Indicates the j-th dimension coordinate value of the i-th drone in the current t-th iteration time slice of the development drone subcluster P2, and the position of each drone The update formula is:

[0195]

[0196] Among them, d rj Indicates the j-th dimension coordinate value of the current UAV individual position and the j-th dimension coordinate value of the global optimal position g j distance, add random direction v rj The perturbation is used to enhance diversity and the updated position is in g j Randomly generate nearby and gradually converge to the optimal solution area;

[0197] Archive drone sub-cluster P3: The archive drone sub-cluster uses the historical optimal solution to search and generates the next generation of positions near the positions of individual drones that were eliminated in the previous search process. It represents the j-th dimension coordinate value of the i-th drone in the drone subcluster P3 of the current t-th iteration time slice archive. Its position update formula is:

[0198]

[0199] Among them, a rj The historical optimal solutions randomly selected from the historical archive A provide a stable and diverse reference by storing solutions with improved fitness over the generations.

[0200] (3) Information sharing and dynamic adjustment of subgroup size:

[0201] After obtaining the individual positions of the next generation of drones based on different strategies of the three subgroups, the drones exchange information with each other. Each drone compares its current position information with the position information of the previous time slice to determine whether it is closer to the target. If successful, it will be included in the winner drone cluster of this generation. If unsuccessful, the current position will be stored in the position history archive.

[0202] For the location set X of all drone clusters at the tth time slice t ={x1 t ,x2 t ,…,x N t}, Updated winner drone cluster S t The relationship with location history file A is expressed as:

[0203] S t ={x i t ∈X t |f(x i t )≥f(x i t-1 )}

[0204] A=A∪{x i t |x i t ∈X t and f(x i t ) <f(x i t-1 )}

[0205] Among them, f(x i t ) represents the target signal strength value of the i-th UAV in the t-th time slice;

[0206] When placing individual positions into historical archive A, if the number of individual positions exceeds the historical archive size A h , first randomly delete an individual position, and then perform the add operation:

[0207] x remove ∈A

[0208] A=(A\{x remove})∪{x new}

[0209] Among them, x remove represents a random individual position in the historical archive A, x new Indicates the individual to be placed in historical archive A.

[0210] Then, the size of the sub-UAV cluster is adjusted according to the optimization performance of the sub-UAV cluster, that is, the proportion of successful optimization of individual drones in the sub-UAV cluster:

[0211]

[0212] in, For subgroup P k The number of successfully optimized drones, R k is the optimization performance of the kth UAV subgroup in this iteration. According to the optimization performance R k , dynamically adjust the t+1 generation of sub-drone cluster P k t+1 The number of drone individuals N k t+1 , the total number of drones N remains unchanged, To dynamically adjust the t-th generation of sub-UAV cluster P k t The number of drone individuals in R i is the optimization performance of the kth drone subgroup in this iteration.

[0213] The fourth step is the path planning and obstacle avoidance of the UAV cluster: calculate the optimal moving path for each UAV, adopt a real-time obstacle avoidance mechanism, and adjust the path according to the information fed back by the sensor during the flight. The path planning and obstacle avoidance of the UAV cluster is the key link to ensure the smooth execution of the mission. Through the optimal path calculation and real-time obstacle avoidance, it can improve the flight efficiency and reduce energy consumption, while ensuring the safety and mission execution capabilities of the UAV in complex and dynamic environments; it is necessary to cope with the high computational complexity of real-time path updates in dynamic environments and solve the path conflicts and coordination problems between UAVs.

[0214] (1) After determining the next generation detection location of the drone cluster, dispatch the drones to the corresponding locations and determine the movement path of each drone:

[0215] The planning objectives mainly include the following two aspects: minimizing the moving distance of the UAV, which is directly related to energy consumption and equipment wear, and minimizing the time to complete the task, which is particularly important for scenarios with high mission urgency. The goal planning of minimizing the moving distance of the UAV and minimizing the time to complete the task is carried out. Multiple cost functions in path planning are combined and optimized in a weighted manner to optimize the objective function F. d as follows:

[0216] F d =min{a·γ+(1-a)·Δ}

[0217] Among them, a represents the weighting coefficient, γ represents the total moving distance of the drone cluster after completing the entire task, and Δ represents the maximum moving time of the drone cluster after completing the task. The specific variables are defined as follows:

[0218]

[0219] in, represents the moving distance of the i-th UAV in the t-th generation, Z(t) represents the total moving distance of all UAVs in the t-th generation, represents the moving time of the i-th UAV in the t-th generation, T span (t) represents the maximum movement time of all drones in the tth generation, and N is the number of drones;

[0220] In the path planning process, the cost matrix F(i,j) is used to represent the i-th UAV from its current position S i (t) to the target position X j The comprehensive cost of (t+1) is calculated as follows:

[0221] F(i,j)=a·μ ij +(1-a)·θ ij

[0222]

[0223] Among them, μ ij is the normalized distance, indicating the length of the path, γ max represents the maximum moving distance of the previous generation of drones, θ ij is the normalized time cost, indicating the time required to complete the path, Δ max represents the maximum moving time of the previous generation of drones, v i represents the flight speed of the i-th UAV, D(S i (t),X j (t+1 represents the distance from the current position Sit to the target position Xjt+1 of the i-th UAV in the t-th generation;

[0224] Based on the generated cost matrix F(i,j), the optimal moving path of each drone in the drone cluster in this time slice is obtained. The schematic diagram of drone moving path planning is shown in Figure 5 shown.

[0225] (2) A real-time obstacle avoidance mechanism based on the force field model is used during the movement of the UAV, and the path is adjusted according to the information fed back by the sensors during the flight:

[0226] When the distance between a drone and other drones or obstacles is less than the set safety radius d, a virtual repulsion field is generated to make the drone deviate from the dangerous area. The repulsion model Urep,ij for:

[0227]

[0228] Among them, d ij is the distance between the i-th UAV and the j-th obstacle or the j-th obstacle. The schematic diagram of the force field model of the UAV cluster collision avoidance and obstacle avoidance is as follows: Figure 6 shown.

[0229] The total repulsion of a drone is the vector sum of its repulsions with all other drones and obstacles:

[0230]

[0231] Among them, F i (t) is the total repulsion of the ith UAV in the tth generation, r ij and r io are the unit vectors between the UAV and other UAVs and obstacles,

[0232] When considering collision avoidance between drones, a fitness value priority mechanism is adopted, and the formula is expressed as:

[0233]

[0234] Among them, f(x i ) represents the signal strength of the individual position of the i-th UAV, and the fraction represents the degree of avoidance of the UAV with poor fitness to the UAV with good fitness, U rep,ij It is a repulsion model that adopts the fitness value priority mechanism.

[0235] Step 5: Update the UAV dispatch position: The dispatch system dynamically dispatches the UAV according to the current state of the cluster. After the UAV is dispatched to the new position, the UAV cluster collaborative target search step continues until the target search task is successfully completed. The update of the UAV dispatch position is the core link to achieve dynamic cluster response and optimize resource utilization. The distribution of UAVs can be adjusted according to the real-time status to avoid resource waste and omission of mission areas, and improve the comprehensiveness, efficiency and accuracy of target search. It is necessary to solve the real-time optimization problem of UAV position scheduling in a dynamic environment, balance the task priority, UAV coverage efficiency and obstacle avoidance, and deal with communication delays, state feedback uncertainty and collaborative conflicts between multiple UAVs.

[0236] (1) Dynamic scheduling based on the current state of the cluster:

[0237] The cluster status includes factors such as the current location of the drone, the degree of completion of the task, and information about obstacles. The scheduling system calculates the optimal scheduling order for each drone in real time to ensure the efficiency and safety of the cluster when performing tasks. Assuming that the current location of the i-th drone is P i (t), the task completion degree is C i (t), the obstacle information is O(t), and the scheduling system dynamically calculates the next position through the following process;

[0238] Calculate the scheduling weight W of each drone i (t), the expression is:

[0239] W i (t) = α·f pos (P i (t))+β·f task (C i (t))+γ·f obs (O(t))

[0240] Among them, α, β, γ are weight coefficients, f pos 、f task , and f obs They represent the influence functions of position, task completion and obstacle information on UAV scheduling respectively;

[0241] f pos (P i (t)0=∥P i (t)-P target (t)∥,f task (C i (t)) = 1-C i (t),

[0242]

[0243] Among them, P target (t) represents the current target position, P o Represents the position of obstacles. Through this weight function, the scheduling system comprehensively considers different task requirements and dynamically adjusts the position of the UAV so that it always moves in the optimal direction for the task.

[0244] like Figure 7a , 7b 7c shows the distribution diagram of individual drones in the drone cluster at three different moments during the target search task. The first generation of drone clusters starts the target search and determines the initial position of each drone individual. Then the drone cluster starts the target search and position update based on multi-subgroup collaborative optimization. Figure 7c As shown, the drone has locked onto the target area.

[0245] (2) Continue the UAV cluster collaborative target search steps until the search mission is successfully completed:

[0246] Assuming that the current time is t, the state of target search is S(t), the target search process of the drone cluster is expressed as:

[0247]

[0248] Among them, v i (t) is the velocity vector of the i-th UAV at time t. When the mission reaches the predetermined target state S goal When the target search task is completed, S(t)→S goal .

[0249] Here, a computer readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, a method for cooperative control of a drone cluster for target search in a dynamic obstacle environment can be implemented. Here, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes, a method for cooperative control of a drone cluster for target search in a dynamic obstacle environment can be implemented.

[0250] In order to efficiently solve the problem of target search and collaborative control of drone clusters in dynamic obstacle environments, the present invention proposes a "target search and collaborative control method for drone clusters in dynamic obstacle environments". This method fully considers the dynamic changes of targets and obstacles in the environment through multi-subgroup collaborative optimization strategies and real-time dynamic adjustment mechanisms, and realizes the target search and obstacle avoidance functions of drone clusters in complex environments. Combined with dynamic subgroup division and information sharing technology, it can flexibly adjust task allocation according to environmental changes, ensuring that drone clusters can still efficiently perform tasks in the face of emergencies.

[0251] This method avoids the limitations of traditional methods that rely on static models by integrating environmental perception, data fusion and force field obstacle avoidance technology, allowing drone clusters to safely and stably search for targets and plan paths in dynamic environments. Combined with cost matrix optimization paths, the efficiency of task execution is further improved, flight distance and time are reduced, and the overall coordination ability of drone clusters is enhanced.

[0252] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for cooperative control of a drone cluster for target search in a dynamic obstacle environment, characterized in that: The following steps are involved: 11) Initialization of the UAV cluster: Initialize the search area according to the mission requirements, set the initial flight parameters of the UAV cluster, and establish the cluster communication network; 12) Dynamic environment perception and obstacle information acquisition: Use drone sensors to collect surrounding environment information in real time, generate real-time environment maps through data fusion technology, and feed back environmental data to each drone in a timely manner; 13) UAV multi-subgroup collaborative optimization of target search: The UAV cluster is divided into multiple subgroups, each of which is responsible for target search tasks in different areas, and the flight mission and path planning of the subgroup are dynamically adjusted according to the target distribution and environmental information; 14) UAV cluster path planning and obstacle avoidance: Calculate the optimal movement path for each UAV, use a real-time obstacle avoidance mechanism, and adjust the path according to the information fed back by the sensors during flight; 15) Update of UAV dispatch position: The dispatch system performs dynamic dispatch according to the current status of the cluster. After the UAV is dispatched to the new position, the UAV cluster collaborative target search step continues until the target search task is successfully completed.

2. The method for cooperative control of a drone cluster for target search in a dynamic obstacle environment according to claim 1 is characterized in that: The initialization of the drone cluster includes the following steps: 21) According to the relative positions between the drone clusters, an initial plane coordinate system is established in the target working area. When the initial coordinate system C is set in the target working area, it is assumed that the target area R is: R=[x min ,x max ]×[y min ,y max ], Among them, x max 、x min and represent the upper and lower boundaries of the x-axis, y max ,y min and represent the upper and lower boundaries of the y-axis respectively; 22) Set the initial flight parameters of each drone, including: the number of sub-drone clusters N s represents the number of sub-UAV clusters in the target search of UAV multi-sub-swarm collaborative optimization, and the historical archive size a h Indicates the size of the archive storing historical information about the environment, and the initial speed v0 of the drone; The setting formula for initial speed and flight mode is: v0=MinSpeed+(MaxSpeed-MinSpeed)·β (β∈[0,1]), Among them, MinSpeed ​​and MaxSpeed ​​are the minimum and maximum speeds of the drone respectively, and β is a random factor between [0,1], which is used to simulate the randomness of speed; 23) The drone cluster communication network is assumed to be implemented through the wireless communication protocol Wi-Fi, and its topology is represented by graph theory: Each drone is considered as a node in the graph. If the distance d between two drones ij Less than the communication threshold d th , then there is an edge between the two drones, and the construction formula of the topological structure is as follows: G=(V,E),V={1,2,…,N},W={(i,j)∣∣d ij ≤d th } Among them, G is the graph structure of the communication network, V is all the nodes in the drone cluster, E is the connected edge set, and d ij is the distance between UAV i and UAV j, and d th is the set communication distance threshold; 24) According to the target search task of the drone cluster in the dynamic obstacle environment, a fitness value function f(x) is specified to represent the signal strength of the search target: Assuming that the target signal strength is calculated based on the target position x and environmental conditions ∈, the fitness function is expressed as: Where S(x) is the signal strength of the target at position x, and d(x,x goal ) is from the current position x to the target position x goal ∈(x) represents the degree of environmental obstacles or interference at that location, α is the attenuation coefficient, which determines the degree of environmental impact, and x represents the regional coordinate position. The fitness value f(x) is used to guide the adjustment of the search strategy. The stronger the target signal strength, the greater the fitness, indicating that the search priority of the area is higher; 25) Initialize the location set of the drone cluster: Set the initial position set P init When the location is selected according to the regional division and the distribution of the target, it is assumed that the drone cluster is in the target area R = [x min ,x max ]×[y min ,y max ] distribution, the initialization position of each drone P i It is set by the following formula: P init ={P1,P2,…,P N },P i =(x i ,y i ),x i ∈[x min ,x maz ],y i ∈[y min ,y max ] Where N is the number of drones in the swarm, P i is the initial position of the i-th UAV, P init The position set is the initial state of the UAV cluster. During the task execution, the cluster dynamically adjusts its position according to target distribution, environmental changes and task requirements; 26) Initialize history file A: Put the position of a random drone cluster in the initialization drone cluster in the historical file A: A=A∪x i ,i∈[1,N] Historical archive A is used to save the individual positions of drones that are eliminated due to failure in target strength comparison during the iterative position update process during the execution of the drone cluster target search mission.

3. The method for cooperative control of a drone cluster for target search in a dynamic obstacle environment according to claim 1 is characterized in that: The dynamic environment perception and obstacle information acquisition includes the following steps: 31) Collect sensor data: The sensors equipped by the drones are used to collect real-time information about changes in the surrounding environment, including other drones, static and dynamic obstacles. The sensor perception range of each drone is set to r. sensor , then the perception area of ​​each UAV i is expressed as: Among them, S i represents the area perceived by drone i, (x j ,y j ) is the position of the jth object, and r sensor is the maximum sensing radius of the sensor; 32) Classification and identification of sensor data: According to the perceived data, different types of obstacles and target objects are identified and classified. The drone uses infrared sensors and visual sensors to identify static obstacles and targets. static and dynamic obstacles O dynamic , the target object is represented by G, and the boundary between each obstacle and the target in the environment is approximated by a circular model. The detection formula for static and dynamic obstacles is: If d i,j ≤r static , d i,j ≤r dynamic , it is considered that UAV i detects static or dynamic obstacle j, where r static and r dynamic are the safety distances of static obstacles and dynamic obstacles respectively, d i,j is the distance between the drone and the obstacle; 33) Data fusion technology is used to integrate the input data of multiple sensors. UAV i is equipped with K sensors for data collection. The output of sensor K is D k , then the environmental information D after sensor data fusion fusion , calculated by weighted average method, Among them, w k is the weight of sensor k; 34) Build real-time environment map: The fused data is used to build a real-time environment map, and each drone i is based on its current position P i =(x i ,y i ) and the perceived obstacle information D fusion Updated environment map M env , the environment map is represented as a matrix, where each element M i,j Indicates the position (x i ,y i ) is the environmental state at which the update rules are as follows: Among them, M env (x i ,y i )=0 means there is no object at this location, M env (x i ,y i )=1 indicates a static obstacle, M env (x i ,y i )=2 means there is a dynamic obstacle, M env (x i ,y i )=3 means that there is a drone individual in the drone cluster at this location, M env (x i ,y i )=4 means that the position is the target object position.

4. The method for cooperative control of a drone cluster for target search in a dynamic obstacle environment according to claim 1 is characterized in that: The multi-subgroup collaborative optimization target search of UAVs includes the following steps: 41) Divide the entire drone cluster into three drone sub-groups: Initially, the cluster is divided into three sub-clusters: exploration drone sub-cluster P1, development drone sub-cluster P2, and archive drone sub-cluster P3. The size of the initial drone sub-cluster satisfies the following conditions: N1+N2+N3=N. The initial division of drones within the sub-cluster is completed by random selection: Among them, N1, N2, and N3 represent the number of drones in subgroups P1, P2, and P3, respectively. N1, N2, and N3 are positive integers. The size of a single sub-UAV cluster is allowed to be 0 in the later stage of target search. k Represents the subgroup P k The drone collection included; 42) Set subgroup target search strategy: Explore UAV sub-cluster P1: Explore UAV sub-cluster for global search, generate the next generation of positions near the current UAV cluster random UAV positions, Indicates the j-th dimension coordinate value of the i-th drone in the drone sub-cluster P1 in the current t-th iteration time slice, and explores the position of each drone in the drone sub-cluster P1 The update strategy is: Randomly select the j-th dimension coordinate value p of the position of an individual drone in the drone cluster in the current t-th iteration time slice rj As the reference individual position, the current position is updated based on its position and random disturbance, rand is a random number in [0,1], and r is a dynamically decreasing random factor; Develop drone sub-cluster P2: Develop drone sub-cluster responsible for local search, generating the next generation of positions around the current drone cluster's optimal signal strength position, Indicates the j-th dimension coordinate value of the i-th drone in the current t-th iteration time slice of the development drone subcluster P2, and the position of each drone The update formula is: V rj =2·row-1 Among them, d rj Indicates the j-th dimension coordinate value of the current UAV individual position and the j-th dimension coordinate value of the global optimal position g j distance, add random direction v rj The perturbation is used to enhance diversity and the updated position is in g j Randomly generate nearby and gradually converge to the optimal solution area; Archive drone sub-cluster P3: The archive drone sub-cluster uses the historical optimal solution to search and generates the next generation of positions near the positions of individual drones that were eliminated in the previous search process. It represents the j-th dimension coordinate value of the i-th drone in the drone subcluster P3 of the current t-th iteration time slice archive. Its position update formula is: Among them, a rj The historical optimal solution is randomly selected from the historical archive A. The historical archive provides a stable and diverse reference by storing solutions with improved fitness over generations; 43) Information sharing and dynamic adjustment of subgroup size: After obtaining the individual positions of the next generation of drones based on different strategies of the three subgroups, the drones exchange information with each other. Each drone compares its current position information with the position information of the previous time slice to determine whether it is closer to the target. If successful, it will be included in the winner drone cluster of this generation. If unsuccessful, the current position will be stored in the position history archive. For the location set X of all drone clusters at the tth time slice t ={x1 t ,x2 t ,…,x N t }, Updated winner drone cluster S t The relationship with location history file A is expressed as: S t ={x i t ∈X t |f(x i t )≥f(x i t-1 )} A=A∪{x i t |x i t ∈X t and f(x i t )<f(x i t-1 )} Among them, f(x i t ) represents the target signal strength value of the i-th UAV at the t-th time slice; When placing individual positions into historical archive A, if the number of individual positions exceeds the historical archive size A h , first randomly delete an individual position, and then perform the add operation: x remove ∈A A=(A\{x remove })∪{x new } Among them, x remove represents a random individual position in the historical archive A, x new Indicates the individual to be placed in historical archive A. Then, the size of the sub-UAV cluster is adjusted according to the optimization performance of the sub-UAV cluster, that is, the proportion of successful optimization of individual drones in the sub-UAV cluster: in, For subgroup P k The number of successfully optimized drones, R k is the optimization performance of the kth UAV subgroup in this iteration. According to the optimization performance R k , dynamically adjust the t+1 generation of sub-drone cluster P k t+1 The number of drone individuals N k t+1 , the total number of drones N remains unchanged, To dynamically adjust the t-th generation of sub-UAV cluster P k t The number of drone individuals in R i is the optimization performance of the kth drone subgroup in this iteration.

5. The method for cooperative control of a drone cluster for target search in a dynamic obstacle environment according to claim 1 is characterized in that: The UAV cluster path planning and obstacle avoidance process includes the following steps: 51) After determining the next generation detection location of the drone cluster, dispatch the drones to the corresponding locations and determine the movement path of each drone: The goal planning is to minimize the moving distance of the UAV and the time to complete the task. The multiple cost functions in the path planning are combined and optimized in a weighted manner to optimize the objective function F. d as follows: F d =min{a·γ+(1-a)·Δ} Among them, a represents the weighting coefficient, γ represents the total moving distance of the drone cluster after completing the entire task, and Δ represents the maximum moving time of the drone cluster after completing the task. The specific variables are defined as follows: in, represents the moving distance of the i-th UAV in the t-th generation, Z(t) represents the total moving distance of all UAVs in the t-th generation, represents the moving time of the i-th UAV in the t-th generation, T span (t) represents the maximum movement time of all drones in the tth generation, and N is the number of drones; In the path planning process, the cost matrix F(i,j) is used to represent the i-th UAV from its current position S i (t) to the target position X j The comprehensive cost of (t+1) is calculated as follows: F(i,j)=a·μ ij +(1-a)·θ ij Among them, μ ij is the normalized distance, indicating the length of the path, γ max represents the maximum moving distance of the previous generation of drones, θ ij is the normalized time cost, indicating the time required to complete the path, Δ max represents the maximum moving time of the previous generation of drones, v i represents the flight speed of the i-th UAV, D(S i (t),X j (t+1)) indicates that the i-th UAV in the t-th generation moves from the current position S i (t) to the target position X j The distance of (t+1); Based on the generated cost matrix F(i,j), the optimal moving path of each drone in the drone cluster in this time slice is obtained; 52) A real-time obstacle avoidance mechanism based on the force field model is used during the movement of the drone, and the path is adjusted according to the information fed back by the sensors during the flight: When the distance between a drone and other drones or obstacles is less than the set safety radius d, a virtual repulsion field is generated to make the drone deviate from the dangerous area. The repulsion model U rep,ij for: Among them, d ij is the distance between the i-th UAV and the j-th obstacle or the j-th obstacle, The total repulsion of a drone is the vector sum of its repulsions with all other drones and obstacles: Among them, F i (t) is the total repulsion of the ith UAV in the tth generation, r ij and r io are the unit vectors between the UAV and other UAVs and obstacles, When considering collision avoidance between drones, a fitness value priority mechanism is adopted, and the formula is expressed as: Among them, f(x i ) represents the signal strength of the individual position of the i-th UAV, and the fraction represents the degree of avoidance of the UAV with poor fitness to the UAV with good fitness, U rep,ij It is a repulsion model that adopts the fitness value priority mechanism.

6. The method for cooperative control of a drone cluster for target search in a dynamic obstacle environment according to claim 1 is characterized in that: The updating of the UAV dispatching position includes the following steps: 61) Dynamic scheduling based on the current state of the cluster: Assume that the current position of the i-th drone is P i (t), the task completion degree is C i (t), the obstacle information is O(t), and the scheduling system dynamically calculates the next position through the following process; Calculate the scheduling weight W of each drone i (t), the expression is: W i (t)=α·f pos (P i (t))+β·f task (C i (t))+γ·f obs (O(t)) Among them, α, β, γ are weight coefficients, f pos 、f task , and f obs They represent the influence functions of position, task completion and obstacle information on UAV scheduling respectively; f pos (P i (t))=∥P i (t)-P target (t)∥,f task (C i (t))=1-C i (t), Among them, P target (t) represents the current target position, P o Represents the position of obstacles. Through this weight function, the scheduling system comprehensively considers different task requirements and dynamically adjusts the position of the UAV so that it always moves in the optimal direction for the task. 62) Continue the UAV cluster collaborative target search steps until the search mission is successfully completed: Assuming that the current time is t, the state of target search is S(t), the target search process of the drone cluster is expressed as: Among them, v i (t) is the velocity vector of the i-th UAV at time t. When the mission reaches the predetermined target state S goal When the target search task is completed, S(t)→S goal .

7. A computer-readable storage medium, characterized in that: A computer program is stored on the storage medium. When the computer program is executed by the processor, the drone cluster collaborative control method for target search in a dynamic obstacle environment as described in any one of claims 1 to 6 can be implemented.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes, a drone cluster collaborative control method for target search in a dynamic obstacle environment as described in any one of claims 1 to 6 can be implemented.

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