A method for deploying unmanned aerial vehicle sensor networks based on entropy feedback control

Abstract

The present invention discloses a method for deploying a drone sensor network based on entropy feedback control. The real-time feedback mechanism of local entropy, speed entropy and topological connection entropy is introduced based on the Olfati-Saber model. By controlling the entropy reduction process of the cluster system, the interaction force and obstacle avoidance strategy of each drone are dynamically adjusted, the robustness and adaptability of the sensor network in a complex environment are improved, and the connectivity and collaborative operation efficiency of the network are effectively guaranteed. The present invention controls the communication complexity through entropy feedback, effectively reduces unnecessary communication connections in the cluster, avoids redundancy caused by excessive communication, reduces the overhead of information transmission, dynamically adjusts the allocation weight of communication resources according to entropy value feedback, preferentially establishes connections with important nodes, improves the utilization efficiency of communication resources, monitors the topological connection entropy in real time, adaptively adjusts the network structure, flexibly optimizes the communication topology according to task requirements, and ensures efficient communication and cluster stability in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle sensor networks, and in particular to a method for deploying unmanned aerial vehicle sensor networks based on entropy feedback control. Background Art

[0002] Unmanned aerial vehicle sensor networks are widely used in environmental monitoring, emergency response, disaster relief and other fields due to their flexibility, high efficiency and large-scale collaborative operation. However, how to effectively deploy unmanned aerial vehicle sensor networks in complex and dynamic environments has become an important challenge. In complex environments such as urban buildings and dense forests, the deployment of unmanned aerial vehicle sensor networks faces uncertain factors such as limited perception range, communication interruption, and obstacles. These factors not only affect the collaborative work between drones, but may also reduce the connectivity of the network, making it impossible for drones to effectively complete their tasks. In addition, traditional multi-drone deployment methods usually rely only on fixed control rules and lack real-time feedback control of the overall system status. Especially in the face of changing environments, it is difficult to dynamically adjust the deployment plan to maintain the stability and efficient operation of the network. Summary of the invention

[0003] Purpose of the invention: The present invention aims to provide a method for deploying an unmanned aerial vehicle sensor network by introducing a real-time feedback mechanism of local entropy, velocity entropy and topological connection entropy to maintain stable connectivity and efficient collaborative work of the unmanned aerial vehicle sensor network in a complex and dynamic environment.

[0004] Technical solution: The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control of the present invention comprises the following steps:

[0005] (1) According to the relative distance between each UAV in the UAV swarm system and its neighboring UAVs within the communication range, the adjacency matrix is ​​determined and the UAV swarm communication topology network is constructed;

[0006] (2) Calculate the entropy value of each UAV at the current moment based on the location information, speed information, and detected obstacle location information of each UAV and its neighboring UAVs within the communication range. The entropy value includes local entropy, speed entropy, and topological connection entropy.

[0007] (3) Each drone regularly broadcasts its own entropy information, and dynamically evaluates the local and global states of the drone cluster based on its own entropy information and the entropy values ​​of neighboring drones received through communication, and dynamically adjusts the position, speed, and communication connection of the drone;

[0008] (4) The local entropy, velocity entropy, and topological connection entropy obtained in step (2) are introduced into the swarming control model Olfati-Saber, the control rate of the Olfati-Saber model is calculated, the total control input of each UAV is determined, and the movement direction, speed, and communication topology of the UAV are adjusted;

[0009] (5) During the flight, repeat steps (2) to (4) to dynamically adjust the position and speed of the drone until the drone completes the specified flight mission.

[0010] Furthermore, the adjacency matrix for

[0011] ;

[0012] in, and Respectively represent i Frame and j is the position vector of the UAV, and R is the communication radius between UAVs; is 1, indicating i The first drone and j The distance between the two drones is within the communication radius and there is a communication connection; If it is 0, it means i The first drone and j There is no communication link between the drones.

[0013] Furthermore, the local entropy is

[0014] ;

[0015] in, is the local entropy, which is used to reflect the spatial distribution of drones in the current area; is the number of grids used to divide the UAV's motion area. For the i The probability density of drones in a grid.

[0016] Furthermore, the velocity entropy is

[0017] ;

[0018] in, is the speed entropy, which is used to reflect the speed consistency of the drone cluster; For speed falls in i The probability of an interval.

[0019] Furthermore, the topological connection entropy is

[0020] ;

[0021] in, is the topological connection entropy, which is used to reflect the network connectivity of the drone cluster. is the ratio of nodes with degree k, and the degree represents the number of neighboring drones that are connected to the drone for communication.

[0022] Furthermore, the control rate of the Olfati-Saber model includes the interaction force, velocity consistency term, and obstacle avoidance term.

[0023] Further, i Interaction forces of drones for

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] in, is the first control coefficient; For the i The set of neighboring drones within the communication range of the drone; is the action function; and Respectively represent i Frame and j The position vector of the drone, is a unit vector, indicating the i Frame and j The relative directions between the drones; is a smoothness function used to control the influence of the interaction force between UAVs at different distances; z is the distance between the two UAVs; is the Sigma normalization function for distance, which is used to ensure the stability of the control system and the reliability of numerical calculation; R is the communication radius between UAVs; D is the expected distance between UAVs; is the local entropy control coefficient, which is used to measure the basic control strength of the UAV in adjusting the spatial distribution uniformity; is the adjustment function based on the local entropy value.

[0029] Further, i The speed consistency of the drone for

[0030] ;

[0031] ;

[0032] in, is the second control coefficient; For the i The set of neighboring drones within the communication range of the drone; is an element of the adjacency matrix, indicating the i Frame and j Whether there is communication connection between the drones; and Respectively represent i Frame and j The speed of the drone; is the speed entropy control coefficient, which is used to measure the basic control strength of the speed consistency of UAVs within the cluster; is the adjustment function based on speed entropy.

[0033] Further, i Obstacle avoidance for drones for

[0034] ;

[0035] in, It is an obstacle o to the i The repulsion coefficient of the drone, For the i The direction vector between the UAV and the obstacle o.

[0036] Further, i Total control input for the drone for

[0037] ;

[0038] in, represents the mth control coefficient, ; For the i The position vector of the drone; is the target point position of the UAV cluster migration, the third control coefficient for

[0039] ;

[0040] in, is the topology connection entropy control coefficient, which is used to measure the basic control strength of the cluster network connection status; is the adjustment function based on the topological connection entropy.

[0041] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. Based on the Olfati-Saber model, the present invention introduces a real-time feedback mechanism of local entropy, speed entropy and topological connection entropy, thereby solving the problems of low deployment efficiency and insufficient cluster stability caused by the lack of real-time feedback regulation in the existing model; 2. In terms of entropy feedback mechanism, the present invention is based on the local entropy and speed entropy measurement functions of sensor node distribution and speed consistency, and dynamically adjusts the interaction force and obstacle avoidance strategy of each drone by controlling the entropy reduction process of the cluster system, thereby improving the robustness and adaptability of the sensor network in complex environments, and effectively ensuring the connectivity and collaborative operation efficiency of the network; 3. When the local entropy is high, it indicates that the drones are unevenly distributed in space, and the system will prompt the drones to change their formation to reduce the complexity of the cluster network; when the speed entropy is high, it indicates that the drones are unevenly distributed in space, and the system will prompt the drones to change their formation to reduce the complexity of the cluster network; when the speed entropy is high, it indicates that the drones are unevenly distributed in space, and the system will prompt the drones to change their formation to reduce the complexity of the cluster network; When the speed difference between the two is large, the system will adjust the speed of the drone to make it consistent with the speed of its neighbors, thereby enhancing the synchronization of the cluster; when the topological connection entropy is high, it means that the cluster network connectivity is poor or the network connection redundancy is high. The system will prompt the drone to get closer to its neighbors, increase the connection density, and adjust the formation to cut off redundant communication connections, thereby improving the communication and collaboration capabilities of the cluster; 4. The present invention controls the communication complexity through entropy feedback, effectively reduces unnecessary communication connections in the cluster, avoids the redundancy caused by excessive communication, and reduces the overhead of information transmission; 5. The present invention dynamically adjusts the allocation weight of communication resources according to entropy value feedback, gives priority to establishing connections with important nodes, improves the utilization efficiency of communication resources, monitors the topological connection entropy in real time, adaptively adjusts the network structure, and flexibly optimizes the communication topology according to task requirements, thereby ensuring efficient communication and cluster stability in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0043] The following is combined with Figure 1 The present invention is further described.

[0044] The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control of the present invention comprises the following steps:

[0045] According to the relative distance between each drone in the drone cluster system and its neighboring drones within the communication range, the adjacency matrix is ​​determined and the drone cluster communication topology network is constructed.

[0046] The drone establishes a communication network with neighboring drones within its communication range, determines which neighboring drones are within its communication radius by calculating the relative distance between drones, and generates an adjacency matrix to ensure the connectivity of the drone in the local network.

[0047] Consider a drone swarm system consisting of n drones in a three-dimensional Euclidean space. Represents a collection of drones, the communication radius between drones is R, for any drone , No. i A collection of other drones within the sensing range of the drone for

[0048] ;

[0049] Adjacency Matrix Indicates the connection relationship between drones. i Frame and j When the distance between drones is less than or equal to the communication radius R, the matrix element is set to 1, otherwise it is set to 0. i Frame and j The distance calculation formula between drones is:

[0050] ;

[0051] in, and are the position vectors of the i-th and j-th UAVs respectively.

[0052] Adjacency Matrix The definition is as follows:

[0053] ;

[0054] If i The first drone and j The distance between the two drones is within the communication radius. If it is 1, it means there is a communication connection between them; otherwise A value of 0 indicates that there is no communication connection between them.

[0055] No. i The drone obtains the j The location and speed information of the neighboring drones are as follows:

[0056] ;

[0057] Among them, the neighbor set are all drones within the drone communication radius R.

[0058] According to the location information, speed information and detected obstacle location information of each drone and its neighboring drones within the communication range at the current moment, the entropy value of each drone at the current moment is calculated. The entropy value includes local entropy, speed entropy and topological connection entropy.

[0059] Each drone uses its sensors to sense the location information of obstacles within the detection range to ensure that it can identify and avoid obstacles in complex environments. Each drone obtains the location and speed information of neighboring drones within the communication range at the current moment, and sends its own location and speed information to neighboring drones.

[0060] Each drone calculates the entropy value at the current moment based on its own position and speed information at the current moment, combined with the position and speed information of neighboring drones within the communication range, and the detected obstacle position information, through the entropy measurement function of local entropy, speed entropy and topological connection entropy. The entropy value reflects the status of the drone in terms of spatial distribution, speed consistency and network connectivity.

[0061] Each drone detects obstacles within its detection range in real time through its onboard sensors. The obstacles are represented as (Obstacle location).

[0062] in, For the i The position of drone i at time t, is the position of the ith obstacle, is the distance vector between the two, Indicates the range of influence of obstacles. When an obstacle enters the detection range of the drone, the drone will adjust its flight trajectory according to the distance from the obstacle to avoid collision. Obstacle avoidance strategies usually use repulsive force to influence the movement direction of the drone. The closer the obstacle is, the greater the repulsive force of the drone to avoid the obstacle. The magnitude of the repulsive force can be calculated by the following formula:

[0063] ;

[0064] The lower the local entropy value, the more evenly the drones are distributed; the higher the local entropy value, the more likely the drones are too concentrated or too dispersed in space. First, the drone movement area is divided into The grids are each containing the number of drones at the current moment. The calculation formula for local entropy is:

[0065] ;

[0066] in, is the probability density of drones in the ith grid, that is, the proportion of the number of drones in this grid to the total number of drones; the entropy value It reflects the spatial distribution of drones in the current area. By calculating the local entropy, the system can detect the concentration and distribution uniformity of drones in the area, and adjust the movement trajectory of drones according to this entropy value to ensure that the cluster is reasonably distributed in space.

[0067] Speed ​​entropy is used to evaluate the speed consistency of a drone cluster. The lower the speed entropy value, the more consistent the speed of the drones, and the higher the synchronization of the cluster in coordinated motion; the higher the speed entropy value, the greater the speed difference of the drones, and the poorer the coordination of the cluster. The speed information of the drones is divided into multiple speed intervals according to a certain range. The speed values ​​of all drones are counted, and the probability distribution of each speed interval is calculated. The calculation formula of speed entropy is:

[0068] ;

[0069] in, is the probability that the speed falls in the ith interval. By calculating the speed entropy, the system can detect the speed difference of the drones in the cluster and then adjust the control strategy to reduce the speed difference and improve the synchronization of the cluster.

[0070] Topological connection entropy is used to measure the network connectivity of a cluster. Each drone in a cluster has a different number of connections (i.e., degree) with other drones. Topological connection entropy can reflect the connectivity of a cluster by calculating the degree distribution. The lower the topological connection entropy, the more stable and connected the network structure of the cluster is; if the entropy value is too high, isolated drones or redundant communication connections will appear, resulting in ineffective information transmission. First, the degree of each drone is calculated. , which is the number of neighbors connected to it. According to the degree distribution of the drone, calculate the proportion of nodes with degree k to the total nodes . It is used to reflect the topological connection entropy of the network connectivity of the cluster. The calculation formula is

[0071] .

[0072] Each drone broadcasts the entropy information it calculates to neighboring nodes through its communication module at certain time intervals. These entropy values ​​include local entropy, speed entropy, and topological connection entropy, which reflect the status of the network area where the drone is currently located. Through the exchange of entropy values, neighboring drones can receive this information and use it as part of subsequent collaborative decision-making.

[0073] Each drone i calculates the entropy value at the current moment, including local entropy, speed entropy and topological connection entropy. The i-th drone sends its entropy value information to all neighboring drones within the communication range at the current moment t. .

[0074] ;

[0075] in, is the set of neighbors of UAV i within the communication range.

[0076] (3) Each drone regularly broadcasts its own entropy information. Based on its own entropy information and the entropy values ​​of neighboring drones received through communication, it dynamically evaluates the local and global states of the drone cluster and dynamically adjusts the position, speed, and communication connection of the drone.

[0077] After receiving the entropy information from the i-th neighbor drone, the j-th drone stores this information, compares its entropy with its own calculated entropy, and updates its local judgment and adjustment strategy. The entropy information between drones is exchanged through wireless communication, using a broadcast mechanism. Within a certain time interval, each drone will regularly broadcast its entropy information, including local entropy, speed entropy, and topological connection entropy, which will be received by its neighbors within its communication radius. Neighboring drones will also broadcast their entropy values ​​at similar time intervals, so that each drone can obtain information about its neighbors and form a dynamic assessment of the local and global state of the cluster. Through this information exchange mechanism, drones can update their understanding of the surrounding environment in real time, provide a continuous data source for adaptive control, and ensure that each node has a relatively comprehensive understanding of the current state. Real-time entropy feedback and adaptive control are achieved through the comparison and adjustment of entropy information between drones. This feedback mechanism is decentralized, that is, each drone independently calculates entropy and adjusts control inputs. By comparing the entropy information of itself and its neighbors, drones dynamically adjust their positions, speeds, and communication connections to optimize local behavior and enhance the overall collaboration of the cluster.

[0078] When the local entropy When it is high, it indicates that the UAVs are unevenly distributed in space, and the system will prompt the UAVs to change their formation to reduce the complexity of the cluster network. When it is high, it means that the speed difference between drones is large. The system will adjust the speed of the drone to make it consistent with the speed of its neighbors to enhance the synchronization of the cluster. When it is higher, it means that the cluster network connectivity is poor or the network connection redundancy is high, and isolated nodes and redundant communication connections may appear. The system will prompt drones to move closer to neighbors, increase connection density, and adjust the formation to cut off redundant communication connections, thereby improving the communication and collaboration capabilities of the cluster.

[0079] (4) The local entropy, velocity entropy, and topological connection entropy obtained in step (2) are introduced into the swarming control model Olfati-Saber, the control rate of the Olfati-Saber model is calculated, the total control input of each UAV is determined, and the movement direction, speed, and communication topology of the UAV are adjusted.

[0080] The Olfati-Saber model is used for the distributed control of drone swarms. By calculating the interaction force, velocity consistency term, and obstacle avoidance force, the control input of each drone is obtained to adjust its motion direction and speed. , speed consistency item , obstacle avoidance composition.

[0081] Interaction force It is used to control the attraction and repulsion between the drone and its neighbors to ensure that the drones maintain an appropriate distance. The calculation formula is:

[0082] ;

[0083] in, is the first control coefficient; is the action function, defined as

[0084] ;

[0085] in, is a smoothness function used to control the influence of the interaction force between UAVs at different distances; z is the distance between the two UAVs; is the Sigma normalization function for distance, which is used to ensure the stability of the control system and the reliability of numerical calculation; R is the communication radius between UAVs; D is the expected distance between UAVs.

[0086] The expression of the smoothness function is as follows:

[0087] ;

[0088] Among them, g is the input value, and h is the threshold in the smoothing function, which is used to determine the change in the strength of the interaction force within different distance ranges.

[0089] The expression of the Sigma normalization function is as follows:

[0090] ;

[0091] Where s is the input value, Coefficients for the Sigma normalization function, which aims to introduce smoothness into the distance calculation and ensure that the system remains stable at distances close to zero.

[0092] is a unit vector, indicating the relative direction between drones i and j, as follows:

[0093] .

[0094] Speed ​​consistency term It is used to adjust the speed of the drone to keep it consistent with the speed of its neighbors and ensure the synchronization of the cluster. The calculation formula is:

[0095] ;

[0096] in, is an element of the adjacency matrix, indicating the i Frame and j Check whether there is communication connection between the drones.

[0097] Obstacle Avoidance To ensure that the drone can avoid obstacles, the system calculates the repulsive force between the drone and the obstacle, prompting the drone to adjust its flight path to avoid collision; the calculation formula is

[0098] ;

[0099] ;

[0100] in, It is an obstacle For i The repulsion coefficient of the drone determines the strength of the repulsion force exerted by the obstacle on the drone. Its value is equal to the repulsion force exerted by the drone on the obstacle. The size of is the direction vector between UAV i and obstacle o.

[0101] (5) During the flight, the drone continuously repeats steps (2) to (4), dynamically adjusting its position and speed until the drone completes the specified flight mission. Through repeated iterations, the system can adapt to changes in complex environments and ensure the deployment efficiency and mission execution capabilities of the drone.

[0102] The entropy feedback mechanism proposed in this paper dynamically adjusts the state of the UAV cluster to ensure the stability, coordinated movement and efficient communication of the system. The entropy feedback mechanism is based on local entropy, speed entropy and topological connection entropy, which are used to measure the spatial distribution, speed consistency and network connectivity of the cluster respectively, and optimize the control input in real time.

[0103] Local entropy feedback is used to measure the uniformity of the UAV distribution in space, and its control coefficient The calculation formula is as follows:

[0104] ;

[0105] Among them, among them, is the first control coefficient; is the local entropy control coefficient, which is used to measure the basic control strength of the UAV in adjusting the spatial distribution uniformity; is the adjustment function based on the local entropy value.

[0106] Speed ​​entropy feedback is used to measure the consistency of the UAV speed, and its control coefficient The calculation formula is as follows:

[0107] ;

[0108] in, is the second control coefficient; is the speed entropy control coefficient, which is used to measure the basic control strength of the speed consistency of UAVs within the cluster; is the adjustment function based on speed entropy.

[0109] Topological connection entropy feedback is used to measure the network connectivity of the cluster, and its control coefficient The calculation formula is as follows:

[0110] ;

[0111] in, is the topology connection entropy control coefficient, which is used to measure the basic control strength of the cluster network connection status; is the adjustment function based on the topological connection entropy.

[0112] The total control input of the UAV is composed of interaction force, velocity consistency term, obstacle avoidance term, target position term, and entropy feedback term, which ensures that the UAV can fly in coordination and avoid obstacles. The calculation formula of the total control input is

[0113] .

[0114] The present invention realizes efficient coordination and dynamic optimization of drone clusters by combining the Olfati-Saber model with the entropy feedback mechanism. The entropy feedback mechanism ensures system stability and avoids cluster collapse and communication complexity by monitoring spatial distribution, speed consistency and network connectivity. Speed ​​entropy feedback enhances the synchronization between drones, and topological connection entropy optimizes communication efficiency and ensures timely information transmission. The system has excellent obstacle avoidance capability and environmental adaptability, can maintain high robustness and collaboration in complex tasks, and significantly improves the task execution efficiency and safety of drone clusters.

Claims

1. A method for deploying an unmanned vehicle sensor network based on entropy feedback control, characterized in that: The following steps are involved: (1) According to the relative distance between each UAV in the UAV swarm system and its neighboring UAVs within the communication range, the adjacency matrix is ​​determined and the UAV swarm communication topology network is constructed; (2) Calculate the entropy value of each UAV at the current moment based on the location information, speed information, and detected obstacle location information of each UAV and its neighboring UAVs within the communication range. The entropy value includes local entropy, speed entropy, and topological connection entropy. (3) Each drone regularly broadcasts its own entropy information, and dynamically evaluates the local and global states of the drone cluster based on its own entropy information and the entropy values ​​of neighboring drones received through communication, and dynamically adjusts the position, speed, and communication connection of the drone; (4) The local entropy, velocity entropy, and topological connection entropy obtained in step (2) are introduced into the swarming control model Olfati-Saber, the control rate of the Olfati-Saber model is calculated, the total control input of each UAV is determined, and the movement direction, speed, and communication topology of the UAV are adjusted; (5) During the flight, repeat steps (2) to (4) to dynamically adjust the position and speed of the UAV until the UAV completes the designated flight mission; The local entropy is ； in, is the local entropy, which is used to reflect the spatial distribution of drones in the current area; is the number of grids used to divide the UAV's motion area. For the i The probability density of drones in a grid; The velocity entropy is ； in, is the speed entropy, which is used to reflect the speed consistency of the drone cluster; For speed falls in i The probability of an interval; The topological connection entropy is ； in, is the topological connection entropy, which is used to reflect the network connectivity of the drone cluster. is the ratio of nodes with degree k, and the degree represents the number of neighboring drones that are connected to the drone for communication.

2. The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control according to claim 1, characterized in that: Adjacency Matrix for ； in, and Respectively represent i Frame and j is the position vector of the UAV, and R is the communication radius between UAVs; is 1, indicating i The first drone and j The distance between the two drones is within the communication radius and there is a communication connection; If it is 0, it means i The first drone and j There is no communication link between the drones.

3. The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control according to claim 1, characterized in that: The control rate of the Olfati-Saber model includes the interaction force, velocity consistency term, and obstacle avoidance term.

4. The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control according to claim 3 is characterized in that: No. i Interaction forces of drones for ； ； ； ； in, is the first control coefficient; For the i The set of neighboring drones within the communication range of the drone; is the action function; and Respectively represent i Frame and j The position vector of the drone, is a unit vector, indicating the i Frame and j The relative directions between the drones; is a smoothness function used to control the influence of the interaction force between UAVs at different distances; z is the distance between the two UAVs; is the Sigma normalization function for distance, which is used to ensure the stability of the control system and the reliability of numerical calculation; R is the communication radius between UAVs; D is the expected distance between UAVs; is the local entropy control coefficient, which is used to measure the basic control strength of the UAV in adjusting the spatial distribution uniformity; is the adjustment function based on the local entropy value.

5. The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control according to claim 4 is characterized in that: No. i The speed consistency of the drone for ； ； in, is the second control coefficient; For the i The set of neighboring drones within the communication range of the drone; is an element of the adjacency matrix, indicating the i Frame and j Whether there is communication connection between the drones; and Respectively represent i Frame and j The speed of the drone; is the speed entropy control coefficient, which is used to measure the basic control strength of the speed consistency of UAVs within the cluster; is the adjustment function based on speed entropy.

6. The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control according to claim 5 is characterized in that: No. i Obstacle avoidance for drones for ； in, It is an obstacle o to the i The repulsion coefficient of the drone, For the i The direction vector between the UAV and the obstacle o.

7. The method for deploying an unmanned aerial vehicle sensor network based on entropy feedback control according to claim 6, characterized in that: No. i Total control input for the drone for ； in, represents the mth control coefficient, ; For the i The position vector of the drone; is the target point position of the UAV cluster migration, the third control coefficient for ； in, is the topology connection entropy control coefficient, which is used to measure the basic control strength of the cluster network connection status; is the adjustment function based on the topological connection entropy.

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