Operator / unmanned aerial vehicle cluster hybrid intelligent cooperative control system and method

Abstract

The invention discloses an operator / unmanned aerial vehicle cluster hybrid intelligent cooperative control system and method. The system comprises an operator credibility calculation module, an operator interaction instruction module, a cluster credibility calculation module, a distributed cooperative control calculation module and a visual display module. The method comprises the following steps: 1, giving a given agent cluster model and an unmanned aerial vehicle cluster model, and carrying out the mapping; 2, designing an unmanned aerial vehicle cluster communication model; 3, designing an unmanned aerial vehicle cluster distributed cooperative controller; and 4, designing a man-machine mutual trust model and judging the trust degree. According to the invention, by considering the influence of external environment interference on the unmanned aerial vehicle cluster and introducing the advantages of intelligent decision of human operators and distributed autonomous control of unmanned aerial vehicles, an efficient, flexible and extensible hybrid intelligent cooperative control framework is constructed, and the task execution efficiency and safety of the unmanned aerial vehicle cluster in a dynamic environment are improved.

Description

Technical Field

[0001] The present invention relates to an operator / unmanned aerial vehicle (UAV) cluster hybrid intelligent collaborative control system and method, belonging to the field of unmanned aerial vehicle navigation and control. Background Art

[0002] In recent years, the progress of drone swarm technology has promoted the rapid development of various fields, among which the research on hybrid intelligent systems in collaborative control has attracted much attention. Although drone swarms have shown great potential and high efficiency in many tasks, their intelligence in autonomous control still has certain limitations. These limitations make it difficult to achieve efficient and safe task execution in complex and dynamic environments relying solely on the autonomous capabilities of drones. Therefore, hybrid intelligent systems that combine artificial intelligence and human wisdom have become a key way to solve this problem.

[0003] Drone swarms usually rely on preset algorithms and rules for task allocation and path planning. However, these algorithms often fail to make optimal decisions quickly when faced with highly dynamic and interfering environments. For example, at a disaster rescue site, drones need to quickly assess the environment, identify trapped people, and flexibly adjust the mission path, but current autonomous control systems may lag or make errors when handling these tasks. Therefore, it becomes necessary to consider interference in the mathematical model of drones and introduce the intelligence of human operators. Human operators, with their unique intuitive judgment and real-time decision-making capabilities, can effectively supplement and enhance the autonomous control capabilities of drone swarms.

[0004] The core of the hybrid intelligent system lies in human-machine collaboration, which requires not only technical complementarity, but also the establishment of an interactive model based on trust. In the hybrid intelligent framework, the operator is not only a decision maker and supervisor, but also a partner of the drone cluster. By establishing a human-machine mutual trust model, the two parties can dynamically share information and instructions to ensure the flexibility and accuracy of task execution. When the drone encounters a complex situation that cannot be handled during the execution of the mission, the mission requirements can be transmitted to the operator in real time. The operator can give the best solution based on the on-site situation and experience, and then control the action of the drone through methods such as distributed optimization algorithms and control obstacle functions. In view of the problems that the existing drone cluster control intelligence is not high, the flexibility and accuracy are not strong when facing mission scenarios, the present invention introduces the intelligence of human operators, and designs an operator / drone cluster hybrid intelligent collaborative control system and method. Summary of the invention

[0005] The purpose of this invention is to invent an operator / UAV cluster hybrid intelligent collaborative control system and method, aiming to provide an operator / UAV mutual trust model and a UAV distributed optimization collaborative control method to improve the task execution efficiency and safety of UAV clusters in dynamic environments. The system constructs an efficient, flexible and scalable hybrid intelligent collaborative control framework by introducing the advantages of human operator intelligent decision-making and UAV autonomous control.

[0006] The present invention proposes an operator / UAV cluster hybrid intelligent collaborative control system and method, which is specifically implemented as follows:

[0007] System framework such as Figure 1 As shown in the figure, the operator / UAV cluster hybrid intelligent collaborative control system consists of five parts, namely: 1) operator trust calculation module; 2) operator interaction instruction module; 3) cluster trust calculation module; 4) distributed collaborative control calculation module; 5) scene display module.

[0008] 1) The operator trust calculation module calculates the incoming UAV cluster flight status, including the order parameter calculation unit, the UAV cluster obstacle avoidance effect calculation unit, and the target tracking effect calculation unit. The order parameter calculation unit calculates the speed consistency of the UAV cluster to evaluate the overall collaborative performance of the cluster; the UAV cluster obstacle avoidance effect calculation unit is used to monitor and quantify the cluster's obstacle avoidance performance in real time and evaluate the execution effect of the obstacle avoidance strategy; the target tracking effect calculation unit is used to calculate the distance between the UAV cluster and the target and evaluate the cluster's tracking accuracy and response speed to the target. The operator trust calculation module is the core decision-making module, which receives the operator interaction instruction module, the cluster trust calculation module, and the distributed collaborative control calculation module; the output of the three modules assigns different proportional coefficients to the three values ​​and obtains the final trust, and determines whether to intervene in the UAV cluster based on the trust. When the trust drops below the threshold, the operator adopts corresponding interactive strategies in the interactive instruction module according to the untrustworthy reasons to improve the task execution efficiency of the UAV cluster.

[0009] 2) Operator interaction command module, which divides the operator / UAV cluster interaction into a three-layer architecture, and designs interaction commands according to these three layers. The first layer is the "scenario" layer. There are different task types in the same scenario, so this layer has a task type selection unit. For example, in the target tracking scenario, the task type can be selected as "task reallocation", "target reacquisition" and other types, and this type is used as an interactive operation command to quickly adjust the group behavior. After selecting the interactive operation command in the first layer, the second layer is the "planning" layer, which realizes the collaborative decision-making of the operator and the UAV cluster in terms of motion planning, communication reconnection, etc., and realizes effective information exchange and decision sharing between the operator and the UAV. Therefore, this layer has a planning type selection unit. The third layer is the "control" layer, which has a control type selection unit. For the underlying control of the UAV, the operator can choose to optimize the UAV performance by adjusting the control parameters, or choose to directly remotely control the takeover through manual intervention to complete the goal when necessary, to ensure reliability and safety in emergency situations. The operator judges and selects one of the interactive commands according to the untrustworthy reason.

[0010] 3) Cluster trust calculation module. After each aircraft receives the target position message sent by the operator, it uses the obstacle distance calculation unit contained in this module to determine whether the instructions given by the operator meet flight safety requirements. If there are obstacles in the aircraft path, the flight is judged to be unsafe and the trust is reduced. The drone will start obstacle avoidance control and recalculate the path, entering the distributed collaborative control calculation module.

[0011] 4) Distributed collaborative control module, including observer state estimation unit and distributed control quantity calculation unit. When the UAV starts obstacle avoidance control, the observer state estimation unit estimates the UAV state information while considering the external environment interference, and transmits it to the distributed control quantity calculation unit to calculate the control quantity.

[0012] 5) Visual display module, including the UAV cluster flight status display unit, flight trajectory display unit, UAV communication status monitoring unit, target position display unit, and operator interactive command operation unit. Through this operation interface, the operator can monitor the UAV cluster tracking target situation in real time and implement intervention control of the UAV cluster.

[0013] A hybrid intelligent collaborative control method of an operator / UAV cluster is implemented in the following steps:

[0014] In this method, there are the following concepts, which are defined as follows:

[0015] Operator / UAV cluster: refers to the operator who commands and controls a cluster of 3 or more UAVs through the operator interactive command operation unit in the vision module.

[0016] Drone swarm: refers to a swarm formed by three or more drones. The dynamic model of each drone is formula (3).

[0017] Unmanned system agent cluster: refers to a cluster consisting of 3 or more individuals, each of which is an agent. The dynamic model is formula (1)-(2). Based on this model, the control quantity u of individual a can be calculated. pa , and then map the control quantity to the dynamic model (3) of UAV i through formulas (4)-(5).

[0018] Individual a: refers to individual a in the unmanned system intelligent agent cluster.

[0019] Step 1: Given the unmanned system agent cluster model and the drone cluster model and map them

[0020] Assuming there are N individuals in total, the unmanned system agent cluster model with interference is:

[0021]

[0022] in, is the position state of individual a, k C ,k G ,k U is the proportional gain, n is the number of neighbors of individual a, x j For x a Neighbors of individual a, Ne is the neighbor set of individual a, R c is the distance between each individual and other individuals, x G is the target position of the individual, ω(t) is the bounded interference, f a (·) is the abbreviation of the model, p a is the integral form of the repulsive force and is calculated as follows:

[0023]

[0024] Among them, u pa is the control quantity to be calculated.

[0025] Given the drone model:

[0026]

[0027] Where g is the acceleration due to gravity, [x i ,y i ,z i ] is the position of UAV i in the inertial coordinate system, V i is the airspeed, ψ i is the heading angle, γ i is the pitch angle, is the UAV i control instruction vector, is the time constant coefficient vector, n max is the maximum lateral overload, V i min is the minimum speed of UAV i, V i max is the maximum speed of UAV i, is the minimum pitch angle of UAV i, is the maximum pitch angle of UAV i.

[0028] Mapping equation (2) to equation (3) yields the control command vector of drone i to drive the drone model:

[0029] [v xc ,v yc ,v zc ]=u pa (4)

[0030]

[0031] Among them, v xc ,v yc ,v zc for u pa 3 components.

[0032] Step 2: Design of UAV Cluster Communication Model

[0033] Design the UAV cluster communication model, that is, the adjacency matrix, which is designed as L = (l ij ) n×n , where l ij represents the element in row i and column j, l ii is the element in row i and column i, and l ii >0, the sum of the elements in each row is 1.

[0034] Step 3: Design of Distributed Collaborative Controller for UAV Cluster

[0035] The deterministic Kalman filter observer is used to observe the state of the UAV:

[0036]

[0037] in, is the state estimator of individual a estimated by the observer, x a is the real quantity, represents the set of neighbor state estimators of individual a, is the symbol of partial derivative, P, Q, R are the designed matrix parameters, θ is a positive constant, and T is the transpose.

[0038] Considering whether the drone cluster takes obstacle avoidance measures, the controller design is divided into two cases. When obstacle avoidance is not required, the control quantity u pa =u anom ,u anom is the nominal control quantity designed. When obstacle avoidance is required, the specific design process is as follows:

[0039] Assuming the obstacle is a sphere, design the control obstacle function:

[0040] h=||x a -o k || 2 -(r b +R o ) 2 (7)

[0041] Among them, k is the center position of obstacle k, R o is the obstacle radius, r b is a safe distance. Assuming that there are extended K-type functions, define the set in:

[0042]

[0043] Among them, B is the feasible domain of individual states, x is the set of real quantities of the neighbor states of individual a, and r is the relative order of the control barrier function. On this basis, the safe set is defined as:

[0044]

[0045] in, is the set of neighbor state estimators of individual a, and satisfies Defining functions With the positive constant γ φ satisfy:

[0046]

[0047] Defining functions Satisfies formula (10) and satisfies:

[0048]

[0049] And make all drones' initial states satisfy:

[0050]

[0051] Then the problem can be formulated as a quadratic programming problem:

[0052]

[0053] Among them, uanom is the nominal control quantity of the design. The constraint in equation (14) is transformed into A a u pa -b a In the form of, the distributed subgradient descent method is used to solve it. The steps are as follows:

[0054] S31. Initialize multiplier κ a (0)=0, a=1,...,N, the number of initialization iterations is t=0.

[0055] S32. For a=1,…,N, calculate the coefficients

[0056] S33, solve the control quantity:

[0057]

[0058] S34. Solve the projection amount:

[0059]

[0060] S35, repeat S32-S34 until the maximum number of iterations T is reached max , the calculated control quantity is:

[0061]

[0062] Step 4: Design of human-machine mutual trust model and trust judgment

[0063] First, a method for calculating the operator's trust in the drone is designed. The order parameter, obstacle avoidance effect, and target tracking performance of the drone cluster are considered. The order parameter index is expressed as:

[0064]

[0065] Among them, v a (t) is the speed of individual a at time t, and v0 is the uniform initial speed designed for each individual.

[0066] The obstacle avoidance effect index is expressed as:

[0067]

[0068] Among them, h a (t) is the control barrier function value of individual a at time t, h a ′(t) is the value of the control obstacle function without considering the safety distance.

[0069] The target tracking index is expressed as:

[0070]

[0071] in, is the average initial distance between the cluster and the target.

[0072] Operator trust T h The comprehensive expression is:

[0073]

[0074] Among them, ω φ ,ω h ,ω χ is the design constant coefficient.

[0075] Secondly, a method for calculating the trust of the drone in the operator is designed. Considering the safety, reliability and efficiency of the operator's instructions, the drone's trust is designed as:

[0076]

[0077] Among them, T sa (t) is the trustworthiness of individual a, N e is the number of available drones, D a is the straight-line distance from individual a to the center of the obstacle.

[0078] When the operator's trust in the UAV cluster decreases, the operator selects the task type, planning type and control type through the operator interaction instruction operation unit in the visual display module to intervene in the UAV cluster. For example, the operation content may include task reallocation, changing the UAV cluster communication topology, and controlling the UAV movement to change the real-time flight status, such as position, speed, and altitude.

[0079] When the drone's trust in the operator's instructions decreases, the automatic obstacle avoidance function is enabled and a notification is displayed in the vision module to the operator.

[0080] The present invention proposes a hybrid intelligent collaborative control system and method for operators / drone clusters. The main advantages of the hybrid intelligent collaborative control system are as follows: 1. The present invention provides a complete operator / drone cluster interaction system framework and workflow, including trust model establishment and evaluation and interaction strategy; 2. The present invention proposes a distributed collaborative optimization control method for drone clusters, which has obstacle avoidance and route planning capabilities, and is easy to expand and flexibly configure; 3. The present invention takes into account the impact of external environmental interference on drone clusters, and performs stable control of drone clusters by designing a controller that takes interference into account. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a diagram of the operator / UAV cluster hybrid intelligent collaborative control system of the present invention.

[0082] Figure 2 This is the location trajectory map of the example drone cluster.

[0083] Figure 3a is the trust curve of drone cluster.

[0084] Figure 3b is the operator confidence curve.

[0085] Figure 4 This is the operator / UAV cluster hybrid intelligent collaborative control flow chart of the present invention. DETAILED DESCRIPTION

[0086] See Figures 1 to 4 The following is a specific example to verify the effectiveness of the operator / drone cluster hybrid intelligent collaborative control system proposed in the present invention. The operator / drone cluster hybrid intelligent collaborative control system and method thereof, the implementation flow chart is as follows Figure 4 As shown, the position trajectory of the drone cluster is as follows Figure 2 This example is based on Intel(R) Core(TM) i9-14900HX 2.20GHz, and the specific practical steps of MATLAB 2020b are as follows:

[0087] Step 1: Given the unmanned system agent cluster model and the drone cluster model and map them

[0088] According to formulas (1)-(3), N=5 individuals are used, and the individual positions at the initial moment are [-200,80,80]m, [-100,100,60]m, [-150,-30,100]m, [-200,40,100]m, [-200,100,40]m. The parameter in formula (1) is K c =0.02, K G =0.1, K u =0.2, R c = 10m, bounded interference ω(t) is simulated as Gaussian noise and ||ω(t)||≤1. The parameter in formula (3) is τ vi =2,τ ψi =2,τ γi =2,V i min =10,V i max =30, n max =3. The initial target position is [100,-50,0]m. Gravitational acceleration g = 9.81m / s 2 , the simulation step size is 0.08s.

[0089] Step 2: Design of UAV Cluster Communication Model

[0090] Design the UAV cluster communication model, that is, the adjacency matrix, which is designed as:

[0091]

[0092] Step 3: Design of Distributed Collaborative Controller for UAV Cluster

[0093] The deterministic Kalman filter observer is used to observe the state of the UAV. For formula (6), the observer parameters are designed as follows:

[0094]

[0095] Calculated Therefore, a distributed cooperative controller for drone clusters is designed. The center position of obstacle k is o k is [-100,40,0]m, obstacle radius R o is 50m, r b The safety distance is 10m. Considering whether the drone cluster takes obstacle avoidance measures, the controller design is divided into two cases. When obstacle avoidance is not required, the control quantity u pa =u anom ,u anom is the nominal control quantity designed. When obstacle avoidance is required, the specific design process is as follows:

[0096] Assuming the obstacle is a sphere, use formula (7) as the control obstacle function. Assuming there is an extended K-type function, define the set Satisfy formula (8) and define the security set Satisfying formula (9), define the function With the positive constant γ φ Satisfying formulas (10)-(11), finally calculated by formula (14) The control amount of each individual is calculated according to S31-35 in the aforementioned step three.

[0097] Step 4: Design of human-machine mutual trust model and trust judgment

[0098] For the calculation of the operator's trust in the drone, the drone cluster order parameter, obstacle avoidance effect and target tracking effect are calculated according to formulas (18)-(20), and then the operator's trust is calculated comprehensively according to formula (21). When one of the drones is damaged due to obstacle avoidance failure, the operator needs to re-formulate the communication topology structure, sort the isolated drone and other drones from near to far according to their positions, and sort them according to the trust of each available drone, and connect the isolated drone to the drone with the closest position and the drone with the highest trust.

[0099] Each drone calculates the straight line path between its own position and the target point position, and calculates the operator's command trust according to formulas (22)-(23). When there are obstacles in the path, the drone's trust is reduced, and the distributed optimization collaborative control method considering obstacle avoidance is used to calculate the control quantity to drive the drone movement, and the operator is notified in the vision module. Through simulation, we can get Figure 2 The drone cluster location trajectory diagram shown, and Figure 3a , Figure 3b The cluster trust and operator trust change curves are shown.

Claims

1. An operator / UAV cluster hybrid intelligent collaborative control system, characterized by: The system includes: an operator trust calculation module; an operator interaction instruction module; a cluster trust calculation module; a distributed collaborative control calculation module; The operator trust calculation module calculates the incoming UAV cluster flight status, including an order parameter calculation unit, a UAV cluster obstacle avoidance effect calculation unit, and a target tracking effect calculation unit; the order parameter calculation unit calculates the speed consistency of the UAV cluster and evaluates the overall collaborative performance of the cluster; the UAV cluster obstacle avoidance effect calculation unit is used to monitor and quantify the cluster's obstacle avoidance performance in real time and evaluate the execution effect of the obstacle avoidance strategy; the target tracking effect calculation unit is used to calculate the distance between the UAV cluster and the target and evaluate the cluster's tracking accuracy and response speed to the target; the operator trust calculation module, as the core decision-making module, receives the operator interaction instruction module, the cluster trust calculation module, and the distributed collaborative control calculation module; the output of the three modules assigns different proportional coefficients to the three values ​​and obtains the final trust, and determines whether to intervene in the UAV cluster based on the trust; when the trust drops below the threshold, the operator adopts corresponding interaction strategies in the interaction instruction module according to the untrustworthy reasons to improve the task execution efficiency of the UAV cluster; The operator interaction instruction module divides the operator / UAV cluster interaction into a three-layer architecture, and designs interaction instructions according to the three-layer architecture; the first layer is the scene layer, and there are different task types in the same scene. This layer has a task type selection unit; after selecting the interactive operation instruction in the first layer, the second layer is the planning layer, which realizes the collaborative decision-making of the operator and the UAV cluster, and realizes the effective information exchange and decision sharing between the operator and the UAV. This layer has a planning type selection unit; the third layer is the control layer, which has a control type selection unit. For the underlying control of the UAV, the operator chooses to optimize the UAV performance by adjusting the control parameters, or chooses to directly remotely control and take over through manual intervention to achieve the goal, to ensure reliability and safety in emergency situations; the operator judges and selects one of the interaction instructions according to the untrustworthy reason; Cluster trust calculation module: After each aircraft receives the target position message sent by the operator, it uses the obstacle distance calculation unit contained in this module to determine whether the instructions given by the operator meet the flight safety requirements. If there are obstacles in the aircraft path, the flight is judged to be unsafe, and the trust is reduced. The UAV will start obstacle avoidance control and recalculate the path, and enter the distributed collaborative control calculation module; The distributed collaborative control module includes an observer state quantity estimation unit and a distributed control quantity calculation unit. When the UAV starts obstacle avoidance control, the observer state quantity estimation unit estimates the UAV state quantity information while taking into account the interference of the external environment, and transmits it to the distributed control quantity calculation unit to calculate the control quantity.

2. The system according to claim 1, characterized in that: The system also includes: a visual display module, which specifically includes a UAV cluster flight status display unit, a flight trajectory display unit, a UAV communication status monitoring unit, a target position display unit, and an operator interactive instruction operation unit; through this operation interface, the operator can monitor the UAV cluster tracking target situation in real time and realize intervention control of the UAV cluster.

3. A method for cooperative control of operator / UAV cluster hybrid intelligence, which is implemented by the system described in claims 1-2, and the specific steps are as follows: Step 1: Given the unmanned system agent cluster model and the drone cluster model and map them Assuming there are N individuals in total, the unmanned system agent cluster model with interference is: in, is the position state of individual a, k C ,k G ,k U is the proportional gain, n is the number of neighbors of individual a, x j For x a Neighbors of individual a, Ne is the neighbor set of individual a, R c is the distance between each individual and other individuals, x G is the target position of the individual, ω(t) is the bounded interference, f a (·) is the abbreviation of the model, p a is the integral form of the repulsive force and is calculated as follows: Among them, u pa is the control quantity to be calculated; Given the drone model: Where g is the acceleration due to gravity, [x i ,y i ,z i ] is the position of UAV i in the inertial coordinate system, V i is the airspeed, ψ i is the heading angle, γ i is the pitch angle, is the UAV i control instruction vector, is the time constant coefficient vector, n max is the maximum lateral overload, V i min is the minimum speed of UAV i, V i max is the maximum speed of UAV i, is the minimum pitch angle of UAV i, is the maximum pitch angle of UAV i; Mapping equation (2) to equation (3) yields the control command vector of drone i to drive the drone model: [v xc ,v yc ,v zc ]=u pa (4) Among them, v xc ,v yc ,v zc for u pa 3 components of; Step 2: Design of UAV Cluster Communication Model Design the UAV cluster communication model, that is, the adjacency matrix, which is designed as L = (l ij ) n×n , where l ij represents the element in row i and column j, l ii is the element in row i and column i, and l ii > 0, the sum of the elements in each row is 1; Step 3: Design of Distributed Collaborative Controller for UAV Cluster The deterministic Kalman filter observer is used to observe the state of the UAV: in, is the state estimator of individual a estimated by the observer, x a is the real quantity, represents the set of neighbor state estimators of individual a, is the symbol of partial derivative, P, Q, R are designed matrix parameters, θ is a positive constant, and T is the transpose; Considering whether the drone cluster takes obstacle avoidance measures, the controller design is divided into two cases. When obstacle avoidance is not required, the control quantity u pa =u anom ,u anom is the nominal control quantity designed. When obstacle avoidance is required, the specific design process is as follows: Assume the obstacle is a sphere and design the function to control the obstacle: h=||x a -o k || 2 -(r b +R o ) 2 (7) Among them, k is the center position of obstacle k, R o is the obstacle radius, r b is a safe distance; suppose there exists an extended K-type function, defining the set in: Among them, B is the feasible domain of individual state, x is the set of real quantities of neighbor states of individual a, and r is the relative order of the control barrier function; on this basis, the safe set is defined as: in, is the set of neighbor state estimators of individual a, and satisfies Defining functions With the positive constant γ φ satisfy: Defining functions Satisfies formula (10) and satisfies: And make all drones' initial states satisfy: Designed as a quadratic programming problem: Among them, u anom is the nominal control quantity of the design; Step 4: Design of human-machine mutual trust model and trust judgment First, a method for calculating the operator's trust in the UAV is designed, and secondly, a method for calculating the UAV's trust in the operator is designed; When the operator's trust in the UAV cluster decreases, the operator selects the mission type, planning type and control type through the operator interaction instruction operation unit in the visual display module to intervene in the UAV cluster; When the drone's trust in the operator's instructions decreases, the automatic obstacle avoidance function is enabled and a notification is displayed in the vision module to the operator.

4. The method according to claim 3, characterized in that: In step 3, the constraint in equation (14) is transformed into A a u pa -b a In the form of, the distributed subgradient descent method is used to solve it. The steps are as follows: S31. Initialize multiplier κ a (0) = 0, a = 1, ..., N, initialization iteration number t = 0; S32. For a=1,…,N, calculate the coefficients S33, solve the control quantity: S34. Solve the projection amount: S35, repeat S32-S34 until the maximum number of iterations T is reached max , the calculated control quantity is:

5. The method according to claim 3, characterized in that: The fourth step is to design a method for calculating the operator's trust in the drone, which is as follows: Consider the order parameters, obstacle avoidance effect and target tracking performance of the drone cluster; the order parameter index is expressed as: Among them, v a (t) is the speed of individual a at time t, and v0 is the uniform initial speed designed for each individual; The obstacle avoidance effect index is expressed as: Among them, h a (t) is the control barrier function value of individual a at time t, h a ′(t) is the value of the control obstacle function without considering the safety distance; The target tracking index is expressed as: in, is the average initial distance between the cluster and the target; Operator trust T h The comprehensive expression is: Among them, ω φ ,ω h ,ω χ is the design constant coefficient.

6. The method according to claim 3, characterized in that: The fourth step is to design a method for calculating the trust of the UAV to the operator, which is as follows: Considering the safety, reliability and efficiency of the operator's instructions, the UAV trust is designed as: Among them, T sa (t) is the trustworthiness of individual a, N e is the number of available drones, D a is the straight-line distance from individual a to the center of the obstacle.

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