A collaborative control system and method for operator / UAV cluster hybrid intelligence

Abstract

The present invention discloses an operator / UAV cluster hybrid intelligent collaborative control system and method, the system includes an operator trust calculation module, an operator interaction instruction module, a cluster trust calculation module, a distributed collaborative control calculation module, and a visual display module. The method of the present invention is as follows: Step 1: Given a given intelligent agent cluster model and a UAV cluster model and mapping them; Step 2: UAV cluster communication model design; Step 3: UAV cluster distributed collaborative controller design; Step 4: Human-machine mutual trust model design and trust judgment. The present invention considers the impact of external environmental interference on the UAV cluster and introduces the advantages of human operator intelligent decision-making and UAV distributed autonomous control to construct an efficient, flexible, and scalable hybrid intelligent collaborative control framework, and improves the task execution efficiency and safety of the UAV cluster in a dynamic environment.

Description

Technical Field

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

[0002] In recent years, advances in drone swarm technology have driven rapid development across various fields, with research on hybrid intelligent systems in collaborative control garnering significant attention. While drone swarms demonstrate significant potential and efficiency in many tasks, their autonomous control capabilities remain limited. These limitations make it difficult to achieve efficient and safe mission execution solely through autonomous drone capabilities in complex and dynamic environments. Therefore, hybrid intelligent systems—combining artificial intelligence and human intelligence—are emerging as a key approach to addressing this challenge.

[0003] Drone swarms typically rely on pre-defined algorithms and rules for task allocation and path planning. However, these algorithms often fail to make optimal decisions quickly in highly dynamic and disruptive environments. For example, at a disaster rescue site, drones must rapidly assess the environment, identify trapped individuals, and flexibly adjust their mission paths. However, current autonomous control systems can experience lags or errors when handling these tasks. Therefore, it is essential to account for interference and incorporate the intelligence of human operators into drone mathematical models. Human operators, with their unique intuitive judgment and real-time decision-making abilities, can effectively complement 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 it cannot handle during the execution of the mission, the mission requirements can be transmitted to the operator in real time. The operator can then provide the best solution based on the on-site situation and experience, and then control the drone's actions through methods such as distributed optimization algorithms and control obstacle functions. In response to the problems of low intelligence in existing drone cluster control and poor flexibility and accuracy in 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] This invention aims to develop a hybrid intelligent collaborative control system and method for operator / UAV swarms. This system combines an operator / UAV mutual trust model with a distributed optimized collaborative control method for UAVs to improve the efficiency and safety of UAV swarm mission execution in dynamic environments. By incorporating the advantages of intelligent decision-making by human operators and autonomous control by UAVs, this system constructs an efficient, flexible, and scalable hybrid intelligent collaborative control framework.

[0006] This invention proposes a hybrid intelligent collaborative control system and method for operators and drone swarms, 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: 1) operator trust calculation module; 2) operator interaction instruction module; 3) cluster trust calculation module; 4) distributed collaborative control calculation module; 5) visual display module.

[0008] 1) The operator trust calculation module calculates the incoming UAV swarm flight status and includes an order parameter calculation unit, a UAV swarm obstacle avoidance effectiveness calculation unit, and a target tracking effectiveness calculation unit. The order parameter calculation unit calculates the UAV swarm's velocity consistency to evaluate the swarm's overall collaborative performance. The UAV swarm obstacle avoidance effectiveness calculation unit monitors and quantifies the swarm's obstacle avoidance performance in real time, evaluating the effectiveness of the obstacle avoidance strategy. The target tracking effectiveness calculation unit calculates the distance between the UAV swarm and the target, assessing the swarm's tracking accuracy and response speed. The operator trust calculation module, serving as the core decision-making module, receives the outputs of the operator interaction command module, the cluster trust calculation module, and the distributed collaborative control calculation module. The outputs of these three modules are assigned different scaling coefficients to obtain a final trust level, which is then used to determine whether to intervene in the UAV swarm. If the trust level drops below a threshold, the operator, based on the reason for the untrustworthiness, implements an interaction strategy in the interaction command module to improve the UAV swarm's mission execution efficiency.

[0009] 2) The operator interaction command module divides the operator / UAV swarm interaction into a three-tiered architecture, with interaction commands designed based on these three layers. The first layer is the "scenario" layer. Within the same scenario, different task types exist, so this layer features a task type selection unit. For example, in a target tracking scenario, task types such as "task reallocation" and "target reacquisition" can be selected, and these types are used as interaction commands to quickly adjust swarm behavior. After selecting interaction commands in the first layer, the second layer, the "planning" layer, is entered. This layer enables collaborative decision-making between the operator and the UAV swarm in areas such as motion planning and communication reconnection, enabling effective information exchange and decision-making between the operator and the UAVs. Therefore, this layer features a planning type selection unit. The third layer is the "control" layer, featuring a control type selection unit. For the underlying control of the UAVs, the operator can choose to optimize UAV performance by adjusting control parameters or, if necessary, directly take over remote control to achieve the goal through manual intervention, ensuring reliability and safety in emergency situations. The operator then selects one of the interaction commands based on the reason for the untrustworthiness.

[0010] 3) Cluster trust calculation module. After each aircraft receives the target position message from the operator, it uses the obstacle distance calculation unit contained in this module to determine whether the operator's instructions are consistent with flight safety. If there is an obstacle in the aircraft's path, the flight is judged to be unsafe and the trust level 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, which includes an observer state estimation unit and a distributed control variable calculation unit. When the UAV initiates obstacle avoidance control, the observer state estimation unit estimates the UAV state information, taking into account external environmental interference, and transmits this information to the distributed control variable calculation unit to calculate the control variable.

[0012] 5) The visual display module includes a swarm drone flight status display unit, a flight trajectory display unit, a swarm drone communication status monitoring unit, a target location display unit, and an operator interaction command operation unit. Through this operation interface, the operator can monitor the swarm drone's tracking target in real time and implement intervention control of the swarm drone.

[0013] A hybrid intelligent collaborative control method for operators and drone swarms is implemented as follows:

[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 interaction instruction operation unit in the vision module.

[0016] Drone swarm: refers to a swarm formed by three or more drones, and 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 is x a Neighbors, 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 a 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 instruction vector for UAV i to drive the UAV 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 UAV cluster takes obstacle avoidance measures, the controller design is divided into two categories. When obstacle avoidance is not required, the control quantity u pa =u anom ,u anom is the nominal control quantity of the design. 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 Assume that there is an extended K-type function, 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 a function With the positive constant γ φ satisfy:

[0046]

[0047] Defining a function Satisfy formula (10), and satisfy:

[0048]

[0049] And make the initial states of all drones 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, and 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 control quantity is calculated as:

[0061]

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

[0063] First, a method is designed to calculate the operator's trust in the drone. 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 velocity of individual a at time t, and v0 is the uniform initial velocity 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 control obstacle function value without considering the safety distance.

[0069] The target tracking indicator 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 confidence in the swarm decreases, they can use the operator interaction command unit in the visual display module to select the mission type, planning type, and control type to intervene in the swarm. These actions can include reassigning tasks, changing the swarm's communication topology, and controlling the movement of drones to alter 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 visual module to the operator.

[0080] This invention proposes a hybrid intelligent collaborative control system and method for operator / drone swarms. The main advantages of this hybrid intelligent collaborative control system are: 1. It provides a complete operator / drone swarm interaction system framework and workflow, including trust model establishment and evaluation, and interaction strategies; 2. It proposes a distributed collaborative optimization control method for drone swarms, which has obstacle avoidance and route planning capabilities, and is easily scalable and flexibly configured; 3. It considers the impact of external environmental interference on drone swarms and achieves stable control of drone swarms 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 the drone cluster.

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

[0085] Figure 4 This is the flow chart of the operator / UAV cluster hybrid intelligent collaborative control 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 its method, 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 MATLAB 2020b. The specific practical steps 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 initial individual positions 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, the 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 follows:

[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 UAV clusters is designed. The center position of obstacle k is o k is [-100,40,0]m, and the obstacle radius is R o 50m, r b The safety distance is 10m. Considering whether the UAV cluster takes obstacle avoidance measures, the controller design is divided into two categories. When obstacle avoidance is not required, the control quantity u pa =u anom ,u anom is the nominal control quantity of the design. 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 Satisfy formulas (10)-(11), and finally calculate by formula (14) The control amount of each individual is calculated according to S31-35 in the above step three.

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

[0098] To calculate the operator's trust in the drones, 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-establish the communication topology structure, sorting the isolated drone from near to far based on the position of the isolated drone and other drones, and sorting them according to the trust of each available drone, connecting the isolated drone to the drone with the closest position and the drone with the highest trust.

[0099] Each UAV 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 UAV's trust is reduced, and a distributed optimization collaborative control method considering obstacle avoidance is used to calculate the control quantity to drive the UAV movement, and the operator is notified in the visual module. Through simulation, we can get Figure 2 The drone cluster position trajectory diagram shown, and Figure 3a 、 Figure 3b The cluster trust and operator trust change curves are shown.

Claims

1. An operator / UAV swarm hybrid intelligent collaborative control system, characterized by: The system includes: operator trust calculation module; operator interaction instruction module; cluster trust calculation module; distributed collaborative control calculation module; The operator trust calculation module calculates the incoming UAV cluster flight status and includes 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 serves as the core decision-making module and 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 reason for untrustworthiness 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 based on these three layers. The first layer is the scenario layer. Different task types exist in the same scenario. This layer has a task type selection unit. After the interactive operation instruction is selected 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 effective information exchange and decision sharing between the operator and the UAVs. 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 take over through manual intervention to achieve the goal, ensuring reliability and safety in emergency situations. The operator can judge and select one of the interaction instructions based on the reason for untrustworthiness. Cluster trust calculation module: After each aircraft receives the target position message from the operator, it uses the obstacle distance calculation unit contained in this module to determine whether the operator's instructions are consistent with flight safety. If there is an obstacle in the aircraft's path, it is judged as flight unsafe and the trust level is reduced. The drone will initiate obstacle avoidance control and recalculate the path, entering 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, wherein: The system also includes: a visual display module, which specifically includes a drone cluster flight status display unit, a flight trajectory display unit, a drone communication status monitoring unit, a target position display unit, and an operator interaction instruction operation unit; through this operation interface, the operator can monitor the drone cluster's tracking target situation in real time and implement intervention control of the drone cluster.

3. A method for hybrid intelligent collaborative control of an operator / drone swarm, implemented by the system of claims 1-2, comprising the following steps: 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 is x a Neighbors, 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 a 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 instruction vector for UAV i to drive the UAV model: [v xc ,v yc ,v zc ]=u pa (4) Among them, v xc ,v yc ,v zc for u pa 3 components; 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 the designed matrix parameters, θ is a positive constant, and T is the transpose; Considering whether the UAV cluster takes obstacle avoidance measures, the controller design is divided into two categories. When obstacle avoidance is not required, the control quantity u pa =u anom ,u anom is the nominal control quantity of the design. 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 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: in, is the set of neighbor state estimators of individual a, and satisfies Defining a function With the positive constant γ φ satisfy: Defining a function Satisfy formula (10), and satisfy: And make the initial states of all drones satisfy: The design is 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 is designed to calculate the operator's trust in the drone, and secondly, a method is designed to calculate the drone's trust in the operator; When the operator's trust in the UAV swarm 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 swarm; 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 visual module to the operator.

4. The method according to claim 3, wherein: In step 3, the constraint in formula (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 control quantity is calculated as:

5. The method according to claim 3, wherein: Step 4 designs a method for calculating the operator's trust in the drone, which is as follows: Considering the order parameters, obstacle avoidance effect and target tracking performance of the UAV 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 control obstacle function value without considering the safety distance; The target tracking indicator 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, wherein: The fourth step is to design a method for calculating the trust of the UAV in the operator. Specifically, the UAV trust is designed 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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