A distributed flapping-wing aircraft cluster collaborative coverage method and device

Abstract

The present invention relates to the technical field of aircraft control, and particularly to a distributed flapping-wing aircraft cluster cooperative coverage method and device. The method includes: modeling each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft; according to the information of neighboring flapping-wing aircraft, the position and angle of each flapping-wing aircraft, and the joint detection probability of the flapping-wing aircraft cluster, initially judging the next position of each flapping-wing aircraft by using the optimal gradient descent method. If the next position does not exceed the boundary of the mission area, calculate the total displacement cost of all flapping-wing aircraft. If the total displacement cost is less than 2, it is determined that the convergence criterion is satisfied, and the optimal coverage position is obtained; control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position. By adopting the present invention, optimal coverage of an area with obstacles can be achieved under the condition of decentralization and with polynomial-level time complexity.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft control, and particularly to a distributed cooperative coverage method and device for a flapping-wing aircraft cluster. Background Art

[0002] Coverage control refers to that a multi-agent system reasonably and effectively allocates non-overlapping monitoring sub-regions for each agent according to the sensor model and the distribution state of the information to be sensed in the environment to be covered. Coverage control is widely applied to tasks such as search and rescue of trapped persons, forest fires, and military reconnaissance. In such applications, flapping-wing aircraft have great development potential because of their good concealment, strong endurance, fast flight speed, etc., and can be equipped with different sensors to meet different task requirements.

[0003] Currently, the work on coverage control is mainly based on the coverage of dividing the task space. The area to be covered is divided into multiple sub-regions based on map information, probability distribution or task requirements, and then different unmanned aerial vehicles use algorithms such as the plowing method, the spanning tree method, and the backtracking spiral method to cover these sub-regions to minimize overlapping coverage and ensure complete coverage of the entire area. These algorithms do not consider the fact of improving the overall sensing performance by sharing the observation results of multiple sensors, and most of the algorithms are based on global information, resulting in a large amount of complex information that the agent cluster needs to process, which is not suitable for the lightweight computing and processing capabilities of flapping-wing aircraft. Summary of the Invention

[0004] In order to solve the technical problem that the existing technology is not suitable for the lightweight computing and processing capabilities of flapping-wing aircraft, embodiments of the present invention provide a distributed cooperative coverage method and device for a flapping-wing aircraft cluster. The technical solution is as follows:

[0005] On the one hand, a distributed cooperative coverage method for a flapping-wing aircraft cluster is provided. This method is implemented by a cooperative coverage device for a flapping-wing aircraft cluster, and the method includes:

[0006] S1. Define a flapping-wing aircraft cluster and a task area;

[0007] S2. Model each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft;

[0008] S3. Obtain the coordinate point information of the ground projection of the spatial position of the sensor node of each flapping-wing aircraft and the sensor perception model;

[0009] S4. Obtain the information of the neighbor flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft, and calculate the joint detection probability according to the information of the neighbor flapping-wing aircraft and the sensor perception model;

[0010] S5. Based on the information of neighboring flapping-wing aircraft, the position, angle of each flapping-wing aircraft, the coordinate point information of the ground projection of the spatial position of the sensor nodes, and the joint detection probability of the flapping-wing aircraft cluster, initially determine the next position of each flapping-wing aircraft using the optimal gradient descent method;

[0011] S6. Determine whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area. If it does not exceed the boundary of the mission area, execute S7; if it exceeds the boundary of the mission area, project the point to the interior of the mission area;

[0012] S7. Calculate the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster. If the total displacement cost is less than 2, it is determined that the convergence criterion is met, and the optimal coverage position is obtained; otherwise, go back to execute step S4;

[0013] S8. Control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position.

[0014] On the other hand, a distributed cooperative coverage device for a flapping-wing aircraft cluster is provided. This device is applied to the distributed cooperative coverage method for a flapping-wing aircraft cluster, and the device includes:

[0015] A definition unit for defining a flapping-wing aircraft cluster and a mission area;

[0016] A modeling unit for modeling each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft;

[0017] An acquisition unit for acquiring the coordinate point information of the ground projection of the spatial position of the sensor nodes of each flapping-wing aircraft and the sensor perception model;

[0018] A calculation unit for acquiring the information of neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft and calculating the joint detection probability according to the sensor perception model;

[0019] A first judgment unit for initially judging the next position of each flapping-wing aircraft using the optimal gradient descent method based on the position, angle of each flapping-wing aircraft, the coordinate point information of the ground projection of the spatial position of the sensor nodes, and the joint detection probability of the flapping-wing aircraft cluster;

[0020] A second judgment unit for judging whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area. If it does not exceed the boundary of the mission area, execute S7; if it exceeds the boundary of the mission area, project the point to the interior of the mission area;

[0021] A determination unit, configured to calculate the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster. If the total displacement cost is less than 2, it is determined that the convergence criterion is met, and the optimal coverage position is obtained; otherwise, it proceeds to execute the steps performed by the calculation unit.

[0022] A control unit, configured to control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position.

[0023] On the other hand, a flapping-wing aircraft cluster cooperative coverage device is provided. The flapping-wing aircraft cluster cooperative coverage device includes: a processor; a memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, any one of the methods in the above-mentioned distributed flapping-wing aircraft cluster cooperative coverage method is implemented.

[0024] On the other hand, a computer-readable storage medium is provided. At least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the methods in the above-mentioned distributed flapping-wing aircraft cluster cooperative coverage method.

[0025] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0026] In the embodiments of the present invention, a flapping-wing aircraft cluster and a mission area are defined; each flapping-wing aircraft in the flapping-wing aircraft cluster is modeled to obtain the position and angle of each flapping-wing aircraft; the coordinate point information of the spatial position of the sensor node of each flapping-wing aircraft projected on the ground and the sensor perception model are obtained; the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft is obtained, and the joint detection probability is calculated according to the information of the neighboring flapping-wing aircraft and the sensor perception model; according to the information of the neighboring flapping-wing aircraft, the position and angle of each flapping-wing aircraft, the coordinate point information of the spatial position of the sensor node projected on the ground, and the joint detection probability of the flapping-wing aircraft cluster, the optimal gradient descent method is used to preliminarily judge the next position of each flapping-wing aircraft; it is judged whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area, and the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster is calculated. If the total displacement cost is less than 2, it is determined that the convergence criterion is met, and the optimal coverage position is obtained; otherwise, the above steps are cyclically executed; the flapping-wing aircraft cluster is controlled to perform cooperative coverage according to the optimal coverage position. The present invention finds the position where random events occur in the maximized detection task space according to the known probability map while maintaining the connectivity constraint between flapping-wing aircraft; by transforming the problem into a distributed constraint optimization problem, each flapping-wing aircraft can consider the information of neighboring flapping-wing aircraft during the process of solving the problem, cooperate with neighboring flapping-wing aircraft, and consider the discontinuity of the sensor detection probability caused by the existence of polygonal obstacles and the limited sensor field of view constraints of each flapping-wing aircraft, and use the distributed optimal gradient method for solution. This algorithm can ensure that the optimal coverage of the area with obstacles is achieved under the condition of decentralization and polynomial-level time complexity. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a flowchart of a distributed cooperative coverage method for a flapping-wing aircraft cluster provided by an embodiment of the present invention;

[0029] Figure 2 It is a graph of the total movement cost curve and the total detection probability curve provided by an embodiment of the present invention;

[0030] Figure 3 It is a graph of the total movement cost curve and the total detection probability curve provided by an embodiment of the present invention;

[0031] Figure 4It is a block diagram of a distributed flapping-wing aircraft cluster collaborative coverage device provided by an embodiment of the present invention;

[0032] Figure 5 It is a schematic structural diagram of a flapping-wing aircraft cluster collaborative coverage device provided by an embodiment of the present invention. Specific embodiments

[0033] The following describes the technical solutions in the present invention with reference to the accompanying drawings.

[0034] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0035] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0036] In the embodiments of the present invention, sometimes subscripts such as W1 may be miswritten as non-subscript forms such as W1. When the difference is not emphasized, the meanings they express are the same.

[0037] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0038] The embodiments of the present invention provide a distributed flapping-wing aircraft cluster collaborative coverage method. This method can be implemented by a flapping-wing aircraft cluster collaborative coverage device, and this flapping-wing aircraft cluster collaborative coverage device can be a terminal or a server. As Figure 1 shown in the flowchart of the distributed flapping-wing aircraft cluster collaborative coverage method, the processing flow of this method can include the following steps:

[0039] S1. Define a flapping-wing aircraft cluster and a mission area.

[0040] Optionally, defining a flapping-wing aircraft cluster and a mission area in S1 includes:

[0041] Define the coverage mission space, each flapping-wing aircraft in the flapping-wing aircraft swarm, each obstacle, the position state of each flapping-wing aircraft, the event occurrence probability function, the sensor detection probability, the communication radius, and the total time detection probability of the flapping-wing aircraft swarm. The total time detection probability of the flapping-wing aircraft swarm is as shown in Equation (1) below:

[0042] (1)

[0043] where the variable node represents the position state of each flapping-wing aircraft; represents the coverage mission space; the event occurrence probability function represents the probability of a reconnaissance target appearing at point . For all points , ; represents the joint detection probability of all sensors at position , represents the feasible region of the position state of each flapping-wing aircraft.

[0044] In a feasible implementation, a feasible definition method is exemplified below:

[0045] 1) Define a 600m×600m rectangular mission area to represent the coverage mission space;

[0046] 2) The entity represents each flapping-wing aircraft, where represents the total number of flapping-wing aircraft;

[0047] 3) The obstacle represents each obstacle, where represents the number of obstacles, and the internal space of the obstacle ;

[0048] 4) The variable node represents the position state of each flapping-wing aircraft, and its feasible space is ;

[0049] 5) The event occurrence probability function represents the probability of a reconnaissance target appearing at point . For all points , ;

[0050] 6) The sensor detection probability represents the probability of the variable node detecting the occurrence of an event at position , represents the position where all sensors are located The combined detection probability at

[0051] 7) Flapping-wing aircraft node Communication radius Represents the communication range of each flapping-wing aircraft.

[0052] 8) The objective function of the optimization problem, that is, the total event detection probability of the flapping-wing aircraft is expressed as:

[0053] (1)

[0054] It should be noted that the data defined above is set according to a specific implementation scenario. In addition to this method, specific data can be defined according to the specific implementation scenario, and the present invention does not limit this.

[0055] S2. Model each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft.

[0056] Optionally, modeling each flapping-wing aircraft in the flapping-wing aircraft cluster in S2 to obtain the position and angle of each flapping-wing aircraft includes:

[0057] Assume that each flapping-wing aircraft in the flapping-wing aircraft cluster flies at a fixed speed and controls its movement only through the speed direction. Model each flapping-wing aircraft in the flapping-wing aircraft cluster as follows in Equation (2):

[0058] (2)

[0059] Where, Represents the flapping-wing aircraft At The position at time Represents the flapping-wing aircraft At The speed at time , Is the real part, representing the flapping-wing aircraft At The abscissa value at time Is the imaginary unit, Is the imaginary part, representing the flapping-wing aircraft At The ordinate value at time; Represents the flapping-wing aircraft The speed magnitude, Is the base of the natural logarithm, Represents the flapping-wing aircraft At The angular direction at time, Represents the flapping-wing aircraft At The angular rate of change at a moment denotes a flapping-wing aircraft at the moment of control input.

[0060] In a feasible implementation, in this embodiment, it can be assumed that the flapping-wing aircraft flies at a fixed airspeed with an initial heading angle , and the flapping-wing aircraft is modeled as a circle with a radius .

[0061] S3. Obtain the coordinate point information of the projection of the spatial position of the sensor node of each flapping-wing aircraft on the ground and the sensor perception model.

[0062] In a feasible implementation, each flapping-wing aircraft node obtains the coordinate point information of the projection of the current spatial position of the sensor node on the ground and the sensor perception model , where denotes the sensor detection power coefficient of node , denotes the sensor attenuation coefficient of node . In this embodiment, it can be set that , .

[0063] S4. Obtain the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft, and calculate the joint detection probability according to the information of the neighboring flapping-wing aircraft and the sensor perception model.

[0064] Among them, the information of the neighboring flapping-wing aircraft may include information such as the detection probability of the sensors of the neighboring flapping-wing aircraft, the set of neighboring flapping-wing aircraft nodes, and the position status of the neighboring flapping-wing aircraft nodes.

[0065] Optionally, calculating the joint detection probability according to the information of the neighboring flapping-wing aircraft and the sensor perception model includes:

[0066] According to the following formula (3), calculate the joint detection probability of all visible points of the flapping-wing aircraft node within the communication radius :

[0067] (3)

[0068] Among them, denotes the sensor perception model, the position status of the flapping-wing aircraft , denotes at the point The detection probability of the flapping-wing aircraft sensor at Represents the visible area, represents the invisible area, and N represents the number of flapping-wing aircraft in the flapping-wing aircraft cluster.

[0069] S5. Based on the information of neighboring flapping-wing aircraft, the position and angle of each flapping-wing aircraft, the coordinate point information of the sensor node spatial position projected on the ground, and the joint detection probability of the flapping-wing aircraft cluster, the optimal gradient descent method is used to preliminarily determine the next step position of each flapping-wing aircraft.

[0070] Optionally, S5 uses an optimal gradient descent method to preliminarily determine the next position of each flapping-wing aircraft based on information about neighboring flapping-wing aircraft, the position and angle of each flapping-wing aircraft, the coordinate point information of the projection of the sensor node's spatial position on the ground, and the joint detection probability of the flapping-wing aircraft cluster, including:

[0071] S51. If there are no obstacles in the task area, the optimization function is as follows (4):

[0072] (4)

[0073] Use the following formula (5) to determine the next position:

[0074] (5)

[0075] Among them, k represents the neighbor flapping-wing aircraft node, represents the set of neighboring flapping-wing aircraft nodes, Represents the neighboring flapping-wing aircraft node The sensor detection probability, Represents the neighboring flapping-wing aircraft node Position status, Representation node The sensor attenuation coefficient;

[0076] along The gradient descent formula in the axial direction is as follows (6):

[0077] (6)

[0078] in, Represents a flapping-wing aircraft node The coordinates of a point within the sensor detection range, Represents a flapping-wing aircraft node The horizontal coordinate of the current position,

[0079] along The gradient descent formula in the axial direction is as follows (7):

[0080] (7)

[0081] If there are obstacles in the task area, the optimization function is as shown in the following formula (8):

[0082] (8)

[0083] Where represents the visible area, represents the invisible area, and it is defined that if point , point , and for all , , then the set of visible area of point is the set of all points that satisfy the conditions; when , , when , ; represents of the set; the following formula (9) is used for the next position judgment:

[0084] (9)

[0085] Where represents the velocity vector at the boundary point of the visible area;

[0086] The gradient descent formula along the axis is as shown in the following formula (10):

[0087] (10)

[0088] The gradient descent formula along the axis is as shown in the following formula (11):

[0089] (11)

[0090] Where, q represents the number of reflection vertices , represents a line connecting the starting point to the intersection points of the rays drawn in the direction of the connection between each obstacle reflection vertex and the starting point with other obstacles or the boundary, represents a point pointing to direction, starting from with a length of is to distance value, representing the reflection vertex of the obstacle, representing the flapping-wing aircraft sensor detection model function.

[0091] In a feasible implementation manner, the derivation process of formula (9) is introduced below:

[0092] Generally speaking, first, the following initial formula (9') is used to make the next position judgment in the case of obstacles in the task area:

[0093] (9')

[0094] represents the boundary of the visible area, represents the "velocity" vector at the boundary of the visible area. The first term on the right side of equation (9') does not involve any change in the integration domain, and its derivative implementation is the same as formula (5). The second term on the right side of equation (9') needs to consider the integration along the detection boundary. Define that for the projection of each obstacle in the two-dimensional plane, if the interior angle corresponding to each vertex is less than 180°, then the vertex is a reflection vertex, otherwise the vertex is a non-reflection vertex. The obstacle detection boundary is defined as starting from the current flapping-wing sensor node as the starting point, making rays in the direction of the connection line between each obstacle reflection vertex and the starting point, and the connection line with the intersection point of the ray and other obstacles or boundaries as the end point, and its normal vector points to the inside of the visible area. Assume that for the flapping-wing aircraft node there are reflection vertices. At this time, equation (9') can be written as formula (9):

[0095] (9)

[0096] The derivation processes of formulas (10) and (11) are introduced below:

[0097] Based on the above theory, considering that the point only moves along the x-axis under small perturbations, so , , , let be to the distance value as the new integration variable, so , . Let be in the direction from pointing to direction, Starting from and the length is

[0098] (12)

[0099] In formula (12), represents the current position coordinates of flapping-wing aircraft i ( ). Therefore, formula (12) can be split into formula (10) for gradient descent along the axis and formula (11) for gradient descent along the axis.

[0100] S6. Determine whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area. If it does not exceed the boundary of the mission area, execute S7. If it exceeds the boundary of the mission area, project the point to the inside of the mission area.

[0101] Optionally, determining whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area in S6 includes:

[0102] Determine whether the next position of each flapping-wing aircraft is the feasible region F. If the next position of the flapping-wing aircraft is the feasible region F, it is determined that the flapping-wing aircraft does not exceed the boundary of the mission area. If the next position of the flapping-wing aircraft is not the feasible region F, it is determined that the flapping-wing aircraft exceeds the boundary of the mission area;

[0103] Projecting the point to the inside of the mission area includes:

[0104] If exceeds the boundary of the mission area, adjust the abscissa of the boundary point of the mission area so that is inside the mission area;

[0105] If exceeds the boundary of the mission area, adjust the ordinate of the boundary point of the mission area so that is inside the mission area.

[0106] In a feasible implementation, compare with the abscissa of the boundary point of the mission area. If is less than the minimum value of the abscissa of the boundary point of the mission area, adjust the minimum value of the abscissa of the boundary point of the mission area. Specifically, subtract g from the minimum value of the abscissa of the boundary point of the mission area, where g is a preset constant; if is greater than the maximum value of the abscissa of the boundary point of the mission area, adjust the maximum value of the abscissa of the boundary point of the mission area. Specifically, add g to the maximum value of the abscissa of the boundary point of the mission area so that Inside the task area.

[0107] Compare with the ordinate of the boundary point of the task area. If it is less than the minimum value of the ordinate of the boundary point of the task area, then adjust the minimum value of the ordinate of the boundary point of the task area. Specifically, subtract g from the minimum value of the ordinate of the boundary point of the task area, where g is a preset constant; if it is greater than the maximum value of the ordinate of the boundary point of the task area, then adjust the maximum value of the ordinate of the boundary point of the task area. Specifically, add g to the maximum value of the ordinate of the boundary point of the task area to make inside the task area.

[0108] S7. Calculate the total displacement cost sum of all flapping-wing aircraft in the flapping-wing aircraft cluster. If the total displacement cost sum is less than 2, it is determined that the convergence criterion is met, and the optimal coverage position is obtained; otherwise, go to step S4.

[0109] Among them, the optimal coverage position may include the target coordinates of each flapping-wing aircraft.

[0110] Optionally, calculating the total displacement cost sum of all flapping-wing aircraft in the flapping-wing aircraft cluster in S7 includes:

[0111] Let the total displacement cost sum of each iteration , and calculate according to the following formula (12) :

[0112] (12)

[0113] Among them, represents the coordinate of the flapping-wing aircraft node at time represents the coordinate of the flapping-wing aircraft node at the previous moment.

[0114] In a feasible implementation manner, as the number of iterations increases, the total movement cost curve and the total detection probability curve are respectively as shown in Figure 2 and Figure 3 , it can be seen that the algorithm converges to obtain the optimal coverage position.

[0115] S8. Control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position.

[0116] In a feasible implementation manner, according to the target coordinates of each flapping-wing aircraft in the optimal coverage position, control each flapping-wing aircraft to travel towards the corresponding target coordinates to complete cooperative coverage.

[0117] In the embodiments of the present invention, a flapping-wing aircraft cluster and a mission area are defined; each flapping-wing aircraft in the flapping-wing aircraft cluster is modeled to obtain the position and angle of each flapping-wing aircraft; the coordinate point information of the spatial position of the sensor node of each flapping-wing aircraft projected on the ground and the sensor perception model are obtained; the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft is obtained, and the joint detection probability is calculated according to the information of the neighboring flapping-wing aircraft and the sensor perception model; according to the information of the neighboring flapping-wing aircraft, the position and angle of each flapping-wing aircraft, the coordinate point information of the spatial position of the sensor node projected on the ground, and the joint detection probability of the flapping-wing aircraft cluster, the optimal gradient descent method is used to preliminarily judge the next position of each flapping-wing aircraft; it is judged whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area, and the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster is calculated. If the total displacement cost is less than 2, it is determined that the convergence criterion is met, and the optimal coverage position is obtained; otherwise, the above steps are executed in a loop; the flapping-wing aircraft cluster is controlled to perform cooperative coverage according to the optimal coverage position. The present invention finds the position where a random event occurs in the maximized detection task space according to the known probability map while maintaining the connectivity constraint between flapping-wing aircraft; by transforming the problem into a distributed constraint optimization problem, each flapping-wing aircraft can consider the information of neighboring flapping-wing aircraft, cooperate with neighboring flapping-wing aircraft, and consider the discontinuity of the sensor detection probability caused by the existence of polygonal obstacles and the limited sensor field of view constraints of each flapping-wing aircraft during the process of solving the problem, and the distributed optimal gradient method is used for solving. This algorithm can ensure the optimal coverage of the area with obstacles under the condition of decentralization and polynomial-level time complexity.

[0118] Figure 4 FIG. 4 is a block diagram of a distributed cooperative coverage device for a flapping-wing aircraft cluster according to an exemplary embodiment, and the device is used for the distributed cooperative coverage method of the flapping-wing aircraft cluster. Wherein:

[0119] A definition unit 410, configured to define a flapping-wing aircraft cluster and a mission area;

[0120] A modeling unit 420, configured to model each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft;

[0121] An acquisition unit 430, configured to acquire the coordinate point information of the spatial position of the sensor node of each flapping-wing aircraft projected on the ground and the sensor perception model;

[0122] A calculation unit 440, configured to acquire the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft, and calculate the joint detection probability according to the sensor perception model;

[0123] The first judgment unit 450 is configured to preliminarily judge the next position of each flapping-wing aircraft by using the optimal gradient descent method according to the position of each flapping-wing aircraft, the angle, the coordinate point information of the spatial position of the sensor node projected on the ground, and the joint detection probability of the flapping-wing aircraft cluster;

[0124] The second judgment unit 460 is configured to judge whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area. If it does not exceed the boundary of the mission area, then execute S7. If it exceeds the boundary of the mission area, then project the point to the inside of the mission area;

[0125] The determination unit 470 is configured to calculate the total displacement cost of all the flapping-wing aircraft in the flapping-wing aircraft cluster. If the total displacement cost is less than 2, it is determined that the convergence criterion is met, and the optimal coverage position is obtained; otherwise, go to execute the steps performed by the calculation unit;

[0126] The control unit 480 is configured to control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position.

[0127] Optionally, the definition unit 410 is configured to:

[0128] Define the coverage mission space, each flapping-wing aircraft in the flapping-wing aircraft cluster, each obstacle, the position state of each flapping-wing aircraft, the event occurrence probability function, the sensor detection probability, the communication radius, and the total time detection probability of the flapping-wing aircraft cluster. The total time detection probability of the flapping-wing aircraft cluster is as follows in formula (1):

[0129] (1)

[0130] Among them, the variable node represents the position state of each flapping-wing aircraft; represents the coverage mission space; the event occurrence probability function represents the probability that the reconnaissance target appears at point For all points , ; represents the joint detection probability of all sensors at position , represents the feasible region of the position state of each flapping-wing aircraft.

[0131] Optionally, the modeling unit 420 is configured to:

[0132] Assume that each flapping-wing aircraft in the flapping-wing aircraft cluster flies at a fixed speed and controls its movement only through the speed direction, and model each flapping-wing aircraft in the flapping-wing aircraft cluster, as follows in formula (2):

[0133] (2)

[0134] Wherein, represents the position of the flapping-wing aircraft at moment, represents the velocity of the flapping-wing aircraft at moment, , is the real part, representing the abscissa value of the flapping-wing aircraft at moment, is the imaginary unit, is the imaginary part, representing the ordinate value of the flapping-wing aircraft at moment; represents the magnitude of the velocity of the flapping-wing aircraft , is the base of the natural logarithm, represents the angular direction of the flapping-wing aircraft at moment, represents the angular rate of change of the flapping-wing aircraft at moment, represents the control input of the flapping-wing aircraft at moment.

[0135] Optionally, the calculation unit 440 is configured to:

[0136] Calculate the combined detection probability of all visible points within the communication radius of the flapping-wing aircraft node according to the following formula (3):

[0137] (3)

[0138] Wherein, represents the sensor perception model, the position state of the flapping-wing aircraft , represents the detection probability of the flapping-wing aircraft sensor at point , represents the visible area, represents the invisible area, and N represents the number of flapping-wing aircraft in the flapping-wing aircraft cluster.

[0139] Optionally, the first determination unit 450 is configured to:

[0140] If there are no obstacles in the mission area, the optimization function is as follows in formula (4):​

[0141] (4)

[0142] Use the following formula (5) to make the next position judgment:

[0143] (5)

[0144] where k represents the neighbor flapping-wing aircraft node, represents the set of neighbor flapping-wing aircraft nodes, represents the neighbor flapping-wing aircraft node 's sensor detection probability, represents the neighbor flapping-wing aircraft node 's position state, represents the node 's sensor attenuation coefficient;

[0145] The gradient descent formula along the axis is as follows in formula (6):

[0146] (6)

[0147] where, represents a point coordinate within the sensor detection range of the flapping-wing aircraft node , represents the abscissa of the current position of the flapping-wing aircraft node ,

[0148] The gradient descent formula along the axis is as follows in formula (7):

[0149] (7)

[0150] If there are obstacles in the mission area, the optimization function is as follows in formula (8):

[0151] (8)

[0152] where, represents the visible area, represents the invisible area, and it is defined that if point , point , and for all , , then the visible area set of point is the set of all points that meet the conditions; , ; Use the following formula (9) to make the next position judgment:

[0153] (9)

[0154] where represents the velocity vector at the boundary point of the visible region ;

[0155] Along the axis, the gradient descent formula is as follows in Equation (10):

[0156] (10)

[0157] Along the axis, the gradient descent formula is as follows in Equation (11):

[0158] (11)

[0159] where q represents the number of reflection vertices ; represents the line connecting the starting point to the intersection point of the ray drawn in the direction of the line connecting each obstacle reflection vertex and the starting point with other obstacles or the boundary; represents the point pointing in the direction of , starting from and having a length of is to the distance value; represents the reflection vertex of the obstacle; represents the sensor detection model function of the flapping-wing aircraft .

[0160] Optionally, the second determination unit 460 is configured to:

[0161] Determine whether the next position of each flapping-wing aircraft is the feasible region F. If the next position of the flapping-wing aircraft is the feasible region F, it is determined that the flapping-wing aircraft does not exceed the boundary of the mission area. If the next position of the flapping-wing aircraft is not the feasible region F, it is determined that the flapping-wing aircraft exceeds the boundary of the mission area;

[0162] If exceeds the boundary of the mission area, adjust the abscissa of the boundary point of the mission area to make inside the mission area;

[0163] If exceeds the boundary of the mission area, adjust the ordinate of the boundary point of the mission area to make inside the mission area.

[0164] Optionally, the determination unit 470 is configured to:

[0165] Set the total displacement cost of each iteration , and calculate according to the following formula (12) :

[0166] (12)

[0167] where represents the coordinate of the flapping-wing aircraft node at time , represents the coordinate of the flapping-wing aircraft node at the previous moment ;

[0168] The obtaining of the optimal coverage position includes:

[0169] Obtaining the target coordinates of each flapping-wing aircraft.

[0170] In the embodiment of the present invention, a flapping-wing aircraft cluster and a mission area are defined; each flapping-wing aircraft in the flapping-wing aircraft cluster is modeled to obtain the position and angle of each flapping-wing aircraft; the coordinate point information of the spatial position of the sensor node of each flapping-wing aircraft projected on the ground and the sensor perception model are obtained; the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft is obtained, and the joint detection probability is calculated according to the information of the neighboring flapping-wing aircraft and the sensor perception model; according to the information of the neighboring flapping-wing aircraft, the position, angle, the coordinate point information of the spatial position of the sensor node projected on the ground of each flapping-wing aircraft, and the joint detection probability of the flapping-wing aircraft cluster, the optimal gradient descent method is used to preliminarily determine the next position of each flapping-wing aircraft; determine whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area, calculate the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster, if the total displacement cost is less than 2, it is determined that the convergence criterion is satisfied, and the optimal coverage position is obtained; otherwise, the above steps are cyclically executed; the flapping-wing aircraft cluster is controlled to perform cooperative coverage according to the optimal coverage position. The present invention finds the position where random events occur in the maximized detection task space according to the known probability map, while maintaining the connectivity constraint between flapping-wing aircraft; by transforming the problem into a distributed constraint optimization problem, each flapping-wing aircraft can consider the information of neighboring flapping-wing aircraft, cooperate with neighboring flapping-wing aircraft, and consider the discontinuity of the sensor detection probability caused by the existence of polygonal obstacles and the limited sensor field of view constraints of each flapping-wing aircraft, and uses the distributed optimal gradient method for solution. This algorithm can ensure the optimal coverage of the area with obstacles under the condition of decentralization and polynomial-level time complexity.

[0171] Figure 5 This is a schematic structural diagram of a flapping-wing aircraft cluster collaborative coverage device provided by an embodiment of the present invention. As shown in Figure 5 In the figure, the flapping-wing aircraft cluster collaborative coverage device may include the above-mentioned Figure 4 distributed flapping-wing aircraft cluster collaborative coverage device shown. Optionally, the flapping-wing aircraft cluster collaborative coverage device 510 may include a first processor 2001.

[0172] Optionally, the flapping-wing aircraft cluster collaborative coverage device 510 may further include a memory 2002 and a transceiver 2003.

[0173] Among them, the first processor 2001 is connected to the memory 2002 and the transceiver 2003, for example, through a communication bus.

[0174] Next, in conjunction with Figure 5 each component of the flapping-wing aircraft cluster collaborative coverage device 510 will be specifically introduced:

[0175] Among them, the first processor 2001 is the control center of the flapping-wing aircraft cluster collaborative coverage device 510, which can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or it can be an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as: one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0176] Optionally, the first processor 2001 can execute various functions of the flapping-wing aircraft cluster collaborative coverage device 510 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0177] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 5 CPU0 and CPU1 shown in

[0178] In a specific implementation, as an embodiment, the flapping-wing aircraft cluster collaborative coverage device 510 may also include multiple processors, such as Figure 5The first processor 2001 and the second processor 2005 shown in []. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0179] Among them, the memory 2002 is used to store the software program for implementing the solution of the present invention and is controlled by the first processor 2001 for execution. The specific implementation manner can refer to the above method embodiments and will not be elaborated here.

[0180] Optionally, the memory 2002 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 can be integrated with the first processor 2001 or exist independently and is coupled to the first processor 2001 through an interface circuit (not shown in []) of the flapping-wing aircraft cluster collaborative coverage device 510. The embodiments of the present invention do not make specific limitations on this. Figure 5 The transceiver can be used to communicate with a network device or with a terminal device.

[0181] The transceiver 2003 is used to communicate with a network device or with a terminal device.

[0182] Optionally, the transceiver 2003 can include a receiver and a transmitter (not shown separately in []). Among them, the receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. Figure 5 The transceiver 2003 is used to communicate with a network device or with a terminal device.

[0183] Optionally, the transceiver 2003 can be integrated with the first processor 2001 or exist independently and is coupled to the first processor 2001 through an interface circuit (not shown in []) of the flapping-wing aircraft cluster collaborative coverage device 510. The embodiments of the present invention do not make specific limitations on this. Figure 5 The transceiver 2003 is used to communicate with a network device or with a terminal device.

[0184] It should be noted that Figure 5 the structure of the flapping-wing aircraft cluster cooperative coverage device 510 shown in [the figure] does not limit the router. The actual knowledge structure recognition device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0185] In addition, the technical effects of the flapping-wing aircraft cluster cooperative coverage device 510 can refer to the technical effects of the distributed flapping-wing aircraft cluster cooperative coverage method described in the above method embodiments, and will not be elaborated here.

[0186] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0187] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0188] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0189] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0190] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0191] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0192] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0193] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0194] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0195] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0197] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0198] As described above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A distributed collaborative coverage method for a flapping-wing aircraft cluster, characterized in that, The method includes: S1. Define a flapping-wing aircraft cluster and a mission area; S2. Model each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft; S3. Obtain the coordinate point information of the spatial position of the sensor node of each flapping-wing aircraft projected on the ground and the sensor perception model; S4. Obtain the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft, and calculate the joint detection probability according to the information of the neighboring flapping-wing aircraft and the sensor perception model; S5. According to the information of the neighboring flapping-wing aircraft, the position, angle, coordinate point information of the spatial position of the sensor node projected on the ground of each flapping-wing aircraft, and the joint detection probability of the flapping-wing aircraft cluster, initially judge the next position of each flapping-wing aircraft by using the optimal gradient descent method; S6. Judge whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area. If it does not exceed the boundary of the mission area, execute S7. If it exceeds the boundary of the mission area, project the point to the inside of the mission area; S7. Calculate the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster. If the total displacement cost is less than 2, it is determined that the convergence criterion is met and the optimal coverage position is obtained; otherwise, go back to execute step S4; S8. Control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position.

2. The distributed flapping-wing aircraft cluster collaborative coverage method according to claim 1, wherein The defining of the flapping-wing aircraft cluster and the mission area in S1 includes: Define the coverage mission space, each flapping-wing aircraft in the flapping-wing aircraft cluster, each obstacle, the position state of each flapping-wing aircraft, the event occurrence probability function, the sensor detection probability, the communication radius, and the total time detection probability of the flapping-wing aircraft cluster. The total time detection probability of the flapping-wing aircraft cluster is as shown in the following formula (1): （1） Among them, the variable node represents the position state of each flapping-wing aircraft; represents the coverage mission space; the event occurrence probability function represents at point the probability of the reconnaissance target appearing, for all points , ; represents the joint detection probability of all sensors at position ; represents the feasible region of the position state of each flapping-wing aircraft.

3. The distributed flapping-wing aircraft cluster cooperative coverage method according to claim 2, wherein, The modeling of each flapping-wing aircraft in the flapping-wing aircraft cluster in S2 to obtain the position and angle of each flapping-wing aircraft includes: Assume that each flapping-wing aircraft in the flapping-wing aircraft cluster flies at a fixed speed and controls its movement only by the speed direction. Model each flapping-wing aircraft in the flapping-wing aircraft cluster as shown in the following formula (2): （2） Among them, represents the position of the flapping-wing aircraft at moment, represents the velocity of the flapping-wing aircraft at moment, , is the real part, representing the abscissa value of the flapping-wing aircraft at moment, is the imaginary unit, is the imaginary part, representing the ordinate value of the flapping-wing aircraft at moment; represents the magnitude of the velocity of the flapping-wing aircraft at is the base of the natural logarithm, represents the angular direction of the flapping-wing aircraft at moment, represents the angular rate of change of the flapping-wing aircraft at moment, represents the control input of the flapping-wing aircraft at moment.

4. The distributed flapping-wing aircraft cluster collaborative coverage method according to claim 3, characterized in that The calculating of the joint detection probability according to the information of the neighboring flapping-wing aircraft and the sensor perception model includes: Calculate the nodes of the flapping-wing aircraft according to the following formula (3) within the communication radius the combined detection probability of all visible points （3） Among them, represents the sensor perception model, the flapping-wing aircraft 's position state, represents at the point the detection probability of the flapping-wing aircraft sensor, represents the visible area, represents the invisible area, and N represents the number of flapping-wing aircraft in the flapping-wing aircraft cluster.

5. The distributed flapping-wing aircraft cluster collaborative coverage method according to claim 4, wherein The initially judging of the next position of each flapping-wing aircraft by using the optimal gradient descent method according to the information of the neighboring flapping-wing aircraft, the position, angle, coordinate point information of the spatial position of the sensor node projected on the ground of each flapping-wing aircraft, and the joint detection probability of the flapping-wing aircraft cluster in S5 includes: If there are no obstacles in the mission area, the optimization function is as shown in the following formula (4): （4） Use the following formula (5) to judge the next position: （5） Among them, k represents the neighbor flapping-wing aircraft node, represents the set of neighbor flapping-wing aircraft nodes, represents the neighbor flapping-wing aircraft node 's sensor detection probability, represents the neighbor flapping-wing aircraft node 's position state, represents the node 's sensor attenuation coefficient; Along The gradient descent formula in the axial direction is as follows in Equation (6): （6） Among them, represents a point coordinate within the sensor detection range of a flapping-wing aircraft node and represents the abscissa of the current position of a flapping-wing aircraft node represents a flapping-wing aircraft node which is the abscissa of the current position. It should be noted that there seems to be some confusion or inaccuracy in the original text structure. The description about " " in the original text is a bit unclear in terms of its complete logical relationship. The above translation tries to make sense of it as best as possible while maintaining the original tags and text content. Along The gradient descent formula in the axial direction is as follows in Equation (7): （7） If there are obstacles in the mission area, the optimization function is as shown in the following formula (8): （8） Among them, represents the visible area, represents the invisible area, and it is defined that if point , point , and for all , , then the set of visible area of point is the set of all points that satisfy the conditions; , ; Use the following formula (9) to make the next position judgment: （9） Among them represents the velocity vector at the boundary point of the visible region ; Along The gradient descent formula along the axis direction is as follows in Equation (10): （10） Along The gradient descent formula in the axial direction is as follows in Equation (11): （11） Among them, q represents the number of reflection vertices . represents a line segment starting from and taking the direction of the line connecting each obstacle reflection vertex to the starting point as the ray, and the intersection point of the ray and other obstacles or boundaries as the end point. represents a point starting from pointing to the direction, with a length of . is the distance value from . represents the reflection vertex of the obstacle. represents the sensor detection model function of the flapping-wing aircraft .

6. The distributed flapping-wing aircraft cluster collaborative coverage method according to claim 5, wherein The judging of whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area in S6 includes: Determine whether the next position of each flapping-wing aircraft is the feasible region F. If the next position of the flapping-wing aircraft is the feasible region F, then determine that the flapping-wing aircraft does not exceed the boundary of the mission area. If the next position of the flapping-wing aircraft is not the feasible region F, then determine that the flapping-wing aircraft exceeds the boundary of the mission area; The projecting the point into the interior of the mission area includes: If exceeds the boundary of the task area, adjust the abscissa of the boundary point of the task area so that is inside the task area; If exceeds the boundary of the task area, adjust the ordinate of the boundary point of the task area so that is inside the task area.

7. The distributed flapping-wing aircraft cluster collaborative coverage method according to claim 6, characterized in that The calculating the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster in S7 includes: Set the total displacement cost for each iteration , and calculate according to the following formula (12) : （12） Among them, represents the coordinates of the flapping-wing aircraft node at a certain moment, and representsthe coordinates of the flapping-wing aircraft node at the previous moment; The obtaining the optimal coverage position includes: Obtain the target coordinates of each flapping-wing aircraft.

8. A distributed flapping-wing aircraft cluster collaborative coverage device, which is used to implement the distributed flapping-wing aircraft cluster collaborative coverage method according to any one of claims 1-7, characterized in that The device includes: A defining unit, configured to define a flapping-wing aircraft cluster and a mission area; A modeling unit, configured to model each flapping-wing aircraft in the flapping-wing aircraft cluster to obtain the position and angle of each flapping-wing aircraft; An obtaining unit, configured to obtain the coordinate point information of the spatial position of the sensor node of each flapping-wing aircraft projected on the ground and the sensor perception model; A calculating unit, configured to obtain the information of the neighboring flapping-wing aircraft within the preset communication radius of each flapping-wing aircraft, and calculate the joint detection probability according to the information of the neighboring flapping-wing aircraft and the sensor perception model; A first judging unit, configured to preliminarily judge the next position of each flapping-wing aircraft by using the optimal gradient descent method according to the information of the neighboring flapping-wing aircraft, the position and angle of each flapping-wing aircraft, the coordinate point information of the spatial position of the sensor node projected on the ground, and the joint detection probability of the flapping-wing aircraft cluster; A second judging unit, configured to judge whether the next position of each flapping-wing aircraft exceeds the boundary of the mission area. If it does not exceed the boundary of the mission area, then execute S7. If it exceeds the boundary of the mission area, then project the point into the interior of the mission area; A determining unit, configured to calculate the total displacement cost of all flapping-wing aircraft in the flapping-wing aircraft cluster. If the total displacement cost is less than 2, then determine that the convergence criterion is satisfied and obtain the optimal coverage position; otherwise, go to execute the steps performed by the calculating unit; A control unit, configured to control the flapping-wing aircraft cluster to perform cooperative coverage according to the optimal coverage position.

9. A flapping-wing aircraft cluster collaborative coverage device, characterized in that, The flapping-wing aircraft cluster cooperative coverage device includes: A processor; A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code, and the program code can be called by the processor to execute the method described in any one of claims 1 to 7.

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