Multi-photoelectric-agent coverage control method in dynamic environment

Abstract

The invention relates to a multi-photoelectric-agent coverage control technology, and provides a multi-photoelectric-agent coverage control method in a dynamic environment in order to solve the problems that an existing search method is weak in autonomy, low in observation efficiency and the like, and the multi-photoelectric-agent coverage control method comprises the steps that n photoelectric agents are arranged in a target area to form a photoelectric network, according to the coordinates of the pointing point of the single photoelectric agent and the coordinates of the current distribution station of the single photoelectric agent, calculating an initial angle corresponding to the pointing point of the single photoelectric agent; carrying out Voronoi segmentation on the target area, and calculating an initial distribution result of each photoelectric agent Voronoi sub-area; according to the distribution condition of moving obstacles in the target area, obtaining the angle cost required for rotating a pointing point of each photoelectric intelligent body to a target point in the target area, and then obtaining the generalized Voronoi centroid of the pointing point; and designing a control law of the photoelectric intelligent agent, and updating an initial angle of a pointing point of each photoelectric intelligent agent by adopting a gradient descent method to complete autonomous planning and control of multi-photoelectric intelligent agent coverage in a dynamic environment.

Description

Technical Field

[0001] The present invention relates to a multi-photoelectric intelligent body optimal coverage control technology, and in particular to a multi-photoelectric intelligent body coverage control method in a dynamic environment. Background Art

[0002] Photoelectric intelligent body is a system that combines photoelectric technology and intelligent technology, and is mainly used to realize the perception, identification and control of objects; specifically, the photoelectric intelligent body realizes the perception and changes of ambient light through photoelectric sensors and intelligent algorithms, so as to automatically adjust and control it.

[0003] In large-scale sky survey missions, optoelectronic intelligent agents can be installed on telescopes to form observation equipment. This observation equipment has the advantages of long observation distance, strong operability, and mutual communication, and has become an important means of astronomical observation. Since optoelectronic intelligent agents are limited by their own observation aperture and imaging time, it takes a long time to complete regional monitoring using a single optoelectronic intelligent agent, and the observation range is limited. Therefore, multiple optoelectronic intelligent agents are required to cooperate with each other to conduct a comprehensive coverage search of the area.

[0004] In a dynamic observation environment, when multiple observation devices perform observation tasks, they usually artificially divide the observation area to realize the joint search operation of multiple optoelectronic intelligent agents. However, the autonomy of this search method is weak, and the observation equipment cannot quickly and effectively observe the dynamically changing environment. The observation efficiency is low and the observation resources are wasted. At the same time, this search method lacks a certain degree of autonomous control ability, and cannot reasonably plan the search area and conduct efficient and comprehensive coverage search of areas where target information exists. Summary of the invention

[0005] In order to solve the problems of weak autonomy, low observation efficiency and inability to conduct efficient and comprehensive coverage search of target information areas in existing multi-photoelectric intelligent body joint search operation methods, the present invention proposes a multi-photoelectric intelligent body coverage control method in a dynamic environment.

[0006] The design idea of ​​the present invention is: to establish mathematical models according to the characteristics of the optoelectronic intelligent body and the observation scene, combine the coverage control theory, calculate the generalized Voronoi center of mass of the dynamic observation area, design the control law of the optoelectronic intelligent body, and make the system finally stabilize to the generalized Voronoi center of mass, so as to realize the autonomous planning and control of the multi-optoelectronic intelligent body system in a dynamic environment, and improve the search efficiency and autonomy of the system.

[0007] To achieve the above objectives, the technical solution proposed by the present invention is:

[0008] A multi-photoelectric intelligent body coverage control method in a dynamic environment, which is special in that it includes the following steps:

[0009] S1. Arrange n optoelectronic agents in the target area to form an optoelectronic network. According to the coordinates of the pointing point of a single optoelectronic agent and the coordinates of the current deployment site of a single optoelectronic agent, calculate and obtain the initial angle corresponding to the pointing point of a single optoelectronic agent;

[0010] S2, performing Voronoi segmentation on the target area according to the initial angle corresponding to the pointing point of each optoelectronic intelligent agent, and calculating and obtaining the initial allocation result of the Voronoi sub-area of ​​each optoelectronic intelligent agent;

[0011] S3. According to the distribution of mobile obstacles in the target area, the angle cost required for each optoelectronic intelligent body to rotate the pointing point to the target point in the target area is obtained through the coverage control objective function of each optoelectronic intelligent body, and then the generalized Voronoi centroid of each optoelectronic intelligent body Voronoi sub-area is calculated;

[0012] S4. Based on the generalized Voronoi centroid of each optoelectronic intelligent body's Voronoi sub-region, the control law of the optoelectronic intelligent body is designed. The gradient descent method is used to update the initial angle of each optoelectronic intelligent body's pointing point, so that each optoelectronic intelligent body rotates to the specified position until each optoelectronic intelligent body is in the optimal distribution state, thereby completing the autonomous planning and control of multi-optoelectronic intelligent body coverage in a dynamic environment.

[0013] Furthermore, step S1 is specifically as follows:

[0014] S1.1. Set the pointing point set of n optoelectronic intelligent agents arranged in the target area as

[0015] S1.2. Establish a three-dimensional coordinate system and obtain the coordinates of the pointing point of a single optoelectronic intelligent body: p i =[x i ,y i ,z i ] T , the current site coordinates of a single optoelectronic intelligent body are: p i0 =[x i0 ,y i0 ,z i0 ] T ,i=1,2,…,n;

[0016] S1.3, according to the coordinates of the single photoelectric intelligent body pointing point obtained in step S1.2 and the coordinates of the current deployment site of the single photoelectric intelligent body, calculate and obtain the initial angle corresponding to the single photoelectric intelligent body pointing point

[0017] Among them, p i =[x i ,y i ,z i ] T Medium iis the X-coordinate value of the point pointed by the i-th photoelectric intelligent agent, y i is the Y coordinate value of the point pointed by the i-th photoelectric intelligent agent, z i is the Z coordinate value of the point pointed by the i-th photoelectric intelligent body; p i0 =[x i0 ,y i0 ,z i0 ] T Medium i0 is the X-coordinate value of the current deployment site of the i-th optoelectronic intelligent body, y i0 is the Y coordinate value of the current deployment site of the i-th optoelectronic intelligent body, z i0 is the Z coordinate value of the current deployment site of the i-th optoelectronic intelligent body.

[0018] Furthermore, in step S1, the coordinates of the point pointed by the single photoelectric intelligent body, the coordinates of the current deployment point of the single photoelectric intelligent body and the initial angle corresponding to the point pointed by the single photoelectric intelligent body satisfy the following equation:

[0019] When x i >x i0 ,y i >y i0 or x i >x i0 ,y i <y i0 hour,

[0020]

[0021] When x i <x i0 ,y i >y i0 hour,

[0022]

[0023] When x i <x i0 ,y i <y i0 hour,

[0024]

[0025] in, is the azimuth of the i-th optoelectronic intelligent body, with the Z axis as the rotation axis, when the pointing point points to the positive direction of the X axis, it is 0 degrees, and the counterclockwise direction along the Z axis is the positive rotation direction; is the pitch angle of the ith photoelectric intelligent body, and the direction obeys the right-hand rule; when the coordinates of the pointing point of the ith photoelectric intelligent body coincide with the X-axis, the azimuth angle of the photoelectric intelligent body is and pitch angle Both are at zero position.

[0026] Furthermore, step S2 is specifically as follows:

[0027] According to the pointing point set P of n optoelectronic agents, the target area is Voronoi segmented to obtain the initial allocation result of each optoelectronic agent Voronoi sub-area, so that each Voronoi sub-area contains a pointing point of the optoelectronic agent;

[0028] When performing Voronoi segmentation, the initial allocation result of each Voronoi sub-region of the optoelectronic intelligent agent is obtained by the following formula:

[0029]

[0030] Among them, V i is the Voronoi subregion of the i-th optoelectronic agent, D is the target region, q is the target point in the target region, q∈D, θ q is the pointing angle of the i-th optoelectronic agent when it points to the target point q, is the current pointing angle of the jth photoelectric intelligent agent, and N is the set of individual photoelectric intelligent agents;

[0031] The target point q in the target area and its corresponding pointing angle θ q The following conversion relationships exist:

[0032] θ q =π i (q), where π i is a relational function.

[0033] Furthermore, step S3 is specifically as follows:

[0034] S3.1. Establish a mobile obstacle model in the target area. The mobile obstacle model is a Gaussian function. The environmental information function φ(q, t) in the target area is calculated by the following formula:

[0035]

[0036] Among them, t is the current time, T1 is the time constant, y t 0 is the y-axis coordinate of the mean value of the environmental information function, z t 0 is the z-axis coordinate of the mean value of the environmental information function, set T = 5, y t 0=1, z t 0=2;

[0037] S3.2. Calculate the coverage control objective function H(P,t) of each optoelectronic intelligent agent by the following formula to obtain the minimum value of the coverage control objective function:

[0038]

[0039] in, is the measurement cost function, which represents a single photoelectric agent pointing to point p i The angle cost required to rotate to the target point q;

[0040] Said The measurement cost function satisfies the following formula:

[0041]

[0042] S3.3. According to the coverage control objective function of each optoelectronic agent, the generalized Voronoi centroid of each optoelectronic agent Voronoi sub-region is calculated by the following formula:

[0043]

[0044] in, is the quality of the Voronoi subregion of the i-th optoelectronic agent,

[0045] Further, step S4 is specifically as follows:

[0046] S4.1. The control law of the designed optoelectronic intelligent agent satisfies the following formula:

[0047]

[0048] The control model of the i-th optoelectronic agent is:

[0049]

[0050] Among them, u i is the control model of the i-th optoelectronic agent, is the angular acceleration of the ith photoelectric agent, ranging from 0 to 20° / s 2 ;

[0051] The derivation process is as follows:

[0052]

[0053] Among them, k p is the proportionality coefficient, k v is the differential coefficient, and k p , k v are greater than zero, is the angular velocity of the i-th optoelectronic intelligent agent, ranging from 0 to 20° / s;

[0054] S4.2, using the gradient descent method, to derive the control objective function H(P,t);

[0055] The derivative calculation formula of the control objective function H(P,t) is:

[0056]

[0057] The first-order derivative calculation of the control objective function H(P,t) is performed:

[0058]

[0059]

[0060] because are bounded, that is, when k v When big enough, satisfy

[0061] Where: S i is the energy function, ε is the Lyapunov function;

[0062] S4.3, according to the control law of the optoelectronic intelligent body in step S4.1, update the initial angle of each optoelectronic intelligent body pointing point, so that each optoelectronic intelligent body rotates to the specified position. When , each optoelectronic intelligent agent is in the optimal distribution state; when When , it returns to step S2 and re-performs Voronoi segmentation until each optoelectronic intelligent agent is in the optimal distribution state, thereby completing the autonomous planning and control of multi-optoelectronic intelligent agent coverage in a dynamic environment.

[0063] Beneficial effects of the present invention:

[0064] [1] The present invention provides a multi-photoelectric intelligent body coverage control method in a dynamic environment. The method divides a large target area reasonably through Voronoi segmentation, so that each photoelectric intelligent body is allocated to the best scanning area to avoid cloud cover, and the target area is comprehensively and collaboratively covered. The photoelectric intelligent body control algorithm is designed to allocate resources to the best observation area, which effectively improves the search efficiency of multiple photoelectric intelligent bodies, avoids missed scans and repeated searches, improves the search efficiency and autonomy of the system, saves search time, and ensures the validity and reliability of search data information.

[0065] [2] The present invention uses Voronoi segmentation to coordinate coverage and observation of the target area, providing an effective coverage control method for multi-photoelectric intelligent systems. It can perform real-time regulation and control for environments with dynamic cloud coverage, reduce human intervention, improve observation efficiency, and realize large-scale sky survey tasks in the fields of environmental monitoring, target orbit determination, situation awareness, etc., with strong practicality.

[0066] [3] The present invention regards multiple optoelectronic intelligent agents as second-order systems, performs Voronoi segmentation on the target pointing area, solves the generalized Voronoi center of mass of the Voronoi area, constructs a control objective function at the cost of the rotation range of the optoelectronic intelligent agent, designs the control law of the optoelectronic intelligent agent using a gradient descent method, updates the pointing points of each optoelectronic intelligent agent, and achieves efficient coverage of the observable area in an environment where cloud cover is partially obscured. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of an embodiment of a multi-photoelectric intelligent body coverage control method in a dynamic environment of the present invention;

[0068] Figure 2 A schematic diagram of a single optoelectronic intelligent body pointing point in an embodiment of the present invention;

[0069] Figure 3 This is a schematic diagram of the results of cooperative coverage control of a pentagonal convex plane area by 50 optoelectronic intelligent bodies in an embodiment of the present invention;

[0070] in:

[0071] (a) is the schematic diagram of coverage control at t=1s;

[0072] (b) is the schematic diagram of coverage control at t=10s;

[0073] (c) is the schematic diagram of coverage control at t = 20s;

[0074] (d) is the schematic diagram of coverage control at t=30s;

[0075] (e) is the schematic diagram of coverage control at t = 40s;

[0076] (f) is the schematic diagram of coverage control at t = 50s;

[0077] (g) is the schematic diagram of coverage control at t=60s;

[0078] (h) is the coverage control diagram at t=70s. DETAILED DESCRIPTION

[0079] like Figure 1 As shown, a multi-photoelectric intelligent body coverage control method in a dynamic environment includes the following steps:

[0080] S1. Arrange n optoelectronic agents in the target area to form an optoelectronic network. According to the coordinates of the pointing point of a single optoelectronic agent and the coordinates of the current deployment site of a single optoelectronic agent, calculate and obtain the initial angle corresponding to the pointing point of a single optoelectronic agent;

[0081] S1.1. Set the pointing point set of n optoelectronic intelligent agents arranged in the target area as

[0082] S1.2, such as Figure 2 As shown, a three-dimensional coordinate system is established to obtain the coordinates of the pointing point of a single optoelectronic intelligent body: p i =[x i ,y i ,z i ] T , the current site coordinates of a single optoelectronic intelligent body are: p i0 =[x i0 ,y i0 ,z i0 ] T ,i=1,2,…,n;

[0083] S1.3. Calculate and obtain the initial angle corresponding to the pointing point of a single photoelectric intelligent body according to step S1.2

[0084] The coordinates of the point pointed by a single photoelectric intelligent body, the coordinates of the current deployment site of a single photoelectric intelligent body and the initial angle corresponding to the point pointed by a single photoelectric intelligent body satisfy the following equation:

[0085] When x i >x i0 ,y i >y i0 or x i >x i0 ,y i <y i0 hour,

[0086]

[0087] When x i <x i0 ,y i >y i0 hour,

[0088]

[0089] When x i <x i0 ,y i <y i0 hour,

[0090]

[0091] in, is the azimuth of the i-th optoelectronic intelligent body, with the Z axis as the rotation axis, when the pointing point points to the positive direction of the X axis, it is 0 degrees, and the counterclockwise direction along the Z axis is the positive rotation direction; is the pitch angle of the ith photoelectric intelligent body, the direction obeys the right-hand rule, and T is the time constant; when the coordinates of the pointing point of the ith photoelectric intelligent body coincide with the X-axis, the azimuth angle of the photoelectric intelligent body and pitch angle Both are at zero position.

[0092] p i =[x i ,y i ,z i ] T Medium i is the X-coordinate value of the point pointed by the i-th photoelectric intelligent agent, y i is the Y coordinate value of the point pointed by the i-th photoelectric intelligent agent, z i is the Z coordinate value of the point pointed by the i-th photoelectric intelligent body; p i0 =[x i0 ,y i0 ,z i0 ] T Medium i0 is the X-coordinate value of the current deployment site of the i-th optoelectronic intelligent body, y i0 is the Y coordinate value of the current deployment site of the i-th optoelectronic intelligent body, z i0 is the Z coordinate value of the current deployment site of the i-th optoelectronic intelligent body.

[0093] S2, perform Voronoi segmentation on the target area according to the observation effect of each optoelectronic intelligent body's pointing point, and calculate and obtain the initial allocation result of each optoelectronic intelligent body's Voronoi sub-area;

[0094] According to the pointing point set P of n optoelectronic agents, the target area is Voronoi segmented to obtain the initial allocation result of each optoelectronic agent Voronoi sub-area, so that each Voronoi sub-area contains a pointing point of the optoelectronic agent;

[0095] When performing Voronoi segmentation, the initial allocation result of each Voronoi sub-region of the optoelectronic intelligent agent is obtained by the following formula:

[0096]

[0097] Among them, V i is the Voronoi subregion of the i-th optoelectronic agent, D is the target region, q is the target point in the target region, q∈D, θ q is the pointing angle of the i-th optoelectronic agent when it points to the target point q, is the current pointing angle of the jth photoelectric intelligent agent, and N is the set of individual photoelectric intelligent agents;

[0098] The target point q in the target area and its corresponding pointing angle θ q The following conversion relationships exist:

[0099] θ q =π i (q), where π i is a relational function.

[0100] S3, according to the distribution of mobile obstacles in the target area, the initial allocation result is quantified by the coverage control objective function of each optoelectronic intelligent agent, and the generalized Voronoi centroid of each optoelectronic intelligent agent Voronoi sub-area in step S2 is calculated;

[0101] S3.1. Establish a mobile obstacle model in the target area. The mobile obstacle model is a Gaussian function. The environmental information function φ(q, t) in the target area is calculated by the following formula:

[0102]

[0103] Among them, t is the current time, y t 0 is the y-axis coordinate of the mean value of the environmental information function, z t 0 is the z-axis coordinate of the mean value of the environmental information function, set T = 5, y t 0=1, z t 0=2;

[0104] S3.2. Calculate the coverage control objective function H(P,t) of each optoelectronic intelligent agent by the following formula to obtain the minimum value of the coverage control objective function:

[0105]

[0106] in, is the measurement cost function, which represents a single photoelectric agent pointing to point p i The angle cost required to rotate to the target point q;

[0107] Said The measurement cost function satisfies the following formula:

[0108]

[0109] S3.3. According to the coverage control objective function of each optoelectronic agent, the generalized Voronoi centroid of each optoelectronic agent Voronoi sub-region is calculated by the following formula:

[0110]

[0111] in, is the quality of the Voronoi subregion of the i-th optoelectronic agent,

[0112] S4. Based on the generalized Voronoi centroid of each optoelectronic intelligent body's Voronoi sub-region, the control law of the optoelectronic intelligent body is designed. The gradient descent method is used to update the initial angle of each optoelectronic intelligent body's pointing point, so that each optoelectronic intelligent body rotates to the specified position until each optoelectronic intelligent body is in the optimal distribution state, thereby completing the autonomous planning and control of multi-optoelectronic intelligent body coverage in a dynamic environment.

[0113] S4.1. The control law of the designed optoelectronic intelligent agent satisfies the following formula:

[0114]

[0115] The control model of the i-th optoelectronic agent is:

[0116]

[0117] Among them, u i is the control model of the i-th optoelectronic intelligent agent; is the angular acceleration of the ith photoelectric agent, ranging from 0 to 20° / s 2 ;

[0118] The derivation process is as follows:

[0119]

[0120] Among them, k p is the proportionality coefficient, k v is the differential coefficient, and k p , k v are greater than zero, is the angular velocity of the i-th optoelectronic intelligent agent, ranging from 0 to 20° / s;

[0121] S4.2, using the gradient descent method, to derive the control objective function H(P,t);

[0122] The derivative calculation formula of the control objective function H(P,t) is:

[0123]

[0124] The first-order derivative calculation of the control objective function H(P,t) is performed:

[0125]

[0126] because are bounded, that is, when k v When big enough, satisfy

[0127] Where: S i is the energy function, ε is the Lyapunov function;

[0128] S4.3, according to the control law of the optoelectronic intelligent body in step S4.1, update the initial angle of each optoelectronic intelligent body pointing point, so that each optoelectronic intelligent body rotates to the specified position. When , each optoelectronic intelligent agent is in the optimal distribution state; when When , it returns to step S2 and re-performs Voronoi segmentation until each optoelectronic intelligent agent is in the optimal distribution state, thereby completing the autonomous planning and control of multi-optoelectronic intelligent agent coverage in a dynamic environment.

[0129] According to the above-mentioned multi-photoelectric intelligent body coverage control method, a multi-agent system is composed of 50 photoelectric intelligent bodies to collaboratively cover the target area as a pentagonal convex plane area. The x-axis range of the photoelectric intelligent body pointing point coordinates in the target area is [-1.6, 2], the y-axis range is [-1.9, 1.9], the z-axis range = 10, and the current deployment site coordinates of the photoelectric intelligent body are [-3+0.01*(i-5*X), -3+0.01*

[0130] (i%5+1),0], the unit is km.

[0131] Among them: X represents the largest integer that makes 5X>i, % represents the remainder, that is, the optoelectronic intelligent agents are arranged in a square array, and the distance between adjacent intelligent agents in the same row or column is 10m. The mathematical model of information distribution in a dynamic environment is φ(q,t); the maximum speed is 20° / s, and the maximum acceleration is 20° / s2; Figure 3 As shown in (a)-(h) in the figure, the optimal distribution states of the pointing points of each optoelectronic intelligent agent are obtained at t=1s, 10s, 20s, 30s, 40s, 50s, 60s, and 70s, respectively, completing the autonomous planning and control of multi-optoelectronic intelligent agent coverage in a dynamic environment.

Claims

1. A multi-photoelectric intelligent coverage control method in a dynamic environment, characterized in that: The following steps are involved: S1. Arrange n optoelectronic agents in the target area to form an optoelectronic network. According to the coordinates of the pointing point of a single optoelectronic agent and the coordinates of the current deployment site of a single optoelectronic agent, calculate and obtain the initial angle corresponding to the pointing point of a single optoelectronic agent; S2, performing Voronoi segmentation on the target area according to the initial angle corresponding to the pointing point of each optoelectronic intelligent agent, and calculating and obtaining the initial allocation result of the Voronoi sub-area of ​​each optoelectronic intelligent agent; S3. According to the distribution of mobile obstacles in the target area, the angle cost required for each optoelectronic intelligent body to rotate the pointing point to the target point in the target area is obtained through the coverage control objective function of each optoelectronic intelligent body, and then the generalized Voronoi centroid of each optoelectronic intelligent body Voronoi sub-area is calculated; S4. Based on the generalized Voronoi centroid of each optoelectronic intelligent body's Voronoi sub-region, the control law of the optoelectronic intelligent body is designed. The gradient descent method is used to update the initial angle of each optoelectronic intelligent body's pointing point, so that each optoelectronic intelligent body rotates to the specified position until each optoelectronic intelligent body is in the optimal distribution state, thereby completing the autonomous planning and control of multi-optoelectronic intelligent body coverage in a dynamic environment.

2. According to the multi-photoelectric intelligent body coverage control method in a dynamic environment as described in claim 1, it is characterized in that: Step S1 is specifically as follows: S1.

1. Set the pointing point set of n optoelectronic intelligent agents arranged in the target area as S1.

2. Establish a three-dimensional coordinate system and obtain the coordinates of the pointing point of a single optoelectronic intelligent body: p i =[x i ,y i ,z i ] T , the current site coordinates of a single optoelectronic intelligent body are: p i0 =[x i0 ,y i0 ,z i0 ] T ,i=1,2,…,n; S1.3, according to the coordinates of the single photoelectric intelligent body pointing point obtained in step S1.2 and the coordinates of the current deployment site of the single photoelectric intelligent body, calculate and obtain the initial angle θ corresponding to the single photoelectric intelligent body pointing point pi .

3. According to the multi-photoelectric intelligent body coverage control method in a dynamic environment as described in claim 2, it is characterized by: In step S1.3, the coordinates of the point pointed by a single photoelectric intelligent body, the coordinates of the current deployment site of a single photoelectric intelligent body and the initial angle corresponding to the point pointed by a single photoelectric intelligent body satisfy the following equation: When x i >x i0 ,y i >y i0 or x i >x i0 ,y i <y i0 hour, When x i <x i0 ,y i >y i0 hour, When x i <x i0 ,y i <y i0 hour, in, is the azimuth of the i-th optoelectronic agent, is the pitch angle of the ith photoelectric intelligent body; when the coordinates of the pointing point of the ith photoelectric intelligent body coincide with the X-axis, the azimuth angle of the photoelectric intelligent body and pitch angle Both are at zero position.

4. According to the multi-photoelectric intelligent body coverage control method in a dynamic environment as described in claim 3, it is characterized in that: Step S2 is specifically as follows: Perform Voronoi segmentation on the target area according to the pointing point set P of n optoelectronic agents, and obtain the initial allocation result of each optoelectronic agent Voronoi sub-area, so that each Voronoi sub-area contains a pointing point of the optoelectronic agent; When performing Voronoi segmentation, the initial allocation result of each optoelectronic agent Voronoi sub-region is obtained by the following formula: Among them, V i is the Voronoi subregion of the i-th optoelectronic agent, D is the target region, q is the target point in the target region, q∈D, θ q is the pointing angle of the i-th optoelectronic agent when it points to the target point q, is the current pointing angle of the jth photoelectric intelligent agent, and N is the set of individual photoelectric intelligent agents; The target point q in the target area and its corresponding pointing angle θ q The following conversion relationships exist: θ q =π i (q), where π i is a relational function.

5. The multi-photoelectric intelligent coverage control method in a dynamic environment according to claim 4, characterized in that: Step S3 is specifically as follows: S3.

1. Establish a mobile obstacle model in the target area. The mobile obstacle model is a Gaussian function. The environmental information function φ(q, t) in the target area is calculated by the following formula: Among them, t is the current time, T1 is the time constant, y t0 is the y-axis coordinate of the mean value of the environmental information function, z t0 is the z-axis coordinate of the mean value of the environmental information function, set T1 = 5, y t0 =1, z t0 =2; S3.

2. Calculate the coverage control objective function H(P,t) of each optoelectronic intelligent agent by the following formula: Among them, f(θ pi ,θ q ) is the measurement cost function, which indicates that a single optoelectronic agent points to point p i The angle cost required to rotate to the target point q; The f(θ pi ,θ q )The measurement cost function satisfies the following formula: S3.

3. According to the coverage control objective function of each optoelectronic agent, the generalized Voronoi centroid of each optoelectronic agent Voronoi sub-region is calculated by the following formula: in, is the quality of the Voronoi subregion of the i-th optoelectronic agent, 6. The multi-photoelectric intelligent body coverage control method in a dynamic environment according to claim 5, characterized in that: Step S4 is specifically as follows: S4.

1. The control law of the designed optoelectronic intelligent agent satisfies the following formula: The control model of the i-th optoelectronic agent is: Among them, u i is the control model of the i-th optoelectronic agent, is the angular acceleration of the ith photoelectric agent, ranging from 0 to 20° / s 2 ; k p is the proportionality coefficient, k v is the differential coefficient, and k p , k v are greater than zero, is the angular velocity of the i-th optoelectronic intelligent agent, ranging from 0 to 20° / s; S4.2, using the gradient descent method, to derive the control objective function H(P,t); The derivative calculation formula of the control objective function H(P,t) is: The first-order derivative calculation of the control objective function H(P,t) is performed: because are bounded, that is, when k v When big enough, satisfy Where: S i is the energy function, ε is the Lyapunov function; S4.3, according to the control law of the optoelectronic intelligent body in step S4.1, update the initial angle of each optoelectronic intelligent body pointing point, so that each optoelectronic intelligent body rotates to the specified position. When , each optoelectronic intelligent agent is in the optimal distribution state; when When , it returns to step S2 and re-performs Voronoi segmentation until each optoelectronic intelligent agent is in the optimal distribution state, thereby completing the autonomous planning and control of multi-optoelectronic intelligent agent coverage in a dynamic environment.

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