A multi-target unmanned boat formation collaborative capture method and related equipment

Abstract

The present invention provides a multi-target unmanned boat formation collaborative capture method and related equipment. The method performs distributed task allocation on each unmanned boat in the unmanned boat formation to obtain an initial task allocation result, and performs centralized allocation through constraint conditions to determine the number of unmanned boats for capturing each capture target. The method plans the path of each unmanned boat to its rotation area based on the set virtual speed component and the hybrid task allocation result, thereby improving the obstacle avoidance performance. When each unmanned boat enters the rotation area, the first rotation speed in the virtual speed component is adaptively adjusted to obtain a second rotation speed, and the unmanned boats entering the rotation area are controlled based on the second rotation speed to capture multiple capture targets. The method can control any number of unmanned boats to capture targets in a uniformly distributed formation, thereby overcoming the uncertainty caused by the number of unmanned boats in the formation and obstacles in the environment during the dynamic formation process.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat intelligent control, and in particular to a multi-target oriented unmanned boat formation collaborative capture method and related equipment. Background Art

[0002] In recent years, the role of the ocean in national security has become increasingly prominent. It not only provides vital strategic resources but also plays a crucial role in maintaining national border security and ensuring maritime trade and freedom of navigation. Against this backdrop, unmanned boats (UAVs) have emerged as a new type of maritime vehicle. Their efficiency, flexibility, and safety have made them a crucial tool for ocean exploration and patrol. The development of UAV technology offers a new approach and approach to the development and utilization of the ocean, and is of vital importance to safeguarding national security.

[0003] Due to the complex and ever-changing ocean environment, it is difficult for a single unmanned vehicle to complete diverse maritime tasks. The collaborative completion of multiple unmanned vehicles is an important approach to this problem. Therefore, the collaborative control of multiple unmanned vehicles has received great attention and extensive research. The problem of collaborative capture by multiple unmanned vehicles can be divided into three processes: task allocation, path planning, and dynamic formation capture. During the task allocation process, the allocation schemes of each unmanned vehicle are coupled with each other and change dynamically with the mission scenario. How to quickly obtain a reasonable allocation scheme is the current research difficulty. Path planning mainly considers real-time issues. As the number of unmanned vehicles increases, the computational complexity of the system will increase exponentially. In scenarios where unmanned vehicles and targets move simultaneously, some global path search algorithms such as the A* algorithm and the RRT algorithm are difficult to meet the higher real-time requirements. Dynamic formation capture can reduce the probability of target escape during the capture process. The formation maintenance and adjustment during obstacle avoidance are important tests of the complexity and response speed of the formation capture algorithm, and it is necessary to balance capture efficiency and system stability. Summary of the Invention

[0004] The present invention provides a multi-target unmanned boat formation collaborative capture method and related equipment, the purpose of which is to achieve reasonable target allocation and obstacle avoidance during the process of capturing targets.

[0005] In order to achieve the above object, the present invention provides a multi-target unmanned boat formation collaborative capture method, comprising:

[0006] Step 1: Obtain the estimated distance between each unmanned boat in the unmanned boat formation and each encirclement target, and the number of unmanned boats around each encirclement target;

[0007] Step 2: Distribute tasks among the unmanned boats in the unmanned boat formation according to the estimated distance between each unmanned boat and each target and the number of unmanned boats around each target to obtain an initial task allocation result. The initial task allocation result is then centrally allocated based on the constraint conditions to obtain a hybrid task allocation result, which is the number of unmanned boats required to capture each target.

[0008] Step 3: For each unmanned boat in the unmanned boat formation, the path of the unmanned boat to its rotation area is planned based on the set virtual speed components and the hybrid task allocation results. The virtual speed components include the target approach speed, the first repulsive speed between the unmanned boats, the second repulsive speed between the unmanned boat and the target, the rotation obstacle avoidance speed, and the first rotation speed.

[0009] Step 4: When each unmanned boat enters the rotation area, the first rotation speed is adaptively adjusted to obtain a second rotation speed, and the unmanned boats entering the rotation area are controlled based on the second rotation speed to capture multiple capture targets.

[0010] To further explain, the initial task allocation results are:

[0011]

[0012]

[0013] in, Indicates the serial number value of the target selected by unmanned boat i, represents the initial decision matrix, i=1,2,…,n, w1 and w2 represent the weights of decision indicators, s ij represents the estimated distance between the unmanned boat i and the capture target j, n ij represents the number of unmanned boats surrounding the capture target j, c ij represents the decision score of UAV i on the capture target j, j = 1, 2, ..., m, d ij represents the straight-line distance between the unmanned boat i and the capture target j, μ represents the obstacle influence factor, and n io represents the number of obstacles that may collide on the straight line connecting the unmanned boat i and the capture target j, represents the vertical distance from the center of the obstacle k to the straight line, r o represents the safety radius of obstacle k.

[0014] Furthermore, by centrally allocating the initial task allocation results through constraints, a hybrid task allocation result is obtained, including:

[0015] Grouping all the unmanned boats in the unmanned boat formation according to the capture targets selected by each unmanned boat in the unmanned boat formation;

[0016] When the number of unmanned boats selecting the same capture target is greater than or equal to a preset threshold, the unmanned boats selecting the same capture target and the selected capture targets are divided into a high-density group, wherein the selected capture targets include multiple targets;

[0017] When the number of unmanned boats selecting the same capture target is lower than a preset threshold, the unmanned boats selecting the same capture target and the selected capture targets are divided into a low-density group, wherein the selected capture targets include multiple targets;

[0018] Determine whether the initial task allocation results meet the constraints;

[0019] When the initial task allocation result meets the constraint conditions, the initial task allocation result is used as the task allocation result of the unmanned boat formation;

[0020] When the initial task allocation result does not meet the constraint conditions, the sum of the motion path lengths of the unmanned boats in the low-density group is used as the optimization target. According to the optimization target, the unmanned boats in the low-density group are optimized and allocated on the basis of the initial task allocation result to obtain a hybrid task allocation result, which is the number of unmanned boats required to capture each target.

[0021] More specifically, the constraints include:

[0022] Each unmanned boat in the high-density group can only select one of the multiple selected capture targets in the low-density group;

[0023] The number of UAVs assigned to each selected capture target in the high-density group cannot be lower than the preset threshold;

[0024] The number of unmanned boats assigned to each selected capture target in the low-density group must be equal to a preset threshold.

[0025] Furthermore, the optimization equation for optimizing the allocation of unmanned boats in the low-density group based on the initial task allocation results according to the optimization objective is:

[0026]

[0027]

[0028] Among them, H k represents the decision matrix of each high-density group for the low-density group, S k Represents the estimated distance matrix of each high-density group to the low-density group, G represents the optimization objective function, Denotes the decision matrix H kand distance matrix The trace of the matrix resulting from the multiplication, represents the decision variable of the mth unmanned boat in the kth group in the high-density group for the nth target in the low-density group, r represents the number of high-density groups, q represents the number of low-density groups, and p k Indicates the number of unmanned boats in the kth group in the high-density group, n b Indicates the baseline value of the number of unmanned boats required to capture a single target, n n Indicates the number of unmanned boats initially assigned to the nth capture target in the low-density group.

[0029] Furthermore, the virtual velocity component v itotal for:

[0030]

[0031] Among them, α represents the attenuation factor, α n represents the nth-order attenuation factor, v iatt represents the target approach speed, represents the first repulsive velocity, represents the second repulsive velocity, v irot Indicates the speed of rotation to avoid obstacles, v icircle1 Indicates the first rotation speed.

[0032] Furthermore, the target approach speed is used to control the unmanned boat to approach the target. The expression of the target approach speed is:

[0033] v iatt =[v0+C t ·D(r ig -r e , a t , p t )]·r iatt

[0034] Among them, v0 represents the minimum approach speed, C t represents the approach velocity gain coefficient, D(·) represents the velocity function, a t Indicates the maximum permissible acceleration, p t Represents the gain of the braking process, r ig represents the straight-line distance between the unmanned boat and the target, r e represents the expected capture radius, r iatt Represents the unit approach velocity vector, the direction from the unmanned boat to the capture target:

[0035] The first repulsive velocity is the repulsive velocity between the unmanned boats. The expression of the first repulsive velocity is:

[0036]

[0037] Among them, k ar Represents the repulsion coefficient between unmanned boats, r ia represents the straight-line distance between the two unmanned boats, r a Indicates the safe distance between unmanned boats. Represents the unit repulsive velocity vector between the UAVs, with the direction from other UAVs to the current UAV;

[0038] The second repulsive speed is the repulsive speed between the unmanned boat and the target. The expression of the second repulsive speed is:

[0039]

[0040] Among them, k gr Represents the repulsive force coefficient between the unmanned boat and the target, r g Indicates the safe distance between the unmanned boat and the target. Represents the unit repulsive velocity vector between the UAV and the target, with the direction from the target to the current UAV;

[0041] The rotation obstacle avoidance speed is used to avoid obstacles. The expression of the rotation obstacle avoidance speed is:

[0042]

[0043] Among them, k rot Represents the gain coefficient of the rotation obstacle avoidance speed, r irot Represents the unit rotation obstacle avoidance velocity vector, and Flag represents the obstacle avoidance flag;

[0044] The first rotational speed is used to prevent the capture target from escaping. The expression of the first rotational speed is:

[0045]

[0046] Among them, k c Represents the rotation speed gain coefficient, r icircle1 represents the first unit rotation velocity vector, which is perpendicular to the line between the unmanned boat and the target, r c Indicates the desired rotation radius.

[0047] Furthermore, the second rotation speed is:

[0048]

[0049] Among them, v m Indicates the maximum rotation speed, r icircle2 Represents the second unit rotation velocity vector, which is perpendicular to the line between the unmanned boat and the target.

[0050] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, a multi-target unmanned boat formation collaborative capture method is implemented.

[0051] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, a multi-target unmanned boat formation collaborative capture method is implemented.

[0052] The above solution of the present invention has the following beneficial effects:

[0053] Compared with the prior art, the present invention performs distributed task allocation on each unmanned boat in the unmanned boat formation by obtaining the estimated distance between each unmanned boat and each capture target in the unmanned boat formation, and the number of unmanned boats around each capture target, to obtain an initial task allocation result, and centrally allocates the initial task allocation result through constraint conditions to determine the number of unmanned boats to capture each capture target, effectively balancing the efficiency and quality of task allocation; based on the set virtual speed component and the hybrid task allocation result, the path of each unmanned boat to its rotation area is planned, which solves the dead zone stagnation and local oscillation problems of multiple unmanned boats in the obstacle avoidance process and improves the obstacle avoidance performance; when each unmanned boat enters the rotation area, the first rotation speed in the virtual speed component is adaptively adjusted to obtain a second rotation speed, and based on the second rotation speed, the unmanned boats after entering the rotation area are controlled to capture multiple capture targets, which can control any number of unmanned boats to capture targets in a uniformly distributed formation, overcoming the uncertainty caused by the number of formation unmanned boats and obstacles in the environment during the dynamic formation process.

[0054] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a flow chart of an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of the motion path of the unmanned boat in an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of simulation results of distributed and hybrid task allocation in an embodiment of the present invention;

[0058] Figure 4 Optimization process curve diagram of the genetic particle swarm algorithm in the embodiment of the present invention;

[0059] Figure 5 Schematic diagram of the process of selecting the rotation obstacle avoidance speed according to an embodiment of the present invention;

[0060] Figure 6 Schematic diagram comparing the obstacle avoidance effects of repulsion speed and rotation speed in an embodiment of the present invention;

[0061] Figure 7 Schematic diagram of the round-up process in an embodiment of the present invention;

[0062] Figure 8 Schematic diagram comparing distance error and angle error in an embodiment of the present invention;

[0063] Figure 9 Schematic diagram of simulation results of adaptive uniform capture according to an embodiment of the present invention;

[0064] Figure 10 Schematic diagram of simulation results of dynamic multi-target capture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To make the technical problems, technical solutions, and advantages to be solved by the present invention more clear, the following is a detailed description with reference to the accompanying drawings and specific embodiments. It is obvious that the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0066] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0067] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to a locking connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0068] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0069] In response to the existing problems, the present invention provides a multi-target unmanned boat formation collaborative capture method and related equipment.

[0070] like Figure 1 As shown, an embodiment of the present invention provides a multi-target unmanned boat formation collaborative capture method, comprising:

[0071] Step 1: Obtain the estimated distance between each unmanned boat in the unmanned boat formation and each encirclement target, and the number of unmanned boats around each encirclement target;

[0072] Step 2: Distribute tasks for each unmanned boat in the unmanned boat formation according to the estimated distance between each unmanned boat and each target and the number of unmanned boats around each target to obtain an initial task allocation result. Then, perform a centralized allocation on the initial task allocation result based on the constraint conditions to obtain a hybrid task allocation result. The hybrid task allocation result is the number of unmanned boats required to capture each target.

[0073] Step 3: For each unmanned boat in the unmanned boat formation, the path of the unmanned boat to its rotation area is planned based on the set virtual speed components and the hybrid task allocation results. The virtual speed components include the target approach speed, the first repulsive speed between the unmanned boats, the second repulsive speed between the unmanned boat and the target, the rotation obstacle avoidance speed, and the first rotation speed.

[0074] Step 4: When each unmanned boat enters the rotation area, the first rotation speed is adaptively adjusted to obtain a second rotation speed, and the unmanned boats entering the rotation area are controlled based on the second rotation speed to capture multiple capture targets.

[0075] Specifically, step 1 includes:

[0076] Assume that there are n unmanned boats, m capture targets and l obstacles in the maritime capture scenario, and the unmanned boat formation is A = {a1, a2, ..., a n}, the capture target set is T = {t1, t2, ..., t m}, the obstacle set is O = {o1, o2, ..., o l In order to ensure that the unmanned boats can effectively capture the targets, the number of unmanned boats must be strictly greater than the number of targets, and the specific relationship that needs to be satisfied is n≥n b *m,n b Indicates the minimum number of unmanned boats required to capture a target. In this embodiment of the present invention, n b =3, the capture target can be an unmanned boat, a ship to be rescued, etc.

[0077] There are two types of task allocation methods for cluster systems: centralized and distributed. The present invention combines the advantages of both and adopts a two-stage task allocation method. The first stage uses a distributed method to reduce task allocation time and improve task allocation efficiency. The second stage uses a centralized planning algorithm based on the initial allocation results to perform a secondary allocation for unreasonable allocation plans to ensure the effectiveness of the final allocation plan. The specific implementation process is as follows:

[0078] In the distributed task allocation process, all UAVs make autonomous decisions based solely on the information between themselves and the targets. The decision-making information used in this embodiment of the present invention includes the estimated distance between the UAVs and the targets, as well as the number of UAVs surrounding each target. The decision matrix is ​​expressed as follows:

[0079]

[0080] in, represents the initial decision matrix, i = 1, 2, ..., n, w1 and w2 represent the weights of the decision indicators, which include the estimated distance and the number of unmanned boats around the target, s ij It represents the estimated distance between the unmanned boat i and the target j, n ij represents the number of unmanned boats surrounding the capture target j, c ij represents the decision score of UAV i on the capture target j, j = 1, 2, ..., m; the lower the decision score, the higher the priority of the capture target;

[0081] Since the impact of obstacles on the movement path needs to be considered during the roundup process, Figure 2 It can be seen that the movement path of the unmanned boat will become longer when passing through obstacles, and the estimated distance s can be designed ij The expression is as follows:

[0082]

[0083] Among them, d ij represents the straight-line distance between the unmanned boat i and the capture target j, μ represents the obstacle influence factor, and n io represents the number of obstacles that may collide on the straight line connecting the unmanned boat i and the capture target j, represents the vertical distance from the center of the obstacle k to the straight line, r o represents the safety radius of obstacle k.

[0084] According to the above analysis, the initial task allocation result can be expressed as the following expression:

[0085]

[0086] in, Indicates the sequence number of the target selected by unmanned boat i.

[0087] Specifically, the initial task allocation results are centrally allocated through constraints to obtain hybrid task allocation results, including:

[0088] Grouping all the unmanned boats in the unmanned boat formation according to the capture targets selected by each unmanned boat in the unmanned boat formation;

[0089] When the number of unmanned boats selecting the same encirclement target is greater than or equal to a preset threshold, the unmanned boats selecting the same encirclement target and the selected encirclement targets are divided into a high-density group, where the selected encirclement targets include multiple ones;

[0090] When the number of unmanned boats selecting the same encirclement target is lower than a preset threshold, the unmanned boats selecting the same encirclement target and the selected encirclement targets are divided into a low-density group, where the selected encirclement targets include multiple ones;

[0091] Determine whether the initial task allocation results meet the constraints;

[0092] When the initial task allocation result meets the constraint conditions, the initial task allocation result is used as the task allocation result of the unmanned boat formation. The initial allocation result is as follows: Figure 3 (a)

[0093] When the initial task allocation result does not meet the constraint conditions, the sum of the motion path lengths of the unmanned boats in the low-density group is used as the optimization target. Based on the optimization target and the initial task allocation result, the unmanned boats in the low-density group are optimized and allocated to obtain a hybrid task allocation result. The hybrid task allocation result is the number of unmanned boats that capture each of the capture targets, as shown in the following example: Figure 3 (b) shown.

[0094] Specifically, the constraints include:

[0095] Each unmanned boat in the high-density group can only select one of the multiple selected capture targets in the low-density group;

[0096] The number of UAVs assigned to each selected capture target in the high-density group cannot be lower than the preset threshold;

[0097] The number of unmanned boats assigned to each selected capture target in the low-density group must be equal to a preset threshold.

[0098] Since each unmanned boat makes decisions based only on its own observation information, without considering the impact of other unmanned boats’ decision-making on the whole, this may result in the number of unmanned boats selected for certain targets being less than the basic value required for the capture, thus failing to capture all the capture targets. The present invention uses the genetic particle swarm algorithm to perform secondary distribution on the initial task allocation results, and through the constraints of the conditions, ensures that the number of unmanned boats for capturing each target is not less than the baseline value. The optimization process of the genetic particle swarm algorithm is as follows: Figure 4 As shown, the specific process is as follows:

[0099] First, all the unmanned boats in the unmanned boat formation are grouped into high-density groups and low-density groups according to the capture targets selected by each unmanned boat in the unmanned boat formation. Assuming that the high-density group can be divided into r groups according to the target category, and the low-density group can be divided into q groups, the decision matrix H of each high-density group for the low-density group can be obtained. k and the distance matrix S k , the expression is:

[0100]

[0101] According to the optimization goal, the optimization equation for optimizing the allocation of unmanned boats in the low-density group based on the initial task allocation results is:

[0102]

[0103]

[0104] Among them, H k represents the decision matrix of each high-density group for the low-density group, S k Represents the estimated distance matrix of each high-density group to the low-density group, represents the decision variable of the mth unmanned boat in the kth group in the high-density group for the nth target in the low-density group, r represents the number of high-density groups, q represents the number of low-density groups, and p k represents the number of unmanned boats in the kth group in the high-density group, G represents the optimization objective function, Denotes the decision matrix H k and distance matrix The trace of the result matrix of the multiplication, n b Indicates the baseline value of the number of unmanned boats required to capture a single target, n n It represents the number of unmanned boats initially assigned to the nth capture target in the low-density group. The objective function represents the minimum sum of the estimated distances of all unmanned boats assigned from the high-density group to the low-density group.

[0105] In the embodiment of the present invention, the optimization equation can be optimized and solved by using a genetic particle swarm algorithm.

[0106] The embodiment of the present invention adopts a virtual velocity component control mechanism to control the motion behavior of each unmanned boat in the unmanned boat formation. It is also a distributed path planning method. The virtual velocity components of each unmanned boat include the target approach speed, the first repulsive speed between the unmanned boats, the second repulsive speed between the unmanned boat and the captured target, the rotation obstacle avoidance speed, and the first rotation speed. The embodiment of the present invention takes the unmanned boat as an example to illustrate the solution method of these speeds respectively:

[0107] (1) Target approach speed. The embodiment of the present invention adopts the D function as the speed function of the UAV approaching the target. The D function has good speed smooth attenuation performance. When the distance to the target is far, the output value is large and the attenuation is slow. Therefore, the UAV can quickly approach the target. When it reaches a certain distance range, it decelerates to zero with a constant acceleration. The expression of the D function is:

[0108]

[0109] Where r represents the estimated distance between the UAV and the target, p represents the gain, which determines the intersection point of the two deceleration stages, and a represents the expected acceleration.

[0110] Based on the above D function, the final speed expression for controlling the unmanned boat to approach the target can be designed as follows:

[0111] v iatt =[v0+C t ·D(r ig -r e , a t , p t )]·r iatt

[0112] Among them, v0 represents the minimum approach speed, C t represents the approach velocity gain coefficient, D(·) represents the velocity function, a t Indicates the maximum permissible acceleration, p t Represents the gain of the braking process, r ig represents the straight-line distance between the unmanned boat and the target, r e represents the expected capture radius, r iatt Represents the unit approach velocity vector, the direction is from the unmanned boat to the capture target;

[0113] (2) The first repulsive velocity is the repulsive velocity between the unmanned boats. The expression of the first repulsive velocity is:

[0114]

[0115] Among them, k ar Represents the repulsion coefficient between unmanned boats, ria represents the straight-line distance between the two unmanned boats, r a Indicates the safe distance between unmanned boats. Represents the unit repulsive velocity vector between the UAVs, with the direction from other UAVs to the current UAV;

[0116] (3) The second repulsive speed is the repulsive speed between the unmanned boat and the target. The expression of the second repulsive speed is:

[0117]

[0118] Among them, k gr Represents the repulsive force coefficient between the unmanned boat and the target, r g Indicates the safe distance between the unmanned boat and the target. Represents the unit repulsive velocity vector between the UAV and the target, with the direction from the target to the current UAV;

[0119] (4) Rotational obstacle avoidance speed

[0120] When the unmanned boat approaches the target, due to the existence of a large number of static obstacles in the environment, the use of a simple repulsive speed obstacle avoidance method will inevitably lead to dead zone stagnation and local oscillation problems, thus causing the failure of target capture.

[0121] Inspired by the flow trajectory formed by a fluid after hitting an obstacle, the present invention adopts a surface-circling obstacle avoidance method. Therefore, the expression of the rotational obstacle avoidance speed can be expressed as:

[0122]

[0123] Among them, k rot Represents the gain coefficient of the rotation obstacle avoidance speed, r irot Represents the unit rotation obstacle avoidance velocity vector, Flag represents the obstacle avoidance flag, which is determined based on the estimated distance between the unmanned boat and the obstacle and the speed direction of the unmanned boat;

[0124] When the unmanned boat enters the dangerous area near an obstacle for the first time, it will determine whether the unmanned boat needs to avoid the obstacle. If it needs to avoid the obstacle, the Flag will be set to 1, and then the appropriate obstacle avoidance speed vector will be selected to perform obstacle avoidance movement until the obstacle avoidance is completed, and then it will be set to 0; otherwise, it will continue to move forward at the original speed.

[0125] like Figure 5 As shown, there are currently only two feasible obstacle avoidance velocity vectors, n1 and n2. According to the movement direction of the unmanned boat, n1 will be selected as the current obstacle avoidance velocity vector. The specific steps of the algorithm are as follows:

[0126] Input: Position information of the unmanned boat and the obstacle Pos = [agnets_Pos, obstacle_Pos], selected speed for selecting the obstacle avoidance speed vector The speed v used to determine when to start and end obstacle avoidance d =v total -v rot , used to ensure the attenuation rate of obstacle avoidance effect

[0127] Step 1: Calculate the estimated distance D between the unmanned boat and all obstacles ao , determine whether there is a safety radius r smaller than the obstacle k o If it exists, determine the obstacle to be avoided according to Step 3-Step 5, and use the vector pointing from the unmanned boat to the obstacle as the obstacle avoidance normal vector n o ;

[0128] Step 2: If v d ·n o >0, Flag=1; otherwise, Flag=0;

[0129] Step 3: if Flag = 1

[0130] Step 4: Determine which obstacles are currently in the danger zone and record their number as z;

[0131] Step 5: Calculate the feasible obstacle avoidance velocity vector set: ( is the unit tangent vector perpendicular to the line connecting the unmanned boat and the center of the obstacle in the dangerous area, and Q is the normal vector matrix formed by the line connecting the unmanned boat and the centers of all obstacles in the dangerous area, as shown in Figure 3 shown);

[0132] Step 6: Calculate the optimal obstacle avoidance speed: (V a V ava The velocity matrix formed by

[0133] Step 7: Speed ​​decay: v s =α n ·v s (α is the attenuation factor, n is the corresponding order);

[0134] Step 8: Determine whether the obstacle avoidance process is completed;

[0135] Step 9: if ( Indicates v d With n oThe angle formed

[0136] Step 10: Flag = 0;

[0137] Step 11: end

[0138] Step 12: end

[0139] Output: optimal obstacle avoidance speed v rot

[0140] (5) First rotation speed

[0141] When the UAV is close to the target, it takes a circling approach to prevent the target from escaping and improve the success rate of the capture. The expression of the first circling speed is:

[0142]

[0143] Among them, k c Represents the rotation speed gain coefficient, r icircle1 represents the first unit rotation velocity vector, which is perpendicular to the line between the unmanned boat and the target, r c Indicates the desired rotation radius.

[0144] The virtual velocity component v is calculated based on the target approach velocity, the first repulsive velocity, the second repulsive velocity, the obstacle avoidance rotation velocity and the first rotation velocity. itotal for:

[0145]

[0146] Among them, α represents the attenuation factor, α n represents the nth-order attenuation factor, v iatt represents the target approach speed, represents the first repulsive velocity, represents the second repulsive velocity, v irot Indicates the speed of rotation to avoid obstacles, v icircle1 Indicates the first rotation speed.

[0147] In the embodiment of the present invention, the value of n is 2. If Flag=1, then α=0.01; if Flag=0, then α=1.

[0148] Depend on Figure 6 The simulation results shown in the figure show that for the static obstacle avoidance problem, the repulsive velocity obstacle avoidance results are as follows: Figure 6 As shown in (a), the results of the obstacle avoidance by designing the rotation speed according to the embodiment of the present invention are as follows: Figure 6 As shown in (b), compared with the two, the method designed in the embodiment of the present invention can effectively solve the dead zone stagnation and local oscillation problems in the obstacle avoidance process.

[0149] To prevent the target from escaping during pursuit, when the unmanned boats are close to the target, they must coordinate control to quickly intercept the target and prevent it from escaping. In this embodiment of the present invention, the unmanned boats in the formation coordinate their actions, employing a tight, circular rotation to restrict the target's freedom of movement. During this rotation, the unmanned boats must maintain a certain distance from each other to avoid collision.

[0150] In order to achieve a uniform distribution of any number of unmanned boats around the target, the first rotation speed is redesigned in the embodiment of the present invention. Figure 7 As shown in the figure, when the unmanned boat enters its rotation area, the rotation area is an area formed by taking the capture target as the center and the preset threshold as the radius. When the distance between the unmanned boat and the capture target is less than or equal to the preset threshold, it is considered that the unmanned boat has entered the rotation area. Due to the dual effects of the approach speed and the rotation speed, the unmanned boat will rotate counterclockwise around the target while approaching it, and the rotation speed will be adaptively adjusted with the angular relationship between the unmanned boats entering the rotation area, ultimately achieving the unmanned boat rotating around the target evenly distributed within the desired radius. The first rotation speed is redesigned to obtain the second rotation speed:

[0151]

[0152] Among them, v m Indicates the maximum rotation speed, r icircle2 represents the second unit rotation velocity vector, which is perpendicular to the line between the unmanned boat and the target, k c is an adaptive coefficient, which can be adjusted based on the number of unmanned boats in the turning area and the angle between adjacent unmanned boats, so that the unmanned boats can be evenly distributed around the target. Its expression is as follows:

[0153]

[0154] Among them, θ f represents the angle difference between the current unmanned boat and its adjacent forward unmanned boat, θ b represents the angle difference between the current unmanned boat and its adjacent back lane unmanned boat. The forward and backward directions are defined according to the rotation direction of the unmanned boat. m represents the number of unmanned boats in the rotation area. From the above expression, we can see that k c The value range is between [-1, 1].

[0155] Combine the formula with Figure 7 It can be seen that considering θ f and θ b Take special value, that is, when k c = -1, vicircle2 = 0, the unmanned boat stops rotating. c =0, v icircle2 The size of The unmanned boats rotate at the reference speed, which is the rotation speed of multiple unmanned boats when they are stable in the dynamic formation capture process. c = 1, the unmanned boat rotates at the maximum speed v m According to the above analysis, the angle difference between the unmanned boats will eventually converge to

[0156] The circular formation capture not only requires a uniform distribution between the UAVs, but also ensures that all UAVs move within the desired radius. Therefore, the concept of the stopping area is introduced, that is, when the UAV enters the stopping area, its target approach speed v iatt = 0, at this time the UAV only has a rotational speed. Since the sampling time of the control process cannot reach infinitesimal, the rotational speed alone cannot control the UAV to move constantly on the circumference. Therefore, a smaller approaching speed is required to keep the UAV moving on the desired radius.

[0157] Since the radius of the stopping area has the same meaning as the expected capture radius in the target approach speed, it should be noted that the expected capture radius r e must be greater than the rotation radius r c On the one hand, it ensures that the unmanned boat can move constantly within the desired radius, and on the other hand, it makes the movement process of the unmanned boat more stable when it is encircling the target. The embodiment of the present invention takes three unmanned boats as an example. The comparison results of the distance error and angle error of each unmanned boat in the process of encircling the target are as follows: Figure 8 As shown in the figure, each unmanned boat simulates the capture process of a capture target, and the simulation results are as follows: Figure 9 shown.

[0158] The present invention can not only realize the capture of multiple static obstacles, but also can well complete the capture of multiple randomly moving dynamic targets, and can ensure that the number of unmanned boats capturing each capture target is not less than the benchmark value. The simulation results are as follows: Figure 10 As shown, Figure 10 (a) is the simulation result of the round-up at 0s. Figure 10 (b) is the simulation result of the roundup at 12s. Figure 10 (c) is the simulation result of the roundup at 30s. Figure 10 (d) is the simulation result of the roundup at 48 seconds. Figure 10 (e) is the simulation result of the roundup at 60s. Figure 10 (f) is the simulation result of the roundup at 90s.

[0159] Compared with the prior art, the embodiments of the present invention perform distributed task allocation for each unmanned boat in the unmanned boat formation by obtaining the estimated distance between each unmanned boat and each capture target in the unmanned boat formation and the number of unmanned boats around each capture target to obtain an initial task allocation result, and centrally allocate the initial task allocation result through constraint conditions to determine the number of unmanned boats to capture each capture target, effectively balancing the efficiency and quality of task allocation; based on the set virtual speed component and the hybrid task allocation result, the path of each unmanned boat to its rotation area is planned, solving the dead zone stagnation and local oscillation problems of multiple unmanned boats in the obstacle avoidance process, and improving the obstacle avoidance performance; when each unmanned boat enters the rotation area, the first rotation speed in the virtual speed component is adaptively adjusted to obtain a second rotation speed, and based on the second rotation speed, the unmanned boats after entering the rotation area are controlled to capture multiple capture targets, which can control any number of unmanned boats to capture targets in a uniformly distributed formation, overcoming the uncertainty caused by the number of formation unmanned boats and obstacles in the environment during the dynamic formation process.

[0160] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, a multi-target unmanned boat formation collaborative capture method is implemented.

[0161] If the integrated module 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 present invention implements all or part of the processes in the above-mentioned method embodiments, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a construction device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0162] An embodiment of the present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a multi-target unmanned boat formation collaborative capture method is implemented.

[0163] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a server, a server cluster, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0164] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0165] In some embodiments, the memory may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. In other embodiments, the memory may also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart memory card (SMC, Smart Media Card), a secure digital (SD, Secure Digital) card, a flash card, etc. Furthermore, the memory may include both an internal storage unit of the terminal device and an external storage device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is about to be output.

[0166] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the embodiment of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0167] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the embodiments of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0168] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A multi-target unmanned boat formation collaborative capture method, characterized by: include: Step 1: Obtain the estimated distance between each unmanned boat in the unmanned boat formation and each encircled target, and the number of unmanned boats around each encircled target; Step 2: Performing distributed task allocation on each unmanned boat in the unmanned boat formation according to the estimated distance between each unmanned boat and each encircled target and the number of unmanned boats around each encircled target to obtain an initial task allocation result, and performing centralized allocation on the initial task allocation result according to the constraint condition to obtain a hybrid task allocation result, wherein the hybrid task allocation result is the number of unmanned boats required to encircle each encircled target; Step 3: For each unmanned boat in the unmanned boat formation, a path for the unmanned boat to move toward its rotation area is planned based on the set virtual speed components and the hybrid task allocation result, where the virtual speed components include the target approach speed, the first repulsive speed between the unmanned boats, the second repulsive speed between the unmanned boat and the encircled target, the rotation obstacle avoidance speed, and the first rotation speed; Step 4: When each of the unmanned boats enters the rotation area, the first rotation speed is adaptively adjusted to obtain a second rotation speed, and the unmanned boats entering the rotation area are controlled based on the second rotation speed to capture the multiple capture targets.

2. The multi-target unmanned boat formation collaborative capture method according to claim 1 is characterized in that: The initial task allocation result is: in, Indicates the serial number value of the target selected by unmanned boat i, represents the initial decision matrix, i=1,2,…,n, w1 and w2 represent the weights of decision indicators, s ij represents the estimated distance between the unmanned boat i and the capture target j, n ij represents the number of unmanned boats surrounding the target j, c ij represents the decision score of UAV i on the capture target j, j = 1, 2, ..., m, d ij represents the straight-line distance between the unmanned boat i and the capture target j, μ represents the obstacle influence factor, and n io represents the number of obstacles that may collide on the straight line connecting the unmanned boat i and the capture target j, represents the vertical distance from the center of the obstacle k to the straight line, r o represents the safety radius of obstacle k.

3. The multi-target unmanned boat formation collaborative capture method according to claim 2 is characterized in that: Centrally allocating the initial task allocation results according to the constraint conditions to obtain a hybrid task allocation result, including: Grouping all the unmanned boats in the unmanned boat formation according to the capture target selected by each unmanned boat in the unmanned boat formation; When the number of unmanned boats selecting the same capture target is greater than or equal to a preset threshold, the unmanned boats selecting the same capture target and the selected capture targets are divided into a high-density group, wherein the selected capture targets include multiple targets; When the number of unmanned boats selecting the same capture target is lower than a preset threshold, the unmanned boats selecting the same capture target and the selected capture targets are divided into a low-density group, wherein the selected capture targets include multiple targets; Determining whether the initial task allocation result satisfies the constraint conditions; When the initial task allocation result satisfies the constraint condition, the initial task allocation result is used as the task allocation result of the unmanned boat formation; When the initial task allocation result does not meet the constraint conditions, the sum of the motion path lengths of the unmanned boats in the low-density group is used as the optimization target. According to the optimization target, the unmanned boats in the low-density group are optimized and allocated on the basis of the initial task allocation result to obtain a hybrid task allocation result, which is the number of unmanned boats used to capture each of the capture targets.

4. The multi-target unmanned boat formation collaborative capture method according to claim 3 is characterized in that: The constraints include: Each unmanned boat in the high-density group can only select one of the multiple selected capture targets in the low-density group; The number of UAVs assigned to each selected capture target in the high-density group cannot be lower than the preset threshold; The number of unmanned boats assigned to each selected capture target in the low-density group must be equal to a preset threshold.

5. The multi-target unmanned boat formation collaborative capture method according to claim 4 is characterized in that: The optimization equation for optimizing the allocation of unmanned boats in the low-density group based on the initial task allocation result according to the optimization goal is: Among them, H k represents the decision matrix of each high-density group for the low-density group, S k Represents the estimated distance matrix of each high-density group to the low-density group, G represents the optimization objective function, Denotes the decision matrix H k and distance matrix The trace of the matrix resulting from the multiplication, represents the decision variable of the mth unmanned boat in the kth group in the high-density group for the nth target in the low-density group, r represents the number of high-density groups, q represents the number of low-density groups, and p k Indicates the number of unmanned boats in the kth group in the high-density group, n b Indicates the baseline value of the number of unmanned boats required to capture a single target, n n Indicates the number of unmanned boats initially assigned to the nth capture target in the low-density group.

6. The multi-target unmanned boat formation collaborative capture method according to claim 5 is characterized in that: The virtual velocity component v itotal for: Among them, α represents the attenuation factor, α n represents the nth-order attenuation factor, v iatt represents the target approach speed, represents the first repulsive velocity, represents the second repulsive velocity, v irot Indicates the speed of rotation to avoid obstacles, v icircle1 Indicates the first rotation speed.

7. The multi-target unmanned boat formation collaborative capture method according to claim 6, characterized in that: The target approaching speed is used to control the unmanned boat to approach the target. The expression of the target approaching speed is: v iatt =[v0+C t ·D(r ig -r e ,a t ,p t )]·r iatt Among them, v0 represents the minimum approach speed, C t represents the approach velocity gain coefficient, D(·) represents the velocity function, a t Indicates the maximum permissible acceleration, p t Represents the gain of the braking process, r ig represents the straight-line distance between the unmanned boat and the target, r e represents the expected capture radius, r iatt Represents the unit approach velocity vector, the direction is from the unmanned boat to the capture target; The first repulsive speed is the repulsive speed between the unmanned boats. The expression of the first repulsive speed is: Among them, k ar Represents the repulsion coefficient between unmanned boats, r ia represents the straight-line distance between the two unmanned boats, r a Indicates the safe distance between unmanned boats. Represents the unit repulsive velocity vector between the UAVs, with the direction from other UAVs to the current UAV; The second repulsive speed is the repulsive speed between the unmanned boat and the target, and the expression of the second repulsive speed is: Among them, k gr Represents the repulsive force coefficient between the unmanned boat and the target, r g Indicates the safe distance between the unmanned boat and the target. Represents the unit repulsive velocity vector between the UAV and the target, with the direction from the target to the current UAV; The rotation obstacle avoidance speed is used to avoid obstacles. The expression of the rotation obstacle avoidance speed is: Among them, k rot Represents the gain coefficient of the rotation obstacle avoidance speed, r irot Represents the unit rotation obstacle avoidance velocity vector, and Flag represents the obstacle avoidance flag; The first rotational speed is used to prevent the capture target from escaping. The expression of the first rotational speed is: Among them, k c Represents the rotation speed gain coefficient, r icircle1 represents the first unit rotation velocity vector, which is perpendicular to the line between the unmanned boat and the target, r c Indicates the desired rotation radius.

8. The multi-target unmanned boat formation collaborative capture method according to claim 7, characterized in that: The second rotation speed is: Among them, v m Indicates the maximum rotation speed, r icircle2 Represents the second unit rotation velocity vector, which is perpendicular to the line between the unmanned boat and the target.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-target unmanned boat formation collaborative capture method as described in any one of claims 1 to 8 is implemented.

10. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the multi-target unmanned boat formation collaborative capture method as described in any one of claims 1 to 8 is implemented.

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