Task allocation method for distributed cooperative formation reconstruction of underactuated unmanned surface vehicles

Abstract

The invention discloses a task allocation method for distributed cooperative formation reconstruction of underactuated unmanned surface vehicles. The method comprises the following steps: acquiring an initial cluster scale N of the underactuated unmanned surface vehicles and the position of a preset formation target point; according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned ship, designing a cost function based on the Durbin curve, calculating the income value of the underactuated unmanned ship, enabling the distribution result to be closer to the path distance from the unmanned ship to the target point, and autonomously deciding the bid of the interested target point; and according to an auction algorithm principle, a distributed auction task allocation strategy is designed, and optimal matching of the unmanned ship formation and the target point position is realized.

Description

Technical Field

[0001] The present invention is applied to the field of task allocation for unmanned boat formation reconstruction, and in particular relates to a distributed collaborative task allocation method for under-actuated unmanned boats. Background Art

[0002] Since the 21st century, my country has made great achievements in the field of science and technology, especially in big data, cloud computing and artificial intelligence. The development of these technologies has promoted the rise of unmanned boat technology research. Unmanned boats have attracted much attention in the military and civilian fields due to their easy assembly, high mobility, low maintenance cost and high autonomy. With the expansion of the scope of application, unmanned boat formations responsible for coordinated search and rescue, hydrological surveys and other tasks have emerged. When performing tasks, the formation formation needs to be frequently adjusted to adapt to the environment and needs. Before the formation is reconstructed, the allocation of the target position of the unmanned boats in the formation becomes the key. An efficient allocation strategy is crucial to ensure the timeliness of the formation reconstruction and the success of the mission. Therefore, the study and analysis of the task allocation strategy of the formation reconstruction of the unmanned boat formation is not only of great significance for combat missions in the military field, but also plays an important role in the fields of marine resource exploration, environmental monitoring, disaster relief and so on.

[0003] Many relevant research results have been achieved in the research on task allocation at home and abroad. Ran Huaming, in view of the problem of coordinated jamming task allocation in air combat, comprehensively considered the jamming power, jamming frequency and jamming style of the enemy and friendly aircraft and the combat performance of the enemy and friendly aircraft, established the total advantage function and solved it using genetic algorithm. Kim proposed a resource-oriented decentralized auction algorithm for multi-robot task allocation. Each robot generates the robot's bid value in a probabilistic manner according to its remaining resources. Finally, the auctioneer and the robot communicate with each other to assign the task to the robot with the shortest completion time. Qiao C, in view of the problem of dynamic task allocation of multiple UAVs under multiple constraints, proposed to establish a mathematical model of UAVs with the goal of minimizing the cost of UAVs, and considered multiple constraints when calculating the bid cost of UAVs. Choi HL, in view of the tasks that require robot cooperation, relaxed the limit on the number of robots for each task on the basis of the auction algorithm, added robot cooperation constraints, and proposed a new scoring mechanism. Zaman S proposed a combinatorial auction algorithm to solve the problem that the total cost of a robot performing a set of tasks is lower than the sum of the individual costs of performing these tasks separately. Each robot bids for a combination of multiple tasks instead of a single task, thereby obtaining a near-optimal allocation solution. Zhen used an improved contract network algorithm to handle one-to-many and many-to-one task allocation problems in response to constraints such as task timing and heterogeneous loads of drones. Chen proposed a dynamic group allocation method based on an auction algorithm to improve the quality of the solution under a certain amount of computation in distributed task allocation scenarios. However, the existing unmanned boat formation task allocation methods still have the following shortcomings:

[0004] First, in the existing centralized allocation strategy, the unmanned boat cluster faces the problem of limited global communication in harsh environments, which increases the dependence of the centralized allocation strategy on the communication center and also reduces the robustness and scalability of the strategy.

[0005] Second, among the existing distributed allocation strategies, the task allocation problems based on particle swarm optimization algorithm, distributed model prediction allocation algorithm and ant colony algorithm have problems such as high computational complexity, serious redundancy and low execution efficiency.

[0006] Third, after completing the task assignment, the existing unmanned boat formation reorganizes the formation. In most cases, the straight path distance for the fully driven unmanned boat is adopted, which fails to fully consider the kinematic constraints caused by the characteristics of the under-actuated unmanned boat. Therefore, after arriving at the task point, the bow angles of the unmanned boats are not unified, presenting a chaotic and disordered state, which brings great trouble to the subsequent control of the unmanned boat formation. Summary of the invention

[0007] In order to solve the above problems, the technical solution adopted by the present invention is: a task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats, comprising the following steps:

[0008] Obtain the initial underactuated unmanned boat cluster size N and the position of the preset formation target point;

[0009] According to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, a cost function based on the Durbin curve is designed to calculate the benefit value of the underactuated unmanned boat, so that the allocation result is closer to the path distance from the unmanned boat to the target point and the bid for the target point of interest is decided autonomously;

[0010] Based on the principle of auction algorithm, a distributed auction task allocation strategy is designed to achieve the optimal matching of the unmanned boat formation and the target point position.

[0011] Further: the process of designing a cost function based on the Durbin curve according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, calculating the benefit value of the underactuated unmanned boat, making the allocation result closer to the path distance from the unmanned boat to the target point and autonomously deciding the bid for the target point of interest is as follows:

[0012] Under the condition that the heading angle of the starting point and the minimum turning radius of the target point are known, the shortest path consists of a straight line segment and a minimum radius turning arc segment. Then, any target position point m j With unmanned boat i There is a corresponding Durbin curve L between ij Make unmanned boat u i Arrival at target point m j , L ij The length of the unmanned boat u i To the target point m j The cost of ij , use the Durbin curve to calculate the corresponding curve length, that is, the corresponding cost;

[0013] Assume that the unmanned boat u i The state vector at the starting point Target location point m j Final position state vector The minimum turning radius r is at the starting point A and the ending point C of the unmanned boat, respectively, and a circle is drawn with the forward speed direction of the unmanned boat as the tangent;

[0014] The coordinates of the center O1 and O2 are obtained as follows:

[0015]

[0016]

[0017] in: are the horizontal and vertical coordinates of the center O1, are the horizontal and vertical coordinates of the center O2, the horizontal and vertical coordinates of the end positions of x1 and y1, and the horizontal and vertical coordinates of the starting point positions of x0 and y0. and Yes, it is an unmanned boat i The headings corresponding to the starting point and the target point;

[0018] The coordinates of the tangent point B are:

[0019]

[0020] Where: x B and B are the horizontal and vertical coordinates of the tangent point B;

[0021] The Durbin curve of the unmanned boat from the starting point A to the target position point C is composed of the straight line AB and the arc BC. The central angle of the arc BC is β, and the cost is c ij for:

[0022]

[0023] Then there will be n unmanned boats and n target points. 2 Cost element c ij , and define the decision variable x ij as follows.

[0024]

[0025] Further: The process of designing a distributed auction task allocation strategy based on the auction algorithm principle to achieve the optimal matching of the unmanned boat formation and the target point position is as follows:

[0026] S31: Acquisition phase: Acquire the benefit value calculated by the unmanned boat and make an independent decision to bid for the target point of interest;

[0027] S32: Bidding decision stage: record the highest bid unmanned boat at each target point and update the price accordingly;

[0028] S33: Price information exchange update phase: consensus adjustment is performed based on the communication between adjacent unmanned boats and the principle of consistency to achieve the target availability consensus and the global minimum benefit consensus.

[0029] S34: Allocation phase: determine whether the number of bidding ships at the target point is unique. If the number of bidding ships at the target point is not unique, return to step S31; if the number of bidding ships at the target point is unique, the target point is allocated to the ship with the highest bid;

[0030] S34: consistency stage: determine whether the target point allocation is completed. If not, return to S31 and count again; if completed, the auction ends. In this way, the optimal match between the unmanned boat and the target point is achieved.

[0031] Furthermore: This method is applicable to unmanned boats based on distributed communication topology.

[0032] Furthermore, in the bidding decision stage, the process of recording the highest bid unmanned boat at each target point and updating the price accordingly is as follows:

[0033] Unmanned boat i According to formula (8), select the interest point l with the minimum total cost i , record the minimum total cost as v i And remove the target point l i The minimum total cost w i , and for the target point l i Bid according to formula (11)

[0034]

[0035]

[0036]

[0037]

[0038] Where: c is The target point s is the unmanned boat u i The resulting revenue value, p is For unmanned ships i The price of the target point;

[0039] ε is a supplementary increment in each bid, which is used to ensure that each bid increases by at least ε, avoiding i With v i If they are equal, the bid price P ili Constant.

[0040] Furthermore, the process of consensus adjustment based on the communication between adjacent unmanned boats and the consistency principle to achieve the target availability consensus and the global minimum benefit consensus is as follows:

[0041] Unmanned boat i After autonomously selecting the target point of interest and bidding, a set of price vectors P is generated oldi , unmanned boat i The set of price vectors P generated in the bidding decision stage oldi To neighboring ships Send and receive from neighboring ships The price information sent is recorded and updated according to formula (12) for each target point, which is used as the starting price vector for the auction of a group of target points in the next iteration.

[0042]

[0043] Further: the calculation formula of the upper limit D of the maximum number of iterations in the consistency stage is as follows:

[0044]

[0045] A task allocation device for distributed cooperative formation reconstruction of underactuated unmanned boats, comprising:

[0046] Acquisition module: used to obtain the initial underactuated unmanned boat cluster size N and the position of the preset formation target point;

[0047] The bid module for the target point of interest is used to design a cost function based on the Durbin curve according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, calculate the benefit value of the underactuated unmanned boat, make the allocation result closer to the path distance from the unmanned boat to the target point, and independently decide on the bid for the target point of interest;

[0048] Target point location allocation module: used to design a distributed auction task allocation strategy based on the auction algorithm principle to achieve the optimal match between the unmanned boat formation and the target point location.

[0049] The present invention provides a distributed collaborative task allocation method for under-actuated unmanned boats, which not only focuses on the problem of limited global communication of unmanned boat clusters and low task allocation efficiency, but also considers the cluster control problem under the kinematic constraints of under-actuated unmanned boats, and proposes a distributed target collaborative auction allocation method for unmanned boat formation reconstruction based on the combination of Durbin curve and auction algorithm theory to achieve unmanned boat cluster task allocation. At the level of kinematic constraints of under-actuated unmanned boats, a cost function based on the Durbin curve is constructed to make the allocation result closer to the path distance from the unmanned boat to the target point, thereby improving the executability. At the level of unmanned boat cluster task allocation, a distributed auction task allocation strategy is designed based on the auction algorithm principle to achieve the optimal matching of the unmanned boat formation and the target point position.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] First, compared with the existing centralized communication method for unmanned boat formations, the present invention proposes a distributed communication strategy for unmanned boat formations. This strategy does not need to rely on a communication center node for centralized planning and unified calculation, thereby saving a lot of hardware resources and improving allocation accuracy.

[0052] Second, compared with the existing distributed allocation strategy, the error between the total cost result and the optimal result of the auction algorithm designed by the present invention is controlled within 1%, and the allocation time and communication times are shortened, and the allocation efficiency is greatly improved.

[0053] Third, compared with the existing unmanned boat task allocation method, the allocation method designed by the present invention fully considers the kinematic constraint problem caused by the under-actuated characteristics of the unmanned boat, so that the allocation result is closer to the path distance from the unmanned boat to the target point, thereby improving the executability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0055] Figure 1 This is the flow chart of the distributed collaborative auction algorithm for underactuated unmanned boats.

[0056] Figure 2 This is a schematic diagram of the unmanned boat formation reconstruction task allocation.

[0057] Figure 3 It is a schematic diagram of the distance from the unmanned boat to the target point.

[0058] Figure 4 This is the principle diagram of the Durbin curve.

[0059] Figure 5 This is the communication topology diagram of the unmanned boat cluster.

[0060] Figure 6 It is a schematic diagram of the target point of the unmanned boat formation from H-formation to E-formation.

[0061] Figure 7 It is a schematic diagram of the target point of the unmanned boat formation from E-formation to L-formation.

[0062] Figure 8 It is a schematic diagram of the target point of the unmanned boat formation from L-formation to T-formation.

[0063] Fig. 9 It is a schematic diagram of the terminal position state from the starting position state of the unmanned boat to the target position point. DETAILED DESCRIPTION

[0064] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Figure 1 The flow chart of the distributed collaborative auction algorithm for underactuated unmanned boats is shown.

[0067] A task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats comprises the following steps:

[0068] S1: Obtain the initial underactuated unmanned boat cluster size N and the position of the preset formation target point;

[0069] S2: According to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, a cost function based on the Durbin curve is designed to calculate the benefit value of the underactuated unmanned boat, so that the allocation result is closer to the path distance from the unmanned boat to the target point and autonomously decides the bid for the target point of interest;

[0070] S3: Based on the principle of auction algorithm, a distributed auction task allocation strategy is designed to achieve the optimal matching between the unmanned boat formation and the target point position.

[0071] Steps S1 / S2 / S3 are executed sequentially;

[0072] Consider the formation reconstruction problem of an unmanned boat formation composed of n unmanned boats. The n unmanned boats in the formation constitute the unmanned boat set U = {u1,u2,…u n}, the n target positions in the preset formation constitute the target point set M = {m1,m2,…m n}, each unmanned boat is assumed to be able to reach any point. The allocation of target points in the formation reconstruction is based on the cost of the unmanned boat to the target point. j Assigned to the unmanned boat u in the set U i , so that the total cost is minimized, as shown in formula (1):

[0073]

[0074] In the formula, C i (·) is the cost function of the unmanned boat from the starting point to the target position, X i,0 For unmanned boati The starting position state, X i,f For unmanned boat i The target position point position state satisfies the constraints:

[0075]

[0076] Furthermore, according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, a cost function based on the Durbin curve is designed to calculate the benefit value of the underactuated unmanned boat, so that the allocation result is closer to the path distance from the unmanned boat to the target point and the process of autonomously deciding the bid for the target point of interest is as follows:

[0077] In the task allocation process, the path distance from the starting position to the target position of the unmanned boat can be regarded as the cost of the unmanned boat to the target point. The shorter the distance, the lower the cost. Assume that there are two unmanned boats located on circles with the target position as the center and a radius of r, and the heading angles are different. If the straight-line distance is used as the cost measurement standard, then the distance from ship 1 and ship 2 to the target point is r, and the cost is the same. However, due to the under-actuated characteristics, inertia and maximum rudder angle limit of unmanned boat 2, it is impossible to reach the target point in a straight line after turning in place. The actual path distance L2 is much greater than the path distance L1 from ship 1 to the target point, so the cost will also increase. If the straight-line path distance is used as the cost allocation target, the actual feasibility of the allocation result is low, which may cause the unmanned boat to fail to reach the allocated target point.

[0078] According to the kinematic constraints of the underactuated unmanned vehicle, under the conditions of the known heading angle of the starting point and the minimum turning radius of the target point, the shortest path consists of a straight line segment and a minimum radius turning arc segment. Then, any target position point m j With unmanned boat i There is a corresponding Durbin curve L between ij Make unmanned boat u i Arrival at target point m j , L ij The length of the unmanned boat u i To the target point m j The cost of ij , use the Durbin curve to calculate the corresponding curve length, that is, the corresponding cost;

[0079] Assume that the unmanned boat u i The state vector at the starting point Target location point m j Final position state vector The minimum turning radius r is at the starting point A and the ending point C of the unmanned boat, respectively, and a circle is drawn with the forward speed direction of the unmanned boat as the tangent;

[0080] The coordinates of the center O1 and O2 are obtained as follows:

[0081]

[0082]

[0083] in: are the horizontal and vertical coordinates of the center O1, are the horizontal and vertical coordinates of the center O2, the horizontal and vertical coordinates of the end positions of x1 and y1, and the horizontal and vertical coordinates of the starting point positions of x0 and y0. and It is an unmanned boat i The headings corresponding to the starting point and the target point;

[0084] The coordinates of the tangent point B are:

[0085]

[0086] Where: x B and B are the horizontal and vertical coordinates of the tangent point B;

[0087] The Durbin curve of the unmanned boat from the starting point A to the target position point C is composed of the straight line AB and the arc BC. The central angle of the arc BC is β, and the cost is c ij for:

[0088]

[0089] Then there will be n unmanned boats and n target points. 2 Cost element c ij , and define the decision variable x ij as follows.

[0090]

[0091] Furthermore, after the coordinate information of the n target points constituting the preset formation is sent to each unmanned boat, the auction process begins. According to the auction algorithm principle, a distributed auction task allocation strategy is designed to achieve the optimal matching process between the unmanned boat formation and the target point position as follows:

[0092] S31: Acquisition phase: Acquire the benefit value calculated by the unmanned boat and make an independent decision to bid for the target point of interest;

[0093] S32: Bidding decision stage: record the highest bid unmanned boat at each target point and update the price accordingly;

[0094] S33: Price information exchange update phase: consensus adjustment is performed based on the communication between adjacent unmanned boats and the principle of consistency to achieve the target availability consensus and the global minimum benefit consensus.

[0095] S34: Allocation phase: Determine whether the number of bidding ships at the target point is unique. If the number of bidding ships at the target point is not unique, return to step S31 and continue iterating until the counter reaches the maximum number of iterations. When the counter reaches the maximum number of iterations, the target point is allocated to the unmanned boat with a smaller ship index value; if the number of bidding ships at the target point is unique, the target point is allocated to the ship with the highest bid;

[0096] S34: consistency stage: determine whether the target point allocation is completed. If not, return to S31 and count again; if completed, the auction ends. In this way, the optimal match between the unmanned boat and the target point is achieved. In this way, the optimal match between the unmanned boat and the target point is achieved.

[0097] The bidding decision stage: the process of recording the highest bid unmanned boat at each target point and updating the price accordingly is as follows:

[0098] Unmanned boat i According to formula (8), select the interest point l with the minimum total cost i , where c is The target point s is the unmanned boat u i The resulting revenue value, p is For unmanned ships i Price the target point; record the minimum total cost as v i And remove the target point l i The minimum total cost w i , and for the target point l i Bid according to formula (11)

[0099]

[0100]

[0101]

[0102]

[0103] In formula (11), ε is a supplementary increment in each bid, which is used to ensure that each bid increases by at least ε, avoiding i With v i If they are equal, the bid price P ili Constant.

[0104] Furthermore: consensus adjustment is performed based on the communication between adjacent unmanned boats and the consistency principle to achieve target availability consensus and global minimum benefit consensus. In this process, the consistency principle ensures that the unmanned boats follow a unified communication protocol and data format, achieve equal participation and decision-making, and thus effectively coordinate and adjust, enhance the overall efficiency and stability of the system, and improve the adaptability of the unmanned boat cluster in a dynamic environment.

[0105] Unmanned boat i After autonomously selecting the target point of interest and bidding, a set of price vectors P is generated oldi , unmanned boat i The set of price vectors P generated in the bidding decision stage oldi To neighboring ships Send and receive from neighboring ships The price information sent is recorded and updated according to formula (12) for each target point, which is used as the starting price vector for the auction of a group of target points in the next iteration.

[0106]

[0107] After exchanging and updating information with neighboring ships, the unmanned boat enters the consistency phase. i The updated price P i and the price P in the decision-making stage oldi Compare them, if they are the same, the counter count i Add one, if the two are different, the counter count i Set to zero, and select the target point of interest again according to formula (8) to make a bid in the next round of iterative bidding decision phase until count i When the count reaches the maximum iteration limit D, it is ensured that the price vectors recorded by each unmanned boat are consistent, and the global price of each target point is obtained. The target point obtained by each unmanned boat according to formula (8) is the target position point assigned to each unmanned boat, thereby completing the allocation. In the above allocation method, the calculation formula of the maximum iteration limit D in the consistency stage is as follows:

[0108]

[0109] Furthermore: This method is applicable to unmanned boats based on distributed communication topology.

[0110] The task allocation method of the unmanned boat formation is mainly divided into centralized and distributed methods according to the type of communication topology. Unlike the centralized communication topology, the distributed communication topology does not rely on the communication center node for centralized planning and unified calculation. Each unmanned boat in the formation acts as a relatively independent intelligent node and only communicates with neighboring nodes to complete information exchange. The unmanned boat formation distributed communication network topology is abstractly described by an undirected tree G(V,E), where V is the set of nodes in the topology, that is, the unmanned boat set U in this article; E is an edge connecting any communication node. If (u i ,u k )∈E, then the unmanned boat u i With unmanned boat k Communication can be realized. i The neighbor nodes of are defined as: The designed allocation method is based on distributed communication topology, which makes full use of the autonomous decision-making and coordination capabilities of unmanned boats and reduces the dependence on the communication center, thereby improving the robustness of the formation system to a certain extent.

[0111] A task allocation device for distributed cooperative formation reconstruction of underactuated unmanned boats, comprising:

[0112] Acquisition module: used to obtain the initial underactuated unmanned boat cluster size N and the position of the preset formation target point;

[0113] The bid module for the target point of interest is used to design a cost function based on the Durbin curve according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, calculate the benefit value of the underactuated unmanned boat, make the allocation result closer to the path distance from the unmanned boat to the target point, and independently decide on the bid for the target point of interest;

[0114] Target point location allocation module: used to design a distributed auction task allocation strategy based on the auction algorithm principle to achieve the optimal match between the unmanned boat formation and the target point location.

[0115] Figure 2 A schematic diagram of the unmanned boat formation reconstruction task allocation is shown, and it can be seen that the unmanned boat formation achieves conflict-free allocation of target points.

[0116] Figure 3 The distance relationship from the initial position to the target point of the unmanned boat is described, and the motion characteristics of the under-actuated unmanned boat are clearly revealed.

[0117] Figure 4 The principle diagram of the Durbin curve is displayed to make it closer to the path distance from the unmanned boat to the corresponding target point.

[0118] Figure 5The communication topology diagram of the unmanned boat cluster is displayed. By comparing the centralized and distributed communication topologies, it is shown that the distributed communication topology reduces the dependence on the communication center, thereby improving the robustness of the formation system to a certain extent.

[0119] Figures 6 to 8 The simulation results for the allocation problem of 16 target positions that form a preset formation are shown. The initial heading angle of the unmanned boat is set to be random, the heading angle of the target position is 1 rad, and the minimum turning radius is 0.2 m. The unmanned boat formation is first reorganized from the H-formation to the E-formation, then from the E-formation to the L-formation, and finally from the L-formation to the T-formation. It can be seen from the figure that through the auction algorithm, the unmanned boat formation can accurately reach the task allocation point.

[0120] Fig. 9 It describes the state of the unmanned boat from its starting position to its terminal position at the target point, and clearly presents the starting and terminal position information of the unmanned boat.

[0121] Finally, in order to further compare the performance of the proposed algorithm with the classic Hungarian method, the total cost of the target point allocation in the three-time formation reconstruction was compared with the optimal total cost obtained by the Hungarian algorithm. It was found that the error between the total cost result of the proposed method and the optimal result was controlled within 1%, and the allocation time was shortened compared with the Hungarian method. In addition, the number of communications was also reduced compared with the Hungarian method, thus verifying the reliability of the algorithm.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats, characterized by: The following steps are involved: Obtain the initial underactuated unmanned boat cluster size N and the position of the preset formation target point; According to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, a cost function based on the Durbin curve is designed to calculate the benefit value of the underactuated unmanned boat, so that the allocation result is closer to the path distance from the unmanned boat to the target point and the bid for the target point of interest is decided autonomously; Based on the principle of auction algorithm, a distributed auction task allocation strategy is designed to achieve the optimal matching of the unmanned boat formation and the target point position.

2. The task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats according to claim 1 is characterized by: The process of designing a cost function based on the Durbin curve according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, calculating the benefit value of the underactuated unmanned boat, making the allocation result closer to the path distance from the unmanned boat to the target point and autonomously deciding the bid for the target point of interest is as follows: Under the condition that the heading angle of the starting point and the minimum turning radius of the target point are known, the shortest path consists of a straight line segment and a minimum radius turning arc segment. Then, any target position point m j With unmanned boat i There is a corresponding Durbin curve L between ij Make unmanned boat u i Arrival at target point m j , L ij The length of the unmanned boat u i To the target point m j The cost of ij , use the Durbin curve to calculate the corresponding curve length, that is, the corresponding cost; Assume that the unmanned boat u i The state vector at the starting point Target location point m j Final position state vector The minimum turning radius r is at the starting point A and the ending point C of the unmanned boat, respectively, and a circle is drawn with the forward speed direction of the unmanned boat as the tangent; The coordinates of the center O1 and O2 are obtained as follows: in: are the horizontal and vertical coordinates of the center O1, are the horizontal and vertical coordinates of the center O2, the horizontal and vertical coordinates of the end positions of x1 and y1, and the horizontal and vertical coordinates of the starting point positions of x0 and y0. and Yes, it is an unmanned boat i The headings corresponding to the starting point and the target point; The coordinates of the tangent point B are: Where: x B and B are the horizontal and vertical coordinates of the tangent point B; The Durbin curve of the unmanned boat from the starting point A to the target position point C is composed of the straight line AB and the arc BC. The central angle of the arc BC is β, and the cost is c ij for: Then there will be n unmanned boats and n target points. 2 Cost element c ij , and define the decision variable x ij as follows.

3. The task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats according to claim 1 is characterized by: The process of designing a distributed auction task allocation strategy based on the auction algorithm principle to achieve the optimal matching of the unmanned boat formation and the target point position is as follows: S31: Acquisition phase: Acquire the benefit value calculated by the unmanned boat and make an independent decision to bid for the target point of interest; S32: Bidding decision stage: record the highest bid unmanned boat at each target point and update the price accordingly; S33: Price information exchange update phase: consensus adjustment is performed based on the communication between adjacent unmanned boats and the principle of consistency to achieve the target availability consensus and the global minimum benefit consensus. S34: Allocation phase: determine whether the number of bidding ships at the target point is unique. If the number of bidding ships at the target point is not unique, return to step S31; if the number of bidding ships at the target point is unique, the target point is allocated to the ship with the highest bid; S34: consistency stage: determine whether the target point allocation is completed. If not, return to S31 and count again; if completed, the auction ends. In this way, the optimal match between the unmanned boat and the target point is achieved.

4. The task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats according to claim 1 is characterized by: This method is applicable to unmanned boats based on distributed communication topology.

5. The task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats according to claim 1 is characterized by: The bidding decision stage: the process of recording the highest bid unmanned boat at each target point and updating the price accordingly is as follows: Unmanned boat i According to formula (8), select the interest point l with the minimum total cost i , record the minimum total cost as v i And remove the target point l i The minimum total cost w i , and for the target point l i Bid according to formula (11) Where: c is The target point s is the unmanned boat u i The resulting revenue value, p is For unmanned ships i The price of the target point; ε is a supplementary increment in each bid, which is used to ensure that each bid increases by at least ε, avoiding i With v i If they are equal, the bid price P ili Constant.

6. The task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats according to claim 1 is characterized by: The process of reaching the target availability consensus and the global minimum benefit consensus by adjusting the consensus based on the communication between adjacent unmanned boats and the consistency principle is as follows: Unmanned boat i After autonomously selecting the target point of interest and bidding, a set of price vectors P is generated oldi , unmanned boat i The set of price vectors P generated in the bidding decision stage oldi To neighboring ships Send and receive from neighboring ships The price information sent is recorded and updated according to formula (12) for each target point, which is used as the starting price vector for the auction of a group of target points in the next iteration.

7. The task allocation method for distributed cooperative formation reconstruction of underactuated unmanned boats according to claim 1 is characterized by: The calculation formula for the upper limit D of the maximum number of iterations in the consistency phase is as follows:

8. A task allocation device for distributed cooperative formation reconstruction of underactuated unmanned boats, characterized by: include: Acquisition module: used to obtain the initial underactuated unmanned boat cluster size N and the position of the preset formation target point; The bid module for the target point of interest is used to design a cost function based on the Durbin curve according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned boat, calculate the benefit value of the underactuated unmanned boat, make the allocation result closer to the path distance from the unmanned boat to the target point, and independently decide on the bid for the target point of interest; Target point location allocation module: used to design a distributed auction task allocation strategy based on the auction algorithm principle to achieve the optimal match between the unmanned boat formation and the target point location.

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