A method for position decision-making of multiple autonomous underwater vehicles based on global optimization

Abstract

The present invention relates to a method for position decision-making of multiple autonomous underwater vehicles based on global optimization, and belongs to the field of underwater vehicle control technology. A reasonable division of the area around the dynamic target is achieved, and decision allocation is completed according to the principle of global cost optimization to determine the position point guided by each cluster monomer. The key to the present invention is to propose a cost function that comprehensively considers three factors including distance, speed, and heading, and adopt a global optimization method to minimize the overall cost of the cluster. The decision allocation strategy is screened out to achieve position decision-making. Starting from the perspective of the cluster as a whole, the present invention uses enumeration to find a position decision method with the lowest cost as the best method, which effectively guarantees global optimality.

Description

Technical Field

[0001] The present invention belongs to the field of underwater vehicle control technology, and relates to a method for autonomous underwater vehicle cluster target position decision-making based on global optimization. More specifically, by minimizing the cost value of the cluster as a whole and the target position space, the decision and allocation of multiple autonomous underwater vehicles to the target position space is realized. Background Art

[0002] Since the end of the last century and the beginning of this century, when underwater unmanned autonomous vehicles entered the scientific research stage, they have been widely used in various fields of marine research. Underwater dynamic target guidance is one of the research directions with a wide range of applicable scenarios. The vehicle can efficiently locate dynamic targets through acoustic wave sensor equipment such as active sonar, thereby achieving dynamic guidance tasks. However, with the diversification of ocean exploration, simple target guidance is no longer competent for complex tasks in ocean exploration. In scenarios such as the capture, expulsion, and exploration of dynamic targets, people hope that the vehicle can stably track the surrounding positions of dynamic targets (such as several relatively fixed positions around the target, etc.) to meet the task requirements. Therefore, the decision-making and allocation research on the surrounding positions of dynamic targets is gradually becoming a hot topic.

[0003] The research on location decision mainly includes two parts: location division and decision allocation. Location division refers to selecting a number of points in a certain area around the target to form a location space according to certain rules, and decision allocation refers to mapping the location points in the location space to the monomers in the intelligent agent cluster according to certain constraints. At present, the research on location decision of multi-autonomous underwater vehicle clusters for dynamic targets in water generally directly selects equidistant points in the four directions of due south, due north, due west, and due east of the target to form a location space, and maps the location space to the cluster based on the principle of the shortest distance between the location point and the monomer.

[0004] However, the above method does not take the target motion state into consideration, but directly performs mechanical position division. In the decision-making allocation link, it only satisfies the local shortest distance and does not guarantee the optimal cost of the cluster as a whole. Therefore, it may sometimes cause greater resource consumption. Summary of the invention

[0005] The technical problems to be solved by the present invention are:

[0006] In order to solve the cost non-optimality caused by mechanical position division and single decision allocation in the prior art, the present invention provides a multi-autonomous underwater vehicle position decision method based on global optimization.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A method for position decision of multiple autonomous underwater vehicles based on global optimization is characterized by comprising the following two parts:

[0009] Position division: select position points based on the target position and heading to form a position space;

[0010] Decision allocation: Comprehensively consider the three factors of distance, heading and speed of each single unit, and select the globally optimal decision allocation strategy from the perspective of the entire cluster.

[0011] A further technical solution of the present invention: The position division is as follows:

[0012] During the navigation of multiple unmanned underwater vehicles, the dynamic target position (x T ,y T ) and heading ψ T Based on the information, several points with a distance r from the target in a certain direction are selected to form the position space S;

[0013] S={(x,y)|x=x T -rsin(ψ T -iπ),y=y T +rcos(ψ T -iπ),i∈[0,2)}.

[0014] A further technical solution of the present invention: the decision allocation steps are as follows:

[0015] S1: According to the one-to-one injection method, that is, a position point in the position space can correspond to at most one monomer in the cluster, select an allocation strategy I, and calculate the cost c of each monomer under strategy I based on the target motion information and self-motion information collected by the sensor. Ik :

[0016] k represents each monomer that makes up the cluster

[0017] in, represents the speed cost, where v T is the target movement speed, v0 is the movement speed of monomer k; represents the distance cost, d is the distance between monomer k and the target location, D max , D min The maximum detection distance and minimum guidance distance are set. Within this range, the sensor can effectively collect the target's motion information; represents the heading cost, ψ T and ψ0 are the headings of the target and monomer k, respectively;

[0018] S2: Calculate the overall cost C under strategy I I :

[0019] n is the number of monomers in the cluster

[0020] S3: Determine whether the cost values ​​of all allocation strategies have been calculated. If so, execute step 5; otherwise, repeat steps 2 and 3 until the cost values ​​of all allocation strategies are calculated;

[0021] S4: Find all allocation strategies with a cost of C min Strategy I optimal This is the optimal strategy. According to strategy I optimal Map the position points to the clusters and determine the position points guided by each monomer;

[0022] C min =min{C1,C2,C3,...}.

[0023] A further technical solution of the present invention is to collect the position and heading information of the dynamic target through an acoustic wave sensor.

[0024] A computer system, characterized in that it comprises: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0025] A computer-readable storage medium is characterized by storing computer-executable instructions, which are used to implement the above method when executed.

[0026] The beneficial effects of the present invention are:

[0027] The present invention provides a target location decision method based on global optimization: in location division, location points are selected based on the target location and heading to form a location space; in decision allocation, the three factors of distance, heading and speed of each monomer are comprehensively considered, and the global optimal decision allocation strategy is selected from the perspective of the entire cluster. Compared with traditional location decision technology, it has the following significant advantages:

[0028] 1. The present invention takes into account the movement characteristics of the target in position division and does not perform mechanical absolute position division, but adopts a more flexible relative position division method.

[0029] 2. The present invention combines three cost factors, namely, distance, heading and speed, to make the decision-making process more reasonable and avoid decision-making errors caused by a single factor.

[0030] 3. The present invention starts from the perspective of the cluster as a whole and uses enumeration to find a location decision method with the minimum cost as the best method, which effectively ensures global optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0032] Figure 1 Schematic diagram of the initial scene of the instance;

[0033] Figure 2 Target location division effect;

[0034] Figure 3 Effect of position decision of aircraft cluster. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] This embodiment provides a multi-autonomous underwater vehicle position decision method based on global optimization, which realizes the reasonable division of the area around the dynamic target, completes the decision allocation according to the principle of global cost optimization, and determines the position point guided by each cluster monomer. The key of the present invention is to propose a cost function that comprehensively considers the three factors of distance, speed and heading, adopt a global optimization method, take the minimization of the overall cost of the cluster as the goal, screen out the decision allocation strategy, and realize the position decision accordingly.

[0037] The implementation steps of the present invention are as follows:

[0038] Step 1: Position division. During the navigation of multiple unmanned underwater vehicles, the dynamic target position (x T ,y T ) and heading ψ T Based on the information, several points with a distance r from the target in a certain direction are selected to form the position space S;

[0039] S={(x,y)|x=x T -rsin(ψ T -iπ),y=y T +rcos(ψ T -iπ),i∈[0,2)}

[0040] Step 2. According to the one-to-one injection method, that is, a position point in the position space can correspond to at most one monomer in the cluster, select an allocation strategy I, and calculate the cost c of each monomer under strategy I based on the target motion information and self-motion information collected by the sensor. Ik :

[0041] k represents each monomer that makes up the cluster. represents the speed cost, where v T is the target movement speed, v0 is the movement speed of monomer k; represents the distance cost, d is the distance between monomer k and the target location, D max , D min The maximum detection distance and minimum guidance distance are set. Within this range, the sensor can effectively collect the target's motion information; represents the heading cost, ψ T and ψ0 are the headings of the target and monomer k respectively. The above required motion information can be collected or calculated by sensors;

[0042] Step 3. Calculate the overall cost C under strategy I I :

[0043] n is the number of monomers in the cluster

[0044] Step 4. Determine whether the cost values ​​of all allocation strategies have been calculated. If so, proceed to step 5; otherwise, repeat steps 2 and 3 until the cost values ​​of all allocation strategies are calculated;

[0045] Step 5. Find all allocation strategies with a cost of C min Strategy I optimal This is the optimal strategy. According to strategy I optimal Map the position points to the clusters and determine the position points guided by each monomer.

[0046] C min =min{C1,C2,C3,...}

[0047] In order to enable those skilled in the art to better understand the present invention, the present invention is described in detail below in conjunction with specific embodiments.

[0048] Embodiment 1:

[0049] In the position division of step 1, the target position (x T ,y T ) and heading ψ T As a benchmark, select the position space S as follows, and the position division effect is as follows Figure 2 shown.

[0050]

[0051] Set up aircraft cluster D max is 1000, D min is 100, according to steps 2, 3, and 4, combined with step 1 and Figure 1 For example, we know that: the cluster has 3 monomers, the position space has 4 position points, and under the condition that the position point can only correspond to one aircraft at most, there are 24 allocation strategies, as shown in Table 2, and the cost C of each strategy can be calculated in turn. I ,I=1,2,...,24.

[0052] In step 5, the minimum cost C can be obtained min And its corresponding optimal strategy I optimal , I optimal This is the optimal location decision method.

[0053] C min =min{C1,C2,C3,...,C 24}

[0054] according to Figure 1 In this example, a simulation experiment was designed. The cluster and target initialization data are shown in Table 1. The 24 allocation strategies obtained according to the present invention are shown in Table 2. According to Table 2, in strategy 1, when monomer 1 guides position point 1, monomer 2 guides position point 2, and monomer 3 guides position point 3, the overall cost of the cluster is the smallest, so it is selected as the optimal strategy. The position decision effect diagram is shown in Figure 3 This fully verifies the advancement and effectiveness of the present invention in location decision making.

[0055] Table 1 Initial conditions of simulation examples

[0056]

[0057] Table 2 Allocation strategy and cost value

[0058]

[0059]

[0060] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. A method for position decision of multiple autonomous underwater vehicles based on global optimization, characterized in that: It consists of the following two parts: Position division: select position points based on the target position and heading to form a position space; the details are as follows: During the navigation of multiple autonomous underwater vehicles, the dynamic target position (x T ,y T ) and target heading ψ T Based on the information, several points with a distance r from the target in a certain direction are selected to form the position space S; S={(x,y)|x=x T -resin(ψ T -iπ),y=y T +rcos(ψ T −iπ),i∈[0,2)} Decision allocation: Comprehensively consider the distance, heading, and speed of each monomer, and select the globally optimal decision allocation strategy from the perspective of the entire cluster; the specific steps are as follows: S1: According to the one-to-one injection method, that is, a position point in the position space can correspond to at most one monomer in the cluster, select an allocation strategy I, and calculate the cost c of each monomer under strategy I based on the target motion information and self-motion information collected by the sensor. Ik : k represents each monomer that makes up the cluster in, represents the speed cost, where v T is the target movement speed, v0 is the movement speed of monomer k; represents the distance cost, d is the distance between monomer k and the target location, D max , D min The maximum detection distance and minimum guidance distance are set. Within this range, the sensor can effectively collect the target's motion information; represents the heading cost, ψ T and ψ0 are the headings of the target and monomer k, respectively; S2: Calculate the overall cost C under strategy I I : n is the number of monomers in the cluster S3: Determine whether the cost values ​​of all allocation strategies have been calculated. If so, execute step 5; otherwise, repeat steps 2 and 3 until the cost values ​​of all allocation strategies are calculated; S4: Find all allocation strategies with a cost of C min Strategy I optimal This is the optimal strategy. According to strategy I optimal Map the position points to the clusters and determine the position points guided by each monomer; C min =min{C1,C2,C3,...}。 2. The method for determining the position of multiple autonomous underwater vehicles based on global optimization according to claim 1, characterized in that: The dynamic target position and heading information are collected through acoustic wave sensors.

3. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

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