Cooperative control method for unmanned information group dynamic interaction

Abstract

The invention belongs to the technical field of unmanned vehicle intelligent control, and particularly relates to a cooperative control method for unmanned information group dynamic interaction, and the method mainly relates to three main parts of sub-body information group initialization judgment, feature-based group global planning operation and feature-based group local planning operation. The method comprises the following steps: firstly, identifying and separating a daughter information group and extracting and calculating the daughter information group according to limited information which can be obtained by daughters in the group; according to the calculation result, the state of each daughter in the group in different environments can be effectively judged, so that the connection state between the daughters is obtained. On the basis of considering reasonable input and effective interaction of system information, the distance between different daughters is dynamically adjusted by taking group comprehensive performance as a target, so that the global and local planning operation and control process of the unmanned group overall architecture under the action of information interaction is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control of unmanned vehicles, and specifically relates to a collaborative control method for dynamic interaction of unmanned information groups. The collaborative control method for dynamic interaction of unmanned information groups mainly involves a master planning and control method under the interaction of limited information sets of unmanned groups in different environments. During the operation of the unmanned group, considering the interaction and influence of the dynamic changes of the group architecture and the information interaction between each sub-body, the group needs to carry out multi-dimensional master planning and effective control of the sub-body operation status according to the group characteristics. In this process, it is necessary to take into account the overall performance requirements and the achievable goals of the sub-body, and complete the global and local planning of the group under conditional constraints, so as to complete a scalable and extensible unmanned group intelligent operation process that is adaptable to different environments. Background Art

[0002] During the operation of unmanned swarms, the group configuration will change accordingly due to changes in the environment, such as Figure 1 When the configuration changes, individuals need to make dynamic adjustments based on group changes to adapt to group performance requirements in complex environments.

[0003] like Figure 1 As shown in the figure, under different operating conditions, the group's configuration undergoes various complex and diverse changes. However, actual group configuration changes are not limited to the types described above and can be even more complex. During these dynamic changes, the interactions between sub-bodies also undergo corresponding follow-up changes based on demand. During these dynamic changes, the interactive information between sub-bodies also changes with changes in the environment and configuration. Considering the sub-bodies' information input as a dynamic information group and further considering the differences in interactions between group information and individual information groups, it is necessary to consider a group control process based on the dynamic interactions of information groups.

[0004] In this process, swarm information collection relies on information uploaded and shared by each sub-unit, and the effective issuance of sub-unit commands also relies on effective decision-making based on global information input. When sub-units are located in different spatial locations, the external data they collect varies, and this variation increases with the distance between individuals. Integrated control of unmanned swarms relies on the effective input of external environmental data, but it also requires a good interconnection mechanism between sub-units. If the distance between sub-units is too small, the external information obtained will overlap significantly, hindering the swarm's effective adaptation in unknown environments. If the distance between sub-units is too large, the external information received by each sub-unit will not overlap, and the correlation between them will be very low, which will hinder the swarm's effective control of the sub-units. Therefore, swarm control based on the fusion of spatial layout and sub-unit distance has become a key scientific issue in the operation of unmanned swarms in location environments. Summary of the Invention

[0005] (1) Technical issues to be resolved

[0006] The technical problem to be solved by the present invention is: how to provide a collaborative control method for dynamic interaction of unmanned information groups.

[0007] (2) Technical solution

[0008] To solve the above technical problems, the present invention provides a collaborative control method for dynamic interaction of an unmanned information group, the collaborative control method comprising:

[0009] Step 1: Initialization and determination of sub-body information group;

[0010] Step 2: Feature-based group global planning operation;

[0011] Step 3: Feature-based group local planning operation.

[0012] The initialization determination process of the child information group in step 1 includes:

[0013] Step 1.1: Complete the process of identifying and separating sub-body information groups;

[0014] The unmanned platform group is defined as consisting of the front vehicle A, the middle vehicle B and the rear vehicle C, and the corresponding information group sets are S A 、S B and S C In the actual driving process, the driving trajectories of different vehicles are not always on the same straight line, so the straight-line distance between the front vehicle A and the middle vehicle B is L AB , the straight-line distance between the middle car B and the rear car C is L BC , the straight-line distance between the front car A and the rear car C is L AC ;

[0015] For the unmanned platform group, the information group S corresponding to the front vehicle A, the middle vehicle B and the rear vehicle C A 、S B and S C Identify and separate the information and calculate the common information set R of the three sub-information groups, then we have:

[0016] R=S A ∩S B ∩S C

[0017] On this basis, the local information set R between different sub-body information groups is calculated AB 、R BC and R AC , then we have:

[0018]

[0019] Finally, calculate the sub-body information set R unique to each sub-body information group A 、R B and R C , then we have:

[0020]

[0021] In the information group calculation process, the public information set R and the local information set R of the information group AB 、R BC 、R AC It is related to the distance between the three sub-bodies. The closer the distance, the more information data there is in the intersection; the farther the distance, the less information data there is in the intersection. Therefore, data feature calculation and collaborative control of the information group are carried out based on the sub-body distance.

[0022] Step 1.2: Complete the sub-body information group extraction calculation process;

[0023] Information groups S for unmanned platform groups A 、S B and S C , first calculate the information sharing rate η ABC and information difference rate η A|B|C As shown below:

[0024]

[0025] Where M(x) represents the size corresponding to the calculation set x;

[0026] According to the local information set R between different sub-information groups AB 、R BC and R AC , it is necessary to calculate the information free rate η corresponding to each information group separately A|B ,η B|C and η A|C , then:

[0027]

[0028] During the planning and operation of unmanned groups, global planning is given priority. After the global planning is completed, local planning and operation are carried out according to the characteristics of the group.

[0029] The feature-based group global planning operation in step 2 includes:

[0030] Step 2.1: Complete the group global feature calculation process;

[0031] When the information sharing rate η ABC= 1, which means that the system information within the group is completely shared and there is no external information input, corresponding to the information difference rate η A|B|C =0, which means that the group has no external information reference in an unknown environment and cannot make decisions;

[0032] The information difference rate and information sharing rate η A|B|C = 1, which means that the group is in a state of complete input of external information, and the corresponding η ABC =0, which indicates that the sub-bodies within the group are too separated and the information between them cannot be shared at all. At this time, the information interaction between the individuals in the group is lost, and the group loses its cluster interconnection function;

[0033] Under ideal conditions, the information sharing rate η ABC and information difference rate η A|B|C It is necessary to achieve a dynamic balance within a certain range to realize the real-time input of external information and the reasonable sharing of internal information, so as to provide a reasonable judgment reference for the system sub-bodies and overall decision-making;

[0034] The information sharing rate η ABC and information difference rate η A|B|C The calculation method of the system span Φ is:

[0035]

[0036] The system span Φ has dynamic fluctuations during actual operation. Its corresponding dynamic equilibrium range is [ω1, ω2], and the upper and lower limits of the range are:

[0037]

[0038] Step 2.2: Complete the group global planning process;

[0039] According to the system span Φ and distance L AB 、L BC The following situations occur when the value is determined:

[0040] (1) If the system span |Φ|∈[ω1,ω2], then the shared information within the group in the external environment and the unknown information detected by the external environment reach a dynamic equilibrium. At this time, the group is in a normal driving process and there is no need to adjust the group state;

[0041] (2) If the system span If Φ>0, the group in the external environment is too compact. If the group continues to move, the information it obtains in the unknown environment will be insufficient, and the information overlap between different sub-bodies will be high, which is not conducive to the group making full decisions in a complex environment. At this time, the minimum distance L between different sub-bodies needs to be increased. min =min(LAB , L BC ) to achieve group optimization results for different information groups;

[0042] In this case, if L AB ≠L BC , minimum distance L min Need to expand to the target distance L t , then:

[0043]

[0044] If L AB =L BC , distance L AB and L BC Need to expand to the target distance L t , then the calculation equation is as follows:

[0045]

[0046] (3) If the system span If Φ<0, then the group is too relaxed in the external environment. If the group continues to move, the information in the unknown environment will be too dispersed. Without human sub-bodies, it will be impossible to make comprehensive judgments based on the associated data, and thus it will be impossible to make effective decisions in complex environments.

[0047] At this time, it is necessary to reduce the maximum distance L between different sub-bodies max =max(L AB , L BC ) to achieve the group optimization results of different information groups; under this working condition, the maximum distance L max Need to reduce to target distance L t , then:

[0048]

[0049] If L AB =L BC , distance L AB and L BC Need to expand to the target distance L t , then the calculation equation is as follows:

[0050]

[0051] The feature-based group local planning operation in step 3 includes:

[0052] Step 3.1: Complete the group local feature calculation process;

[0053] Calculation: Information Freedom Rate η A|B ,ηB|C and η A|C The intermediate value ω3 of , then:

[0054]

[0055] On this basis, according to the information free rate η A|B ,η B|C and η A|C If the following information judgment conditions are set, the following constraints will apply:

[0056] Condition 1: (n A|C +η A|B )>η B|C And (η A|C +η B|C )>η A|B ;

[0057] Condition two:

[0058] Step 3.2: Complete the group local planning operation process;

[0059] Comprehensively assess the information judgment criteria in step 3.1, and the following situations may occur:

[0060] (1) If both conditions 1 and 2 are met at the same time under the current working conditions, the operating structure and spatial layout of each sub-body within the group are temporarily in a reasonable state, and no dynamic adjustment is required at this time.

[0061] (2) If only condition 2 is satisfied but condition 1 is not satisfied under the current working condition, then within the group, the layout of the front car A, the rear car C and the middle car B is over-coupled. If the car continues to drive, the information processing and transmission time between the front car A and the middle car B will be short, which will affect the response decision of the rear car C to the real-time working condition.

[0062] At this time, the distance L between the front car A and the rear car C is AC It is necessary to increase ΔL to solve the problem of excessive information coupling, then:

[0063]

[0064] (3) If only condition 1 is met but condition 2 is not met under the current working conditions, then within the group, the layout of the front car A, the rear car C and the middle car B is overly decoupled, that is, the degree of separation of the sub-bodies is large. If the group continues to move, the information of the front car A and the middle car B will be at risk of failure for the rear car C. At this time, the following and response of the rear car C to the group will be delayed, which is not conducive to the coordinated operation of the group.

[0065] At this time, the distance L between the front car A and the rear car C is AC It is necessary to reduce ΔL to solve the problem of excessive decoupling of information, then:

[0066]

[0067] (4) If neither condition 1 nor condition 2 is satisfied under the current working condition, then the coupling state of the front car A, the rear car C and the middle car B in the group needs to be further determined; at this time, the information free rate η is determined. A|C Is it equal to ω2? If η A|C ≠ω2, then continue to judge η A|C Is it equal to ω1? If η A|C =ω1, the distance between the sub-bodies is not adjusted temporarily; if η A|C ≠ω1, then the distance L between the front car A and the rear car C AC Need to be reduced to increase the system coupling degree; at the moment T, the distance between A and the following vehicle C is L AC|T At time (T+1), the distance L between A and the following vehicle C is AC|T+1 Need to be adjusted to:

[0068]

[0069] And if η A|C =ω2, then the distance L between the front car A and the rear car C AC Need to increase to reduce the system coupling; at time (T+1), the distance L between the front vehicle A and the rear vehicle C is AC|T+1 Need to be adjusted to:

[0070]

[0071] (3) Beneficial effects

[0072] Compared to existing technologies, this invention, based on the research of minimal unmanned platform groups, proposes a collaborative control method for the dynamic interaction of unmanned information groups, based on the feature extraction and effective fusion of information groups. This method identifies and separates information and extracts features based on the group information group. Based on this, during the planning and operation of the unmanned group, the group status is determined based on the features, prioritizing global planning. After the global adjustment is completed, local planning and control operations are carried out among individuals based on the characteristics of the sub-units, thereby achieving information interaction and collaborative control of the unmanned group under the influence of a limited data set.

[0073] Compared to traditional research methods, this paper primarily studies a limited dataset of sub-bodies within a group. By considering the distribution characteristics of the group under spatial layout and the correlation between sub-bodies' information differences and the group's upper-level control, we propose a method based on the dynamic fusion and effective judgment of information groups. Based on this premise, we conduct global and local planning and control processes during the group's operation, thereby realizing the research of collaborative control methods that are compatible with environmental scalability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 Schematic diagram of the dynamic driving architecture of an unmanned group.

[0075] Figure 2 Schematic diagram of the collaborative control architecture for dynamic interaction of unmanned information groups. DETAILED DESCRIPTION

[0076] In order to make the purpose, content, and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings and examples.

[0077] Under the dynamic operation requirements of unmanned swarms, dynamic information interaction and organic collaborative control between different sub-bodies within the swarm become the core issues for the stable operation of the swarm. The purpose of this invention is to provide a collaborative control method for the dynamic interaction of unmanned information swarms, so as to realize the dynamic information interaction process that changes with the spatial scale of the sub-bodies within the swarm. In this way, the overall planning and operation process of the unmanned swarm is completed with the goal of controllable interactive changes of different sub-bodies' information and overall information optimization in multiple dimensions.

[0078] The collaborative control architecture of the unmanned information group dynamic interaction involved in the present invention is as follows Figure 2 In the system control architecture shown in the figure, the spatial layout of the unmanned platform group changes dynamically in real time, so it is necessary to separate and extract the sub-body information. Based on the obtained information, global and local planning calculations can be performed for the group to achieve the group planning process under the optimal layout.

[0079] First, for the front vehicle A, the middle vehicle B and the rear vehicle C of the unmanned platform group, the public information set R and the local information set R corresponding to the three sub-body information groups are calculated respectively. AB 、R BC and R AC , and the sub-body information set R A 、R B and R C At the same time, according to the calculation of each information group S A 、S B and S C , calculate the information sharing rate η respectively ABC , difference rate η A|B|C , and information free rate η A|B ,η B|C and η A|C .

[0080] On this basis, global and local planning operations are performed based on the extracted group characteristics. In the global planning operation process, according to the information sharing rate η ABC and the difference rate ηA|B|C The system span Φ is calculated based on the change of AB , L BC , then the distance L between the front vehicle A and the middle vehicle B can be calculated AB , and the distance L between the middle car B and the rear car C BC Perform global dynamic adjustments.

[0081] During the local planning operation, according to the information free rate η A|B ,η B|C and η A|C The corresponding constraints are proposed based on the intermediate value ω3 and the dynamic equilibrium limit values ​​ω1 and ω2, so that the straight-line distance L between the front vehicle A and the rear vehicle C under different working conditions can be locally dynamically adjusted according to the real-time changes of the group information. AC .

[0082] Example 1

[0083] This embodiment separates and correlates the limited information of different sub-aggregates within a group, fully considering the dynamic changes in the group's characteristics during operation. This research explores group collaborative control based on the dynamic interaction of sub-aggregate information, thereby achieving information interaction and real-time control of unmanned groups. The following, combined with the accompanying drawings, further illustrates a collaborative control method for dynamic interaction of unmanned information groups.

[0084] In real-world environments, the operation of multiple unmanned platforms requires the coordinated interaction of their sub-bodies to achieve group functionality in complex environments. The selection and decision-making of group characteristics relies on the fusion and mutual compensation of information from each sub-bodies. Through the integration and selection of information from different sub-bodies, organic coordination is achieved during the operation of the group queue.

[0085] According to the actual application requirements, the method is introduced based on the minimum unmanned platform group. In the actual operation process, it is assumed that the unmanned platform group consists of the front vehicle A, the middle vehicle B and the rear vehicle C. The corresponding information group sets are S A 、S B and S C In the actual driving process, the driving trajectories of different vehicles are not always on the same straight line, so the straight-line distance between the front vehicle A and the middle vehicle B is L AB , the straight-line distance between the middle car B and the rear car C is L BC , the straight-line distance between the front car A and the rear car C is L AC Thus, the information interaction process and collaborative operation method of the unmanned platform are realized.

[0086] Step 1: Initialization and determination of child information group

[0087] During the operation of unmanned groups, the first step is to identify and separate information and extract features based on the group information group, and then complete the information initialization judgment within the information domain based on the calculation of information overlap and feature set.

[0088] 1.1 Identification and separation of sub-body information groups

[0089] For the unmanned platform group, the information group S corresponding to the front vehicle A, the middle vehicle B and the rear vehicle C A 、S B and S C , first identify and separate the information, that is, calculate the common information set R of the three sub-information groups, then we have:

[0090] R=S A ∩S B ∩S C

[0091] On this basis, calculate the local information set R between different sub-body information groups AB 、R BC and R AC , then we have:

[0092]

[0093] Finally, calculate the sub-body information set R unique to each sub-body information group A 、R B and R C , then we have:

[0094]

[0095] In the information group calculation process, the public information set R and the local information set R of the information group AB 、R BC 、R AC The distance between the three sub-bodies is related to this. The closer the distance, the more information data is in the intersection; the farther the distance, the less information data is in the intersection. Therefore, the data feature calculation and collaborative control of the information group are carried out based on the sub-body distance.

[0096] 1.2 Extraction and calculation of sub-body information groups

[0097] Based on the calculation results of the identification and separation of the sub-body information group, the feature extraction calculation of the information group is further performed, that is, based on the calculation results of different sets, the group feature extraction calculation is performed, and the numerical results are used as feature reference to complete the information differentiation and fusion calculation process under the group information feature manifestation processing.

[0098] Information groups S for unmanned platform groups A 、S B and SC , first calculate the information sharing rate η ABC and information difference rate η A|B|C The following is shown below:

[0099]

[0100] Where M(x) represents the size of the calculation set x.

[0101] According to the local information set R between different sub-information groups AB 、R BC and R AC , it is necessary to calculate the information free rate η corresponding to each information group separately A|B ,η B|C and η A|C , the calculation method is as follows:

[0102]

[0103] During the planning and operation of unmanned groups, global planning is given priority. After the global planning is completed, local planning and operation are carried out according to the characteristics of the group.

[0104] Step 2: Feature-based group global planning operation

[0105] In the process of global planning of the group, the information sharing rate η ABC and the difference rate η A|B|C The distance L between the front vehicle A and the middle vehicle B is adjusted dynamically in real time based on the changes in AB , and the distance L between the middle car B and the rear car C BC , through distance adjustment, the dynamic adjustment of the information group characteristics is completed to achieve the expansion of the scalability characteristics of the unmanned group in complex environments, thereby completing the demand-based adjustable group intelligent adaptive process.

[0106] 2.1 Calculation of global characteristics of the population

[0107] When η ABC = 1, which means that the system information within the group is completely shared and there is no external information input. The corresponding η A|B|C = 0, which means that the group has no external information reference in an unknown environment and cannot make decisions; A|B|C = 1, which means that the group is in a state of complete input of external information, and the corresponding η ABC = 0, which indicates that the sub-bodies within the group are too separated and the information between them cannot be shared at all. At this time, the information interaction between the individuals in the group is lost and the group loses its cluster interconnection function. In an ideal state, the information sharing rate η ABC and the difference rate η A|B|CIt is necessary to achieve a dynamic balance within a certain range to realize the real-time input of external information and the reasonable sharing of internal information, so as to provide a reasonable judgment reference for system sub-bodies and overall decision-making.

[0108] Assuming the information sharing rate η ABC and the difference rate η A|B|C If there is a system span Φ, the corresponding calculation method is as follows:

[0109]

[0110] The system span Φ has dynamic fluctuations during actual operation. Its corresponding dynamic equilibrium range is [ω1, ω2], and the upper and lower limits of the range are:

[0111]

[0112] 2.2 Group Global Planning Operation

[0113] According to the calculated system span Φ and distance L AB , L BC The following situations occur when the value is determined:

[0114] (1) If the system span |Φ|∈[ω1,ω2], then the shared information within the group in the external environment and the unknown information detected from the outside can reach a dynamic equilibrium. At this time, the group is in a normal driving process and there is no need to adjust the group state.

[0115] (2) If the system span If Φ>0, then the group in the external environment is too compact. If the group continues to move, the information it obtains in the unknown environment will be insufficient, and the information overlap between different sub-bodies will be high, which is not conducive to the group making full decisions in a complex environment. In this case, the minimum distance L between different sub-bodies needs to be increased. min =min(L AB , L BC ) to achieve group optimization results for different information groups.

[0116] In this case, if L AB ≠L BC , minimum distance L min Need to expand to the target distance L t , then:

[0117]

[0118] If L AB =L BC , distance L AB and L BC Need to expand to the target distance L t, then the calculation equation is as follows:

[0119]

[0120] (3) If the system span And Φ<0, then the group in the external environment is too relaxed. If it continues to move, the information of the group in the unknown environment will be too scattered, and the unmanned sub-body will not be able to make a comprehensive judgment through the associated data, and thus will not be able to make effective decisions in a complex environment.

[0121] At this time, it is necessary to reduce the maximum distance L between different sub-bodies max =max(L AB , L BC ) to achieve the group optimization results of different information groups. Under this working condition, the maximum distance L max Need to reduce to target distance L t , then:

[0122]

[0123] If L AB =L BC , distance L AB and L BC Need to expand to the target distance L t , then the calculation equation is as follows:

[0124]

[0125] Step 3: Feature-based group local planning operation

[0126] In the local planning process of the group, the information free rate η A|B ,η B|C and η A|C , to dynamically adjust the straight-line distance L between the front car A and the rear car C in real time AC , in order to realize unmanned group operation with the extension characteristics of reference local information, thereby completing the group autonomous adjustment process based on information sharing.

[0127] 3.1 Calculation of local features of a group

[0128] In the actual group operation process, the information free rate η A|B ,η B|C and η A|CA dynamic equilibrium is necessary. An abnormal increase in the information oscillation rate indicates an over-matching of information between the two sub-bodies, while an abnormal decrease indicates a lack of information oscillation between the two sub-bodies. Whether the information oscillation rate increases or decreases, it indicates deficiencies or anomalies in the current state of the system. Information imbalances between sub-bodies can lead to functional imbalances or performance deficits within the group itself.

[0129] Calculate the information release rate η A|B ,η B|C and η A|C The intermediate value ω3 of , then:

[0130]

[0131] On this basis, according to the information free rate η A|B ,η B|C and η A|C If the following information judgment conditions are set, the following constraints will apply:

[0132] Condition 1: (n A|C +η A|B )>η B|C And (η A|C +η B|C )>η A|B .

[0133] Condition two:

[0134] 3.2 Group Local Planning Operation

[0135] Based on the above information and judgment conditions, a comprehensive assessment is conducted, and the following situations may occur:

[0136] (1) If both conditions 1 and 2 are met at the same time under the current working conditions, the operating structure and spatial layout of each sub-body within the group are temporarily in a reasonable state, and no dynamic adjustment is required at this time.

[0137] (2) If only condition 2 is met but condition 1 is not met under the current working conditions, then within the group, the layout of the front car A, the rear car C and the middle car B is over-coupled. If the car continues to drive, the information processing and transmission time of the front car A and the middle car B will be short, which will affect the response decision of the rear car C to the real-time working conditions.

[0138] At this time, the distance L between the front car A and the rear car C is AC It is necessary to increase ΔL to solve the problem of excessive information coupling, then:

[0139]

[0140] (3) If only condition one is met but condition two is not met under the current working conditions, then within the group, the layout of the front car A, the rear car C, and the middle car B is overly decoupled, that is, the degree of separation of the sub-bodies is large. If the group continues to move, the information of the front car A and the middle car B will be at risk of failure for the rear car C. At this time, the rear car C will lag behind in following and responding to the group, which is not conducive to the coordinated operation of the group.

[0141] At this time, the distance L between the front car A and the rear car C is AC It is necessary to reduce ΔL to solve the problem of excessive decoupling of information, then:

[0142]

[0143] (4) If neither condition 1 nor condition 2 is satisfied under the current working condition, then the coupling state of the front car A, the rear car C and the middle car B in the group needs to be further determined. At this time, the information free rate η is determined. A|C Is it equal to ω2? If η A|C ≠ω2, then continue to judge η A|C Is it equal to ω1? If η A|C =ω1, the distance between the sub-bodies is not adjusted temporarily; if η A|C ≠ω1, then the distance L between the front car A and the rear car C AC It needs to be reduced to increase the system coupling. At the moment T, the distance between A and the following vehicle C is L AC|T At time (T+1), the distance L between A and the following vehicle C is AC|T+1 Need to be adjusted to:

[0144]

[0145] And if η A|C =ω2, then the distance L between the front car A and the rear car C AC Need to increase to reduce the system coupling. At time (T+1), the distance L between the front vehicle A and the rear vehicle C is AC|T+1 Need to be adjusted to:

[0146]

[0147] In summary, the present invention belongs to the technical field of intelligent control of unmanned vehicles, and specifically relates to a collaborative control method for dynamic interaction of unmanned information groups. The method mainly involves three main parts: initialization judgment of sub-body information groups, feature-based group global planning and operation, and feature-based group local planning and operation. First, the sub-body information group is identified and separated, and the sub-body information group is extracted and calculated based on the limited information that can be obtained by the sub-bodies within the group. According to the calculation results, the status of each sub-body within the group under different environments can be effectively judged, thereby obtaining the connection status between the sub-bodies. On the basis of considering the reasonable input and effective interaction of system information, the distance between different sub-bodies is dynamically adjusted with the comprehensive performance of the group as the goal, thereby realizing the global and local planning, operation and control process of the overall architecture of the unmanned group under information interaction.

[0148] The above is only 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 technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A collaborative control method for dynamic interaction of unmanned information groups, characterized in that: The collaborative control method includes: Step 1: Initialization and determination of sub-body information group; Step 2: Feature-based group global planning operation; Step 3: Feature-based group local planning operation.

2. The collaborative control method for dynamic interaction of unmanned information groups according to claim 1, characterized in that: The initialization determination link of the child information group in step 1 includes: Step 1.1: Complete the process of identifying and separating sub-body information groups; The unmanned platform group is defined as consisting of the front vehicle A, the middle vehicle B and the rear vehicle C, and the corresponding information group sets are S A 、S B and S C In the actual driving process, the driving trajectories of different vehicles are not always on the same straight line, so the straight-line distance between the front vehicle A and the middle vehicle B is L AB , the straight-line distance between the middle car B and the rear car C is L BC , the straight-line distance between the front car A and the rear car C is L AC ; For the unmanned platform group, the information group S corresponding to the front vehicle A, the middle vehicle B and the rear vehicle C A 、S B and S C Identify and separate the information and calculate the common information set R of the three sub-information groups, then we have: R=S A ∩S B ∩S C On this basis, the local information set R between different sub-body information groups is calculated AB 、R BC and R AC , then we have: Finally, calculate the sub-body information set R unique to each sub-body information group A 、R B and R C , then we have: In the information group calculation process, the public information set R and the local information set R of the information group AB 、R BC 、R AC It is related to the distance between the three sub-bodies. The closer the distance, the more information data there is in the intersection; the farther the distance, the less information data there is in the intersection. Therefore, data feature calculation and collaborative control of the information group are carried out based on the sub-body distance.

3. The collaborative control method for dynamic interaction of unmanned information groups according to claim 2, characterized in that: The sub-body information group initialization determination step also includes: Step 1.2: Complete the sub-body information group extraction calculation process; Information groups S for unmanned platform groups A 、S B and S C , first calculate the information sharing rate η ABC and information difference rate η A|B|C As shown below: Where M(x) represents the size corresponding to the calculation set x; According to the local information set R between different sub-information groups AB 、R BC and R AC , it is necessary to calculate the information free rate η corresponding to each information group separately A|B ,η B|C and η A|C , then: During the planning and operation of unmanned groups, global planning is given priority. After the global planning is completed, local planning and operation are carried out according to the characteristics of the group.

4. The collaborative control method for dynamic interaction of unmanned information groups according to claim 3, characterized in that: The feature-based group global planning operation link in step 2 includes: Step 2.1: Complete the group global feature calculation process; When the information sharing rate η ABC = 1, which means that the system information within the group is completely shared and there is no external information input, corresponding to the information difference rate η A|B|C =0, which means that the group has no external information reference in an unknown environment and cannot make decisions; The information difference rate and information sharing rate η A|B|C = 1, which means that the group is in a state of complete input of external information, and the corresponding η ABC =0, which indicates that the sub-bodies within the group are too separated and the information between them cannot be shared at all. At this time, the information interaction between the individuals in the group is lost, and the group loses its cluster interconnection function; Under ideal conditions, the information sharing rate η ABC and information difference rate η A|B|C It is necessary to achieve a dynamic balance within a certain range to realize the real-time input of external information and the reasonable sharing of internal information, so as to provide a reasonable judgment reference for the system sub-bodies and overall decision-making; The information sharing rate η ABC and information difference rate η A|B|C The calculation method of the system span Φ is: The system span Φ has dynamic fluctuations during actual operation. Its corresponding dynamic equilibrium range is [ω1, ω2], and the upper and lower limits of the range are:

5. The collaborative control method for dynamic interaction of unmanned information groups according to claim 4, characterized in that: The feature-based group global planning operation link of step 2 further includes: Step 2.2: Complete the group global planning process; According to the system span Φ and distance L AB 、L BC The following situations occur when the value is determined: (1) If the system span |Φ|∈[ω1,ω2], then the shared information within the group in the external environment and the unknown information detected by the external environment reach a dynamic equilibrium. At this time, the group is in a normal driving process and there is no need to adjust the group state; (2) If the system span If Φ>0, the group in the external environment is too compact. If the group continues to move, the information it obtains in the unknown environment will be insufficient, and the information overlap between different sub-bodies will be high, which is not conducive to the group making full decisions in a complex environment. At this time, the minimum distance L between different sub-bodies needs to be increased. min =min(L AB , L BC ) to achieve group optimization results for different information groups; In this case, if L AB ≠L BC , minimum distance L min Need to expand to the target distance L t , then: If L AB =L BC , distance L AB and L BC Need to expand to the target distance L t , then the calculation equation is as follows: (3) If the system span If Φ<0, then the group is too relaxed in the external environment. If the group continues to move, the information in the unknown environment will be too dispersed. Without human sub-bodies, it will be impossible to make comprehensive judgments based on the associated data, and thus it will be impossible to make effective decisions in complex environments. At this time, it is necessary to reduce the maximum distance L between different sub-bodies max =max(L AB , L BC ) to achieve the group optimization results of different information groups; under this working condition, the maximum distance L max Need to reduce to target distance L t , then: If L AB =L BC , distance L AB and L BC Need to expand to the target distance L t , then the calculation equation is as follows:

6. The collaborative control method for dynamic interaction of unmanned information groups according to claim 5, characterized in that: The feature-based group local planning operation in step 3 includes: Step 3.1: Complete the group local feature calculation process; Calculation: Information Freedom Rate η A|B ,η B|C and η A|C The intermediate value ω3 of , then: On this basis, according to the information free rate η A|B ,η B|C and η A|C If the following information judgment conditions are set, the following constraints will apply: condition one: (h A|C +n A|B )>h B|C And(the A|C +n B|C )>h A|B ; Condition 2:

7. The collaborative control method for dynamic interaction of unmanned information groups according to claim 6, characterized in that: The feature-based group local planning operation link of step 3 further includes: Step 3.2: Complete the group local planning operation process; Comprehensively assess the information judgment criteria in step 3.1, and the following situations may occur: (1) If both conditions 1 and 2 are met at the same time under the current working conditions, the operating structure and spatial layout of each sub-body within the group are temporarily in a reasonable state, and no dynamic adjustment is required at this time.

8. The collaborative control method for dynamic interaction of unmanned information groups according to claim 7, characterized in that: In step 3.2, a comprehensive assessment is performed based on the information determination conditions in step 3.

1. If the following conditions occur: (2) If only condition 2 is satisfied but condition 1 is not satisfied under the current working condition, then within the group, the layout of the front car A, the rear car C and the middle car B is over-coupled. If the car continues to drive, the information processing and transmission time between the front car A and the middle car B will be short, which will affect the response decision of the rear car C to the real-time working condition. At this time, the distance L between the front car A and the rear car C is AC It is necessary to increase ΔL to solve the problem of excessive information coupling, then:

9. The collaborative control method for dynamic interaction of unmanned information groups according to claim 8, characterized in that: In step 3.2, a comprehensive assessment is performed based on the information determination conditions in step 3.

1. If the following conditions occur: (3) If only condition 1 is met but condition 2 is not met under the current working conditions, then within the group, the layout of the front car A, the rear car C and the middle car B is overly decoupled, that is, the degree of separation of the sub-bodies is large. If the group continues to move, the information of the front car A and the middle car B will be at risk of failure for the rear car C. At this time, the following and response of the rear car C to the group will be delayed, which is not conducive to the coordinated operation of the group. At this time, the distance L between the front car A and the rear car C is AC It is necessary to reduce ΔL to solve the problem of excessive decoupling of information, then:

10. The collaborative control method for dynamic interaction of unmanned information groups according to claim 9, characterized in that: In step 3.2, a comprehensive assessment is performed based on the information determination conditions in step 3.

1. If the following conditions occur: (4) If neither condition 1 nor condition 2 is satisfied under the current working condition, then the coupling state of the front car A, the rear car C and the middle car B in the group needs to be further determined; at this time, the information free rate η is determined. A|C Is it equal to ω2? If η A|C ≠ω2, then continue to judge η A|C Is it equal to ω1? If η A|C =ω1, the distance between the sub-bodies is not adjusted temporarily; if η A|C ≠ω1, then the distance L between the front car A and the rear car C AC Need to be reduced to increase the system coupling degree; at the moment T, the distance between A and the following vehicle C is L AC|T At time (T+1), the distance L between A and the following vehicle C is AC|T+1 Need to be adjusted to: And if η A|C =ω2, then the distance L between the front car A and the rear car C AC Need to increase to reduce the system coupling; at time (T+1), the distance L between the front vehicle A and the rear vehicle C is AC|T+1 Need to be adjusted to:

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