Collaborative navigation layered fusion topology structure optimization system and method

Abstract

A collaborative navigation hierarchical fusion topology optimization system and method includes: a hierarchical fusion topology construction module, a genetic algorithm search module, and a correlation detection module, wherein: the hierarchical fusion topology construction module establishes a hierarchical fusion topology optimization model and generates a first-layer fusion topology based on the sensor configuration and resource constraints of the intelligent agent; the genetic algorithm search module uses a genetic algorithm to search based on the first-layer fusion topology to obtain a second-layer fusion topology; the correlation detection module performs correlation detection on the second-layer fusion topology, removes any information that has correlation, obtains an updated second-layer fusion topology, and outputs it to the genetic algorithm search module for iterative update until the optimal hierarchical fusion topology is obtained. The present invention uses algebraic graph theory to model the hierarchical fusion topology structure, then establishes an optimization model and designs a genetic algorithm for solution. By optimizing the topology structure, resource consumption is reduced while ensuring that navigation performance meets requirements.

Description

Technical Field

[0001] The present invention relates to a technology in the field of unmanned aerial vehicle control, specifically a collaborative navigation layered fusion topology structure optimization system and method. Background Art

[0002] Collaborative navigation leverages communication and relative observation between intelligent agents to share navigation resources. However, existing technologies cannot select an appropriate topology based on positioning requirements and communication and computing resource constraints, achieving a compromise between positioning accuracy and resource expenditure. This leads to high resource consumption and difficulty adapting to dynamic scenarios. Summary of the Invention

[0003] In response to the problems that the existing technology does not consider topology optimization and consumes excessive communication computing resources, the present invention proposes a collaborative navigation hierarchical fusion topology structure optimization system and method. The hierarchical fusion topology structure is modeled with the help of algebraic graph theory, and then an optimization model is established. A genetic algorithm is designed for solving the problem. By optimizing the topology structure, resource consumption is reduced while ensuring that the navigation performance meets the requirements.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a collaborative navigation hierarchical fusion topology structure optimization system, comprising: a hierarchical fusion topology structure construction module, a genetic algorithm search module and a correlation detection module, wherein: the hierarchical fusion topology structure construction module establishes a hierarchical fusion topology optimization model and generates a first-layer fusion topology according to the sensor configuration and resource constraints of the intelligent body; the genetic algorithm search module searches based on the first-layer fusion topology using a genetic algorithm to obtain a second-layer fusion topology; the correlation detection module performs correlation detection on the second-layer fusion topology, removes information with correlation, obtains an updated second-layer fusion topology and outputs it to the genetic algorithm search module for iterative update until the optimal hierarchical fusion topology is obtained.

[0006] The hierarchical fusion topology structure refers to: in a multi-agent system, the information fused by the agents is layered in order of priority, and the fusion topology of each layer is modeled as a graph variable with the help of the concept of adjacency matrix, and the final mathematical expression of the multi-layer topology structure is obtained.

[0007] The hierarchical fusion topology structure building module includes: an input unit and an initialization unit, wherein: the input unit generates the sensor configuration and resource constraint information of the intelligent agent, and the initialization unit establishes a hierarchical fusion topology optimization model and generates an initial fusion topology.

[0008] The genetic algorithm search module includes: an encoding unit, a fitness unit and a genetic unit, wherein: the encoding unit encodes the fusion topology, the fitness unit evaluates the fitness of each generated offspring and generates a corresponding fitness value, and the genetic unit generates new offspring through mutation operations on the encoding of the surviving offspring, and finally searches for the optimal fusion topology.

[0009] The correlation detection module includes: a correlation mapping unit and a multipath detection unit, wherein: the correlation mapping unit lists all existing layers in parallel for mapping according to the information fusion process; the multipath detection unit detects whether there is correlation based on the results obtained by the correlation mapping unit and removes the information that has correlation.

[0010] Technical Effects

[0011] This invention leverages algebraic graph theory and a hierarchical structure, using multiple layers to describe the propagation and fusion mechanisms of agent cluster information across different levels. This results in a hierarchical fusion topology. This topology is optimized using a genetic algorithm and multipath detection to remove correlated information. Compared to existing technologies, this optimized hierarchical fusion topology reduces resource consumption while ensuring satisfactory navigation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Schematic diagram of the system of the present invention;

[0013] Figure 2 Schematic diagram of the layered structure of the present invention;

[0014] Figure 3 This is an example of a single-layer fusion topology of the present invention;

[0015] Figure 4 This is a schematic diagram of the principle of the correlation checking module of the present invention;

[0016] Figure 5 This is a schematic diagram of the implementation scenario of this embodiment;

[0017] Figure 6 This is a simulation experiment effect diagram of this embodiment. DETAILED DESCRIPTION

[0018] like Figure 1 As shown in FIG. 1 , a collaborative navigation hierarchical fusion topology structure optimization method based on the above system according to this embodiment includes:

[0019] Step 1: Based on the concept of adjacency matrix in algebraic graph theory, a mathematical model of collaborative navigation topology is established, which includes: the fusion topology G of sensor information p =(v p ,ε p) and the fusion topology G of state estimation r =(v r ,ε r ). When the multi-agent system has N agents and M sensors, then G p The nodes can Where i represents the agent i in the multi-agent system, s jk Corresponding to the kth sensor on agent j. G p The edge set Indicates whether any sensor on any agent transmits information to the information fusion center on another specified agent. ij Transmit information to the information fusion center of the designated agent k, then the edge G p The adjacency matrix is ​​expressed as This means that if the sensor s ij , the information is transmitted to the information fusion center on agent k, then otherwise G r Node set Where i represents the agent i in the multi-agent system, The state estimate of agent j corresponding to agent i. r The edge set Represents whether any state estimate on any agent is transmitted to the information fusion center on another agent. If agent i transmits an existing state estimate s to agent k ij , then the edge G r The adjacency matrix of Indicates that Represents the state estimate s on agent i ij Transmit to the fusion center on agent k, otherwise

[0020] Step 2: The hierarchical fusion topology construction module determines the combination optimization model based on the given multi-agent system information, constructs the search problem, and obtains the initial fusion topology, which includes:

[0021] 2.1) Based on the input, an optimization model is established, where: the optimization variable is the hierarchical fusion topology structure; the objective function is the communication and computing resource consumption of the agent; the constraints include the positioning accuracy requirements and communication and computing resource constraints of the agent.

[0022] 2.2) Using a hierarchical structure to construct the search problem, the hierarchical fusion topology is split into multiple layers and described and modeled separately. Figure 2As shown, the positioning accuracy after each layer is taken as the state S k , taking each layer as action A k , the corresponding navigation resource expenditure is as k Execute A k The cost of reaching the next state can be described by a series of S and A interlaced sequences, thus constructing a search problem. As an action, a single-layer fusion topology example is Figure 3 shown.

[0023] 2.3) Initialize the hierarchical fusion topology optimization problem and construct the initial state: set the adjacency matrix of the first-layer fusion topology to zero, indicating that at the beginning of a navigation epoch, there is no information exchange and fusion between the agents; the corresponding positioning error is set to infinity, indicating that the error of the agent navigation solution is infinite, that is, no valid navigation solution is generated; use the concept of the adjacency matrix to generate an initialized single-layer fusion topology.

[0024] Step 3: The genetic algorithm search module encodes the current state and the initialized single-layer fusion topology, measures the quality of each offspring with fitness, generates offspring through genetic operations, constructs a genetic algorithm based on this, and searches, outputting the optimal new-layer fusion topology. Specifically, it includes:

[0025] 3.1) Design coding and generate initial population: Use binary coding and randomly generate an initial population with binary coding.

[0026] 3.2) Design the fitness function and corresponding genetic operations. The fitness function can be expressed as f = (500P + M) - C·S. P represents the number of agents that meet the required positioning accuracy, M represents the amount of information used in the new fusion topology, C = 10E9, and S represents whether any navigation resource exceeds the maximum limit. If so, the value is 1, and otherwise 0. The genetic operations in this step are random mutation and exchange.

[0027] 3.3) Determine the new layer of information fusion through genetic algorithm search: The genetic algorithm module outputs the optimal adjacency matrix of the new layer of information fusion topology as the information that the corresponding intelligent agent in the new layer needs to integrate. The correlation detection module judges the correlation between the candidate information and the original state estimate one by one. If there is information that does not meet the requirements, the corresponding element of the adjacency matrix is ​​set to zero. If the adjacency matrix is ​​not a zero matrix, it is recorded as a new action A. k+1 , the corresponding state is also advanced to S k+1 Otherwise, it returns to the previous state S k-1 Re-search, taking a different action A than before k ', when S k+1If all constraints are met, the search result is successful and output; if it reaches the initial state and still needs to be rolled back, the search result is failed.

[0028] Step 4: The correlation detection module checks whether the information to be fused is correlated with the original state estimation. The correlation mapping unit builds a map based on the hierarchical fusion topology, such as Figure 4 As shown in the figure, the information fusion process is displayed in parallel as multiple layers. The arrows between the layers represent the transmission of information between agents. If any agent sends out multiple arrows and these arrows converge on the same agent, it means that the information corresponding to the agent is correlated. By removing this information, the updated hierarchical fusion topology can be obtained.

[0029] Step 5: Repeat steps 3 and 4 to obtain the optimized hierarchical fusion topology.

[0030] After specific experiments, in a Figure 5 In the setting of a multi-agent cluster consisting of six agents, experiments were conducted with the parameters shown in Table 1. The performance of the fully measured distributed topology structure under the same conditions was used for comparison. The experimental data obtained are shown in Table 2.

[0031] like Figure 5 As shown in Figure 1, a multi-agent cluster consists of six agents. They have relative distance measurements with each other as shown in the figure. It is required that all navigation solutions of agents 1 to 6 exist on agents 2 and 6, and the accuracy of all navigation solutions on agent 2 can be better than 4.2m, and the accuracy of all navigation solutions on agent 6 can be better than 6m. Experiments show that under the simulation parameters shown in Table 1, the obtained topology is as follows: Figure 6 shown.

[0032] Table 1

[0033] Simulation parameters Setting value Number of carriers 6 Pseudorange observation error variance <![CDATA[1m 2 ]]> Relative ranging error variance <![CDATA[0.04m 2 ]]> Maximum computing power resources of the carrier (fusion center) Both are 100000 Non-fusion center vector number 2、6 Maximum communication resources of carrier (fusion center) Both are 1200 Navigation solution accuracy requirements (Agent 2) <![CDATA[≤2.05m(trace(P)≤4.2m 2 )]]> Navigation solution accuracy requirements (agent 6) <![CDATA[≤2.83m(trace(P≤8m 2 )]]>

[0034] We further compared the performance of the commonly used fully-measured distributed topology in collaborative navigation in simulation experiments. While the fully-measured distributed topology can meet the navigation solution accuracy requirements of the simulation experiments, it cannot simultaneously meet the corresponding navigation resource constraints, as shown in Table 2.

[0035] Table 2

[0036]

[0037] In the hierarchical fusion topology output by the present invention, the navigation resource expenditure of each agent has significantly decreased. Compared with the fully measured distributed topology, the maximum communication resource expenditure of each agent in the hierarchical fusion topology decreased by 33.8%, and the total communication resource expenditure decreased by 38.4%. The maximum computing resource expenditure of each agent decreased by 60.7%, and the total computing resource expenditure decreased by 78.9%. However, the positioning error of agent 2 only increased by 24.4%, and the positioning error of agent 6 only increased by 2.0%. The hierarchical fusion topology is more in line with simulation requirements, and the excessive positioning accuracy of agent 2 does not lead to unnecessary navigation resource expenditure.

[0038] Compared with the existing technology, the present invention takes state estimation into consideration as a special observation by modeling the mathematical model of the collaborative navigation topology structure, making full use of the information in the state estimation, reducing repeated information dissemination and fusion, ensuring the accuracy of the navigation solution, and significantly reducing the collaborative navigation resource expenditure. Through the genetic algorithm search module, the communication resources and computing power resource constraints of each agent are taken into account during optimization. Compared with the fully measured distributed topology structure, the present invention also has a significant reduction in the maximum value of the navigation resource expenditure of a single agent, which significantly reduces the requirements for the agent's hardware facilities. In addition, through the genetic algorithm search module, the present invention can consider the accuracy of the navigation solution obtained by each agent in the system and whether there is a navigation solution for a specified agent on each agent in the system for the collaborative navigation positioning accuracy, which is more comprehensive than the existing technology.

[0039] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principles and purpose of the present invention. The scope of protection of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. All implementation schemes within its scope shall be subject to the constraints of the present invention.

Claims

1. A collaborative navigation hierarchical fusion topology structure optimization method, characterized in that: include: Step 1: Establish a mathematical model of collaborative navigation topology based on the adjacency matrix concept of algebraic graph theory, including: sensor information fusion topology Fusion topology of state estimation , when the multi-agent system has N agents and M sensors, then Node ,in Represents an agent in a multi-agent system , Corresponding agent On the sensors, The edge set Indicates whether any sensor on any agent transmits information to the information fusion center on another specified agent. To the specified agent The information fusion center transmits information, then the edge The adjacency matrix is ​​expressed as , which means that if the sensor The information is transmitted to the agent The information fusion center on ,otherwise Node set ,in Represents an agent in a multi-agent system , Corresponding agent Agents existing on The state estimation, The edge set Represents whether any state estimation on any agent is transmitted to the information fusion center on another agent. Towards the Agent State estimation of transmission presence , then the edge The adjacency matrix of Indicates that Representing an agent The state estimation exists on Transfer to Agent Fusion Center on the ; Step 2: The hierarchical fusion topology construction module determines the combination optimization model based on the given multi-agent system information, constructs the search problem, and obtains the initial fusion topology, which includes: 2.1) Based on the input, an optimization model is established, where the optimization variable is the hierarchical fusion topology; the objective function is the communication and computing resource consumption of the agent; and the constraints include the positioning accuracy requirements and communication and computing resource constraints of the agent; 2.2) Use a hierarchical structure to construct the search problem, split the hierarchical fusion topology into multiple layers, describe and model them separately, and use the positioning accuracy after each layer as the state , taking each layer as an action , the corresponding navigation resource expenditure is as in state Next execution The cost of reaching the next state is expressed as a series of and The search problem is constructed by describing the interleaved sequence; 2.3) Initialize the hierarchical fusion topology optimization problem and construct the initial state: Set the adjacency matrix of the first-layer fusion topology to zero, indicating that at the beginning of a navigation epoch, there is no information exchange or fusion between the agents. The corresponding positioning error is set to infinity, indicating that the error of the agent navigation solution is infinite, that is, no valid navigation solution is generated. Use the concept of the adjacency matrix to generate the initial single-layer fusion topology. Step 3: The genetic algorithm search module encodes the current state and the initialized single-layer fusion topology, measures the quality of each offspring with fitness, generates offspring through genetic operations, constructs a genetic algorithm based on this, and searches, outputting the optimal new-layer fusion topology. Specifically, it includes: 3.1) Design coding and generate initial population: Use binary coding to randomly generate an initial population with binary code; 3.2) Design fitness function and corresponding genetic operation, fitness function represents the number of agents that meet the required positioning accuracy, and M represents the amount of information used in the new layer of fusion topology. , S represents whether any of the navigation resources exceeds the maximum limit. If it exceeds, it is 1, and the rest are 0. The genetic operation in this step is random mutation and exchange; 3.3) Determine the new layer of information fusion through genetic algorithm search: The genetic algorithm module outputs the optimal adjacency matrix of the new layer of information fusion topology as the information that the corresponding intelligent agent in the new layer needs to integrate. The correlation detection module judges the correlation between the candidate information and the original state estimate one by one. If there is information that does not meet the requirements, the corresponding element of the adjacency matrix is ​​set to zero. If the adjacency matrix is ​​not a zero matrix, it is recorded as a new action. , the corresponding state also advances to Otherwise, it returns to the previous state Retry the search, using a different action than before If all constraints are met, the search result is successful and output; if it reaches the initial state and still needs to be rolled back, the search result is failed; Step 4: The correlation detection module checks whether the information to be fused is correlated with the original state estimate. The correlation mapping unit constructs a map based on the hierarchical fusion topology. The information fusion process is displayed in parallel as multiple layers. The arrows between the layers represent the transmission of information between the agents. If any agent sends out multiple arrows, and these arrows converge on the same agent, it means that the information corresponding to the agent is correlated. By removing this information, the updated hierarchical fusion topology can be obtained. Step 5: Repeat steps 3 and 4 to obtain the optimized hierarchical fusion topology.

2. A collaborative navigation hierarchical fusion topology structure optimization system implementing the method of claim 1, characterized in that: include: A hierarchical fusion topology structure construction module, a genetic algorithm search module and a correlation detection module, wherein: the hierarchical fusion topology structure construction module establishes a hierarchical fusion topology optimization model and generates a first-layer fusion topology according to the sensor configuration and resource constraints of the intelligent body; the genetic algorithm search module uses a genetic algorithm to search based on the first-layer fusion topology to obtain a second-layer fusion topology; the correlation detection module performs correlation detection on the second-layer fusion topology, removes the information with correlation, obtains the updated second-layer fusion topology and outputs it to the genetic algorithm search module for iterative update until the optimal hierarchical fusion topology is obtained.

3. The collaborative navigation hierarchical fusion topology structure optimization system according to claim 2 is characterized in that: The hierarchical fusion topology structure refers to: in a multi-agent system, the information fused by the agents is layered in order of priority, and the fusion topology of each layer is modeled as a graph variable with the help of the concept of adjacency matrix, and the final mathematical expression of the multi-layer topology structure is obtained.

4. The collaborative navigation hierarchical fusion topology structure optimization system according to claim 2 is characterized in that: The hierarchical fusion topology structure building module includes: an input unit and an initialization unit, wherein: the input unit generates the sensor configuration and resource constraint information of the intelligent agent, and the initialization unit establishes a hierarchical fusion topology optimization model and generates an initial fusion topology.

5. The collaborative navigation hierarchical fusion topology structure optimization system according to claim 2 is characterized in that: The genetic algorithm search module includes: an encoding unit, a fitness unit and a genetic unit, wherein: the encoding unit encodes the fusion topology, the fitness unit evaluates the fitness of each generated offspring and generates a corresponding fitness value, and the genetic unit generates new offspring through mutation operations on the encoding of the surviving offspring, and finally searches for the optimal fusion topology.

6. The collaborative navigation hierarchical fusion topology structure optimization system according to claim 2 is characterized in that: The correlation detection module includes: a correlation mapping unit and a multipath detection unit, wherein: the correlation mapping unit lists all existing layers in parallel for mapping according to the information fusion process; the multipath detection unit detects whether there is correlation based on the results obtained by the correlation mapping unit and removes the information that has correlation.

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