A process-driven AGV topology road network construction method and related components

Abstract

The application discloses a process-driven AGV topology road network construction method and related components, and relates to the technical field of automatic guided vehicles.The method comprises the following steps: analyzing a process flow, and mapping key workstations as core nodes; extracting auxiliary nodes based on passable areas and AGV movement constraints; connecting nodes to generate topology edges, and calculating actual distances and turning numbers; and combining driving distances, turning numbers and process priorities to perform multi-dimensional weighting, and generating a weighted topology map.The application aims at the problems of existing road networks, such as only relying on geometric features, being disconnected with processes, and not being adapted to vehicle constraints, takes key workstations as cores, fuses passable areas and turning radiuses and other constraints, and generates a weighted map which is bound with process depth.The application can reduce invalid detours and empty driving, adapt to AGV movement characteristics, guarantee high-priority process transfer efficiency, and improve overall efficiency of a production line.

Description

Technical Field

[0001] This invention relates to the field of automated guided vehicle (AGV) technology, specifically to a process-driven AGV topology network construction method and related components. Background Technology

[0002] In smart manufacturing and automated warehousing scenarios, Automated Guided Vehicles (AGVs) are core logistics handling equipment, and the efficiency of their path planning and task scheduling directly determines the overall operating efficiency of the production line. A topology map is the foundation for AGV path planning, typically consisting of nodes and topological edges connecting the nodes. Nodes represent key locations where AGVs can stop, turn, or intersect.

[0003] Currently, the construction of AGV topology maps mainly relies on geospatial data such as raster maps or LiDAR point cloud maps. The working principle involves analyzing the geometric features of the map, such as extracting intersections, points of change of direction, or boundary points, and directly using these as topology nodes, connecting adjacent nodes to form topology edges. However, path planning based on this type of AGV topology map results in low overall production line transfer efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a process-driven AGV topology network construction method and related components to improve the overall transfer efficiency of the production line.

[0005] In a first aspect, the present invention provides a process-driven AGV topology network construction method, comprising: parsing production process flow data, extracting key workstations and process sequences, and obtaining the physical coordinates and process priorities of the key workstations to generate a process data object; based on the physical coordinates of the key workstations and a grid map in the process data object, converting the physical coordinates of the key workstations into grid coordinates, establishing a positional mapping relationship between the key workstations and the grid coordinates, and generating workstation space mapping data; based on the workstation space mapping data, determining the key workstations as core nodes, and extracting intersection nodes and [other nodes] based on the passable areas of the grid map and preset automated guided vehicle driving constraint parameters. Turning nodes serve as auxiliary nodes. The core nodes and auxiliary nodes are aggregated to generate a node set. Based on the drivability constraints of the grid map and the node set, matching nodes in the node set are connected to generate topological edges. Actual driving distances and effective turning counts are calculated based on these topological edges to form a basic topological road network. Configurable weighting coefficients are obtained, and combined with the actual driving distance, effective turning counts, and corresponding process priorities of each topological edge in the basic topological road network, a weight value is generated for each topological edge through multi-dimensional weighted calculation. The nodes in the node set and the topological edges with the weight values ​​are integrated to obtain an automated guided vehicle (AGV) topological map.

[0006] According to one embodiment of the present invention, the step of parsing production process flow data, extracting key workstations and process sequences, obtaining the physical coordinates and process priorities of the key workstations, and generating a process data object includes: establishing a communication connection with a manufacturing execution system (MES) and collecting raw process flow data from the MES; parsing the raw process flow data to identify and extract the process names arranged in production sequence, the key workstation identifiers corresponding to each process, and the physical coordinates of each key workstation in physical space; assigning a corresponding process priority to each extracted key workstation according to a preset priority configuration rule; and storing the extracted process sequence, key workstation identifiers, physical coordinates, and process priorities in a structured manner to generate the process data object.

[0007] According to one embodiment of the present invention, the step of converting the physical coordinates of the key workstations into grid coordinates based on the physical coordinates of the key workstations and the grid map in the process data object, establishing a positional mapping relationship between the key workstations and the grid coordinates, and generating workstation space mapping data includes: obtaining the physical coordinates of the map origin and the grid resolution in the grid map; for each key workstation, subtracting the abscissa value of its physical coordinates from the abscissa value of the map origin to obtain a first difference, and dividing the first difference by the grid resolution and rounding down to obtain the grid abscissa; for each key workstation, subtracting the ordinate value of its physical coordinates from the ordinate value of the map origin to obtain a second difference, and dividing the second difference by the grid resolution and rounding down to obtain the grid ordinate; combining the calculated grid abscissa and grid ordinate to generate the grid coordinates of the key workstations, and establishing a mapping table between the identifier of the key workstation and the grid coordinates to generate the workstation space mapping data.

[0008] According to one embodiment of the present invention, the step of determining the key workstations as core nodes based on the workstation space mapping data, and extracting intersection nodes and turning nodes as auxiliary nodes based on the passable area of ​​the grid map and preset automated guided vehicle driving constraint parameters, and summarizing the core nodes and the auxiliary nodes to generate a node set, includes: defining each key workstation as a core node based on the workstation space mapping data, and assigning a node identifier, node type attribute, and corresponding process priority to each core node; obtaining all grid cells marked as passable in the grid map, traversing each passable grid cell, and checking the passability status of grid cells in its surrounding adjacent directions; and statistically analyzing each passable grid cell. The number of traversable directions for a cell is determined. If this number is greater than or equal to a preset intersection determination threshold, the cell's position is identified as a candidate intersection location. After deduplication of the candidate intersection locations, the intersection node is generated. The vehicle length and minimum turning radius of the automated guided vehicle are obtained. Based on the vehicle length and minimum turning radius, the minimum turning angle of the automated guided vehicle is calculated. Direction change points on all traversable paths in the grid map are obtained. Direction change points with actual turning angles greater than or equal to the minimum turning angle are identified as turning nodes. A unique identifier and node type attribute are assigned to each intersection node and each turning node, and these are aggregated with the core node to generate the node set.

[0009] According to one embodiment of the present invention, the method of connecting mutually matching nodes in the node set based on the drivability constraints of the grid map and the node set to generate topological edges, and calculating the actual driving distance and effective number of turns based on the topological edges to form a basic topological road network, includes: traversing each node in the node set, performing a neighborhood search within the drivable area of ​​the grid map starting from the grid coordinates of the current node; in the neighborhood search, if the grid coordinates of another node are found, and all grid cells on the path from the current node to the other node are drivable, then the other node is determined as the neighboring node of the current node; establishing the connection relationship between the current node and the neighboring node, and assigning unique identifiers to the connection relationship. The topological edge is identified, and the identifiers of the current node and the neighboring nodes are recorded as the starting and ending nodes of the topological edge, respectively, to generate the topological edge. The grid coordinates of the starting and ending nodes of the topological edge are obtained, the straight-line distance between them in the grid map is calculated, and the straight-line distance is converted into physical distance based on the grid resolution as the actual driving distance of the topological edge. All path points along the path from the starting node to the ending node are obtained, and the number of positions on the path with a directional change greater than or equal to the minimum turning angle of the automated guided vehicle is counted as the effective turning number of the topological edge. The actual driving distance and effective turning number of each topological edge are associated with the corresponding topological edge to form the basic topological road network.

[0010] According to one embodiment of the present invention, the step of obtaining configurable weighting coefficients and generating a weight value for each topological edge by combining the actual driving distance, effective number of turns, and corresponding process priority of each topological edge in the basic topological road network through multi-dimensional weighted calculation includes: receiving driving distance weighting coefficients, turning number weighting coefficients, and process priority weighting coefficients input by the user through a user configuration interface; normalizing the received three weighting coefficients and using the normalized three weighting coefficients as the configurable weighting coefficients; for each topological edge in the basic topological road network, obtaining its actual driving distance and effective number of turns, and determining whether the starting node or ending node of the topological edge is... The core node; if at least one of the starting node or the ending node is the core node, then the process priority corresponding to the core node is obtained as the process priority of the topology edge; if both the starting node and the ending node are auxiliary nodes, then a preset default priority value is obtained as the process priority of the topology edge; the actual driving distance is multiplied by the driving distance weight coefficient to obtain a first weighted value, the effective number of turns is multiplied by the number of turns weight coefficient to obtain a second weighted value, the process priority is multiplied by the process priority weight coefficient to obtain a third weighted value, and the first weighted value, the second weighted value and the third weighted value are added together to obtain the weight value of the topology edge.

[0011] According to one embodiment of the present invention, the step of integrating the nodes in the node set and the topological edges with the weight values ​​to obtain an automated guided vehicle topology map includes: structuring all node information, all topological edge information, and the weight value corresponding to each topological edge in the node set; generating a topology map file conforming to a preset format from the structured data; and sending the topology map file to the automated guided vehicle's scheduling system.

[0012] Secondly, the present invention also provides a process-driven AGV topology network construction device, comprising: a process analysis module, used to analyze production process flow data, extract key workstations and process sequences, and obtain the physical coordinates and process priority of the key workstations to generate a process data object; a coordinate mapping module, used to convert the physical coordinates of the key workstations into grid coordinates based on the physical coordinates of the key workstations and grid map parameters in the process data object, establish a positional mapping relationship between the key workstations and the grid coordinates, and generate workstation space mapping data; and a node extraction module, used to determine the key workstations as core nodes based on the workstation space mapping data, and extract intersection nodes based on the passable area of ​​the grid map and preset automated guided vehicle driving constraint parameters. The system uses turning nodes as auxiliary nodes, aggregates the core nodes and the auxiliary nodes, and generates a node set. A topology generation module connects matching nodes in the node set based on the drivability constraints of the grid map and the node set, generating topology edges. It then calculates the actual driving distance and effective turning count based on these topology edges to form a basic topology network. A weight calculation module obtains configurable weighting coefficients and, combined with the actual driving distance, effective turning count, and corresponding process priority of each topology edge in the basic topology network, generates a weight value for each topology edge through multi-dimensional weighted calculation. A map generation module integrates the nodes in the node set and the topology edges with the weight values ​​to obtain an automated guided vehicle (AGV) topology map.

[0013] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a process-driven AGV topology network construction method according to the above embodiments.

[0014] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a process-driven AGV topology network construction method according to the above embodiments.

[0015] The present invention produces at least the following beneficial effects:

[0016] This invention analyzes production process data and extracts key workstations, mapping these workstations to core nodes in a topology map. This allows path planning to directly serve the production cycle time, effectively reducing unnecessary trips, backtracking, and empty runs of automated guided vehicles (AGVs) and improving overall production line efficiency. Simultaneously, when constructing the topology map, this invention extracts auxiliary nodes based on traversable areas of the grid map and AGV driving constraints, ensuring that the generated topology path matches the actual movement capabilities of the AGVs and improving the feasibility and safety of path planning. Furthermore, by assigning weights to topology edges through multi-dimensional weighted calculations, incorporating distance, number of turns, and process priority, the AGV scheduling system can dynamically plan the optimal path based on production needs, ensuring efficient material transfer for high-priority processes. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a process-driven AGV topology network construction method according to an embodiment of the present invention;

[0019] Figure 2 yes Figure 1 A schematic diagram of a specific implementation method for step S10;

[0020] Figure 3 yes Figure 1 A schematic diagram of a specific implementation method for step S20;

[0021] Figure 4 yes Figure 1 A schematic diagram of a specific implementation method for step S30;

[0022] Figure 5 yes Figure 1 A schematic diagram of a specific implementation of step S40;

[0023] Figure 6 yes Figure 1 A schematic diagram of a specific implementation method for step S50;

[0024] Figure 7 yes Figure 1 A schematic diagram of a specific implementation method for step S60;

[0025] Figure 8 This is a schematic block diagram of a process-driven AGV topology network construction device according to an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It should be understood that, when used in this specification and the appended claims, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more of its features, integrals, steps, operations, elements, components and / or collections thereof.

[0028] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0030] To facilitate the description of this application, the following concepts related to this application are introduced.

[0031] Automated Guided Vehicle (AGV): A transport vehicle equipped with electromagnetic or optical automatic guidance devices, capable of traveling along a prescribed guidance path, and possessing safety protection and various transfer functions. In this application, the AGV serves as the carrier for performing material transfer tasks, and its travel path planning relies on a pre-constructed topology map.

[0032] Topology map: A graph-based data structure used to describe an abstract map of key locations and their connections in an environment. In this application, the topology map consists of nodes and topology edges. Nodes represent key locations where AGVs can dock, turn, or intersect, and topology edges represent traversable paths between nodes, used for high-level path planning and task scheduling.

[0033] Raster map: A map representation method that divides environmental space into regular grids. In this application, each raster cell is marked as passable or obstructed, which is used to provide underlying geometric constraints and passable area determination, and is the basic data source for topology map construction.

[0034] Manufacturing Execution System (MES): A computerized system used to manage and monitor production processes. In this application, the MES system provides production process flow data, including process sequence, key workstation information, and physical coordinates, serving as the source of process data for topology map construction.

[0035] Minimum turning radius: refers to the radius of the circle formed by the vehicle's center plane when the deflection angle of the center plane of the front wheel or guide wheel relative to the vehicle's longitudinal axis reaches its maximum value during a turn. In this application, the minimum turning angle is used to calculate the AGV and serves as the basis for determining the turning node.

[0036] This application provides a process-driven AGV topology network construction method, referring to... Figure 1 ,include:

[0037] S10: Parse the production process flow data, extract key workstations and process sequences, obtain the physical coordinates and process priority of the key workstations, and generate process data objects.

[0038] The purpose of this step is to establish the connection between the topology map and the actual production process. Specifically, the system interfaces with the MES system via industrial Ethernet to read the raw process flow data. This data is parsed to identify process names such as "loading," "processing," and "unloading," along with their order. Key workstation identifiers (e.g., "Workstation A"), their coordinates in the physical shop floor coordinate system, and the urgency or importance level of each process (i.e., process priority) are extracted. This information is encapsulated into structured process data objects, serving as the core basis for subsequent topology construction.

[0039] S20: Based on the physical coordinates of the key workstations and the grid map in the process data object, convert the physical coordinates of the key workstations into grid coordinates, establish the positional mapping relationship between the key workstations and the grid coordinates, and generate workstation space mapping data.

[0040] The purpose of this step is to map physical workstations to a digital map space. Specifically, a pre-built raster map is read to obtain its origin coordinates and resolution. Using coordinate transformation formulas, the physical coordinates in the process data object are converted to row and column coordinates in the raster map. For example, if the physical coordinates are (5m, 8m), the map origin is (0m, 0m), and the resolution is 0.05m, then the raster coordinates are calculated. A one-to-one mapping relationship between workstation identifiers and raster coordinates is established, generating workstation space mapping data to ensure that topology nodes accurately fall within the traversable area of ​​the map.

[0041] S30: Based on the workstation space mapping data, the key workstations are identified as core nodes, and based on the passable areas of the grid map and the preset automatic guidance vehicle driving constraint parameters, intersection nodes and turning nodes are extracted as auxiliary nodes. The core nodes and the auxiliary nodes are then combined to generate a node set.

[0042] The purpose of this step is to construct a hierarchical topological node network. Specifically, firstly, the mapped key workstations are directly defined as core nodes and assigned high priority attributes. Then, the passable area of ​​the grid map is traversed: locations with three or more passable directions in their surrounding neighborhood are identified as intersection nodes; the minimum turning angle is calculated based on the AGV's body length and minimum turning radius, and locations on the path where the direction change exceeds this angle are identified as turning nodes. The core nodes and the extracted auxiliary nodes are then combined to form a node set containing attributes such as ID, type, and coordinates.

[0043] Optionally, in step S30, the extraction of the intersection node may not depend on a fixed threshold for the number of neighboring directions. Specifically, the system can adaptively adjust the intersection determination threshold according to the resolution of the raster map and the size of the AGV. For example, in a low-resolution map, the threshold is adjusted to 2 to identify Y-shaped intersections; in a high-resolution map, the threshold is adjusted to 4 to filter out slight channel widening areas, thereby improving the robustness of intersection recognition.

[0044] S40: Based on the drivability constraints of the grid map and the node set, connect the matching nodes in the node set to generate topological edges, and calculate the actual driving distance and effective number of turns based on the topological edges to form a basic topological road network.

[0045] The purpose of this step is to construct the connectivity between nodes and quantify path characteristics. Specifically, for each node in the node set, a neighborhood search is performed on the grid map to find other unobstructed nodes within the line of sight (or within the path search range) as neighboring nodes. Topological edges are generated between neighboring nodes, and the actual physical length of the corresponding edge is calculated as the travel distance. At the same time, the trajectory points on the path are analyzed, and the number of times the direction change exceeds the minimum turning angle of the AGV is counted as the effective turning count, thus forming a basic topological road network with attributes.

[0046] S50: Obtain configurable weighting coefficients, and combine them with the actual driving distance, effective number of turns and corresponding process priority of each topological edge in the basic topological road network to generate a weight value for each topological edge through multi-dimensional weighted calculation.

[0047] The purpose of this step is to optimize the rationality of path selection through multi-dimensional assignment. Specifically, it obtains the user-configured distance weight coefficient, turning weight coefficient, and priority weight coefficient. For each topology edge, if it connects to a core node, the process priority of that node is read; if it connects to an auxiliary node, the default priority is used. For each topology edge, a multi-dimensional weighted formula is used to calculate the weight.

[0048] Calculate the overall weight value for each edge. The smaller the weight value, the lower the overall cost of the path or the higher the technological value.

[0049] S60: Integrate the nodes in the node set and the topological edges with the weight values ​​to obtain the automated guided vehicle topology map.

[0050] The purpose of this step is to output the final map data that can be used for scheduling. Specifically, the node information (ID, type, coordinates) and topological edge information (start and end IDs, weights) are structured and organized to generate a topological map file, which is then sent to the scheduling system for the path planning algorithm to use.

[0051] The embodiments of this application employ the above method, which produces at least the following beneficial effects:

[0052] By analyzing production process data and using key workstations as core nodes, a deep integration of the topology map and the production process is achieved. This method solves the problem in existing technologies where topology map construction relies solely on geometric features, leading to a disconnect from actual production. This allows AGV path planning to directly serve the production cycle time, reducing ineffective travel and empty runs.

[0053] By combining AGV driving constraint parameters to extract turning nodes and intersection nodes as auxiliary nodes, a two-layer node structure that balances process requirements and vehicle mobility is constructed. This method solves the problem that purely geometrically extracted nodes may exceed the AGV's driving capabilities, thus ensuring the feasibility and safety of the generated topology path in actual execution.

[0054] By introducing a multi-dimensional weighted calculation that includes actual driving distance, effective number of turns, and process priority, a refined configuration of path weights is achieved. This method solves the problem that a single-dimensional evaluation cannot balance efficiency and process priorities, thus enabling flexible adjustment of scheduling strategies according to production scenario requirements and improving the overall transfer efficiency of the production line.

[0055] This method can be applied to parts delivery scenarios in automotive final assembly workshops. These workshops typically contain multiple parallel production lines with complex processes and tight cycle times. This method automatically maps key assembly stations as core topological nodes based on assembly procedures issued by the MES (Manufacturing Execution System), and generates a topology map by combining this with the workshop's aisle layout. When the scheduling system plans routes based on this topology map, it prioritizes material delivery to high-priority stations while avoiding narrow areas where AGVs have difficulty turning, ensuring uninterrupted and uncongested production line operation.

[0056] This method can be applied to variable-product production scenarios in flexible manufacturing plants. When a factory switches product models, causing changes in process flow and workstation priorities, this method can quickly re-parse process data and update the core node positions and weight configurations in the topology map. This eliminates the need for manual map redrawing, significantly shortening the debugging cycle for production line changeovers and improving the flexibility of the production line.

[0057] This method can be applied to the picking and handling processes in smart warehousing. The warehouse management system dynamically adjusts the priority of picking areas based on order fluctuations. The topology map constructed by this method can reflect these priority changes in real time, and through weight calculation, the AGV cluster automatically favors high-priority picking channels during path planning, thus optimizing the overall throughput efficiency of warehousing operations.

[0058] In some embodiments, reference Figure 2 Step S10 includes:

[0059] S11: Establish a communication connection with the manufacturing execution system and collect raw process flow data from the manufacturing execution system;

[0060] S12: Parse the original process flow data, identify and extract the process names arranged in production sequence, the key station identifiers corresponding to each process, and the physical coordinates of each key station in physical space;

[0061] S13: Assign a corresponding process priority to each extracted key workstation according to the preset priority configuration rules;

[0062] S14: The extracted process sequence, key station identifiers, physical coordinates and process priority are stored in a structured manner to generate the process data object.

[0063] The purpose of this step is to ensure the accuracy and completeness of the data required for topology construction. The specific implementation steps are as follows:

[0064] The first step is to establish communication: establish a communication connection between the topology building device and the factory's MES system through industrial protocol interfaces such as OPCUA or RESTAPI, and pull production plan BOM and process route data in real time or at regular intervals.

[0065] The second step is data parsing: cleaning and parsing the acquired XML or JSON format data to extract key information sequences such as process 1, material loading, and workstation A (10,20);

[0066] The third step is priority allocation: read the preset rule table, for example, set the bottleneck process to priority 1 (highest) and the auxiliary process to priority 3, and label each workstation with a priority tag;

[0067] Step 4, Object Generation: Encapsulate the above information into a process data object, for example, define a class ProcessData containing a List <station>The `stations` attribute contains an ID, coordinates, and priority fields for use by subsequent modules.

[0068] Through deep integration with the MES system and data structuring, automated acquisition and standardization of process data were achieved. This method solves the problems of low efficiency and error-proneness of manual data entry, thus providing a real-time and accurate data foundation for the automatic construction of topology maps and supporting dynamic responses in the production process.

[0069] In some embodiments, reference Figure 3 Step S20 includes:

[0070] S21: Obtain the physical coordinates of the map origin and the grid resolution in the raster map;

[0071] S22: For each key workstation, subtract the horizontal coordinate value of its physical coordinates from the horizontal coordinate value of the map origin to obtain the first difference value, and then divide the first difference value by the grid resolution and round down to obtain the grid horizontal coordinate.

[0072] S23: For each key workstation, subtract the vertical coordinate value of its physical coordinates from the vertical coordinate value of the map origin to obtain a second difference value, and then divide the second difference value by the grid resolution and round down to obtain the grid vertical coordinate.

[0073] S24: Combine the calculated raster horizontal and vertical coordinates to generate the raster coordinates of the key workstation, and establish a mapping table between the identifier of the key workstation and the raster coordinates to generate the workstation space mapping data.

[0074] The purpose of this step is to eliminate the scale difference between physical space and map space. The specific implementation steps are as follows:

[0075] The first step is parameter reading: parsing the physical coordinates of the map origin and the raster resolution from the metadata of the map file.

[0076] The second step is coordinate transformation: converting the physical coordinates of key workstations into grid coordinates on a standardized grid map, establishing a positional mapping relationship between key workstations and the grid map. The transformation formula is as follows:

[0077] ;

[0078] in, For the physical coordinates of the critical workstations, The origin physical coordinates of the raster map. For raster resolution, These are the grid coordinates corresponding to the critical workstations. This is the floor function.

[0079] The third step is mapping generation: generating a key-value pair mapping table, for example, {"Station_A":(100,200)}, which means that workstation A corresponds to the grid cell in the 100th row and 200th column of the grid map.

[0080] By employing a coordinate transformation formula that rounds down, a precise mapping from physical coordinates to discrete grid space is achieved. This method solves the problem of mismatch between continuous physical coordinates and discrete map cells, thereby ensuring the positioning accuracy of core nodes in the topology map and avoiding errors such as nodes landing on obstacles or boundaries.

[0081] In some embodiments, reference Figure 4 Step S30 includes:

[0082] S31: Based on the workstation space mapping data, each key workstation is defined as a core node, and a node identifier, node type attribute, and corresponding process priority are assigned to each core node.

[0083] S32: Obtain all grid cells marked as passable in the grid map, traverse each passable grid cell, and check the passability status of the grid cells in its surrounding adjacent directions.

[0084] S33: Count the number of directions that each passable grid cell can pass in each direction. If the number is greater than or equal to the preset intersection determination threshold, the position of the grid cell is determined as a candidate intersection position. After deduplication of the candidate intersection positions, the intersection node is generated.

[0085] S34: Obtain the vehicle body length and minimum turning radius of the automated guided vehicle, calculate the minimum turning angle of the automated guided vehicle based on the vehicle body length and the minimum turning radius, obtain the direction change points on all passable paths in the grid map, and determine the direction change points whose actual turning angle is greater than or equal to the minimum turning angle as the turning nodes;

[0086] S35: Assign a unique identifier and node type attribute to each intersection node and each turning node, and summarize them with the core node to generate the node set.

[0087] The purpose of this step is to construct a node network that conforms to the kinematic characteristics of AGVs. The specific implementation steps are as follows:

[0088] The first step is to generate core nodes: define the workstation grid coordinates in the mapping data as core nodes and mark them with the attribute "Core";

[0089] The second step is intersection node extraction: Set the intersection determination threshold to 3 (i.e., a crossroads or a T-junction). Traverse the map; for each passable grid cell, check its neighbors in the vertical, horizontal, and diagonal directions. If the number of passable neighbors is ≥3, mark it as an intersection node. Cluster adjacent intersection grid cells or take their center points to remove duplicates.

[0090] The third step is to extract the turning node: calculate the minimum turning angle based on the AGV parameters, using the following formula:

[0091] ;

[0092] in, This is the angle corresponding to the minimum turning radius of the AGV. The length of the AGV body. The minimum turning radius for the AGV is given. Tracking is performed along a passable path, and the change in the tangent direction of the path is calculated. If the angle of change in direction at a point Δθ ≥ If so, mark that point as a turning node;

[0093] The fourth step is to summarize: merge the above three types of nodes, assign them a globally unique ID, and form a node set.

[0094] By distinguishing between core nodes and auxiliary nodes and introducing turning node determination based on AGV motion parameters, this method achieves dual adaptation of topology nodes to both process logic and physical constraints. This approach addresses the problem of traditional methods neglecting vehicle turning capabilities, thereby reducing the risk of AGVs getting stuck due to insufficient turning radius during path execution.

[0095] In some embodiments, reference Figure 5 Step S40 includes:

[0096] S41: Traverse each node in the node set, and perform a neighborhood search within the passable area of ​​the grid map, starting from the grid coordinates of the current node.

[0097] S42: In the neighborhood search, if the grid coordinates of another node are found, and all grid cells on the path from the current node to the other node are passable, then the other node is determined as the neighboring node of the current node.

[0098] S43: Establish a connection relationship between the current node and the adjacent node, assign a unique topological edge identifier to the connection relationship, and record the identifier of the current node and the identifier of the adjacent node as the starting node and ending node of the topological edge, respectively, to generate the topological edge;

[0099] S44: Obtain the grid coordinates of the starting and ending nodes of the topological edge, calculate the straight-line distance between them in the grid map, and convert the straight-line distance into a physical distance based on the grid resolution, which is used as the actual driving distance of the topological edge. Obtain all path points along the path from the starting node to the ending node, count the number of positions on the path whose directional change is greater than or equal to the minimum turning angle of the automated guided vehicle, which is used as the effective turning number of the topological edge. Associate the actual driving distance and effective turning number of each topological edge with the corresponding topological edge to form the basic topological road network.

[0100] The specific implementation steps are as follows:

[0101] The first step is neighborhood search: using BFS (Breadth-First Search) or the line-of-sight method, starting from the current node, expand the search to other nodes in all directions;

[0102] The second step is connectivity verification: For the potential neighboring nodes found, a path planning algorithm (such as the A* algorithm) or a linear rasterization algorithm is called to check whether there are obstacle grids on the connection path. If there are no obstacles, the connection is confirmed;

[0103] The third step is edge generation: create topological edge objects and record the start and end node IDs;

[0104] Step 4, Attribute Calculation: Calculate the Euclidean distance between the start and end nodes, multiply by the resolution to obtain the physical distance D. Sample along the connection path, calculate the path direction vector, and statistically analyze changes in direction exceeding [a certain threshold]. The number of turns is counted to obtain the effective number of turns T. The physical distance D and the effective number of turns T are then written into the edge's attribute list.

[0105] Fifth, repeat steps one through four until all nodes have completed the matching of adjacent nodes, forming a basic topology network without duplication or redundancy.

[0106] By simultaneously calculating the actual travel distance and effective number of turns during the generation of topological edges, a precise quantitative indicator is provided for subsequent weight calculations. This method solves the problem of simple topological connections lacking physical properties, thus enabling path weights to truly reflect the AGV's travel energy consumption and time cost.

[0107] In some embodiments, reference Figure 6 Step S50 includes:

[0108] S51: Receives user input of driving distance weighting coefficient, turning number weighting coefficient, and process priority weighting coefficient through the user configuration interface;

[0109] S52: Normalize the three received weight coefficients and use the normalized three weight coefficients as the configurable weighting coefficients.

[0110] S53: For each topological edge in the basic topological road network, obtain its actual driving distance and effective number of turns, and determine whether the starting node or ending node of the topological edge is the core node.

[0111] S54: If at least one of the starting node or the ending node is the core node, then the process priority corresponding to the core node is obtained as the process priority of the topology edge; if both the starting node and the ending node are auxiliary nodes, then a preset default priority value is obtained as the process priority of the topology edge.

[0112] S55: Multiply the actual driving distance by the driving distance weight coefficient to obtain a first weighted value, multiply the effective number of turns by the number of turns weight coefficient to obtain a second weighted value, multiply the process priority by the process priority weight coefficient to obtain a third weighted value, and add the first weighted value, the second weighted value and the third weighted value to obtain the weight value of the topological edge.

[0113] The purpose of this step is to enable flexible scheduling strategy configuration. The specific implementation steps are as follows:

[0114] The first step, coefficient acquisition and normalization: user input ′, ′, The system normalizes it, making it so that... =1, to ensure the comparability of weights.

[0115] The second step is priority determination: check the node types connected by the topology edges. If the connection is to a core node (such as workstation A), then read the process priority value. (For example, the smaller the value, the higher the priority); if connecting auxiliary nodes (such as intersections), then P takes the default value (such as the average or maximum value of all priorities).

[0116] The third step is to calculate the weights using a multi-dimensional weighted formula, as follows:

[0117] ;

[0118] in, This represents the topological edge weight. The smaller the weight value, the higher the priority of the corresponding path in AGV path planning. This represents the actual travel distance along the topological edge. The effective number of turns for the topological edge (only turns with a turning angle ≥ the minimum turning radius of the AGV are counted). The process priority corresponding to the topology edge (if the topology edge connects to a core node, the process priority corresponding to the core node is taken; if it connects to an auxiliary node, the default priority value is taken). The weighting coefficients are configurable and satisfy the following conditions: The coefficient can be adjusted according to the actual production scenario requirements. If the priority value is defined as smaller values ​​having higher priority, then in the formula... The item needs to be taken as its reciprocal, or a negative coefficient should be used, or a weight value should be defined. Smaller values ​​have higher priority. This embodiment adopts... The logic is that smaller values ​​take precedence.

[0119] By introducing configurable weighting coefficients and a dynamic priority determination mechanism, the topology network can be rapidly adapted to different production strategies. This method solves the problem that fixed-weight algorithms cannot cope with changing production scenarios, thus enabling the scheduling system to flexibly adjust path planning preferences according to actual needs (such as energy-saving mode and high-efficiency mode).

[0120] Optionally, in step S50, the configuration of the weighting coefficients can be independent of manual input. Specifically, the system can introduce a reinforcement learning algorithm to automatically optimize the weighting coefficients based on the execution efficiency of historical scheduling tasks (such as total travel time and task completion rate). This enables dynamic self-evolution of the topological road network weights.

[0121] In some embodiments, reference Figure 7 Step S60 includes:

[0122] S61: Organize all node information, all topological edge information, and the weight value corresponding to each topological edge in the node set in a structured manner;

[0123] S62: Generate a topology map file that conforms to a preset format from the structured data;

[0124] S63: Send the topology map file to the dispatching system of the automated guided vehicle.

[0125] The specific implementation steps are as follows:

[0126] The first step is data organization: serialize the node list and edge list into JSON or XML format. Node information includes {id, type, x, y, priority}, and edge information includes {id, start_id, end_id, weight}.

[0127] The second step is file generation: generating a .topo map file, along with metadata such as version number and generation time;

[0128] The third step is system distribution: the map file is pushed to the map server of the AGV scheduling system through ROS topic or TCP interface, triggering map loading and update events.

[0129] By standardizing the structure and file generation, the standardized output of topology map data is achieved. This method solves the data compatibility problem between heterogeneous systems, thereby ensuring that the constructed topology map can be correctly parsed and efficiently utilized by the scheduling system, improving the system integration efficiency.

[0130] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0131] Reference Figure 8 This invention provides a process-driven AGV topology network construction device 100, comprising:

[0132] The process analysis module 101 is used to analyze the production process flow data, extract key workstations and process sequences, obtain the physical coordinates and process priority of the key workstations, and generate process data objects.

[0133] The coordinate mapping module 102 is used to convert the physical coordinates of the key workstations into grid coordinates based on the physical coordinates of the key workstations and the grid map parameters in the process data object, establish the position mapping relationship between the key workstations and the grid coordinates, and generate workstation space mapping data.

[0134] The node extraction module 103 is used to determine the key workstations as core nodes based on the workstation space mapping data, and extract intersection nodes and turning nodes as auxiliary nodes based on the passable area of ​​the grid map and the preset automatic guidance vehicle driving constraint parameters, and summarize the core nodes and the auxiliary nodes to generate a node set.

[0135] The topology generation module 104 is used to connect mutually matching nodes in the node set based on the drivability constraints of the grid map and the node set to generate topological edges, and to calculate the actual driving distance and effective number of turns based on the topological edges to form a basic topological road network.

[0136] The weight calculation module 105 is used to obtain configurable weighting coefficients and, in combination with the actual driving distance, effective number of turns and corresponding process priority of each topological edge in the basic topological road network, generate a weight value for each topological edge through multi-dimensional weighted calculation.

[0137] The map generation module 106 is used to integrate the nodes in the node set and the topological edges with the weight values ​​to obtain an automated guided vehicle topology map.

[0138] For specific limitations regarding the process-driven AGV topology network construction device 100, please refer to the limitations of the process-driven AGV topology network construction method described above, which will not be repeated here. Each module in the aforementioned process-driven AGV topology network construction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0139] This invention also provides a computer device, which includes a processor, a memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface communicates with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the process-driven AGV topology network construction method provided in the above embodiments.

[0140] This invention also provides a computer-readable storage medium storing a computer program that, when executed, can implement the functions or steps of the process-driven AGV topology network construction method provided in the above embodiments.

[0141] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0142] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0143] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0144] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.< / station>

Claims

1. A process-driven AGV topology network construction method, characterized in that, include: The production process flow data is analyzed, key workstations and process sequences are extracted, and the physical coordinates and process priorities of the key workstations are obtained to generate process data objects. Based on the physical coordinates and grid map of the key workstations in the process data object, the physical coordinates of the key workstations are converted into grid coordinates, the positional mapping relationship between the key workstations and the grid coordinates is established, and workstation space mapping data is generated. Based on the workstation space mapping data, the key workstations are identified as core nodes, and based on the passable area of ​​the grid map and the preset automatic guidance vehicle driving constraint parameters, intersection nodes and turning nodes are extracted as auxiliary nodes. The core nodes and the auxiliary nodes are then summarized to generate a node set. Based on the drivability constraints of the grid map and the node set, the matching nodes in the node set are connected to generate topological edges, and the actual driving distance and effective number of turns are calculated based on the topological edges to form a basic topological road network. Configurable weighting coefficients are obtained, and weight values ​​are generated for each topological edge by combining the actual driving distance, effective number of turns and corresponding process priority of each topological edge in the basic topological road network through multi-dimensional weighted calculation. By integrating the nodes in the node set and the topological edges with the weight values, an automated guided vehicle topology map is obtained. Based on the workstation space mapping data, the key workstations are identified as core nodes. Based on the passable areas of the grid map and preset automated guided vehicle (AGV) driving constraint parameters, intersection nodes and turning nodes are extracted as auxiliary nodes. The core nodes and auxiliary nodes are then aggregated to generate a node set, including: Based on the workstation space mapping data, each key workstation is defined as a core node, and each core node is assigned a node identifier, node type attribute, and corresponding process priority. Obtain all grid cells marked as passable in the grid map, traverse each passable grid cell, and check the passability status of the grid cells in its surrounding adjacent directions; The number of passable directions for each passable grid cell is counted. If the number is greater than or equal to a preset intersection determination threshold, the position of the grid cell is determined as a candidate intersection position. After deduplication of the candidate intersection positions, the intersection node is generated. The vehicle body length and minimum turning radius of the automated guided vehicle are obtained. The minimum turning angle of the automated guided vehicle is calculated based on the vehicle body length and the minimum turning radius. The direction change points on all passable paths in the grid map are obtained. The direction change points whose actual turning angle is greater than or equal to the minimum turning angle are determined as the turning nodes. Each intersection node and each turning node is assigned a unique identifier and node type attribute, and these are aggregated with the core node to generate the node set.

2. The method for constructing a process-driven AGV topology network according to claim 1, characterized in that, The process involves parsing the production process flow data, extracting key workstations and process sequences, obtaining the physical coordinates and process priorities of the key workstations, and generating a process data object, including: Establish a communication connection with the manufacturing execution system and collect raw process flow data from the manufacturing execution system; The original process flow data is parsed to identify and extract the process names arranged in production sequence, the key station identifiers corresponding to each process, and the physical coordinates of each key station in physical space. According to the preset priority configuration rules, assign a corresponding process priority to each extracted key workstation; The extracted process sequence, key workstation identifiers, physical coordinates, and process priorities are structured and stored to generate the process data object.

3. The method for constructing a process-driven AGV topology network according to claim 1, characterized in that, The process involves converting the physical coordinates of the key workstations into raster coordinates based on the physical coordinates and raster map in the process data object, establishing a positional mapping relationship between the key workstations and the raster coordinates, and generating workstation spatial mapping data, including: Obtain the physical coordinates of the map origin and the grid resolution in the raster map; For each key workstation, the horizontal coordinate of its physical coordinates is subtracted from the horizontal coordinate of the map origin to obtain the first difference. The first difference is then divided by the grid resolution and rounded down to obtain the grid horizontal coordinate. For each key workstation, subtract the ordinate value of its physical coordinates from the ordinate value of the physical coordinates of the map origin to obtain a second difference value. Then, divide the second difference value by the grid resolution and round down to obtain the grid ordinate value. The calculated raster horizontal and vertical coordinates are combined to generate the raster coordinates of the key workstation, and a mapping table between the identifier of the key workstation and the raster coordinates is established to generate the workstation space mapping data.

4. The method for constructing a process-driven AGV topology network according to claim 1, characterized in that, The drivability constraints based on the grid map and the node set are used to connect matching nodes in the node set to generate topological edges. Based on these topological edges, the actual driving distance and effective number of turns are calculated to form a basic topological road network, including: Traverse each node in the node set, and perform a neighborhood search within the passable area of ​​the grid map, starting from the grid coordinates of the current node. In the neighborhood search, if the grid coordinates of another node are found, and all grid cells on the path from the current node to the other node are passable, then the other node is determined as the neighbor of the current node. Establish a connection between the current node and the adjacent nodes, assign a unique topological edge identifier to the connection, and record the identifier of the current node and the identifier of the adjacent node as the start node and end node of the topological edge, respectively, to generate the topological edge; Obtain the grid coordinates of the starting and ending nodes of the topological edge, calculate the straight-line distance between them in the grid map, and convert the straight-line distance into a physical distance based on the grid resolution, which is used as the actual driving distance of the topological edge. Obtain all path points along the path from the starting node to the ending node, count the number of positions on the path whose directional change is greater than or equal to the minimum turning angle of the automated guided vehicle, which is used as the effective turning count of the topological edge. Associate the actual driving distance and effective turning count of each topological edge with the corresponding topological edge to form the basic topological road network.

5. The method for constructing a process-driven AGV topology network according to claim 1, characterized in that, The process of obtaining configurable weighting coefficients and, in conjunction with the actual driving distance, effective number of turns, and corresponding process priority of each topological edge in the basic topological road network, generating a weight value for each topological edge through multi-dimensional weighted calculation includes: The system receives user input of driving distance weighting coefficient, turning number weighting coefficient, and process priority weighting coefficient through the user configuration interface. The three received weight coefficients are normalized, and the normalized three weight coefficients are used as the configurable weighting coefficients. For each topological edge in the basic topological road network, obtain its actual driving distance and effective number of turns, and determine whether the starting node or ending node of the topological edge is the core node. If at least one of the starting node or the ending node is the core node, then the process priority corresponding to the core node is obtained as the process priority of the topology edge. If both the starting node and the ending node are auxiliary nodes, then a preset default priority value is obtained as the process priority of the topology edge. The first weighted value is obtained by multiplying the actual driving distance by the driving distance weight coefficient, the second weighted value is obtained by multiplying the effective number of turns by the number of turns weight coefficient, and the third weighted value is obtained by multiplying the process priority by the process priority weight coefficient. The weight value of the topological edge is obtained by adding the first weighted value, the second weighted value and the third weighted value.

6. The method for constructing a process-driven AGV topology network according to claim 1, characterized in that, The process of integrating the nodes in the node set and the topological edges with the weight values ​​to obtain the automated guided vehicle topology map includes: All node information, all topological edge information, and the weight value corresponding to each topological edge in the node set are organized in a structured manner. The structured data is then used to generate a topological map file that conforms to a preset format. The topology map file is sent to the dispatching system of the automated guided vehicle.

7. A process-driven AGV topology network construction device, characterized in that, include: The process analysis module is used to analyze production process flow data, extract key workstations and process sequences, obtain the physical coordinates and process priority of the key workstations, and generate process data objects. The coordinate mapping module is used to convert the physical coordinates of the key workstations into grid coordinates based on the physical coordinates of the key workstations and the grid map parameters in the process data object, establish the positional mapping relationship between the key workstations and the grid coordinates, and generate workstation space mapping data. The node extraction module is used to determine the key workstations as core nodes based on the workstation space mapping data, and extract intersection nodes and turning nodes as auxiliary nodes based on the passable area of ​​the grid map and the preset automatic guidance vehicle driving constraint parameters. The core nodes and the auxiliary nodes are then combined to generate a node set. The topology generation module is used to connect matching nodes in the node set based on the drivability constraints of the grid map and the node set, generate topological edges, and calculate the actual driving distance and effective number of turns based on the topological edges to form a basic topological road network. The weight calculation module is used to obtain configurable weighting coefficients and, in combination with the actual driving distance, effective number of turns and corresponding process priority of each topological edge in the basic topological road network, generate a weight value for each topological edge through multi-dimensional weighted calculation. The map generation module is used to integrate the nodes in the node set and the topological edges with the weight values ​​to obtain the topological map of the automated guided vehicle. The node extraction module is specifically used for: defining each key workstation as a core node based on the workstation space mapping data, and assigning a node identifier, node type attribute, and corresponding process priority to each core node; obtaining all grid cells marked as passable in the grid map, traversing each passable grid cell, and checking the passability status of grid cells in its surrounding adjacent directions; counting the number of passable directions for each passable grid cell in each direction, and if the number is greater than or equal to a preset intersection determination threshold, determining the position of the grid cell as a candidate intersection position, and generating the intersection node after deduplication of the candidate intersection positions; obtaining the vehicle body length and minimum turning radius of the automated guided vehicle, calculating the minimum turning angle of the automated guided vehicle based on the vehicle body length and the minimum turning radius, obtaining the direction change points on all passable paths in the grid map, and determining the direction change points whose actual turning angle is greater than or equal to the minimum turning angle as the turning nodes; assigning a unique identifier and node type attribute to each intersection node and each turning node, and summarizing them with the core nodes to generate the node set.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the process-driven AGV topology network construction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the process-driven AGV topology network construction method as described in any one of claims 1 to 6.

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