Multi-brand AGV (Automatic Guided Vehicle) same-field mixed operation control method and device, electronic equipment and storage medium

Abstract

The invention discloses a multi-brand AGV same-field mixed operation management and control method and device, electronic equipment and a storage medium, and the method comprises the steps: building a standardized data channel with an original scheduling system of each brand AGV, so as to obtain the real-time operation state data and planning path data of each AGV; based on the real-time operation state data and the planned path data, carrying out space intersection judgment on planned paths of all AGVs, and carrying out collision risk initial judgment on AGV pairs with space intersection based on preset limit operation parameters; motion simulation is carried out on the AGV pair with the collision risk through preliminary judgment in the virtual environment based on the actual operation parameters so as to output a quantized collision risk result; and based on the quantized collision risk result, generating an avoidance strategy for the AGV with the low priority in the AGV pair with the collision risk in combination with a preset dynamic priority rule, and issuing and executing a standardized scheduling instruction generated based on the avoidance strategy. According to the invention, safe and efficient cooperative operation of multiple brands of AGVs in a same-field mixed operation environment can be realized.

Description

Technical Field

[0001] This application relates to the field of AGV management technology, specifically to methods, devices, electronic equipment, and storage media for managing multiple brands of AGVs operating in the same location. Background Technology

[0002] Automated Guided Vehicles (AGVs) are becoming increasingly widely used as core equipment in smart factories and intelligent logistics. With the expansion of production scale and the increase in process complexity, AGVs from a single brand or batch often cannot meet all the needs. In scenarios such as production line upgrades and capacity expansion, the collaborative operation of multiple brands of AGVs in the same physical space, i.e., "co-operation," has become a real and urgent requirement.

[0003] However, the mixed operation of multi-brand AGVs faces severe technical challenges. The core pain point lies in the fact that AGVs from different manufacturers typically use proprietary underlying communication protocols, independent and closed Robot Control Systems (RCS), and heterogeneous data standards. This "protocol silo" and "system barrier" prevents real-time communication and sharing of critical information such as equipment status and planned paths between AGVs of different brands. In complex dynamic environments with mixed traffic and intersecting paths, each AGV can only rely on its limited sensors for local obstacle avoidance, lacking global, forward-looking collaborative planning. This makes the system highly susceptible to path conflicts, obstacle avoidance deadlocks, and even physical collisions at intersections and narrow passages due to information opacity and uncoordinated actions, seriously threatening the smoothness, safety, and overall production efficiency of material transportation.

[0004] To address the challenges of multi-AGV coordination, existing technologies have proposed several solutions. For example, some solutions deploy a central scheduling server to centrally allocate tasks and plan paths for AGV groups of a single brand or using a unified protocol, thus optimizing system efficiency and reducing conflicts to some extent. However, these solutions typically cannot penetrate the underlying protocol barriers between different brands of AGVs, making them difficult to apply directly to multi-brand mixed-operation scenarios. Other solutions introduce digital twin technology, using virtual models to monitor and simulate AGV operation, improving the flexibility of status visualization and debugging. However, their core focus remains on monitoring and post-event analysis, or providing convenience for modifying scheduling instructions. They lack a systematic solution for how to accurately and proactively quantify collision risks based on real-time simulation and thereby drive cross-brand dynamic avoidance scheduling that considers production priorities. Specifically, existing solutions generally have the following defects when implementing mixed management of multi-brand AGVs: (1) They lack efficient and universal multi-brand data integration methods, and cannot achieve unified collection of heterogeneous AGV data and standardized issuance of instructions at low cost and fast speed; (2) The conflict prediction methods are relatively simple, either too conservative leading to low efficiency, or not accurate enough leading to the risk of missed judgment, and fail to effectively combine the advantages of rapid coarse screening and high-fidelity calculation; (3) The avoidance scheduling strategy is usually based on simple "first come, first served" or fixed priority rules, and fails to be deeply integrated with the dynamic business logic of the production system, making it difficult to maximize production efficiency while ensuring safety.

[0005] It should be noted that the above statements are only used to provide background information related to this application and do not necessarily constitute prior art. Summary of the Invention

[0006] To address the aforementioned technical issues, embodiments of this application provide a method, apparatus, electronic device, and storage medium for managing multi-brand AGVs operating in the same location. This method overcomes data barriers caused by heterogeneous protocols and systems among multi-brand AGVs, and achieves safe and efficient collaborative operation of multi-brand AGVs in a mixed-location environment through four levels of collaborative management: protocol adaptation, cross-prediction, collision simulation, and dynamic scheduling.

[0007] In a first aspect, embodiments of this application provide a method for managing the mixed movement of multiple brands of AGVs in the same location, the method comprising: Establish standardized data channels with the native scheduling systems of various brands of AGVs to obtain real-time operating status data and planned path data of each AGV; Based on the real-time operating status data and planned path data, spatial intersection judgment is performed on the planned paths of all AGVs, and for AGV pairs with spatial intersection, a preliminary collision risk judgment is made based on preset limit operating parameters. For AGV pairs that are initially identified as having a collision risk, motion simulation is performed in a virtual environment based on actual operating parameters to output quantified collision risk results; wherein, the actual operating parameters are determined at least based on the real-time operating status data; Based on the quantified collision risk results and combined with the preset dynamic priority rules, an avoidance strategy is generated for the low-priority AGV in the AGV pair with collision risk, and the standardized scheduling instructions generated based on the avoidance strategy are sent out and executed through the standardized data channel.

[0008] In some embodiments of this application, the spatial intersection determination of the planned paths of all AGVs includes: In a unified planar coordinate system, the planned path of each AGV is represented as a polyline consisting of a series of continuous line segments; A line segment intersection detection algorithm based on vector cross product is adopted to determine whether there is an intersection point between the planned path segments of any two AGVs. If an intersection exists, the intersection is recorded as a spatial intersection point, and the corresponding two AGVs are associated to form the AGV pair with spatial intersection.

[0009] In some embodiments of this application, establishing a standardized data channel with the native scheduling systems of various AGV brands includes: Establish standardized specifications for two-way data interaction, including a unified communication protocol, a unified data exchange format, and a protocol conversion mechanism; The acquisition of real-time operating status data and planned path data for each AGV includes: Through the aforementioned protocol conversion mechanism, standard uplink data packets reported by each native scheduling system and converted are received; From the standard uplink data message, the real-time position and speed fields, which serve as the real-time operating status data, and the planned path coordinate sequence field, which serves as the planned path data, are parsed out. The execution via the standardized data channel includes: The avoidance strategy is encapsulated into a standard downlink command message that conforms to the standardized specifications; The protocol conversion mechanism converts the standard downlink instruction message into a private instruction that can be recognized by the corresponding native scheduling system and then sends it out.

[0010] In some embodiments of this application, the real-time operating status data includes the current position, and the preset limit operating parameters include a preset maximum operating speed and a preset maximum vehicle length; the preliminary collision risk assessment based on the preset limit operating parameters includes: For AGV pairs that have spatial intersections, the time window required for each AGV to arrive at and completely pass through the spatial intersection point from its current position is calculated based on the preset maximum operating speed and the preset maximum vehicle length of each AGV. The collision risk of the AGV pair is classified based on the overlap duration of the two time windows.

[0011] In some embodiments of this application, the actual operating parameters include current speed and current acceleration; the process of performing motion simulation in a virtual environment on AGV pairs initially determined to have a collision risk, based on the actual operating parameters, to output quantified collision risk results, includes: Obtain the preset body geometry and preset motion constraints of each AGV in the AGV pair; In a virtual environment, based on the current speed, the current acceleration, the preset vehicle body geometry, and the preset motion constraints, the motion trajectory of each AGV is simulated during the overlapping time period of the time window. The collision probability of the AGV pair is calculated based on the motion trajectory and output as the quantified collision risk result.

[0012] In some embodiments of this application, calculating the collision probability of the AGV pair based on the motion trajectory includes: By introducing random disturbance variables that conform to the actual control logic of the AGV into the current speed, current acceleration and the preset motion constraints, multiple independent motion trajectory simulations are performed. The number of times the AGV interfered with the spatial positioning area in the multiple independent motion trajectory simulations was counted. The collision probability is calculated based on the ratio of the number of interference occurrences to the total number of simulations.

[0013] In some embodiments of this application, after calculating the collision probability of the AGV pair based on the motion trajectory and outputting it as the quantified collision risk result, the method further includes risk classification based on the collision probability, specifically: When the collision probability is zero, it is determined that there is no collision risk; When the collision probability is greater than zero but less than a preset probability threshold, it is determined to be a low-risk collision; When the collision probability is greater than or equal to the preset probability threshold, it is determined to be a high-risk collision.

[0014] In some embodiments of this application, the step of generating an avoidance strategy for the low-priority AGV in an AGV pair with collision risk, based on the quantified collision risk result and in combination with a preset dynamic priority rule, includes: Based on the collision risk results, determine the avoidance urgency of each AGV in the AGV pair; Based on the preset dynamic priority rules, the real-time task priority of each AGV in the AGV pair is calculated. Based on the combined urgency of the avoidance operation and the priority of the real-time task, the low-priority AGVs that need to perform the avoidance operation are determined among the AGVs.

[0015] In some embodiments of this application, generating an avoidance strategy for the low-priority AGV in a pair of AGVs with a collision risk further includes: Based on the environmental conditions around the spatial intersection, instructions are selectively generated to control the low-priority AGV to decelerate, to control the low-priority AGV to enter the preset avoidance zone and stop, or to instruct the low-priority AGV to replan its path.

[0016] A second aspect of this application provides a multi-brand AGV mixed-use management device, the device comprising: A module is established to create a standardized data channel between the AGV native scheduling system of each brand and to obtain real-time operating status data and planned path data of each AGV. The preliminary judgment module is used to make spatial intersection judgments on the planned paths of all AGVs based on the real-time operating status data and the planned path data, and to make a preliminary judgment on the collision risk of AGV pairs with spatial intersections based on preset limit operating parameters. The simulation module is used to perform motion simulation in a virtual environment on AGV pairs that are initially judged to have a collision risk, based on actual operating parameters, so as to output a quantified collision risk result; wherein, the actual operating parameters are determined at least based on the real-time operating status data; The avoidance module is used to generate an avoidance strategy for the low-priority AGV in the AGV pair with collision risk based on the quantified collision risk result and the preset dynamic priority rules, and to send and execute the standardized scheduling instructions generated based on the avoidance strategy through the standardized data channel.

[0017] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-brand AGV mixed-use management method described in various embodiments of this application.

[0018] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the multi-brand AGV mixed-use management method described in the various embodiments.

[0019] This application establishes a standardized data channel with the native scheduling systems of various AGV brands to obtain real-time operating status data and planned path data of each AGV. This effectively penetrates the private protocols and data barriers of each brand's AGVs, achieving unified and real-time collection of heterogeneous equipment status and planning information, laying a data foundation for global collaborative management. Based on the aforementioned real-time operating status data and planned path data, this application performs spatial intersection judgment on the planned paths of all AGVs. For AGV pairs with spatial intersections, a preliminary collision risk assessment is performed based on preset extreme operating parameters. Through rapid and conservative coarse judgment of extreme cases, all potential path conflicts can be identified without omission, overcoming the limitations of local obstacle avoidance and the drawbacks of missing global information in existing technologies, and achieving proactive risk screening. Then, for AGV pairs initially identified as having collision risks, motion simulation is performed in a virtual environment based on actual operating parameters to output quantified collision risk results. These actual operating parameters are determined at least based on the real-time operating status data. This constitutes precise simulation and risk quantification, compensating for the inaccuracy of simple collision detection methods. Through algorithms such as Monte Carlo simulation, risk assessment is elevated from qualitative to quantitative, providing a scientific and accurate basis for intelligent decision-making. Finally, based on the quantified collision risk results and combined with preset dynamic priority rules, avoidance strategies are generated for the low-priority AGVs in the AGV pairs with collision risks. Standardized scheduling instructions generated based on these avoidance strategies are then issued and executed through the standardized data channel, deeply integrating safety collision avoidance with production optimization. Dynamic priority and intelligent strategy selection overcome the negative impact of fixed priority or simple rule scheduling on production efficiency. Furthermore, the standardized instruction issuance and monitoring closed loop ensure the reliable execution of the control strategy. Thus, this application solves the problems in the prior art where multi-brand AGVs cannot coordinate due to protocol heterogeneity and system barriers, as well as the problems of inaccurate conflict prediction and rigid scheduling strategies. Through a four-level collaborative management and control architecture of "protocol adaptation, cross prediction, collision simulation and dynamic scheduling", it systematically solves the core problem of multi-brand AGVs running together, and ultimately enables the safe and efficient collaborative operation of multi-brand AGVs in the same mixed environment.

[0020] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram illustrating the steps of a multi-brand AGV mixed-use management method provided in some embodiments of this application; Figure 2 A flowchart for spatial intersection determination of AGV planning path provided in some embodiments of this application; Figure 3 Flowcharts illustrating motion simulation in a virtual environment based on actual operating parameters provided in some embodiments of this application; Figure 4 This is a flowchart of the AGV dynamic obstacle avoidance scheduling provided in some embodiments of this application; Figure 5 A schematic diagram of the structure of a multi-brand AGV mixed-traffic control device provided in some embodiments of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of this application pertain; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings of this application are intended to cover non-exclusive inclusion.

[0024] In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0027] In related technologies, the challenge of managing multi-brand AGVs in mixed traffic lies in how to penetrate the barriers of heterogeneous systems and build a precise, efficient, and intelligent collaborative mechanism on top of that. To address this, this application provides a four-level collaborative management method, which achieves unified, safe, and efficient scheduling of multi-brand AGVs through protocol adaptation, cross-prediction, collision simulation, and dynamic scheduling. This method can be executed through a central management platform that communicates with the native robot control system (RCS) of each brand's AGV. The following section combines... Figures 1 to 4 The flowchart shown illustrates the embodiments of this application in detail.

[0028] Figure 1 This is a schematic diagram illustrating the steps of a multi-brand AGV co-location control method provided in some embodiments of this application, such as... Figure 1 As shown, the method mainly includes the following four steps S101 to S104, which are executed sequentially.

[0029] S101. Establish a standardized data channel with the native scheduling system of each brand of AGV to obtain real-time operating status data and planned path data of each AGV.

[0030] Specifically, to overcome the "information silo" problem caused by heterogeneous underlying protocols and data structures among multiple AGV brands, a standardized specification for two-way data interaction is established. This standardization specification includes a unified communication protocol, a unified data exchange format, and a protocol conversion mechanism. Its purpose is to establish a unified and reliable data foundation for all subsequent collaborative management and control functions. For example, the central management platform first establishes a standardized set of two-way data interaction specifications, including a unified communication protocol such as MQTT (Message Queuing Telemetry Transport) and a unified data exchange format such as JSON. Converting key data fields to JSON format avoids misinterpretation of information due to inconsistent data formats.

[0031] Furthermore, based on the aforementioned standardized specifications, real-time operating status data and planned path data of each AGV are acquired. This includes receiving standard uplink data messages reported by each native scheduling system and converted through a protocol conversion mechanism; parsing the real-time position and speed fields as real-time operating status data, and the planned path coordinate sequence field as planned path data, from the standard uplink data messages. This process achieves unified collection and formatting of heterogeneous AGV data. Execution is then carried out through the standardized data channel, including: encapsulating the avoidance strategy into standard downlink command messages conforming to standardized specifications; and converting the standard downlink command messages into private commands recognizable by the corresponding native scheduling system through the protocol conversion mechanism and then issuing them, thereby completing the command closed loop from central decision-making to execution by the underlying devices.

[0032] In one possible implementation, a protocol conversion plugin is deployed or configured for each brand of AGV's native RCS. This plugin has bidirectional conversion capabilities: on the one hand, it converts the AGV status data reported by the native RCS, based on proprietary protocols, such as position, speed, battery level, task status, and planned path data, into standard uplink data packets conforming to the aforementioned standardized specifications, and reports them to the central control platform; on the other hand, it converts the standard downlink command packets, such as avoidance commands, issued by the central control platform, back into a proprietary command format recognizable by the native RCS. In this way, the platform can obtain the real-time position, speed, and planned path coordinate sequences of all AGVs in a unified manner, laying the foundation for subsequent issuance of standardized scheduling commands.

[0033] In another alternative implementation, data channel standardization can be achieved by building a unified Application Programming Interface (API) gateway. In this approach, the central control platform defines and publishes a unified RESTful API or WebSocket interface specification. Each brand's native RCS system then needs to develop or configure a corresponding API client adaptation module according to this specification. This adaptation module is responsible for encapsulating the RCS's internal private data model into requests that conform to the platform's API requirements, which serve as uplink data, and parsing the API responses or instructions issued by the platform, which serve as downlink data. Compared to the protocol conversion plugin approach, the API gateway approach focuses more on defining clear data contracts and interaction semantics at the application layer, while the protocol conversion plugin may focus more on bridging the underlying communication protocols. In actual deployment, depending on the openness and technical architecture of different brand RCS systems, the protocol conversion plugin and API gateway approaches can be flexibly selected or combined to achieve effective integration with heterogeneous systems.

[0034] Specifically, regarding uplink data interaction, each native RCS periodically, for example, once per second, uses a protocol conversion plugin to convert its internal private AGV status data into standard uplink data packets conforming to JSON format, and publishes them to the topics subscribed to by the central control platform. The reason for choosing a collection frequency of once per second is based on a balance consideration of the typical operating speed of AGVs in the factory, such as 0.5-2 m / s: too low a frequency may miss critical path points during rapid AGV movement, while too high a frequency will bring unnecessary communication and processing load. The standard uplink data packets aim to provide comprehensive status information of the AGVs. The parsed fields include not only real-time position and speed, but also AGV fault status codes, the complete coordinate sequence of the current planned path, and the percentage of task execution progress, ensuring that the central control platform can comprehensively and in real-time grasp the operating status of each AGV.

[0035] Regarding downlink command interaction, the central control platform encapsulates the generated avoidance strategy into structured standard downlink command messages. These commands are centered on avoidance control, with clearly defined actions, including but not limited to pause commands, path adjustment commands, and deceleration commands. Each downlink command message also includes a high-priority command identifier to ensure that the receiver's native RCS prioritizes these avoidance commands, avoiding conflicts with regular task scheduling commands. Upon receiving the standard downlink command message, the protocol conversion plugin converts it back to a private command format recognizable by the target RCS and executes it, thus completing closed-loop control.

[0036] S102. Based on the real-time operating status data and the planned path data, spatial intersection judgment is performed on the planned paths of all AGVs, and for AGV pairs with spatial intersection, a preliminary collision risk judgment is made based on preset limit operating parameters.

[0037] In practical implementation, to achieve comprehensive screening of all potential conflicts among AGVs, the planned path of each AGV must first be represented as a polyline composed of a series of continuous line segments in a unified planar coordinate system. Next, a line segment intersection detection algorithm based on vector cross product is used to efficiently and accurately determine whether the planned path segments of any two AGVs intersect. If an intersection is detected, it is recorded as a spatial intersection point, and the corresponding two AGVs are associated to form the AGV pair with spatial intersection. This spatial judgment step is the geometric basis for subsequent time analysis and risk quantification, ensuring the completeness of conflict detection.

[0038] In one possible implementation, to further assess collision risk from a temporal perspective after identifying spatial intersections, real-time operational status data includes the current position, and preset limit operational parameters include a preset maximum operating speed and a preset maximum vehicle length. Based on these preset limit operational parameters, an initial collision risk assessment is performed, including: for AGV pairs with spatial intersections, using a conservative estimation strategy based on the preset maximum operating speed and preset maximum vehicle length of each AGV, calculating the time window required for each AGV to reach and completely pass through the spatial intersection point from its current position; subsequently, based on the overlap duration of the two time windows, a preset threshold is used to classify the collision risk of the AGV pair. This step constitutes a rapid and conservative initial risk screening layer, aiming to cover potential risks under the most extreme conditions according to the principle of "better to overreport than underreport," providing screened high-value targets for subsequent accurate simulations.

[0039] This can be used as a reference. Figure 2 , Figure 2 A flowchart illustrating the specific steps of AGV path intersection prediction in an embodiment of this application is shown. Figure 2 As shown, this step begins with real-time acquisition of AGV status and planned path data, where the AGV status includes its real-time position.

[0040] For each identified AGV pair, the platform will make an initial collision risk assessment based on preset limit operating parameters. These parameters typically include the preset maximum operating speed and preset maximum vehicle length of the AGV. The specific calculation method is as follows: For each AGV, taking its currently reported position as the starting point and the planned path as a reference, calculate the time window required for it to travel at its maximum speed from the front of the vehicle to the intersection point until the rear of the vehicle completely leaves the spatial intersection point.

[0041] Specifically, first calculate the time t1 when the front end of the AGV vehicle reaches the spatial intersection: t1 = path distance (i.e., from the current position to the intersection) / maximum speed; then calculate the time t2 when the rear end of the vehicle completely passes through the spatial intersection: t2 = t1 + maximum vehicle length / maximum speed; thus forming a time window (t1, t2) for the vehicle to completely pass through the spatial intersection. There is no need to introduce acceleration and deceleration characteristics, avoiding the risk of missed detection due to the current speed being too low. Extreme cases have been covered in actual operating scenarios.

[0042] After obtaining the time windows of the two AGVs, as follows Figure 2 As shown in the process flow, the overlap duration of two time windows is compared, and risk is classified according to the degree of overlap. If the overlap duration is less than a preset threshold, such as 2 seconds, it is determined to be a slight overlap, and the AGV pair is marked as "potential risk". The system pushes the relevant equipment number, intersection coordinates, and time window information to the subsequent collision simulation stage for accurate simulation calculation.

[0043] If the overlap duration is greater than or equal to the preset threshold, such as 2 seconds, it is judged as a severe overlap, and the AGV pair is marked as "high priority risk". In addition to pushing basic information to the collision simulation stage, such as... Figure 2 As shown, the system will synchronously trigger the priority pre-calculation process in dynamic avoidance scheduling, calculate the dynamic priority for the relevant AGVs in advance, thereby shortening the total response time from risk identification to final scheduling decision.

[0044] S103. For AGV pairs that are initially judged to have a collision risk, perform motion simulation in a virtual environment based on actual operating parameters to output quantified collision risk results; wherein, the actual operating parameters are determined at least based on the real-time operating status data.

[0045] Specifically, for AGV pairs pushed from step S102 that are initially judged to have a collision risk, the central control platform will initiate a high-precision motion simulation process. First, the platform needs to obtain the preset vehicle geometry dimensions (such as actual length and width) and preset motion constraints (such as maximum acceleration, maximum deceleration, and minimum turning radius) for each AGV in the pair. Simultaneously, based on the real-time operating status data obtained in step S101, the platform determines the current speed and current acceleration of each AGV as dynamic input for the simulation. The preset vehicle geometry dimensions and preset motion constraints are typically pre-stored in the system's AGV model library.

[0046] Next, in the virtual environment, based on the current speed, the current acceleration, the preset vehicle body geometry, and the preset motion constraints, the motion trajectory of each AGV is simulated within the overlapping time window. This simulation process strictly follows the kinematic model of the AGV, accurately reproducing its position and attitude changes within the overlapping time window calculated in step S102, over a future period.

[0047] The core objective of the simulation is to calculate the collision probability of the AGV pair based on the motion trajectory and output it as the quantified collision risk result. To achieve high-precision probability calculation, a Monte Carlo simulation algorithm is used in a preferred embodiment. Specifically, the platform introduces random disturbance variables that conform to the actual control logic of the AGVs into the current speed, current acceleration, and preset motion constraints. For example, it simulates real uncertainties such as sensor measurement errors, communication delays, and motor response fluctuations, and performs multiple independent motion trajectory simulations, for example, 1000 times. In each simulation, the system recalculates the precise position and attitude of the two AGVs at each simulation time step based on the introduced random disturbances, and determines their spatial occupancy area, typically represented by the circumscribed polygon or bounding box of the AGV geometric model, to determine whether interference occurs. The number of times the spatial occupancy area of ​​the AGV pair interferes in the multiple independent motion trajectory simulations is counted. Finally, the collision probability is calculated based on the ratio of the number of interference occurrences to the total number of simulations. This calculation process can be referenced... Figure 3 The collision simulation steps shown clearly include core steps such as simulating AGV position changes based on actual data, calculating the spatial occupancy area, and using the Monte Carlo algorithm to calculate the collision probability. This statistical method can effectively assess the likelihood of collisions under the influence of uncertain factors and output a quantitative risk indicator.

[0048] After calculating the collision probability, the process also includes risk classification based on the collision probability. For example... Figure 3 As shown, after calculating the collision probability, the system performs logical judgments and distributes results based on the probability value. Specifically: when the collision probability is zero, it is determined to be a no-collision-risk situation; when the collision probability is greater than zero but less than a preset probability threshold, such as 30%, it is determined to be a low-risk collision; when the collision probability is greater than or equal to the preset probability threshold, it is determined to be a high-risk collision. The system checks whether the collision probability is less than or equal to 0. If so, it determines that there is no collision and logs it; if not, it further checks whether it is less than 30%, thus guiding the system to two different processing paths: "low-risk collision" and "high-risk collision." High-risk collisions are processed first, while low-risk collisions are processed normally. The classification results of no-risk, low-risk, and high-risk are the quantified collision risk results finally output in step S103. High-risk collision results will directly and with high priority trigger subsequent dynamic avoidance scheduling. This step, by introducing actual operating parameters and high-fidelity Monte Carlo simulation, transforms the qualitative / grading risk assessment of S102 into a precise and quantifiable collision probability and risk level, significantly improving the accuracy of risk assessment and avoiding unnecessary scheduling interventions that may result from overly conservative coarse judgments. It achieves logical complementarity between coarse screening and precise calculation.

[0049] As an optional optimization implementation, to improve the accuracy of collision probability assessment, the risk quantification model can be further refined in the Monte Carlo simulation. For example, in each simulation, when interference is detected in the space-occupying area, not only is the "interference occurred" event recorded, but the proportion of the overlapping area or volume of the interference region to the area of ​​the AGV's own space-occupying area can also be calculated. Subsequently, based on a large amount of historical factory operation data or experimental data, a mapping relationship model between the overlapping proportion and the probability of the final actual collision can be established. When calculating the final collision probability, the statistically obtained "number of interference occurrences" and the "interference severity" weighted based on the mapping relationship can be combined to output a more accurate risk quantification value.

[0050] S104. Based on the quantified collision risk results and combined with the preset dynamic priority rules, an avoidance strategy is generated for the low-priority AGV in the AGV pair with collision risk, and the standardized scheduling instructions generated based on the avoidance strategy are sent out for execution through the standardized data channel.

[0051] In one specific implementation, the avoidance urgency of each AGV in the AGV pair is first determined based on the collision risk results, such as the risk level output in step S103 or the specific collision probability value. Then, based on the preset dynamic priority rules, the real-time task priority of each AGV in the AGV pair is calculated.

[0052] This dynamic priority rule is a multi-factor weighted calculation model that considers at least three core factors: task urgency, load status, and task execution status. For example, task urgency has the highest weight, such as 50%-70%, followed by load status, such as 20%-40%, with heavy load exceeding idle load. Task execution status has the lowest weight, such as 5%-15%, with executed tasks exceeding tasks awaiting initiation. As a specific example, the following weight allocation can be used: task urgency accounts for 60% of the weight, as it directly affects production progress; load status accounts for 30%; and execution status accounts for 10%. Task urgency can be further tiered; for example, material transfer at production breakpoints is a Level 1 task with the highest priority, urgent order materials are a Level 2 task, regular production materials are a Level 3 task, and empty return trips are a Level 4 task, decreasing in priority. It can be understood that in the load status, heavily loaded AGVs have higher priority than idle AGVs. In the execution status, AGVs with started tasks have higher priority than AGVs with tasks awaiting initiation. Finally, by combining the avoidance urgency and the real-time task priority, the lower-priority AGVs that need to perform avoidance operations in the AGV pair are determined. High-priority AGVs will continue to operate along their original paths and at their original speeds to ensure that critical production tasks are not affected.

[0053] Furthermore, after identifying low-priority AGVs, specific avoidance strategies need to be generated for them. In the avoidance strategy generation stage, priority is given to ensuring the original path and speed of high-priority AGVs. For low-priority AGVs, specific strategies are formulated and selected based on the physical environment surrounding the intersection point. This includes selectively generating control commands based on the environmental conditions around the spatial intersection point. The specific decision-making logic can be flexibly formulated according to the risk level and time urgency, and can be referenced. Figure 4 The flowchart shown is a dynamic obstacle avoidance scheduling process for AGVs. Figure 4 As shown, after obtaining the collision simulation results and determining the AGV priority, the platform first determines whether it is a high-priority AGV. If so, it runs along its original path and speed. If it is a low-priority AGV, it further makes decisions based on time urgency and environmental conditions. If the risk level is high and the collision time is extremely tight, an instruction is generated to control the low-priority AGV to decelerate. For example, when the predicted collision time window is close to 2 seconds, a deceleration instruction can be generated, specifying a target speed, which can typically be set to 50% of the original speed. By reducing the speed, the time difference is widened until the risk is eliminated.

[0054] If there is sufficient time and a preset, available avoidance zone exists near the intersection, a command is generated to control the low-priority AGV to enter and stop within the preset avoidance zone. At this time, if... Figure 4 As shown, after determining that the time window is greater than 2 seconds and the avoidance area is available, an early stop and avoidance instruction is issued to the RCS of the low-priority AGV. The instruction can specify the specific stopping position, such as 5 to 8 meters away from the intersection, and the waiting time, such as covering the entire time window of the high-priority AGV passing through the intersection. A buffer can also be set, such as stopping 1 second in advance and starting 1 second late, to ensure that the high-priority AGV gives way completely.

[0055] If there is sufficient time but no available avoidance zone, or if rerouting is more efficient, an instruction is generated instructing the low-priority AGV to replan its path, requiring it to use its native RCS to plan an alternative path that does not pass through the current conflict point. As before... Figure 4 As shown, when the avoidance area is determined to be unavailable, the system will issue a re-planning route instruction. At this time, the new path can be required to prioritize the shortest path while ensuring that no new intersection risks are introduced, thus providing the AGV with a safe and efficient alternative route.

[0056] Furthermore, after implementing the deceleration strategy, the system continuously checks whether the high-priority AGV has passed the intersection. If it has not, the low-priority AGV remains decelerated or waits; if it has passed, the low-priority AGV can resume normal driving, thus forming a dynamic and adaptive control closed loop.

[0057] Finally, the standardized scheduling instructions generated based on the avoidance strategy are sent out for execution through the standardized data channel established in step S101. The central control platform encapsulates the strategy into standard downlink instruction messages, converts them into private instructions recognizable by the target AGV's native RCS through a protocol conversion mechanism or API gateway, and then sends them out. To ensure the effective execution of the instructions and form a complete risk handling closed loop, instruction execution status monitoring is initiated. For example, the response and movement status of low-priority AGVs are continuously monitored; if they are not executed within the expected time or the execution effect is inconsistent, instruction resending or manual alarm is triggered.

[0058] Specifically, in the instruction issuance and feedback phase, avoidance instructions are issued in real time to the native RCS scheduling system of low-priority AGVs in a standardized format. Simultaneously, the central control platform activates an instruction monitoring mechanism, such as collecting the AGV's response status every second. If the AGV responds with "Instruction received and executed" within one second within a preset time, the platform continuously monitors its subsequent movement status, such as position and speed, to ensure they meet the instruction requirements. If no feedback is received after the preset time or if the feedback is "Execution failed," a resend mechanism is activated, for example, reissuing the instruction every 0.5 seconds, up to a maximum of three times. If the resend is still ineffective, an audible and visual alarm system is triggered, and maintenance personnel are simultaneously notified to conduct on-site investigation, thus ensuring that avoidance instructions are not missed or invalidated. This step, by integrating quantitative risk and dynamic production priorities, achieves a decision-making upgrade from safe collision avoidance to efficient collaboration. Through standardized instruction issuance and closed-loop monitoring, it ensures the reliable implementation of control strategies, ultimately achieving a balance between safety and efficiency in multi-brand AGV mixed-traffic scenarios.

[0059] In some embodiments of this application, a multi-brand AGV mixed-use management device is also provided, such as... Figure 5 As shown, the device includes: The module establishes a standardized data channel with the native scheduling systems of various AGV brands to obtain real-time operating status data and planned path data of each AGV. Through protocol conversion or API adaptation, this module effectively solves the information barrier problem caused by the heterogeneity of underlying protocols and data formats among multiple AGV brands, providing a unified and real-time data foundation for subsequent collaborative management and control.

[0060] The initial judgment module is used to determine the spatial intersection of the planned paths of all AGVs based on the real-time operating status data and the planned path data, and to make an initial judgment on the collision risk of AGV pairs with spatial intersection based on preset limit operating parameters. This module adopts a fast time window calculation and classification method based on limit parameters to achieve efficient and conservative initial screening of potential collision risks, ensuring full coverage of risk identification, and at the same time screening out high-value targets for subsequent accurate simulation.

[0061] The simulation module is used to perform motion simulation in a virtual environment for AGV pairs that are initially judged to have a collision risk, based on actual operating parameters, to output a quantified collision risk result; wherein, the actual operating parameters are determined at least based on the real-time operating status data; by introducing actual operating parameters and using simulation algorithms such as Monte Carlo, this module performs a high-fidelity, quantitative assessment of the initial risk, improving risk judgment from qualitative classification to precise probability calculation, effectively avoiding ineffective scheduling that may be caused by overly conservative initial judgment, and improving the scientific nature and accuracy of decision-making.

[0062] The collision avoidance module is used to generate avoidance strategies for low-priority AGVs in AGV pairs with collision risks based on the quantified collision risk results and preset dynamic priority rules. The module then sends out standardized scheduling instructions generated based on the avoidance strategies through the standardized data channel for execution. This module deeply integrates quantified risk with real-time production priorities to generate the optimal avoidance strategy based on risk urgency and environmental conditions. It forms a closed-loop control through instruction issuance, status monitoring, and alarm re-sending mechanisms, thereby maximizing overall logistics efficiency and production continuity while ensuring operational safety.

[0063] In some embodiments of this application, an electronic device is also provided to perform the above-described method for managing the mixed movement of multiple brands of AGVs in the same location. Please refer to... Figure 6 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 6 As shown, the electronic device 9 includes: a processor 901, a memory 902, a bus 903, and a communication interface 904. The processor 901, the communication interface 904, and the memory 902 are connected via the bus 903. The memory 902 stores a computer program that can run on the processor 901. When the processor 901 runs the computer program, it executes the multi-brand AGV mixed-traffic management method provided in any of the foregoing embodiments of this application.

[0064] The memory 902 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 904 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0065] Bus 903 can be an ISA bus, PCI bus, or EISA bus, etc. Buses can be divided into address buses, data buses, control buses, etc. Memory 902 is used to store programs. After receiving execution instructions, processor 901 executes the programs. The multi-brand AGV mixed-roaming management method disclosed in any of the aforementioned embodiments of this application can be applied to processor 901, or implemented by processor 901.

[0066] The processor 901 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 901 or by instructions in software form. The processor 901 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 902. Processor 901 reads the information in memory 902 and, in conjunction with its hardware, completes the steps of the above method.

[0067] The electronic device provided in this application embodiment and the multi-brand AGV mixed-field management method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0068] This application also provides a computer-readable storage medium corresponding to the multi-brand AGV mixed-roaming management method provided in the foregoing embodiments, which stores a computer program (i.e., a program product). When the computer program is run by a processor, it executes the multi-brand AGV mixed-roaming management method provided in any of the foregoing embodiments.

[0069] It should be noted that computer-readable storage media may include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, optical disc or other optical and magnetic storage media, etc., which will not be elaborated here.

[0070] The computer-readable storage medium provided in this application embodiment and the multi-brand AGV mixed-field management method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0071] This application also provides a computer program product corresponding to the multi-brand AGV mixed-roaming management method provided in the foregoing embodiments, including a computer program that is executed by a processor to implement the above-mentioned multi-brand AGV mixed-roaming management method.

[0072] The computer program product provided in this application embodiment is based on the same inventive concept as the multi-brand AGV mixed-field management method provided in this application embodiment, and has the same beneficial effects as the method implemented by the computer program being executed by a processor.

[0073] It is understood that the description of the various embodiments above tends to emphasize the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, these will not be repeated here.

[0074] Those skilled in the art will understand that, in the methods described above in specific embodiments, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined based on its function and possible internal logic.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application 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 or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A multi-brand AGV same field mixed running management method, characterized in that, The method comprises: establishing a standardized data channel with each brand AGV native scheduling system to obtain real-time running state data and planning path data of each AGV; based on the real-time running state data and planning path data, judging the spatial intersection of the planning paths of all AGVs, and based on the preset limit running parameters, preliminarily judging the collision risk of the AGV pair with spatial intersection; for the AGV pair preliminarily judged to have collision risk, motion simulation is carried out in a virtual environment based on actual running parameters to output a quantitative collision risk result; wherein the actual running parameters are determined based on at least the real-time running state data; based on the quantitative collision risk result, combined with the preset dynamic priority rule, an avoidance strategy is generated for the low-priority AGV in the AGV pair with collision risk, and a standardized scheduling instruction generated based on the avoidance strategy is issued for execution through the standardized data channel.

2. The method of claim 1, wherein, The spatial intersection judgment of the planning paths of all AGVs comprises: in a unified plane coordinate system, the planning path of each AGV is represented as a polyline composed of a series of continuous line segments; using a line segment intersection detection algorithm based on vector cross product, it is judged one by one whether the planning path line segments of any two AGVs exist intersection points; if there is an intersection point, the intersection point is recorded as a spatial intersection point, and the corresponding two AGVs are associated to form the AGV pair with spatial intersection.

3. The method of claim 1, wherein, The establishment of a standardized data channel with each brand AGV native scheduling system comprises: establishing a standardized specification for bidirectional data interaction, which includes a unified communication protocol, a unified data exchange format and a protocol conversion mechanism; The real-time running state data and planning path data of each AGV are obtained, comprising: through the protocol conversion mechanism, receiving the standard uplink data message reported by each native scheduling system and converted; from the standard uplink data message, the real-time position and speed fields as the real-time running state data and the planning path coordinate sequence fields as the planning path data are parsed; The execution through the standardized data channel comprises: packaging the avoidance strategy into a standard downlink instruction message conforming to the standardized specification; through the protocol conversion mechanism, the standard downlink instruction message is converted into a private instruction recognizable by the corresponding native scheduling system and is issued.

4. The method of claim 2, wherein, The real-time running state data includes the current position, and the preset limit running parameters include the preset maximum running speed and the preset maximum vehicle body length; the collision risk preliminary judgment based on the preset limit running parameters comprises: for the AGV pair with spatial intersection, according to the preset maximum running speed and the preset maximum vehicle body length of each AGV, the time window required for each AGV to reach and completely pass through the spatial intersection point from the current position is calculated; based on the overlapping length of the two time windows, the collision risk of the AGV pair is classified.

5. The method of claim 4, wherein, The actual operation parameters include a current speed and a current acceleration; the AGV pair preliminarily judged to have a collision risk is subjected to motion simulation in a virtual environment based on the actual operation parameters to output a quantitative collision risk result, including: obtaining preset vehicle body geometric dimensions and preset motion constraints of each AGV in the AGV pair; in the virtual environment, simulating motion trajectories of the AGVs in an overlapping time window based on the current speed, the current acceleration, the preset vehicle body geometric dimensions and the preset motion constraints; calculating a collision probability of the AGV pair based on the motion trajectories as the quantitative collision risk result.

6. The method of claim 5, wherein, The collision probability of the AGV pair based on the motion trajectories includes: introducing random disturbance variables conforming to the actual control logic of the AGV for the current speed, the current acceleration and the preset motion constraints, and performing multiple independent motion trajectory simulations; counting a number of times of interference of a space occupation area of the AGV pair in the multiple independent motion trajectory simulations; calculating the collision probability according to a proportion of the number of times of interference to a total number of simulations.

7. The method of claim 5, wherein, After the collision probability of the AGV pair is calculated based on the motion trajectories as the quantitative collision risk result, further including risk grading according to the collision probability, specifically: when the collision probability is zero, determining no collision risk; when the collision probability is greater than zero but less than a preset probability threshold, determining a low-risk collision; when the collision probability is greater than or equal to the preset probability threshold, determining a high-risk collision.

8. The method of claim 1, wherein, The quantitative collision risk result is combined with a preset dynamic priority rule to generate an avoidance strategy for a low-priority AGV in the AGV pair having a collision risk, including: determining an avoidance urgency of each AGV in the AGV pair according to the collision risk result; calculating a real-time task priority of each AGV in the AGV pair based on the preset dynamic priority rule; determining a low-priority AGV in the AGV pair that needs to perform an avoidance operation by comprehensively considering the avoidance urgency and the real-time task priority.

9. The method of claim 2, wherein, The avoidance strategy for the low-priority AGV in the AGV pair having a collision risk further includes: selectively generating an instruction to control the low-priority AGV to decelerate, an instruction to control the low-priority AGV to drive into a preset avoidance area for parking, or an instruction to instruct the low-priority AGV to re-plan a path according to environmental conditions around the space intersection.

10. A multi-brand AGV same field mixed running management and control device, characterized in that, The device includes: an establishment module configured to establish a standardized data channel with a native scheduling system of each brand AGV to obtain real-time operation state data and planned path data of each AGV; a preliminary judgment module configured to perform spatial intersection judgment on planned paths of all AGVs based on the real-time operation state data and the planned path data, and perform collision risk preliminary judgment on AGV pairs having spatial intersections based on preset limit operation parameters; The simulation module is configured to perform motion simulation in a virtual environment based on actual operation parameters to output a quantitative collision risk result for the AGV pair determined to have a collision risk, wherein the actual operation parameters are determined based on at least the real-time operation state data. The avoidance module is configured to generate an avoidance strategy for the AGV with a low priority in the AGV pair determined to have a collision risk based on the quantitative collision risk result and in combination with a preset dynamic priority rule, and to issue a standardized scheduling instruction generated based on the avoidance strategy through the standardized data channel for execution.

11. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1-9.

12. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1-9.

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