AGV trolley control system for intelligent logistics

Abstract

The application discloses an AGV control system for intelligent logistics, comprising: a dispatch planning module acquires surrounding environment information and plans an optimal path; a control module issues an instruction according to the optimal path, controls the movement of the AGV, and acquires movement information; an optimization module optimizes and updates the optimal path according to real-time environmental changes and obstacle monitoring; a target positioning module places the transported object in a set target area according to the nature of the transported object after the AGV reaches the target position; and a monitoring and early warning module acquires monitoring system equipment operating parameters and gives an alarm if there is an abnormal range. The application realizes optimal path planning in a complex environment through intelligent path planning and real-time optimization, the AGV can quickly respond to logistics demand, reduces waiting time and walking distance, significantly improves the efficiency of logistics operation, improves the response capability of the AGV to environmental changes, and can adapt to complex and changeable logistics environments.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics control technology, and in particular to an AGV (Automated Guided Vehicle) control system for intelligent logistics. Background Technology

[0002] Today, Automated Guided Vehicles (AGVs) are used in many application areas, such as factories, warehouses, and hospitals. Different AGV systems use different navigation methods. For manufacturing enterprises, the loading, unloading, and handling of production materials is a high-frequency, time-consuming, tedious, and costly production operation in the industrial production logistics process. AGVs are equipped with electromagnetic or photoelectric automatic guidance devices, enabling them to travel along a set guide route. They have programming and mode selection devices, safety protection functions, and various transfer functions. In industrial applications, these driverless transport vehicles are a powerful tool for enterprises to improve efficiency and reduce production costs, and are also important intelligent equipment for the country's promotion of intelligent construction.

[0003] Traditional AGV systems lack effective path planning capabilities, especially in complex or dynamically changing environments. Existing technologies may not fully consider various factors such as dynamic environmental changes and cargo conditions, resulting in suboptimal path planning and an inability to effectively handle dynamic obstacles or real-time traffic conditions. In multi-AGV environments, task allocation is not intelligent enough, leading to uneven or inefficient resource utilization. In actual operation, new obstacles or road congestion may be encountered, but existing technologies cannot optimize paths in a timely and effective manner. Furthermore, limitations exist in precise positioning and target recognition, affecting the accuracy of task execution. Existing AGVs lack effective energy management strategies, resulting in high operating costs. In emergency situations, some AGV systems may lack sufficient safety measures. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides an AGV (Automated Guided Vehicle) control system for intelligent logistics that addresses the issues of insufficiently optimized path planning, unintelligent task allocation, and inaccurate task execution.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an AGV (Automated Guided Vehicle) control system for intelligent logistics, comprising:

[0008] The scheduling and planning module is used to acquire surrounding environmental information, plan the optimal path based on the initial position and the target position, and transmit the optimal path to the control module.

[0009] The control module is used to receive the optimal path generated by the scheduling and planning module, issue instructions according to the optimal path, control the movement of the AGV, and synchronously acquire movement information, including real-time map updates and AGV positioning, and communicate with the optimization module.

[0010] An optimization module is used to receive movement information from the control module, optimize and update the optimal path based on obstacle monitoring of real-time environmental changes, and feed back the optimization and update results to the control module.

[0011] The target positioning module is used to place the AGV in the set target area according to the nature of the transported goods after the AGV arrives at the target location, and record and update the storage status of the target area.

[0012] The monitoring and early warning module is used to acquire the operating parameters of the monitoring system equipment, and to issue an alarm if there are abnormal ranges.

[0013] As a preferred embodiment of the AGV (Automated Guided Vehicle) control system for intelligent logistics described in this invention, the workflow of the scheduling and planning module includes:

[0014] Environmental information is collected through sensors to construct a dynamic environmental map, wherein the grid cells of the environmental map are... Indicates position It can determine the passage status and identify and classify objects in the environment;

[0015] Based on the urgency of the task Importance of the task and task time window Calculate task priority and obtain task content. Based on the initial position and target position in the task content, determine the starting point and target point of the environment map.

[0016] Based on the task's route distance, energy consumption, and travel time, a multi-objective optimization function is constructed, and constraints on AGV movement are defined. The function then calculates and obtains the passable cells in the current task path planning. Multiple intermediate nodes in the multi-objective optimization function Represented as:

[0017]

[0018]

[0019]

[0020]

[0021] in, Indicates the distance of the mission route. Indicates the energy consumption of the task. Indicates the mission travel time. Represents a node Location, , , These represent the coefficients for task adjustment. Indicates the number of path nodes. Indicates the AGV's load capacity. Indicates the distance between nodes. This represents the average speed of the AGV. Indicates the road surface resistance between nodes. Represents a node The expected stopping time, Represents a node The frequency of passage;

[0022] Based on the starting point, target point, and intermediate nodes of the task, and using the multi-objective optimization function and constraints, a preliminary optimal path for the task is planned.

[0023] As a preferred embodiment of the AGV (Automated Guided Vehicle) control system for intelligent logistics described in this invention, the initial optimal path for the planning task includes:

[0024] Based on the starting point, target point, and intermediate nodes of the task, a cost function is constructed to obtain the optimal path. The cost function is expressed as:

[0025]

[0026] in, Represents a node Total estimated cost, Indicates the distance from the starting point to the node. Path cost, Indicates from node Estimated cost to reach the target point;

[0027] in,

[0028]

[0029]

[0030] in, Indicates the distance from the starting point to the node. The actual path cost Indicates from node arrive The cost of moving, Indicates starting from the current node The straight-line distance to the target node. Indicates starting from the current node The estimated energy consumption to reach the target node. A coefficient indicating the importance of adjusting energy consumption.

[0031] As a preferred embodiment of the AGV (Automated Guided Vehicle) control system for intelligent logistics described in this invention, the workflow of the control module includes:

[0032] Obtain the optimal path data generated by the scheduling and planning module, set the initial position and direction of the AGV, and align it with the first node of the optimal path;

[0033] The optimal path data is converted into control commands and sent to the drive module of the AGV vehicle;

[0034] The movement information of the AGV is acquired and merged with the initial environmental map to update the position and attitude of the AGV in the map in real time.

[0035] It detects obstacles on the path in real time, transmits movement and obstacle information to the optimization module in real time, receives feedback from the optimization module, and adjusts the control strategy based on the feedback.

[0036] The control parameters are visualized, allowing users to view and modify them in real time.

[0037] As a preferred embodiment of the AGV (Automated Guided Vehicle) control system for intelligent logistics described in this invention, the workflow of the optimization module includes:

[0038] The system receives movement information of the AGV in the control module, as well as real-time monitoring of the AGV's surrounding environment, including real-time changes in obstacles and traffic flow, and generates an obstacle set.

[0039] The system maps the current optimal path to real-time environmental data, identifies potential conflict points, generates a set of conflict points, and assesses the severity and type of each conflict point.

[0040] Perform local path optimization at each conflict point to avoid obstacles or congested areas;

[0041] The multi-objective optimization function is adjusted according to the new local path, the constraints are updated, and the local optimization path is merged with the original path to form a new optimal path.

[0042] Verify whether the new optimal path satisfies all constraints. If it does, send the verified optimal path to the control module.

[0043] As a preferred embodiment of the AGV (Automated Guided Vehicle) control system for intelligent logistics described in this invention, the working process of the target positioning module includes:

[0044] The AGV (Automated Guided Vehicle) transports goods to the target area via the target point of the task. The target area is divided into multiple target area sets. The placement area of ​​the goods is determined according to the set of attributes of the goods and the mapping relationship with the target area sets.

[0045] Based on the size of the goods, the layout of the placement area, and the storage status of the items already placed, calculate the optimal placement location of the items within the preset target area;

[0046] The AGV (Automated Guided Vehicle) is controlled to place the transported goods in the optimal location within the target area and arrange them in an orderly manner according to preset rules, thus completing the placement of the goods.

[0047] If any abnormality is encountered in the placement of goods, the abnormality handling procedure should be performed and the incident reported to the administrator.

[0048] Update the storage data in the target area and record the data information of the target area and the placement of goods.

[0049] Secondly, the present invention provides an AGV (Automated Guided Vehicle) control method for intelligent logistics, comprising:

[0050] Obtain information about the surrounding environment and plan the optimal path based on the initial and target locations;

[0051] According to the optimal path, the instructions are issued to control the movement of the AGV and the movement information is acquired synchronously. The movement information includes real-time map updates and determination of the AGV's location.

[0052] The system receives the movement information and optimizes and updates the optimal path based on obstacle monitoring of real-time environmental changes.

[0053] Based on the optimized optimal path, after the AGV reaches the target location, it is placed in the set target area according to the nature of the transported goods, and the storage status of the target area is recorded and updated.

[0054] The system acquires the operating parameters of the monitoring system equipment, and issues an alarm if any abnormal range is found.

[0055] Thirdly, the present invention provides an electronic device, comprising:

[0056] Memory and processor;

[0057] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the AGV vehicle control method for intelligent logistics.

[0058] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the AGV vehicle control method for intelligent logistics.

[0059] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention achieves optimal path planning in complex environments through intelligent path planning and real-time optimization. The AGV can quickly respond to logistics needs, reducing waiting time and travel distance, significantly improving the efficiency of logistics operations. Real-time environmental monitoring and dynamic path optimization enhance the AGV's responsiveness to environmental changes, enabling it to adapt to complex and ever-changing logistics environments, including newly emerging obstacles and road congestion, ensuring stable system operation. Intelligent task priority evaluation and scheduling strategies improve overall logistics efficiency and resource utilization. Multi-objective optimization improves logistics driving efficiency and reduces energy consumption. Simultaneously, modular design reduces maintenance costs and system upgrade difficulty, lowering operating costs. Modular system design and real-time environmental adaptability allow the AGV to flexibly cope with different logistics scenarios, improving the overall flexibility and adaptability of the system. Comprehensive safety monitoring and early warning mechanisms, along with precise item placement, improve the safety and reliability of logistics operations, reducing the risk of accidents. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0061] Figure 1 This is a schematic diagram of the overall structure of an AGV (Automated Guided Vehicle) control system for intelligent logistics according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the overall process of an AGV (Automated Guided Vehicle) control method for intelligent logistics according to an embodiment of the present invention.

[0063] Explanation of reference numerals in the attached diagram: 101, Scheduling and planning module; 102, Control module; 103, Optimization module; 104, Target positioning module; 105, Monitoring and early warning module. Detailed Implementation

[0064] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0065] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1

[0068] Reference Figures 1-2 As one embodiment of the present invention, an AGV (Automated Guided Vehicle) control system for intelligent logistics is provided, such as... Figure 1 As shown, it includes:

[0069] The scheduling and planning module 101 is used to acquire surrounding environmental information, plan the optimal path based on the initial position and the target position, and transmit the optimal path to the control module 102.

[0070] The control module 102 is used to receive the optimal path generated by the scheduling and planning module 101, issue instructions according to the optimal path, control the movement of the AGV, synchronously acquire movement information, including real-time map updates and AGV positioning, and communicate with the optimization module.

[0071] The optimization module 103 is used to receive the movement information from the control module 102, and optimize and update the optimal path based on obstacle monitoring of real-time environmental changes, and feed back the optimization and update results to the control module 102.

[0072] The target positioning module 104 is used to place the AGV in the set target area according to the nature of the transported goods after the AGV arrives at the target position, and record and update the storage status of the target area.

[0073] The monitoring and early warning module 105 is used to acquire the operating parameters of the monitoring system equipment. If there is an abnormal range, an alarm will be triggered.

[0074] Preferably, the workflow of the scheduling planning module 101 includes:

[0075] First, environmental information is collected through sensors to construct a dynamic environmental map, which consists of raster cells. Indicates position It can determine the passage status and identify and classify objects in the environment;

[0076] Specifically, raw data of the surrounding environment is acquired through various sensors, including but not limited to lidar, LiDAR, cameras, and ultrasound. The raw data is preprocessed, including filtering and noise reduction, to improve data quality. Object detection and recognition algorithms are used to identify objects in the environment and classify them into different types, such as obstacles and drivable areas. Based on the identification and classification results, a dynamic environment map is constructed using SLAM. The map preferably uses a grid map or other suitable representation method. Each grid or region is marked with information such as its drivability and object type.

[0077] In this method, using either a raster map or a topological map, the environment space is divided into M×N grids, each grid being d×d in size. Each cell of the raster map... Indicates position The passage status includes two states: passable and blocked. If it is passable, it is marked as 1; if it is blocked, it is marked as 0.

[0078] Secondly, based on the urgency of the task Importance of the task and task time window Calculate task priority and obtain task content. Based on the initial position and target position in the task content, determine the starting point and target point of the environment map.

[0079] For example, data from multiple pre-set tasks in the system is acquired and analyzed. First, the priority of each task is determined. Then, the factors affecting task priority, including urgency, task importance, and task time window, are compared pairwise to construct a judgment matrix. Then, by calculating the judgment matrix The eigenvectors are used to obtain the relative weight of each factor;

[0080] Specifically, factors influencing task priority include the urgency of the task. Importance of the task and task time window (i.e., the time limit for task completion). For the rating of each factor, a scale of 1-9 is used, where 1 indicates that both factors are equally important, and 9 indicates that one factor is extremely important to the other. A judgment matrix is ​​constructed. Represented as:

[0081]

[0082] in, Indicates the level of urgency relative to the importance of the task Importance ratio Indicates the level of urgency Relative to the task time window Importance ratio Indicates the importance of the task Relative to the task time window The importance ratio.

[0083] By solving the judgment matrix The eigenvector corresponding to the largest eigenvalue is normalized to obtain the weight vector of each factor. , is represented as:

[0084]

[0085] Based on this, the overall task priority Represented as:

[0086]

[0087] It should be noted that, based on the calculation results of task priority, the task content is obtained in descending order, the starting point and target point of the current task are determined, and the preset tasks are completed in sequence according to the task content.

[0088] Secondly, based on the task's route distance, energy consumption, and travel time, and considering the AGV's dynamic characteristics and environmental factors, a multi-objective optimization function is constructed, and constraints on AGV movement are defined. This process calculates and obtains the passable cells in the current task path planning. Multiple intermediate nodes, multi-objective optimization function Represented as:

[0089]

[0090]

[0091]

[0092]

[0093] in, Indicates the distance of the mission route. Indicates the energy consumption of the task. Indicates the mission travel time. Represents a node Location, , , These represent the coefficients for task adjustment. Indicates the number of path nodes. Indicates the AGV's load capacity. Indicates the distance between nodes. This represents the average speed of the AGV. Indicates the road surface resistance between nodes. Represents a node The expected stopping time, Represents a node The frequency of passage.

[0094] For example, based on the physical characteristics and safety standards of the AGV, constraints are defined to ensure that the path planning meets requirements such as maximum speed, turning radius, and safety distance. The maximum speed constraint, turning radius constraint, and safety distance constraint are expressed as follows:

[0095]

[0096]

[0097]

[0098] in, This indicates the speed of the AGV during operation. Indicates the maximum running speed. Indicates at node Turning radius, Indicates the minimum turning radius. Indicates a safe distance. Indicates the distance between the AGV and the nearest obstacle. This indicates the minimum safe distance. The speed of an AGV must not exceed its designed maximum speed at any time, and its turning radius must not be less than its minimum turning radius when turning. An AGV must maintain a certain safe distance from other objects or obstacles.

[0099] It should be noted that in route planning, not only the shortest path is considered, but also multiple objective factors such as minimum energy consumption and minimum risk are considered. In energy consumption calculation, changes in road conditions are taken into account, and the concept of time window is introduced to ensure that route planning not only considers the shortest path, but also meets the time constraints of the task. These objectives are balanced by using a multi-objective optimization algorithm.

[0100] Finally, based on the starting point, target point, and intermediate nodes of the task, and using a multi-objective optimization function and constraints, a preliminary optimal path for the task is planned.

[0101] Preferably, the initial optimal path for the planning task includes:

[0102] Based on the starting point, target point, and intermediate nodes of the task, a cost function is constructed to obtain the optimal path. The cost function is expressed as:

[0103]

[0104] in, Represents a node Total estimated cost, Indicates the distance from the starting point to the node. Path cost, Indicates from node Estimated cost to reach the target point;

[0105] in,

[0106]

[0107]

[0108] in, Indicates the distance from the starting point to the node. The actual path cost Indicates from node arrive The cost of moving, Indicates starting from the current node The straight-line distance to the target node. Indicates starting from the current node The estimated energy consumption to reach the target node. A coefficient representing the importance of adjusting energy consumption;

[0109] For example, an open list and a closed list are initialized. The open list stores discovered nodes, and the closed list stores evaluated nodes. The starting node is... Add to the open list, loop until the target node is found or the open list is empty. The open list is ordered by... A priority queue sorted by value.

[0110] Specifically, the starting node of Set to 0 and calculate. When the open list is not empty, perform the following operation: Select from the open list the one with the lowest... value node , as the current node; if If it is the target node, then the optimal path has been found, and it can be achieved through backtracking. Reconstruct the path using the parent node, Moved from the open list to the closed list; for Each neighbor node ,if Not in the close list, and can be accessed via arrive If the constraints are not violated (if adjacent nodes are infeasible, then skip), then: calculate... ;like Not on the open list, or via arrive of If smaller, then update. of and and will Set as The parent node; Add to the open list; if the adjacent node is already in the open list, and the result is calculated using the current path. If the value is smaller, update it. Value and parent node; moves the current node from the open list to the closed list. The final optimal path is generated by backtracking from the target node to its parent node when the target node is added to the closed list.

[0111] Apply path smoothing algorithms, such as Bézier curves or dynamic window methods, to the optimal path to reduce path tortuosity and improve the smoothness of AGV operation.

[0112] It should be noted that this embodiment combines multiple factors to design a heuristic function to achieve more comprehensive path optimization. It introduces a path smoothing algorithm to improve the efficiency and safety of AGV driving. It can not only evaluate task priorities more precisely, but also construct a multi-objective function that is more in line with actual needs. Furthermore, it achieves real-time dynamic path planning through an improved A* algorithm, and provides a comprehensive and complex scheduling planning module 101. It comprehensively considers factors such as shortest distance, path smoothness, energy consumption, and traffic congestion to conduct a comprehensive evaluation and select the optimal path.

[0113] Preferably, the workflow of the control module 102 includes:

[0114] The system acquires the optimal path data generated by the scheduling and planning module 101, sets the initial position and direction of the AGV, and aligns it with the first node of the optimal path. The path can be represented as a series of navigation nodes. ;

[0115] The optimal path data is converted into control commands, generating a control command sequence, and sent to the drive module of the AGV to control the vehicle's movement. Specifically, a proportional-integral-derivative (PID) controller is used to adjust the speed and direction of the AGV to track the optimal path. The PID controller can dynamically adjust the control output to reduce errors. Based on environmental conditions and the current state of the AGV, the PID parameters are dynamically adjusted to improve response speed and stability.

[0116] The movement information of the AGV is acquired and merged with the initial environmental map to update the position and attitude of the AGV in the map in real time.

[0117] The system detects obstacles on the optimal path in real time, transmits movement and obstacle information to the optimization module 103 in real time, receives feedback from the optimization module 103, and adjusts the control strategy based on the feedback.

[0118] The control parameters are visualized, allowing users to view and modify them in real time.

[0119] Preferably, the workflow of the optimization module 103 includes:

[0120] The system receives movement information from the AGV (Automated Guided Vehicle) in the control module 102, as well as real-time monitoring of the AGV's surrounding environment, including real-time changes in obstacles and traffic flow, and generates an obstacle set. Specifically, the obstacle set... ,in, Indicates the first The location and size of each obstacle;

[0121] Mapping the current optimal path to real-time environmental data identifies potential conflict points, generating a set of conflict points, represented as... ,in Indicates the first position on the path Each conflict point is assessed for its severity and type, i.e., static or dynamic obstacles, road congestion. The severity of the conflict can be calculated based on the size, location, and expected duration of the obstacles.

[0122] Perform local path optimization at each conflict point to avoid obstacles or congested areas;

[0123] The multi-objective optimization function is adjusted according to the new local path, the constraints are updated to reflect the new environmental limitations, the newly generated local path is smoothed to ensure the continuity and smoothness of the path, and the local optimized path is merged with the original path to form a new optimal path, ensuring safety and efficiency.

[0124] Verify whether the new optimal path meets all constraints. If it does, send the verified optimal path to the control module 102. At the same time, the administrator is allowed to manually adjust the optimization strategy or intervene in the path optimization based on real-time feedback.

[0125] It should be noted that the optimization module 103 can respond quickly in a real-time changing environment, provide effective path optimization measures, and maintain consistency with the multi-objective function and constraints designed by the scheduling and planning module 101. During the planning process, the path is updated in real time to avoid dynamic obstacles, thereby improving the adaptability and efficiency of the AGV.

[0126] Preferably, the workflow of the target positioning module 104 includes:

[0127] The AGV (Automated Guided Vehicle) transports goods to the target area via the target point of the task. The target area is divided into multiple target area sets. The placement area of ​​the goods is determined based on the set of attributes of the goods and the mapping relationship with the target area set.

[0128] Based on the size of the goods, the layout of the placement area, and the storage status of the items already placed, the optimal placement location of the items within the preset target area is calculated. The remaining available space is calculated based on the total available space in the target area, the occupied space, and the volume of the newly identified items. If the remaining available space is greater than or equal to the volume of the new items, the items can be placed there; otherwise, other areas are searched or space is waited for to be released.

[0129] The AGV (Automated Guided Vehicle) is controlled to place the transported goods in the optimal location within the target area and arrange them in an orderly manner according to preset rules, such as first-in-first-out (FIFO) or priority.

[0130] If any abnormality is encountered in the placement of goods, the abnormality handling procedure should be performed and the incident reported to the administrator.

[0131] Update the storage data of the target area, including the coverage or vacancy status of the target area and the information of the placed goods. Divide the target area into multiple grids and dynamically adjust the size of the covering grids according to the size of the placed goods. Dynamically redraw the range of vacant grids and finally record the data information of the target area and the placement of goods.

[0132] Specifically, the monitoring and early warning module 105 uses various sensors and other devices mentioned above to monitor data including the status information of the AGV, the location of obstacles in the environment, and the relative distance to obstacles. The status information of the AGV includes the position, speed, acceleration, battery status, temperature, and load status of the vehicle. The module performs data analysis to identify obstacles and dynamic objects, monitor the health status of the AGV, and periodically check key parameters such as battery status and motor temperature to ensure that the AGV is in good working condition and detect abnormal behavior or potential faults.

[0133] When an anomaly is detected, the monitoring and early warning module 105 will trigger an alert, specifically including: approaching an obstacle or moving object within a safe distance, potentially causing a collision; the AGV's battery level being below a preset threshold; malfunction or wear of AGV components; and the AGV deviating from its planned path or exhibiting abnormal speed. In response to these alerts, corresponding measures can be taken to ensure the safe operation of the AGV, including: slowing down or stopping the AGV to avoid collisions; sending an alert to the administrator to inspect the AGV or its components; adjusting the path planning to avoid obstacles; and triggering the AGV to automatically return to the charging station for recharging.

[0134] It should be noted that this invention achieves optimal path planning in complex environments through intelligent path planning and real-time optimization. The AGV can quickly respond to logistics needs, reduce waiting time and travel distance, and significantly improve the efficiency of logistics operations. Real-time environmental monitoring and dynamic path optimization improve the AGV's responsiveness to environmental changes, enabling it to adapt to complex and ever-changing logistics environments, including newly emerging obstacles and road congestion, ensuring stable system operation. Intelligent task priority evaluation and scheduling strategies improve overall logistics efficiency and resource utilization. Multi-objective optimization improves logistics driving efficiency and reduces energy consumption. At the same time, modular design reduces maintenance costs and the difficulty of system upgrades, thus reducing operating costs. The modular system design and real-time environmental adaptability allow the AGV to flexibly cope with different logistics scenarios, improving the overall flexibility and adaptability of the system. Comprehensive safety monitoring and early warning mechanisms, as well as precise item placement, improve the safety and reliability of logistics operations and reduce the risk of accidents.

[0135] The above is an illustrative scheme of an AGV (Automated Guided Vehicle) control system for intelligent logistics according to this embodiment. It should be noted that the technical solution of this AGV control system for intelligent logistics belongs to the same concept as the AGV control system for intelligent logistics described above. Details not described in detail in the technical solution of the AGV control system for intelligent logistics in this embodiment can be found in the description of the AGV control system for intelligent logistics described above.

[0136] The AGV (Automated Guided Vehicle) control method used in this embodiment for intelligent logistics, such as Figure 2 As shown, it includes:

[0137] S201, Obtain surrounding environmental information and plan the optimal path based on the initial position and the target position;

[0138] S202 issues instructions based on the optimal path to control the movement of the AGV and synchronously acquires movement information, including real-time map updates and AGV positioning.

[0139] S203 receives movement information and optimizes and updates the optimal path based on obstacle monitoring of real-time environmental changes;

[0140] S204, based on the optimized optimal path, after the AGV arrives at the target location, it is placed in the set target area according to the nature of the transported goods, and the storage status of the target area is recorded and updated.

[0141] S205: Obtain the operating parameters of the monitoring system equipment. If there is an abnormal range, issue an alarm.

[0142] This embodiment also provides an electronic device suitable for controlling AGV (Automated Guided Vehicle) carts in intelligent logistics, including:

[0143] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the AGV (Automated Guided Vehicle) control method for intelligent logistics as described in the above embodiments.

[0144] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the AGV vehicle control method for intelligent logistics as proposed in the above embodiments.

[0145] The storage medium proposed in this embodiment and the AGV vehicle control system for intelligent logistics proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0146] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0147] Example 2

[0148] Referring to Table 1, an embodiment of the present invention provides an AGV (Automated Guided Vehicle) control system for intelligent logistics. To verify its beneficial effects, a comparison of two schemes is provided.

[0149] The implementation of this invention includes a scheduling and planning module, a control module, an optimization module, a target positioning module, and a monitoring and early warning module. An experimental environment is set up to simulate a logistics environment, including dynamic and static obstacles, multiple AGV vehicles, a certain number of tasks to be processed, and multiple target storage areas. A smart logistics warehouse scenario of the same scale is constructed, and the same number and type of transportation tasks are set. A comparative experiment is conducted with a traditional AGV control system. The experimental results are shown in Table 1.

[0150] Table 1: Experimental Index Data

[0151]

[0152] As shown in Table 1, the present invention has significant effects on path planning efficiency, energy consumption reduction, task completion rate improvement, collision reduction, system response speed acceleration, and safety enhancement. Through multi-objective optimization and dynamic path planning, the time for AGV to complete tasks is greatly shortened, unnecessary energy waste is reduced, the precise placement of items is ensured, storage efficiency is improved, and collisions and other safety accidents are effectively prevented.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An AGV (Automated Guided Vehicle) control system for intelligent logistics, characterized in that, include: The scheduling and planning module is used to acquire surrounding environmental information, plan the optimal path based on the initial position and the target position, and transmit the optimal path to the control module. The control module is used to receive the optimal path generated by the scheduling and planning module, issue instructions according to the optimal path, control the movement of the AGV, and synchronously acquire movement information, including real-time map updates and AGV positioning, and communicate with the optimization module. An optimization module is used to receive movement information from the control module, optimize and update the optimal path based on obstacle monitoring of real-time environmental changes, and feed back the optimization and update results to the control module. The target positioning module is used to place the AGV in the set target area according to the nature of the transported goods after the AGV arrives at the target location, and record and update the storage status of the target area. The monitoring and early warning module is used to acquire the operating parameters of the monitoring system equipment and to issue an alarm if there are abnormal ranges. The workflow of the scheduling planning module includes: Environmental information is collected through sensors to construct a dynamic environmental map, wherein the grid cells of the environmental map are... Indicates position It can determine the passage status and identify and classify objects in the environment; Based on the urgency of the task Importance of the task and task time window Calculate task priority and obtain task content. Based on the initial position and target position in the task content, determine the starting point and target point of the environment map. Based on the task's route distance, energy consumption, and travel time, a multi-objective optimization function is constructed, and constraints on AGV movement are defined. The function then calculates and obtains the passable cells in the current task path planning. Multiple intermediate nodes in the multi-objective optimization function Represented as: ， ， ， ， in, Indicates the distance of the mission route. Indicates the energy consumption of the task. Indicates the mission travel time. Represents a node Location, , , These represent the coefficients for task adjustment. Indicates the number of path nodes. Indicates the AGV's load capacity. Indicates the distance between nodes. This represents the average speed of the AGV. Indicates the road surface resistance between nodes. Represents a node The expected stopping time, Represents a node The frequency of passage; Based on the starting point, target point, and intermediate nodes of the task, and using the multi-objective optimization function and constraints, a preliminary optimal path for the task is planned. Computation task priorities include: Based on the urgency of the task Importance of the task and task time window Construct a judgment matrix , represented as: ， in, Indicates the level of urgency relative to the importance of the task Importance ratio Indicates the level of urgency Relative to the task time window Importance ratio Indicates the importance of the task Relative to the task time window Importance ratio; By solving the judgment matrix The eigenvector corresponding to the largest eigenvalue is normalized to obtain the weight vector of each factor. Calculate the overall task priority Represented as: ， in, These represent the urgency of the task. Importance of the task and task time window Weighting coefficients; The workflow of the optimization module includes: It receives movement information of the AGV in the control module, as well as real-time monitoring of the AGV's surrounding environment, including real-time changes in obstacles and traffic flow, and generates an obstacle set. Mapping the current optimal path to real-time environmental data identifies potential conflict points, generating a set of conflict points, represented as... ,in Indicates the first position on the path Each conflict point is assessed for severity and type, i.e., static or dynamic obstacles, road congestion. The severity of the conflict is calculated based on the size, location, and expected duration of the obstacles. Perform local path optimization at each conflict point to avoid obstacles or congested areas; The multi-objective optimization function is adjusted according to the new local path, the constraints are updated to reflect the new environmental limitations, the newly generated local path is smoothed to ensure the continuity and smoothness of the path, and the local optimized path is merged with the original path to form a new optimal path. Verify whether the new optimal path satisfies all constraints. If it does, send the verified optimal path to the control module. The workflow of the target localization module includes: The AGV (Automated Guided Vehicle) transports goods to the target area via the target point of the task. The target area is divided into multiple target area sets. The placement area of ​​the goods is determined according to the set of attributes of the goods and the mapping relationship with the target area sets. Based on the size of the goods, the layout of the placement area, and the storage status of the items already placed, calculate the optimal placement location of the items within the preset target area; The AGV (Automated Guided Vehicle) is controlled to place the transported goods in the optimal location within the target area and arrange them in an orderly manner according to preset rules, thus completing the placement of the goods. If any abnormality is encountered in the placement of goods, the abnormality handling procedure should be performed and the incident reported to the administrator. Update the storage data of the target area, including the coverage or idle status of the target area and the information of the placed goods. Divide the target area into multiple grids and dynamically adjust the size of the covered grids according to the size of the placed goods. Dynamically redraw the range of the idle grids and record the data information of the target area and the placement of goods.

2. The AGV control system for intelligent logistics as described in claim 1, characterized in that, The initial optimal path for the planning task includes: Based on the starting point, target point, and intermediate nodes of the task, a cost function is constructed to obtain the optimal path. The cost function is expressed as: ， in, Represents a node Total estimated cost, Indicates the distance from the starting point to the node. Path cost, Indicates from node Estimated cost to reach the target point; in, ， ， in, Indicates the distance from the starting point to the node. The actual path cost Indicates from node arrive The cost of moving, Indicates starting from the current node The straight-line distance to the target node. Indicates starting from the current node The estimated energy consumption to reach the target node. A coefficient indicating the importance of adjusting energy consumption.

3. The AGV control system for intelligent logistics as described in claim 1 or 2, characterized in that, The workflow of the control module includes: Obtain the optimal path data generated by the scheduling and planning module, set the initial position and direction of the AGV, and align it with the first node of the optimal path; The optimal path data is converted into control commands and sent to the drive module of the AGV vehicle; The movement information of the AGV is acquired and merged with the initial environmental map to update the position and attitude of the AGV in the map in real time. It detects obstacles on the path in real time, transmits movement and obstacle information to the optimization module in real time, receives feedback from the optimization module, and adjusts the control strategy based on the feedback. The control parameters are visualized, allowing users to view and modify them in real time.

4. A method for controlling an AGV (Automated Guided Vehicle) for intelligent logistics, based on the AGV control system for intelligent logistics as described in any one of claims 1-3, characterized in that, include, Acquire information about the surrounding environment and plan the optimal path based on the initial and target locations; According to the optimal path, the instructions are issued to control the movement of the AGV and the movement information is acquired synchronously. The movement information includes real-time map updates and determination of the AGV's location. The system receives the movement information and optimizes and updates the optimal path based on obstacle monitoring of real-time environmental changes. Based on the optimized optimal path, after the AGV reaches the target location, it is placed in the set target area according to the nature of the transported goods, and the storage status of the target area is recorded and updated. Obtain the operating parameters of the monitoring system equipment, and issue an alarm if there are abnormal ranges; The workflow of the scheduling planning module includes: Environmental information is collected through sensors to construct a dynamic environmental map, wherein the grid cells of the environmental map are... Indicates position It can determine the passage status and identify and classify objects in the environment; Based on the urgency of the task Importance of the task and task time window Calculate task priority and obtain task content. Based on the initial position and target position in the task content, determine the starting point and target point of the environment map. Based on the task's route distance, energy consumption, and travel time, a multi-objective optimization function is constructed, and constraints on AGV movement are defined. The function then calculates and obtains the passable cells in the current task path planning. Multiple intermediate nodes in the multi-objective optimization function Represented as: ， ， ， ， in, Indicates the distance of the mission route. Indicates the energy consumption of the task. Indicates the mission travel time. Represents a node Location, , , These represent the coefficients for task adjustment. Indicates the number of path nodes. Indicates the AGV's load capacity. Indicates the distance between nodes. This represents the average speed of the AGV. Indicates the road surface resistance between nodes. Represents a node The expected stopping time, Represents a node The frequency of passage; Based on the starting point, target point, and intermediate nodes of the task, and using the multi-objective optimization function and constraints, a preliminary optimal path for the task is planned. Computation task priorities include: Based on the urgency of the task Importance of the task and task time window Construct a judgment matrix , represented as: ， in, Indicates the level of urgency relative to the importance of the task Importance ratio Indicates the level of urgency Relative to the task time window Importance ratio Indicates the importance of the task Relative to the task time window Importance ratio; By solving the judgment matrix The eigenvector corresponding to the largest eigenvalue is normalized to obtain the weight vector of each factor. Calculate the overall task priority Represented as: ， in, These represent the urgency of the task. Importance of the task and task time window Weighting coefficients; The workflow of the optimization module includes: It receives movement information of the AGV in the control module, as well as real-time monitoring of the AGV's surrounding environment, including real-time changes in obstacles and traffic flow, and generates an obstacle set. Mapping the current optimal path to real-time environmental data identifies potential conflict points, generating a set of conflict points, represented as... ,in Indicates the first position on the path Each conflict point is assessed for severity and type, i.e., static or dynamic obstacles, road congestion. The severity of the conflict is calculated based on the size, location, and expected duration of the obstacles. Perform local path optimization at each conflict point to avoid obstacles or congested areas; The multi-objective optimization function is adjusted according to the new local path, the constraints are updated to reflect the new environmental limitations, the newly generated local path is smoothed to ensure the continuity and smoothness of the path, and the local optimized path is merged with the original path to form a new optimal path. Verify whether the new optimal path satisfies all constraints. If it does, send the verified optimal path to the control module. The workflow of the target localization module includes: The AGV (Automated Guided Vehicle) transports goods to the target area via the target point of the task. The target area is divided into multiple target area sets. The placement area of ​​the goods is determined according to the set of attributes of the goods and the mapping relationship with the target area sets. Based on the size of the goods, the layout of the placement area, and the storage status of the items already placed, calculate the optimal placement location of the items within the preset target area; The AGV (Automated Guided Vehicle) is controlled to place the transported goods in the optimal location within the target area and arrange them in an orderly manner according to preset rules, thus completing the placement of the goods. If any abnormality is encountered in the placement of goods, the abnormality handling procedure should be performed and the incident reported to the administrator. Update the storage data of the target area, including the coverage or idle status of the target area and the information of the placed goods. Divide the target area into multiple grids and dynamically adjust the size of the covered grids according to the size of the placed goods. Dynamically redraw the range of the idle grids and record the data information of the target area and the placement of goods.

5. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the AGV vehicle control method for intelligent logistics as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the AGV vehicle control method for intelligent logistics as described in any one of claims 1-3.

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