AGV trolley control system for intelligent logistics

Abstract

The invention discloses an AGV trolley control system for intelligent logistics, and the system comprises a scheduling planning module which obtains surrounding environment information, and carries out the planning of an optimal path; the control module issues an instruction according to the optimal path, controls the movement of the AGV trolley, and obtains movement information; the optimization module optimizes and updates the optimal path according to obstacle monitoring of real-time environment change; the target positioning module is used for placing the AGV in a set target area according to the property of the conveyed object after the AGV reaches the target position; and the monitoring and early warning module is used for acquiring the equipment operation parameters of the monitoring system and giving an alarm prompt if an abnormal range exists. Through intelligent path planning and real-time optimization, optimal path planning in a complex environment is realized, the AGV trolley can quickly respond to logistics requirements, the waiting time and the walking distance are reduced, the efficiency of logistics operation is remarkably improved, the response capability of the AGV to environment change is improved, and the AGV trolley can adapt to the complex and changeable logistics environment.

Description

Technical Field

[0001] The present invention relates to the field of intelligent logistics control technology, and in particular to an AGV trolley control system for intelligent logistics. Background Art

[0002] Nowadays, automatic guided vehicles (AGVs) are used in many application fields, such as factories, warehouses and hospitals. Different AGV systems use different navigation methods. For manufacturing companies, production material loading and unloading is a high-frequency, time-consuming, boring and costly production operation in the industrial production logistics process. The AGV cart is equipped with electromagnetic or photoelectric automatic guidance devices, can travel along the set guidance route, has programming and mode selection devices, safety protection functions and various transfer functions, and does not require a driver in industrial applications. It is a powerful weapon for enterprises to improve efficiency and reduce production costs, and is also an important intelligent equipment for the country to promote intelligent construction.

[0003] Traditional AGV systems lack effective path planning capabilities, especially in complex or dynamically changing environments. Existing technologies may not be able to fully consider multiple factors such as dynamic changes in the environment and load conditions, resulting in insufficiently optimized path planning and inability to effectively handle dynamic obstacles in the environment or real-time changing traffic conditions. In a multi-AGV environment, task allocation is not intelligent enough, resulting in 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. At the same time, there are limitations in precise positioning and target recognition, which affects the accuracy of task execution. Existing AGVs do not have effective energy consumption management strategies, resulting in high operating costs. In emergencies, some AGV systems may lack sufficient safety measures. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an AGV control system for intelligent logistics to solve the problems of insufficient optimization of path planning, insufficient intelligence of task allocation and accuracy of task execution.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

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

[0008] The scheduling and planning module is used to obtain the surrounding environment information, plan the optimal path according to the initial position and the target position, and transmit the optimal path to the control module;

[0009] A 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 obtain movement information, which includes real-time map updates and determination of the AGV location, and communicate with the optimization module;

[0010] An optimization module, used for receiving the movement information of the control module, optimizing and updating the optimal path according to obstacle monitoring of real-time environmental changes, and feeding 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 object after it reaches the target location, and record and update the storage status of the target area;

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

[0013] As a preferred solution of the AGV trolley control system for intelligent logistics described in the present invention, the workflow of the scheduling planning module includes:

[0014] Environmental information is collected by sensors to build a dynamic environmental map. The grid cells of the environmental map Indicates location Traffic status, and identify and classify objects in the environment;

[0015] According to the urgency of the task , Task Importance and task time window , calculate the task priority, obtain the task content, and determine the starting point and target point of the environment map according to the initial position and target position in the task content;

[0016] According to the route distance, energy consumption and driving time of the task, a multi-objective optimization function is constructed, and the constraints of AGV driving are defined to calculate and obtain each traversable cell in the current task path planning. The multiple intermediate nodes in the multi-objective optimization function It is expressed as:

[0017]

[0018]

[0019]

[0020]

[0021] in, Indicates the mission route distance, represents the task energy consumption, represents the mission travel time, Representation Node location, , , They represent the coefficients of task adjustment, Indicates the number of path nodes, Indicates AGV load. represents the distance between nodes, Indicates the average speed of AGV, represents the road resistance between nodes, Representation Node The estimated stop time, Representation Node The frequency of traffic;

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

[0023] As a preferred solution of the AGV trolley control system for intelligent logistics described in the present invention, the preliminary optimal path of the planning task includes:

[0024] According to 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, Representation Node The total estimated cost of From the starting point to the node The path cost, Represents a slave node Estimated cost to the destination point;

[0027] in,

[0028]

[0029]

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

[0031] As a preferred solution of the AGV trolley control system for intelligent logistics described in the present invention, the working process of the control module includes:

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

[0033] Convert the optimal path data into control instructions and send them to the drive module of the AGV vehicle;

[0034] Obtain the movement information of the AGV, integrate it with the initial environment map, and update the position and posture of the AGV in the map in real time;

[0035] Detect obstacles on the path in real time, transmit movement information and obstacle information to the optimization module in real time, receive feedback from the optimization module, and adjust the control strategy based on the feedback;

[0036] The control parameters are displayed visually, and users can view and modify the control parameters in real time.

[0037] As a preferred solution of the AGV trolley control system for intelligent logistics described in the present invention, the workflow of the optimization module includes:

[0038] Receive the movement information of the AGV in the control module, as well as real-time monitoring of the surrounding environment of the AGV, including obstacles and traffic flow that change in real time, and generate an obstacle set;

[0039] Map the current optimal path with real-time environmental data, identify potential conflict points, generate a set of conflict points, and assess the severity and type of each conflict point;

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

[0041] Adjust the multi-objective optimization function according to the new local path, update the constraints, merge the local optimization path with the original path, and form a new optimal path;

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

[0043] As a preferred solution of the AGV trolley control system for intelligent logistics described in the present invention, the working process of the target positioning module includes:

[0044] The AGV transports the goods to the target area through 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 attribute set of the goods and the mapping relationship with the target area set.

[0045] Calculate the best placement of items in the preset target area based on the size of the goods, the layout of the placement area, and the storage status of the items already placed;

[0046] Control the AGV to place the transported objects at the best location in the target area, and arrange them in order according to the preset rules to complete the placement of the goods;

[0047] If there is any abnormality in the placement of goods, perform abnormal processing and report it to the administrator;

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

[0049] In a second aspect, the present invention provides an AGV control method for intelligent logistics, comprising:

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

[0051] According to the optimal path, the command is issued to control the movement of the AGV and synchronously obtain movement information, which includes real-time update of the map and determination of the location of the AGV;

[0052] Receiving the movement information, and optimizing and updating the optimal path according to 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 object, and the storage status of the target area is recorded and updated;

[0054] Obtain the operating parameters of the monitoring system equipment and issue an alarm if there is an abnormal range.

[0055] In a third aspect, 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, the steps of the AGV trolley control method for intelligent logistics are implemented.

[0058] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the AGV trolley control method for intelligent logistics.

[0059] Compared with the prior art, the present invention has the following beneficial effects: the present invention realizes the optimal path planning in a complex environment through intelligent path planning and real-time optimization. The AGV can quickly respond to logistics needs, reduce waiting time and walking distance, and significantly improve the efficiency of logistics operations. Real-time environmental monitoring and dynamic path optimization improve the AGV's ability to respond to environmental changes and can adapt to complex and changeable logistics environments, including newly emerging obstacles, road congestion, etc., to ensure the stable operation of the system. Intelligent task priority evaluation and scheduling strategies improve the 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, and reduces operating costs; modular system design and real-time environmental adaptability enable the AGV to flexibly respond to 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

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

[0062] Figure 2 The figure is a schematic diagram of the overall process of an AGV control method for intelligent logistics according to an embodiment of the present invention.

[0063] Explanation of reference numerals: 101, scheduling and planning module; 102, control module; 103, optimization module; 104, target positioning module; 105, monitoring and early warning module. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1

[0068] Reference Figure 1-Figure 2 , is an embodiment of the present invention, which provides an AGV control system for intelligent logistics, such as Figure 1 As shown, including:

[0069] The scheduling and planning module 101 is used to obtain the surrounding environment information, plan the optimal path according to 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, and synchronously obtain movement information, which includes real-time map updates and determination of the AGV location, and communicate with the optimization module;

[0071] The optimization module 103 is used to receive the movement information of the control module 102, and optimize and update the optimal path according to the obstacle monitoring of the 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 transported object in a set target area according to the nature of the transported object after the AGV vehicle reaches the target location, and record and update the storage status of the target area;

[0073] The monitoring and early warning module 105 is used to obtain the operating parameters of the monitoring system equipment and issue an alarm if there is an abnormal range.

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

[0075] First, the environmental information is collected through sensors to build a dynamic environmental map. The grid cells of the environmental map Indicates location Traffic status, and identify and classify objects in the environment;

[0076] Specifically, raw data of the surrounding environment is obtained through various sensors, including but not limited to laser radar, LiDAR, camera, ultrasound, etc., and the raw data is preprocessed, including filtering, denoising and other operations to improve data quality. Objects in the environment are identified and classified into different types, such as obstacles, drivable areas, etc., and a dynamic environment map is constructed through SLAM based on the results of identification and classification. The map preferably uses a grid map or other suitable representation method, and each grid or area is marked with information such as its passability and object type.

[0077] In this case, using a grid map or a topological map, the environment space is divided into M×N grids, each of which is d×d in size, where each cell of the grid map Indicates location The traffic status includes two states: passable and blocked. If it is passable, it is marked as 1, and if it is blocked, it is inaccessible and marked as 0.

[0078] Secondly, according to the urgency of the task , Task Importance and task time window , calculate the task priority, obtain the task content, and determine the starting point and target point of the environment map according to the initial position and target position in the task content;

[0079] For example, the data of multiple tasks preset in the system are obtained and analyzed. First, the priority of each task is determined, and the various factors affecting the priority of the task, including urgency, task importance, and task time window, are compared pairwise to construct a judgment matrix. , then, by calculating the judgment matrix The eigenvector of , and the relative weight of each factor is obtained;

[0080] Specifically, factors that affect task priority include task urgency , Task Importance and task time window (i.e., the time limit for completing the task), for each factor, a scale of 1-9 is used, where 1 means that the two factors are equally important, and 9 means that one factor is extremely important than the other, to construct a judgment matrix It is expressed as:

[0081]

[0082] in, Indicates urgency Relative to the importance of the task The importance ratio of Indicates urgency Relative to the task time window The importance ratio of 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 maximum eigenvalue of is normalized to obtain the weight vector of each factor , expressed as:

[0084]

[0085] Based on this, the overall task priority It is expressed as:

[0086]

[0087] It should be noted that according to the calculation result of the 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, according to the route distance, energy consumption and driving time of the task, considering the dynamic characteristics and environmental factors of AGV, a multi-objective optimization function is constructed, and the constraints of AGV driving are defined to calculate and obtain the current task path planning. Multiple intermediate nodes in the multi-objective optimization function It is expressed as:

[0089]

[0090]

[0091]

[0092]

[0093] in, Indicates the mission route distance, represents the task energy consumption, represents the mission travel time, Representation Node location, , , They represent the coefficients of task adjustment, Indicates the number of path nodes, Indicates AGV load. represents the distance between nodes, Indicates the average speed of AGV, represents the road resistance between nodes, Representation Node The estimated stop time, Representation Node The frequency of passage.

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

[0095]

[0096]

[0097]

[0098] in, Indicates the speed of the AGV when it is running. Indicates the maximum operating speed. Indicates that at the node The turning radius, Indicates the minimum turning radius, Indicates safe distance. Indicates the distance between the AGV and the nearest obstacle. Indicates the minimum safety distance. The speed of the AGV at any time cannot exceed its designed maximum speed, and when turning, it cannot be less than its minimum turning radius. A certain safety distance must be maintained between the AGV and other objects or obstacles.

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

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

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

[0102] According to 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, Representation Node The total estimated cost of From the starting point to the node The path cost, Represents a slave node Estimated cost to the destination point;

[0105] in,

[0106]

[0107]

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

[0109] Exemplarily, initialize the open list and the closed list, the open list is used to store the nodes that have been found, and the closed list is used to store the nodes that have been evaluated. Add to the open list and loop until the target node is found or the open list is empty. A value-ordered priority queue.

[0110] Specifically, the starting node of Set to 0 and calculate , when the open list is not empty, do the following: select the node with the lowest Value Node , as the current node; if is the target node, then the optimal path has been found and can be found by backtracking The parent node of Move from the open list to the closed list; for Each neighbor node of ,if is not in the closed list and can be arrive Without violating the constraints (if the adjacent node is not feasible, the constraints are violated), then skip it), then: ;like Not in the open list, or through arrive of Smaller, newer of and , and Set to The parent node of Add to the open list; if the adjacent node is already in the open list and is calculated by the current path If the value is smaller, update it value and parent node; move the current node from the open list to the closed list. Finally, the optimal path is generated. When the target node is added to the closed list, the optimal path is constructed by tracing back from the target node to its parent node.

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

[0112] It should be noted that this embodiment combines multiple factors to design a heuristic function to achieve more comprehensive path optimization, introduces a path smoothing algorithm, improves the efficiency and safety of AGV driving, and can not only evaluate task priorities more finely, but also construct a multi-objective function that better meets actual needs. It also implements real-time dynamic path planning through an improved A* algorithm, and provides a comprehensive and complex scheduling planning module 101, which comprehensively considers factors such as the shortest distance, path smoothness, energy consumption, traffic congestion, etc., conducts a comprehensive evaluation, and selects the optimal path.

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

[0114] Obtain the optimal path data generated by the scheduling planning module 101, set the initial position and direction of the AGV, and align it with the first node of the optimal path, where the path can be represented as a series of navigation nodes, represented as ;

[0115] Convert the optimal path data into control instructions, generate a control instruction sequence, and send it to the drive module of the AGV to control the vehicle. Specifically, use a proportional-integral-differential (PID) controller 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 the error. According to the environmental conditions and the current state of the AGV, the PID parameters can be dynamically adjusted to improve the response speed and stability.

[0116] Obtain the movement information of the AGV, integrate it with the initial environment map, and update the position and posture of the AGV in the map in real time;

[0117] Detect obstacles on the optimal path in real time, transmit movement information and obstacle information to the optimization module 103 in real time, receive feedback from the optimization module 103, and adjust the control strategy according to the feedback;

[0118] The control parameters are displayed visually, and users can view and modify the control parameters in real time.

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

[0120] Receive the movement information of the AGV in the control module 102, as well as the real-time monitoring of the environment around the AGV, including the real-time changing obstacles and traffic flow, and generate an obstacle set. Specifically, the obstacle set ,in, Indicates The location and size of each obstacle;

[0121] Map the current optimal path with the real-time environment data, identify potential conflict points, and generate a set of conflict points, expressed as ,in Indicates the first conflict points, assess the severity and type of each conflict point, i.e. static or dynamic obstacles, road congestion, which can be calculated based on the size, location and expected duration of the obstacle to assess the severity of the conflict;

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

[0123] Adjust the multi-objective optimization function according to the new local path, update the constraints to reflect the new environmental restrictions, smooth the newly generated local path to ensure the continuity and smoothness of the path, merge the local optimized path with the original path to form a new optimal path to ensure safety and efficiency;

[0124] Verify whether the new optimal path satisfies all constraints. If so, send the verified optimal path to the control module 102. At the same time, allow the administrator to manually adjust the optimization strategy or intervene in the path optimization according to real-time feedback.

[0125] It should be noted that the optimization module 103 can respond quickly in a real-time changing environment and provide effective path optimization measures, while maintaining consistency with the multi-objective functions and constraints designed by the scheduling planning module 101, and updating the path in real time during the planning process 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 transports the goods to the target area through 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 attribute set of the goods and the mapping relationship with the target area set.

[0128] Calculate the best placement of the item in the preset target area according to the size of the goods, the layout of the placement area, and the storage status of the placed items. Calculate the remaining available space according to the total available space, the occupied space, and the volume of the identified new item in the target area. If the remaining available space is ≥ the volume of the new item, the new item can be placed. Otherwise, find other areas or wait for space to be released.

[0129] Control the AGV to place the transported objects at the best location in the target area and arrange them in order according to the preset rules to complete the placement of the goods. The preset rules can be the first-in-first-out and priority of the goods;

[0130] If there is any abnormality in the placement of goods, perform abnormal processing and report it to the administrator;

[0131] 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, dynamically adjust the size of the covered grids according to the size of the placed goods, and dynamically redraw the range of the idle grids. Finally, record the data information of the target area and the placement of goods.

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

[0133] When abnormal conditions are detected, the monitoring and early warning module 105 will trigger an early warning, including: less than the safe distance, approaching obstacles or dynamic objects, and possibly causing a collision; the battery power of the AGV is lower than the preset threshold; the vehicle components are faulty or worn; the AGV deviates from the predetermined path or the speed is abnormal. For abnormal early warnings, corresponding measures can be taken according to the abnormal behavior to ensure the safe operation of the AGV, including: slowing down or stopping the AGV to avoid collision; sending early warning information to notify the administrator to check the AGV or components; adjusting the path planning to avoid obstacles; triggering the AGV to automatically return to the charging station for charging, etc.

[0134] It should be noted that the present invention realizes 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 walking distance, and significantly improve the efficiency of logistics operations. Real-time environmental monitoring and dynamic path optimization improve the AGV's ability to respond to environmental changes and can adapt to complex and changeable logistics environments, including newly emerging obstacles, road congestion, etc., to ensure the stable operation of the system. 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, and reduces operating costs; modular system design and real-time environmental adaptability enable the AGV to flexibly respond to 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 a schematic scheme of an AGV trolley control system for intelligent logistics in this embodiment. It should be noted that the technical scheme of the AGV trolley control system for intelligent logistics and the technical scheme of the AGV trolley control system for intelligent logistics are of the same concept. For details not described in detail in the technical scheme of the AGV trolley control system for intelligent logistics in this embodiment, please refer to the description of the technical scheme of the AGV trolley control system for intelligent logistics.

[0136] The AGV control method for intelligent logistics in this embodiment is as follows: Figure 2 As shown, including:

[0137] S201, obtaining surrounding environment information, and planning an optimal path according to the initial position and the target position;

[0138] S202, issuing instructions according to the optimal path, controlling the movement of the AGV, and synchronously acquiring movement information, the movement information including real-time update of the map and determination of the AGV positioning;

[0139] S203, receiving movement information, and optimizing and updating the optimal path according to obstacle monitoring of real-time environmental changes;

[0140] S204, 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 object, and the storage status of the target area is recorded and updated;

[0141] S205, obtaining the operating parameters of the monitoring system equipment, and if there is an abnormal range, an alarm is issued.

[0142] This embodiment also provides an electronic device, which is suitable for controlling an AGV vehicle used in intelligent logistics, including:

[0143] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the AGV trolley control method for intelligent logistics as proposed in the above embodiment.

[0144] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the AGV vehicle control method for intelligent logistics proposed in the above embodiment is implemented.

[0145] The storage medium proposed in this embodiment and the AGV trolley control system for intelligent logistics proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0146] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0147] Example 2

[0148] Referring to Table 1, an embodiment of the present invention provides an AGV control system for intelligent logistics. In order to verify its beneficial effects, comparison results of two solutions are provided.

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

[0150] Table 1: Experimental indicator data

[0151]

[0152] It can be seen from Table 1 that the present invention has significant effects in terms of path planning efficiency, energy consumption reduction, task completion rate improvement, collision reduction, system response speed acceleration and safety improvement. Through multi-objective optimization and dynamic path planning, the time for the AGV to complete the task is greatly shortened, unnecessary energy waste is reduced, the precise placement of objects 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 are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An AGV control system for intelligent logistics, characterized in that: include: The scheduling and planning module is used to obtain the surrounding environment information, plan the optimal path according to the initial position and the target position, and transmit the optimal path to the control module; A 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 obtain movement information, which includes real-time map updates and determination of the AGV location, and communicate with the optimization module; An optimization module, used for receiving the movement information of the control module, optimizing and updating the optimal path according to obstacle monitoring of real-time environmental changes, and feeding 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 object after it reaches the target location, and record and update the storage status of the target area; The monitoring and early warning module is used to obtain the operating parameters of the monitoring system equipment and issue an alarm if there is an abnormal range.

2. The AGV control system for intelligent logistics according to claim 1, characterized in that: The workflow of the scheduling planning module includes: Environmental information is collected by sensors to build a dynamic environmental map. The grid cells of the environmental map Indicates location Traffic status, and identify and classify objects in the environment; According to the urgency of the task , Task Importance and task time window , calculate the task priority, obtain the task content, and determine the starting point and target point of the environment map according to the initial position and target position in the task content; According to the route distance, energy consumption and driving time of the task, a multi-objective optimization function is constructed, and the constraints of AGV driving are defined to calculate and obtain each traversable cell in the current task path planning. The multiple intermediate nodes in the multi-objective optimization function It is expressed as: ； ； ； ； in, Indicates the mission route distance, represents the task energy consumption, represents the mission travel time, Representation Node location, , , They represent the coefficients of task adjustment, Indicates the number of path nodes, Indicates AGV load. represents the distance between nodes, Indicates the average speed of AGV, represents the road resistance between nodes, Representation Node The estimated stop time, Representation Node The frequency of traffic; According to the starting point, target point and intermediate nodes of the task, based on the multi-objective optimization function and constraints, a preliminary optimal path for the task is planned.

3. The AGV control system for intelligent logistics according to claim 2, characterized in that: The preliminary optimal path of the planning task includes: According to 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, Representation Node The total estimated cost of From the starting point to the node The path cost, Represents a slave node Estimated cost to the destination point; in, ； ； in, Indicates the starting point to the node The actual path cost, Represents a slave node arrive The cost of moving Indicates that from the current node The straight-line distance to the target node, Indicates that from the current node The estimated energy consumption to the target node, A coefficient that represents the importance of adjusting energy consumption.

4. The AGV control system for intelligent logistics according to claim 1 or 3, characterized in that: The workflow of the control module includes: Obtain the optimal path data generated by the scheduling planning module, set the initial position and direction of the AGV, and align it with the first node of the optimal path; Convert the optimal path data into control instructions and send them to the drive module of the AGV vehicle; Obtain the movement information of the AGV, integrate it with the initial environment map, and update the position and posture of the AGV in the map in real time; Detect obstacles on the path in real time, transmit movement information and obstacle information to the optimization module in real time, receive feedback from the optimization module, and adjust the control strategy based on the feedback; The control parameters are displayed visually, and users can view and modify the control parameters in real time.

5. The AGV control system for intelligent logistics according to claim 4, characterized in that: The workflow of the optimization module includes: Receive the movement information of the AGV in the control module, as well as real-time monitoring of the surrounding environment of the AGV, including obstacles and traffic flow that change in real time, and generate an obstacle set; Map the current optimal path with real-time environmental data, identify potential conflict points, generate a set of conflict points, and assess the severity and type of each conflict point; Perform local path optimization for each conflict point to avoid obstacles or congested areas; Adjust the multi-objective optimization function according to the new local path, update the constraints, merge the local optimization path with the original path, and form a new optimal path; Verify whether the new optimal path satisfies all constraints. If so, send the verified optimal path to the control module.

6. The AGV control system for intelligent logistics according to claim 1 or 5, characterized in that: The workflow of the target positioning module includes: The AGV transports the goods to the target area through 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 attribute set of the goods and the mapping relationship with the target area set. Calculate the best placement of items in the preset target area based on the size of the goods, the layout of the placement area, and the storage status of the items already placed; Control the AGV to place the transported objects at the best location in the target area, and arrange them in order according to the preset rules to complete the placement of the goods; If there is any abnormality in the placement of goods, perform abnormal processing and report it to the administrator; Update the storage data of the target area and record the data information of the target area and the placement of goods.

7. An AGV trolley control method for intelligent logistics, based on the AGV trolley control system for intelligent logistics as claimed in any one of claims 1 to 6, characterized in that: include, Obtain surrounding environment information and plan the optimal path based on the initial position and target position; According to the optimal path, the command is issued to control the movement of the AGV and synchronously obtain movement information, which includes real-time update of the map and determination of the location of the AGV; Receiving the movement information, and optimizing and updating the optimal path according to 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 object, 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 is an abnormal range.

8. 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, the steps of the AGV trolley control method for intelligent logistics described in claim 7 are implemented.

9. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the steps of the AGV trolley control method for intelligent logistics as described in claim 7.

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