Multi-AGV collaborative operation control system

Abstract

The invention relates to the technical field of AGV automatic control, in particular to a multi-AGV collaborative operation control system which is provided with a task scheduling module, an optimal path planning module, a multi-AGV collaborative operation module and a user interface module. The optimal path planning module plans an optimal path in combination with an improved A * algorithm and a time window algorithm, the AGV state is monitored in real time through the multi-AGV collaborative operation module for dynamic decision making, the user interface module provides real-time state monitoring and early warning information display, and in addition, the system is provided with an early warning coordination mechanism, so that the collision risk can be effectively reduced, and the safety of the system is improved. And efficient execution of tasks is ensured, so that the efficiency and reliability of multi-AGV collaborative operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV automatic control, and in particular to a multi-AGV collaborative operation control system. Background Art

[0002] With the rapid development of the logistics industry and the continuous innovation of automation technology, AGVs are increasingly being used in numerous fields. However, facing the ever-increasing logistics demands and increasingly complex logistics environments, the traditional single AGV operation model is no longer able to meet the complex and ever-changing logistics environment. Therefore, multi-AGV collaborative control systems have emerged. Multi-AGV collaborative operation has become a core driving force behind the intelligent and automated development of the modern logistics industry.

[0003] For example, Chinese patent publication / announcement number: CN118629246A. A multi-AGV intersection coordination method for an automated container terminal is disclosed, comprising: determining the current number of intersections and the number of AGVs, and constructing an intersection traffic model based on the current number of intersections and the number of AGVs, wherein the intersection traffic model includes at least a polygonal intersection model; performing a traffic order search on the intersection traffic model according to the MCTS algorithm to obtain the traffic priority of each AGV; and sending the traffic priority of each AGV to the corresponding AGV, so that each AGV can perform transportation operations according to its own traffic priority. The multi-AGV intersection coordination method for an automated container terminal provided by this invention realizes conflict-free deadlock and efficient multi-AGV intersection coordinated traffic.

[0004] However, the prior art still has the following problems:

[0005] In practice, traditional scheduling systems struggle to flexibly adjust task allocations for multiple AGVs under dynamic task demands, and path conflicts are prone to occur in complex environments. Furthermore, the lack of effective early warning and coordination mechanisms increases the risk of collisions, leading to task execution delays and thus compromising the efficiency and reliability of multi-AGV collaborative operations. Summary of the Invention

[0006] To this end, the present invention provides a multi-AGV collaborative operation control system to overcome the problems in the prior art that the scheduling system is difficult to flexibly adjust task allocation, and path conflicts are prone to occur in complex environments. The lack of an effective early warning and coordination mechanism will increase the risk of collision and cause task execution delays, thereby affecting the efficiency and reliability of multi-AGV collaborative operations.

[0007] To achieve the above objectives, the present invention provides a multi-AGV collaborative operation control system, which includes:

[0008] Task scheduling module, used to assign priorities to each task and assign different task levels to different AGVs;

[0009] The optimal path planning module is connected to the task scheduling module and is based on the tasks assigned to each AGV by improving A * The algorithm is combined with the time window algorithm to realize the initial path planning of each AGV, and the optimal path is determined based on the initial path planning;

[0010] A multi-AGV collaborative operation module, which is connected to the optimal path planning module and includes a cloud processing unit for obtaining information about each AGV and transmitting information to each AGV, an early warning unit for obtaining early warning factors based on the operating information of each AGV and issuing early warnings, and a decision-making unit for making decisions based on the AGV status;

[0011] The user interface module is connected to the task scheduling module, the optimal path planning module, and the multi-AGV collaborative operation module respectively, and is used to detect the operating status information of each AGV in real time, wherein the operating status information includes real-time location, task status, and optimal path planning.

[0012] Furthermore, the optimal path planning module is used to implement the initial path planning of each AGV, including:

[0013] Used for environmental modeling and data preparation, including marking key points: mission start and end points, charging stations, and intersections;

[0014] Used to define AGV parameters, including physical constraints and build task lists;

[0015] Used to initialize the time window;

[0016] Based on A * The algorithm generates a collision-free path for each AGV but may violate the time window constraint;

[0017] It is used to resolve conflicts based on time windows, including reserving time windows for high-priority AGVs, replanning low-priority AGVs, and repeating iterations until the conflict is resolved or the maximum number of iterations is reached.

[0018] Furthermore, the optimal path is determined based on the initial path planning.

[0019] The shortest initial path is determined as the optimal path.

[0020] Furthermore, the cloud processing unit acquires the signal of each AGV at a predetermined period, forms an information set, and distributes the information set to each AGV.

[0021] Furthermore, the early warning unit is used to obtain early warning factors for early warning, including:

[0022] The warning factors used to obtain include obstacle warning factor, collision warning factor, path deviation factor and loop warning factor;

[0023] The weighted sum of the obstacle warning factor, collision warning factor, path deviation factor and loop warning factor is used to obtain an overall warning value;

[0024] The overall warning value is compared with the standard warning threshold to determine whether to issue a warning.

[0025] Furthermore, the early warning unit is used to determine whether the early warning includes:

[0026] If the overall warning value is greater than or equal to the standard warning threshold, it is determined that a warning is required.

[0027] Furthermore, the early warning unit is also used to determine the AGV that triggers the early warning, and transmit the information of the AGV to the optimal path planning module to re-plan the path of the AGV that triggers the early warning.

[0028] Furthermore, it is characterized in that the decision-making unit makes a decision based on the AGV status including:

[0029] Determine the AGV status, including idle state, task execution state, power failure state, and fault state;

[0030] If the task is in execution state, no decision is made;

[0031] If it is in idle state, the corresponding AGV is recorded and the control task scheduling module is used to reassign tasks to the AGV;

[0032] If it is in a power-deficient state, the AGV is controlled to charge;

[0033] If it is in a fault state, the early warning unit will be controlled to issue an early warning.

[0034] Furthermore, it is characterized in that the early warning unit is also used to determine the AGV that needs to be warned, record the corresponding position information and fault code of the AGV, and send the position information and fault code to the user interface module.

[0035] Furthermore, the optimal path planning module is also used to change the selection of the optimal path, including:

[0036] If the AGV load exceeds a predetermined load threshold, the number of turns of the initial path is determined, and the initial path with the least number of turns is determined as the optimal path.

[0037] Compared with the prior art, the present invention has the following advantages: the present invention sets a task scheduling module, an optimal path planning module, a multi-AGV collaborative operation module, and a user interface module, and allocates task priorities through the task scheduling module, and the optimal path planning module is combined with the improved A * The algorithm and time window algorithm plan the optimal path, and the multi-AGV collaborative operation module monitors the AGV status in real time to make dynamic decisions. The user interface module provides real-time status monitoring and early warning information display. In addition, the system sets up an early warning coordination mechanism to effectively reduce the risk of collision and ensure efficient execution of tasks, thereby improving the efficiency and reliability of multi-AGV collaborative operations.

[0038] In particular, the present invention improves A * The algorithm is combined with the time window algorithm to realize the initial path planning of each AGV, and the A * By incorporating environmental obstacle probabilities and an adaptive evaluation function, the algorithm can quickly generate a collision-free initial path, improving the efficiency of path planning. The time window algorithm reserves a time window for high-priority AGVs, allowing low-priority AGVs to replan their routes, repeating the process until the conflict is resolved or the maximum number of iterations is reached. This not only effectively resolves path conflicts but also ensures that tasks are completed within a reasonable timeframe, thereby improving the efficiency and reliability of multi-AGV collaborative operations.

[0039] In particular, the present invention determines the overall warning value from four dimensions: obstacle warning factor, collision warning factor, path deviation factor, and loop warning factor. Through multi-dimensional evaluation, it can fully reflect the operating status of AGV in a complex environment, providing a comprehensive and accurate warning mechanism for multi-AGV collaborative operation. The obstacle warning factor and the collision warning factor can timely detect and avoid potential collision risks. The path deviation factor can help each AGV adjust the path planning in real time when performing a task to ensure that each AGV travels along the optimal path, and the loop warning factor avoids excessive turns or detours. By weighting and summing the obstacle warning factor, collision warning factor, path deviation factor, and loop warning factor to determine the overall warning value, not only can potential collision risks be avoided, but also the efficient execution of tasks can be ensured, thereby improving the efficiency and reliability of multi-AGV collaborative operations.

[0040] In particular, the present invention makes different decisions based on the idle state, task execution state, power failure state and fault state of the AGV, thereby realizing intelligent management of each AGV. When the AGV is in the idle state, the task scheduling module reallocates tasks to the AGV to ensure that resources are fully utilized. When the AGV is in the task execution state, no decision is made at this time to ensure that it travels along the optimal planned path so that the task is successfully completed. When the AGV is in the power failure state, the system will automatically arrange for the power-deficient AGV to be charged to ensure the normal operation of the equipment, thereby avoiding task interruption due to insufficient power. In the fault state, the system can promptly control the early warning unit to issue an early warning and carry out repairs in a timely manner. The state decision-making mechanism based on the four dimensions of AGV can not only avoid waste of resources. It also realizes reasonable and efficient intelligent management of each AGV, thereby improving the efficiency and reliability of multi-AGV collaborative operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a system block diagram of a multi-AGV collaborative operation control system according to an embodiment of the invention;

[0042] Figure 2 A logic determination diagram of whether the early warning unit of an embodiment of the invention has issued an early warning;

[0043] Figure 3 A logic determination diagram for determining whether the AGV of an embodiment of the invention needs to be charged;

[0044] Figure 4 This is a logical decision diagram for optimal path selection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0046] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0048] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0049] See also Figure 1 As shown, it is a system block diagram of a multi-AGV collaborative operation control system according to an embodiment of the invention. The embodiment of the invention provides a multi-AGV collaborative operation control system, including:

[0050] Task scheduling module, used to assign priorities to each task and assign different task levels to different AGVs;

[0051] The optimal path planning module is connected to the task scheduling module and is based on the tasks assigned to each AGV by improving A * The algorithm is combined with the time window algorithm to realize the initial path planning of each AGV, and the optimal path is determined based on the initial path planning;

[0052] A multi-AGV collaborative operation module, which is connected to the optimal path planning module and includes a cloud processing unit for obtaining information about each AGV and transmitting information to each AGV, an early warning unit for obtaining early warning factors based on the operating information of each AGV and issuing early warnings, and a decision-making unit for making decisions based on the AGV status;

[0053] The user interface module is connected to the task scheduling module, the optimal path planning module, and the multi-AGV collaborative operation module respectively, and is used to detect the operating status information of each AGV in real time, wherein the operating status information includes real-time location, task status, and optimal path planning.

[0054] In implementation, the present invention does not limit the form of the user interface module. It can be a mobile phone program or a PC interface. It only needs to enable users to detect the status of each AGV's collaborative operation in real time to facilitate management. This will not be repeated here.

[0055] Specifically, the optimal path planning module is used to implement the initial path planning of each AGV, including:

[0056] Used for environmental modeling and data preparation, including marking key points: mission start and end points, charging stations, and intersections;

[0057] Used to define AGV parameters, including physical constraints and build task lists;

[0058] Used to initialize the time window;

[0059] Based on A * The algorithm generates a collision-free path for each AGV but may violate the time window constraint;

[0060] It is used to resolve conflicts based on time windows, including reserving time windows for high-priority AGVs, replanning low-priority AGVs, and repeating iterations until the conflict is resolved or the maximum number of iterations is reached.

[0061] In practice, the environment modeling and data preparation are performed by constructing a grid map model of the environment in which each AGV is located, and dividing the environment into a traversable area and an obstacle area.

[0062] In practice, the physical constraints used to define AGV parameters include the maximum speed, acceleration, and turning radius of each AGV. The task list construction requires constructing a corresponding task list for each AGV to clearly define the task sequence and priority that it needs to perform.

[0063] In practice, time window initialization involves assigning a time window to each task, defining the earliest start time and the latest finish time of the task, and ensuring that the task is completed within a reasonable time frame. High-priority tasks require stricter time window constraints to ensure they can be completed on time.

[0064] In implementation, the maximum number of iterations is predetermined, where

[0065] Those skilled in the art can conduct experiments in actual operation, observe the system performance under different iteration conditions, repeat the experiments, and determine a reasonable maximum number of iterations.

[0066] Specifically, the optimal path is determined based on the initial path planning.

[0067] The shortest initial path is determined as the optimal path.

[0068] Specifically, the cloud processing unit obtains the signal of each AGV at a predetermined period, forms an information set, and distributes the information set to each AGV.

[0069] In implementation, the information set includes the location, operating status, power, load information, path planning and other information of each AGV, among which the real-time position signal of the AGV is obtained by detecting the tag through the UWB positioning system to achieve high-precision positioning requirements.

[0070] See also Figure 2 As shown, it is a logical determination diagram of whether the early warning unit of the embodiment of the invention issues an early warning. Specifically, the early warning unit is used to obtain early warning factors for early warning, including:

[0071] The warning factors used to obtain include obstacle warning factor, collision warning factor, path deviation factor and loop warning factor;

[0072] The weighted sum of the obstacle warning factor, collision warning factor, path deviation factor and loop warning factor is used to obtain an overall warning value;

[0073] The overall warning value is compared with the standard warning threshold to determine whether to issue a warning.

[0074] In implementation, the standard warning threshold is predetermined, wherein,

[0075] Those skilled in the art can analyze historical data to identify the characteristics of data anomalies, and monitor each AGV data and safety events in real time to determine the standard warning threshold.

[0076] In implementation, the weight of the obstacle warning factor is 0.3, the weight of the collision warning factor is 0.3, the weight of the path deviation factor is 0.25, and the weight of the loop warning factor is 0.15.

[0077] Specifically, the early warning unit is used to determine whether the early warning includes:

[0078] If the overall warning value is greater than or equal to the standard warning threshold, it is determined that a warning is required.

[0079] In practice, the present invention does not limit the warning method, which may be a pop-up window prompt or a sound alarm. It only needs to enable the user to perceive the warning information in time and take corresponding measures quickly. This will not be repeated.

[0080] Specifically, the early warning unit is further used to determine the AGV that triggers the early warning, and transmit the information of the AGV to the optimal path planning module to re-plan the path of the AGV that triggers the early warning.

[0081] Specifically, the decision-making unit makes decisions based on the AGV status including:

[0082] Determine the AGV status, including idle state, task execution state, power failure state, and fault state;

[0083] If the task is in execution state, no decision is made;

[0084] If it is in idle state, the corresponding AGV is recorded and the control task scheduling module is used to reassign tasks to the AGV;

[0085] If it is in a power-deficient state, the AGV is controlled to charge;

[0086] If it is in a fault state, the early warning unit will be controlled to issue an early warning.

[0087] See also Figure 3 As shown in FIG, it is a logic determination diagram of whether the AGV needs to be charged according to an embodiment of the invention. In implementation, the battery level of the AGV is compared with a preset battery threshold to determine whether the AGV is in a power-deficient state:

[0088] If the battery level of the AGV is greater than or equal to the preset battery threshold, it is determined that the AGV does not need to be charged;

[0089] If the battery level of the AGV is less than the preset battery threshold, it is determined that the AGV needs to be charged;

[0090] In an implementation, the preset battery threshold is predetermined, wherein,

[0091] Those skilled in the art can analyze the data in the historical operation data to find out the characteristics of the battery power change law, analyze the battery consumption of different AGVs in different environments and different tasks, and thus determine a reasonable preset battery threshold.

[0092] Specifically, the warning unit is further used to determine the AGV that needs to be warned, record the corresponding position information and fault code of the AGV, and send the position information and fault code to the user interface module.

[0093] In implementation, the present invention does not limit the form of recording the corresponding position information and fault code of the AGV, which can be obtained through UWB or QR code navigation. It only needs to accurately record the current position and fault code of the AGV that triggers the warning, which will not be repeated here.

[0094] See also Figure 4 As shown, it is a logical decision diagram for optimal path selection according to an embodiment of the invention. Specifically, the optimal path planning module is also used to change the selection of the optimal path, including:

[0095] If the AGV load exceeds a predetermined load threshold, the number of turns of the initial path is determined, and the initial path with the least number of turns is determined as the optimal path.

[0096] In implementation, the predetermined load threshold is predetermined, wherein,

[0097] Those skilled in the art can analyze historical operating data to find out the failure rate under different load conditions, analyze the battery consumption and task completion rate of AGV under different loads, and thus determine a reasonable predetermined load threshold.

[0098] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A multi-AGV collaborative operation control system, characterized in that: include: Task scheduling module, used to assign priorities to each task and assign different task levels to different AGVs; The optimal path planning module is connected to the task scheduling module and is based on the tasks assigned to each AGV by improving A * The algorithm is combined with the time window algorithm to realize the initial path planning of each AGV, and the optimal path is determined based on the initial path planning; A multi-AGV collaborative operation module, which is connected to the optimal path planning module and includes a cloud processing unit for obtaining information about each AGV and transmitting information to each AGV, an early warning unit for obtaining early warning factors based on the operating information of each AGV and issuing early warnings, and a decision-making unit for making decisions based on the AGV status; The user interface module is connected to the task scheduling module, the optimal path planning module, and the multi-AGV collaborative operation module respectively, and is used to detect the operating status information of each AGV in real time, wherein the operating status information includes real-time location, task status, and optimal path planning.

2. The multi-AGV collaborative operation control system according to claim 1, characterized in that: The optimal path planning module is used to implement the initial path planning of each AGV, including: Used for environmental modeling and data preparation, including marking key points: mission start and end points, charging stations, and intersections; Used to define AGV parameters, including physical constraints and build task lists; Used to initialize the time window; Based on A * The algorithm generates a collision-free path for each AGV but may violate the time window constraint; It is used to resolve conflicts based on time windows, including reserving time windows for high-priority AGVs, replanning low-priority AGVs, and repeating iterations until the conflict is resolved or the maximum number of iterations is reached.

3. The multi-AGV collaborative operation control system according to claim 1, characterized in that: Determine the optimal path based on the initial path planning, The shortest initial path is determined as the optimal path.

4. The multi-AGV collaborative operation control system according to claim 1, characterized in that: The cloud processing unit acquires the signal of each AGV at a predetermined period, forms an information set, and distributes the information set to each AGV.

5. The multi-AGV collaborative operation control system according to claim 1, characterized in that: The early warning unit is used to obtain early warning factors for early warning, including: The warning factors used to obtain include obstacle warning factor, collision warning factor, path deviation factor and loop warning factor; The weighted sum of the obstacle warning factor, collision warning factor, path deviation factor and loop warning factor is used to obtain an overall warning value; The overall warning value is compared with the standard warning threshold to determine whether to issue a warning.

6. The multi-AGV collaborative operation control system according to claim 5, characterized in that: The early warning unit is used to determine whether the early warning includes: If the overall warning value is greater than or equal to the standard warning threshold, it is determined that a warning is required.

7. The multi-AGV collaborative operation control system according to claim 1, characterized in that: The early warning unit is further used to determine the AGV that triggers the early warning, and transmit the information of the AGV to the optimal path planning module to re-plan the path of the AGV that triggers the early warning.

8. The multi-AGV collaborative operation control system according to claim 1, characterized in that: The decision-making unit makes decisions based on the AGV status, including: Determine the AGV status, including idle state, task execution state, power failure state, and fault state; If the task is in execution state, no decision is made; If it is in idle state, the corresponding AGV is recorded and the control task scheduling module is used to reassign tasks to the AGV; If it is in a power-deficient state, the AGV is controlled to charge; If it is in a fault state, the early warning unit will be controlled to issue an early warning.

9. The multi-AGV collaborative operation control system according to claim 1, characterized in that: The warning unit is further used to determine the AGV that needs to be warned, record the corresponding position information and fault code of the AGV, and send the position information and fault code to the user interface module.

10. The multi-AGV collaborative operation control system according to claim 3, characterized in that: The optimal path planning module is also used to change the selection of the optimal path, including: If the AGV load exceeds a predetermined load threshold, the number of turns of the initial path is determined, and the initial path with the least number of turns is determined as the optimal path.

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