A robot cluster scheduling system and method thereof

Abstract

The application provides a kind of robot cluster scheduling system and method thereof, it is related to the field of scheduling system, a kind of robot cluster scheduling system includes user layer, user layer includes the service object of scheduling system, service object is AGV transport robot, further include monitoring module, for monitoring the performance parameter of AGV transport robot, and data are recorded and visualized display, communication module, for the mutual communication cooperation of AGV transport robot in cluster through wireless communication network, state acquisition module, for the task load of AGV transport robot, central controller, central controller includes route planning module and task allocation module, the present application is according to optimal path strategy, the characteristics and current work load of AGV transport robot are allocated task, wherein the task is decomposed into a series of subtasks, and the task order is recombined according to the ability and current position of different AGV transport robots, to ensure the smoothness and continuity of task execution.

Description

Technical Field

[0001] This invention relates to the field of scheduling systems, and more particularly to a robot swarm scheduling system and method. Background Technology

[0002] With the development of communication and electronic technologies, robots are being used more and more widely in the industrial field. In the application of robots, how to schedule them is crucial.

[0003] A current robot scheduling system includes a robot control system and an intermediate control system. The robot control system obtains robot management information from the intermediate control system, generates task instructions based on the robot management information, and sends the task instructions to the associated guide vehicle or external device. The intermediate control system includes a first interface layer, an intermediate business processing layer, and an access layer. The first interface layer includes a business interface for obtaining task packages from the business management system. The intermediate business processing layer generates robot management information based on the task packages obtained from the business management system. The access layer includes a control interface and is connected to the robot control system through the control interface.

[0004] However, when the aforementioned robot scheduling system sends task instructions to the guide vehicle or external device associated with the task instructions, it does not perform optimal task allocation based on the parameters of the task instructions, nor does it allocate the optimal driving route for the robot. This results in low execution efficiency of the scheduled tasks.

[0005] Therefore, it is necessary to provide a new robot swarm scheduling system and method to solve the above-mentioned technical problems. Summary of the Invention

[0006] To address the problem that existing methods do not optimally allocate tasks based on the parameters of the task instructions or assign optimal travel routes to robots when sending task instructions to the associated guide vehicle or external device, resulting in low task execution efficiency after scheduling, this invention provides a robot cluster scheduling system and method.

[0007] The robot cluster scheduling system provided by this invention includes a user layer, which includes the service objects of the scheduling system, namely AGV transport robots, and also includes a monitoring module for monitoring the performance parameters of the AGV transport robots, including position, speed, and power data, and recording and visualizing the data.

[0008] The communication module is used for AGV transport robots in the cluster to communicate and cooperate with each other via a wireless communication network;

[0009] The status acquisition module is used to acquire the task load of the AGV transport robot, which includes task progress and task quantity.

[0010] The central controller includes a route planning module and a task allocation module. The route planning module is used to formulate the movement path of the AGV transport robot, adjust it according to the path deadlock conflict in the movement path of the AGV transport robot, and generate the optimal path strategy for the movement of the AGV transport robot.

[0011] The task allocation module is used to allocate tasks based on the optimal path strategy, the characteristics of the AGV transport robot, and the current workload. The task is decomposed into a series of sub-tasks and the task order is recombined according to the capabilities of different AGV transport robots and their current positions.

[0012] The optimal path strategy includes obtaining the movement direction of surrounding AGV transport robots based on the movement path of the AGV transport robot, determining the conflict type by the number and location information of the AGV transport robots encountered, calculating the energy consumption value of the AGV at the conflict location based on the path already traveled by the AGV transport robot, and setting the priority level of conflict avoidance for each AGV transport robot based on the energy consumption value and operation status of the AGV transport robot.

[0013] Furthermore, the task allocation module establishes a collaborative information table representing the AGV transport robot cluster. The collaborative information table records the task start point, task point, task end point, number of steps at the task start point, distance of steps at the task point, distance of steps at the task end point, AGV transport robot number, etc., for task decomposition and invocation.

[0014] Another aspect of the present invention provides a robot cluster scheduling method applicable to the above-mentioned robot cluster scheduling system, including the following scheduling steps: S1, the AGV transport robots in the cluster are connected to each other through a wireless communication network via a communication module. The AGV transport robots can also share location, status and task information in real time through wireless networks or other forms of communication, so that the scheduling center can make immediate decisions.

[0015] S2. Collect the performance parameters and task load parameters of the AGV transport robot collected by the monitoring module and the status acquisition module, read the data, record it and visualize it.

[0016] S3. When the number of AGV transport robots is greater than 1, the central controller determines the path deadlock conflict between any two AGV transport robots, adjusts the path deadlock conflict between any two AGV transport robots, and obtains the optimal path strategy for the movement of AGV transport robots.

[0017] S4. When any AGV transport robot completes its task, the task of the AGV transport robot with the largest task load is decomposed into a sub-task according to the task allocation module. The sub-task is then taken over by the AGV transport robot that has completed its task. A new path is established for the two AGV transport robots after the planned task is reallocated according to the route planning module. Whenever the AGV transport robot completes its task, step S4 is executed again. If all tasks have been completed, the scheduling of the AGV transport robots ends.

[0018] It should be noted that the above maximum task load refers to the slowest task progress, the heaviest task volume, or the longest task path.

[0019] Furthermore, the specific steps for determining path deadlock conflicts between any two AGV transport robots are as follows:

[0020] S1. Constructing the path model Where ||P i || The path for the AGV transport robot to complete the assigned task;

[0021] S2. Input any two AGV transport robot paths, check if there are any identical road segments on the two paths, and at the same time, determine whether the position points of the AGV transport robots at the next moment are the same. If they are the same, there is a path deadlock conflict. If they are different, it means that there is no path deadlock conflict at the next moment, and the check is repeated.

[0022] Furthermore, the specific steps for handling path deadlock conflicts are as follows: Analyze the cause of the path deadlock conflict. If the paths of two AGV transport robots are in conflict at the current moment, it is necessary to replan the path for one of the AGV transport robots. If the paths of two AGV transport robots are in conflict at the current moment, a random AGV transport robot is selected to wait at the intersection. After the other AGV transport robot passes, the first AGV transport robot can then pass.

[0023] Furthermore, the energy consumption of the AGV transport robot is the sum of the power loss during the AGV transport robot's movement, the power loss of the status acquisition module, and the power loss due to the internal resistance of the AGV transport robot.

[0024] Compared with related technologies, the robot swarm scheduling system and method provided by this invention have the following advantages:

[0025] Beneficial effects:

[0026] 1. In this invention, when the number of AGV transport robots is greater than 1, the central controller determines the path deadlock conflict between any two AGV transport robots, adjusts the path deadlock conflict between any two AGV transport robots, obtains the optimal path strategy for the movement of AGV transport robots, and can plan the optimal path to avoid path deadlock conflict and avoid conflict, so that each AGV transport robot can move to the destination quickly and efficiently in a limited space.

[0027] 2. In this invention, the task allocation module allocates tasks based on the optimal path strategy, the characteristics of the AGV transport robot, and the current workload. After the AGV transport robot completes its task, the collaboration information table is retrieved to reassign tasks. The currently executing task is decomposed into a series of sub-tasks, and the task order is recombined according to the capabilities of different AGV transport robots and their current positions to ensure the smoothness and continuity of task execution. Attached Figure Description

[0028] Figure 1 This is a system block diagram of the robot cluster scheduling system provided by the present invention;

[0029] Figure 2 This is a flowchart of the robot cluster scheduling method provided by the present invention;

[0030] Figure 3 A flowchart for determining path deadlock conflicts provided by the present invention;

[0031] Figure 4 This is a flowchart illustrating the path deadlock conflict handling provided by the present invention. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] Please refer to the following: Figure 1 , Figure 2 , Figure 3 as well as Figure 4 ,in, Figure 1 This is a system block diagram of the robot cluster scheduling system provided by the present invention; Figure 2 This is a flowchart of the robot cluster scheduling method provided by the present invention; Figure 3 A flowchart for determining path deadlock conflicts provided by the present invention; Figure 4 This is a flowchart illustrating the path deadlock conflict handling provided by the present invention.

[0034] In the specific implementation process, such as Figures 1-4As shown, the robot cluster scheduling system provided by this invention includes a user layer, which includes the service objects of the scheduling system, namely AGV transport robots, and also includes a monitoring module for monitoring the performance parameters of the AGV transport robots. The performance parameters of the AGV transport robots include position, speed, and power data, and the data is recorded and visualized in the form of tables, graphs, or curves.

[0035] It should be noted that all AGV transport robots travel at a constant speed of 5m / s, so there is no overtaking conflict.

[0036] The communication module is used for AGV transport robots in the cluster to communicate and cooperate with each other through a wireless communication network. AGV transport robots can also share location, status and task information in real time through wireless networks or other forms of communication so that the scheduling center can make immediate decisions.

[0037] The status acquisition module is used to collect the task load of the AGV transport robot, which includes task progress and task volume.

[0038] The central controller includes a route planning module and a task allocation module. The route planning module is used to formulate the movement path of the AGV transport robot, adjust it according to the path deadlock conflict in the movement path of the AGV transport robot, and generate the optimal path strategy for the movement of the AGV transport robot.

[0039] The task allocation module is used to allocate tasks based on the optimal path strategy, the characteristics of the AGV transport robot, and the current workload. The task is decomposed into a series of sub-tasks, and the task order is recombined according to the capabilities of different AGV transport robots and their current positions to ensure the smoothness and continuity of task execution.

[0040] It should be noted that the optimal path strategy includes obtaining the movement direction of surrounding AGV transport robots based on the movement path of the AGV transport robot, determining the conflict type through the number and location information of the AGV transport robots that meet, calculating the energy consumption value of the AGV at the conflict location based on the path already traveled by the AGV transport robot, and setting the priority level of conflict avoidance for each AGV transport robot based on the energy consumption value and operation status of the AGV transport robot.

[0041] Specifically, the energy consumption of an AGV transport robot is the sum of the power loss during its movement, the power loss of the status acquisition module, and the power loss due to the internal resistance of the AGV transport robot, expressed as:

[0042] E A =E e +E m =∫Pe dt+∫P m dt

[0043] Among them, E e E represents the power consumption of the status acquisition module and the power consumption due to the internal resistance of the AGV transport robot during operation. m Power consumption of AGV transport robots during operation;

[0044] If, at a certain moment, an AGV transport robot performs a transport task, and the AGV transport robot, under heavy load and slow speed conditions, travels at a constant speed of 5 m / s, assuming the ground slope and surface roughness remain constant, the energy loss of the AGV transport robot during the transport process is:

[0045] W = F t *Δ t =(F f +F a +F g )*L

[0046] Where W is the total power of the AGV transport robot during its movement, and F t F is the total traction force during the motion. f F a and F g These represent the frictional force, air resistance, and the horizontal component of gravity on the inclined plane during the motion, respectively, with L being the distance traveled.

[0047] Friction F generated during the motion f and the horizontal component F of the gravity inclined plane g They are respectively:

[0048] F g =mgsinθ

[0049] F f =fmgcosθ

[0050] Where m is the total mass of the AGV transport robot including its own weight and the mass of the goods it carries, g is the acceleration due to gravity, f is the friction coefficient of the transport section, and θ is the slope of the transport section. Since the automated guided vehicle travels at a constant speed during heavy loading, air resistance is negligible. The power lost by the AGV transport robot during its movement is:

[0051] W=mgsinθ*L+fmgcosθ*L

[0052] It should be noted that the task allocation module establishes a collaborative information table representing the AGV transport robot cluster. The collaborative information table records the task start point, task point, task end point, number of steps at the task start point, distance of steps at the task point, distance of steps at the task end point, AGV transport robot number, etc.

[0053] During task allocation, the task allocation module encodes the collaboration information table into grids numbered 1-100, with the first 40 grids representing the AGV transport robot numbers. These grids are then arranged in groups of 10, representing the task start point, task point, task end point, number of steps from the task start point, distance from the task point, and distance from the task end point. The task start point is randomly selected based on AGV transport robot number 1 and AGV transport robot number 2. The task allocation module then assigns path tasks to AGV transport robot number 1 and AGV transport robot number 2 based on the task start point and task end point, respectively. After AGV transport robot number 1 completes its task, the collaboration information table is called back to randomly select a new task start point, and this process is repeated.

[0054] In addition, after AGV transport robot No. 2 completes its task, AGV transport robot No. 1 will re-decompose the task point, separate a new task point, and insert it into the empty grid position in the collaborative information table, and AGV transport robot No. 2 will select the task point.

[0055] Example 1

[0056] In specific implementation, the robot cluster scheduling method provided by the present invention is applicable to the above-mentioned robot cluster scheduling system, and includes the following scheduling steps: S1, the AGV transport robots in the cluster are connected to each other through a wireless communication network via a communication module;

[0057] S2. Collect the performance parameters and task load parameters of the AGV transport robot collected by the monitoring module and the status acquisition module, and record and visualize the data;

[0058] For example, when the performance parameters of the AGV transport robot are collected, the position, speed, and power data in the performance parameters are displayed on the screen in tabular form.

[0059] S3. When the number of AGV transport robots is 2, the central controller determines the path deadlock conflict between the two AGV transport robots, adjusts the path deadlock conflict between the two AGV transport robots, and obtains the optimal path strategy for the movement of the AGV transport robots.

[0060] S4. When any AGV transport robot completes its task, the remaining AGV transport robot's task is decomposed into a sub-task according to the task allocation module, and the sub-task is taken over by the AGV transport robot that has completed its task.

[0061] S5. Based on the route planning module, establish new paths for the two AGV transport robots after the planning tasks have been reassigned;

[0062] S6. Once the task is completed, the scheduling of the AGV transport robot ends.

[0063] In a preferred embodiment, the specific steps for determining path deadlock conflicts between any two AGV transport robots are as follows:

[0064] S1. Constructing the path model Where ||P i || The path for the AGV transport robot to complete the assigned task;

[0065] It should be noted that this path is based on the task start point - task end point path in the above collaboration information table;

[0066] S2. Input any two AGV transport robot paths, check if there are any identical road segments on the two paths, and at the same time, determine whether the position points of the AGV transport robots at the next moment are the same. If they are the same, there is a path deadlock conflict. If they are different, it means that there is no path deadlock conflict at the next moment, and the check is repeated.

[0067] In the visualization, the movement trajectory of the AGV transport robot is represented by planar coordinates (X,Y), that is, the coordinate set {x1,x2,x3…x} along the X-axis. i The set of coordinates along the Y-axis {y1, y2, y3…y} i};

[0068] It should be noted that the method for checking whether two paths have the same road segment is as follows:

[0069]

[0070] Among them, C1(p i ) and C1(p i ,p j The values ​​r represent whether the AGV transport robot's running path collides with static obstacles or other AGV transport robots. i (x i ,y i ) and θ j (x j ,y j ) are robots r i and shelf θ j The coordinates of the location If the paths planned for the AGV transport robot intersect in space, an output result of 1 indicates a path conflict. An output result of 0 indicates no intersection, meaning the AGV transport robot is operating normally.

[0071] When the current coordinates of the AGV transport robot are (x1, y0) and it moves along the positive X-axis, its position at the next moment will be (x2, y0). If the current coordinates of another AGV transport robot are (x2, y1) and it moves along the reverse Y-axis, its position at the next moment will be (x2, y0). This means that there is a path conflict when the robot meets at a corner.

[0072] If the current coordinates of the AGV transport robot are (x1, y0), and it moves along the positive X-axis, its position at the next moment will be (x2, y0). If the current coordinates of another AGV transport robot are (x3, y0), and it moves along the negative X-axis, its position at the next moment will be (x2, y0). This means there is a conflict of opposing paths.

[0073] In a preferred embodiment, the specific steps for handling path deadlock conflicts are as follows: Analyze the cause of the path deadlock conflict. If the paths of two AGV transport robots are in conflict at the current moment, it is necessary to replan the path for one of the AGV transport robots. If the paths of two AGV transport robots are in conflict at a corner, randomly select one AGV transport robot to wait at the intersection. After the other AGV transport robot passes, the first AGV transport robot can then pass. This can plan the optimal path to avoid path deadlock conflicts and prevent conflicts, so that each AGV transport robot can move to its destination quickly and efficiently within a limited space.

[0074] Specifically, if two AGV transport robots are robot A and robot B, when robot A and robot B collide in the opposite direction, that is, when robot A and robot B are on the same line, when robot A and robot B meet at a corner, when robot A and robot B are moving at a constant speed, and at the same point on their next path, the central controller will randomly select one AGV transport robot to wait at the intersection. If robot A waits, it will wait for robot B to pass before robot A passes.

[0075] Example 2

[0076] In the specific implementation process, the same structure as detailed in Embodiment 1 is followed. The difference from Embodiment 1 is that the following scheduling steps are included: S1, the AGV transport robots in the cluster are connected to each other through a wireless communication network via a communication module;

[0077] S2. Collect the performance parameters and task load parameters of the AGV transport robot collected by the monitoring module and the status acquisition module, and record and visualize the data;

[0078] S3. When the number of AGV transport robots is 3, the central controller determines the path deadlock conflict between any two AGV transport robots, adjusts the path deadlock conflict between any two AGV transport robots, and obtains the optimal path strategy for the movement of AGV transport robots.

[0079] S4. After any two AGV transport robots complete their tasks, the remaining AGV transport robots' tasks are broken down into two sub-tasks according to the task allocation module, and the two AGV transport robots that have completed their tasks take over the sub-tasks.

[0080] S5. Based on the route planning module, establish new paths for the three AGV transport robots after the planned tasks have been reassigned. Once the task is completed, the scheduling of the AGV transport robots ends.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, 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 ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0083] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A robot swarm scheduling system, comprising a user layer, wherein the user layer includes service objects of the scheduling system, the service objects being AGV transport robots, characterized in that, It also includes a monitoring module, which monitors the performance parameters of the AGV transport robot and records and visualizes the data; The communication module is used for AGV transport robots in the cluster to communicate and cooperate with each other via a wireless communication network; The status acquisition module is used to acquire the task load of the AGV transport robot. The central controller includes a route planning module and a task allocation module. The route planning module is used to formulate the movement path of the AGV transport robot, adjust it according to the path deadlock conflict in the movement path of the AGV transport robot, and generate the optimal path strategy for the movement of the AGV transport robot. The task allocation module is used to allocate tasks based on the optimal path strategy, the characteristics of the AGV transport robot, and the current workload. The task is decomposed into a series of sub-tasks and the task order is recombined. The task allocation module establishes a collaborative information table representing the AGV transport robot cluster. The collaborative information table records the task start point, task point, task end point, number of steps at the task start point, distance of steps at the task point, distance of steps at the task end point, and AGV transport robot number. The optimal path strategy includes obtaining the movement direction of surrounding AGV transport robots based on the movement path of the AGV transport robot, determining the conflict type through the number and orientation information of the AGV transport robots encountered, calculating the energy consumption value of the AGV at the conflict location based on the path already traveled by the AGV transport robot, and setting the priority level of conflict avoidance for each AGV transport robot based on the energy consumption value and operating status of the AGV transport robot. The energy consumption value of the AGV transport robot is the sum of the power loss during the movement of the AGV transport robot, the power loss of the status acquisition module, and the power loss of the internal resistance of the AGV transport robot.

2. The robot swarm scheduling system according to claim 1, characterized in that, The task allocation module encodes the collaboration information table into grids numbered 1-100, with the first 40 grids representing the AGV transport robot numbers. These grids are then arranged in groups of 10, representing the task start point, task point, task end point, number of steps from the task start point, distance from the task point, and distance from the task end point. The task start point is randomly selected based on AGV transport robot number 1 and AGV transport robot number 2. The task allocation module then assigns path tasks to AGV transport robot number 1 and AGV transport robot number 2 based on the task start point and task end point, respectively. After AGV transport robot number 1 completes its task, the collaboration information table is called back to randomly select a new task start point, and this process is repeated.

3. The robot swarm scheduling system according to claim 2, characterized in that, The performance parameters of the AGV transport robot include position, speed, and battery power data.

4. The robot swarm scheduling system according to claim 3, characterized in that, The task load includes task progress and task volume.

5. A robot swarm scheduling method, applicable to the robot swarm scheduling system according to any one of claims 1-4, characterized in that, The scheduling steps include the following: S1, Establishing a communication connection; S2. Read AGV transport robot data; S3. Determine any path deadlock conflict, adjust the path deadlock conflict, and obtain the optimal path strategy for the AGV transport robot to move. S4. Reassign the AGV transport robot's path: Based on the task allocation module, the task of the AGV transport robot with the largest task load is decomposed into a series of sub-tasks. The scheduling of the AGV transport robot ends when the task is completed.

6. The robot swarm scheduling method according to claim 5, characterized in that, In reading AGV transport robot data, the AGV transport robot's motion trajectory is represented by planar coordinates (X,Y), that is, the coordinate set {x1,x2,x3…x} along the X-axis. i The set of coordinates along the Y-axis {y1, y2, y3…y} i } 7. The robot swarm scheduling method according to claim 6, characterized in that, The specific steps to determine path deadlock conflicts between any two AGV transport robots are as follows: S1. Constructing the path model Where ||P i ||The path for the AGV transport robot to complete the assigned task; S2. Determine if the next position of the AGV transport robot is the same based on its position: When the current coordinate of the AGV transport robot is (x1, y0) and it moves along the positive X-axis, the next position of the AGV transport robot is (x2, y0). If the current coordinate of another AGV transport robot is (x2, y1) and it moves along the negative Y-axis, the next position of the AGV transport robot is (x2, y0), which means there is a path deadlock conflict.

8. The robot swarm scheduling method according to claim 7, characterized in that, The specific steps for handling path deadlock conflicts are as follows: Confirm that the path deadlock conflict is a head-on conflict or a corner encounter conflict: When adjusting a head-on conflict, it is necessary to replan the path for one of the AGV transport robots. When adjusting a corner encounter conflict, randomly select an AGV transport robot to wait at the intersection. After the other AGV transport robot passes, the first AGV transport robot can then pass.

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