Robot cluster management system and method

Abstract

The invention relates to the technical field of robot management, and discloses a robot cluster management system and method.The robot cluster management system comprises a state monitoring module, a task distribution module and a conflict management module.The robot cluster management system calculates a cost function by collecting robot positions, capability parameters and task information, accurately matches tasks with robots and completes the tasks at the minimum cost, and the task cluster management efficiency is improved. The cluster execution efficiency is improved, resources are fully utilized, and complex scenes are adapted; the battery capacity of the robot, the operation state of mechanical parts and the task progress are collected in real time and compared with threshold values, the conditions of insufficient capacity, mechanical faults or task abnormity can be handled in time, stable operation of the robot is guaranteed, delay and loss are reduced, and reliability and continuous operation capacity are enhanced; parameters such as the position and the speed of the robot are obtained to judge conflicts, the sequence is rearranged according to task priorities, collision is avoided, the safety and the orderliness of clusters in a complex environment are guaranteed, the tasks are ensured to be smoothly propelled, and equipment damage and task failures are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of robot management, and in particular to a robot cluster management system and method. Background Art

[0002] With the continuous development of science and technology, robots are increasingly used in various fields, such as industrial production, logistics distribution, rescue operations, etc. In these complex application scenarios, multiple robots are often required to work together to complete a series of tasks, thus forming a robot cluster.

[0003] However, effective management of robot swarms faces many challenges. In actual operation, the robot's own state will constantly change, such as battery consumption, possible failure of mechanical parts, and changes in task execution progress. These factors may affect whether the robot can complete the task continuously and efficiently. If the robot's state is not monitored and adjusted in time, it may lead to problems such as task interruption, low efficiency, and even robot damage.

[0004] At the same time, in a multi-task environment, how to reasonably assign tasks to each robot is a key issue. Different robots have different positions and capability parameters, and the tasks to be assigned also have different requirements, including task location, type, and priority. Unreasonable task allocation will result in waste of resources, prolonged task execution time, and other adverse consequences.

[0005] In addition, robots may affect each other in the motion space during the execution of tasks, especially when they perform tasks in the same area at the same time, there is a risk of collision or interference. Since the robots have different movement speeds, physical sizes, and environmental conditions, accurately determining whether there are conflicts between them and effectively resolving these conflicts is crucial to ensure the safety and efficient operation of the entire cluster.

[0006] Therefore, in order to improve the working efficiency of robot clusters and ensure their safe and stable operation, an effective robot cluster management method and system are urgently needed to solve the above problems. Summary of the invention

[0007] The purpose of the present invention is to provide a robot cluster management system and method, which solves the technical problems raised in the background technology.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A robot cluster management method comprises the following steps:

[0010] Step 1: Status monitoring:

[0011] Collect the robot's own state data in real time, and compare the state data with the corresponding preset state threshold data. According to the comparison result, determine whether the robot's current task execution state needs to be adjusted;

[0012] Step 2: Task allocation:

[0013] Collect the current position, capability parameters and task information of each robot to be assigned, then calculate the cost function of each robot to complete each task, and then select the robot with the smallest cost function value for assignment;

[0014] Step 3: Conflict Management:

[0015] Get the current position of the robot and the corresponding time node, and get the velocity components on the x and y axes. Then, determine whether there is a conflict between the robots based on the obtained results, and rearrange the task order of the robots with conflicts according to their task priorities.

[0016] As a further solution of the present invention: the task allocation method is as follows:

[0017] StepA1, collect the current position, capability parameters and task information of each robot to be assigned;

[0018] in:

[0019] The robot's position information is defined as (x i ,y i ), which is obtained through its built-in positioning system;

[0020] Where i=1, 2, ...n, i represents the number of robots, and n represents the number of robots;

[0021] The robot's capability parameter is defined as C i , is a vector containing multiple sub-parameters, including load capacity, specific task efficiency, etc., which are marked as C i，1 , C i，2 , ...C i，v , where v represents the number of all sub-parameter vectors in the capability parameter;

[0022] The task information of the task to be assigned is defined as T j , which is also a vector containing multiple sub-parameters, including the task location, and is labeled as (x T，j ,y T，j ), which also includes task type, task priority, etc., and is marked as T j，1 、T j，2 ,……T j，v, where v represents the number of all sub-parameter vectors in the task information except the task location, j = 1, 2, ... m, j represents the number of tasks to be assigned, and m represents the number of tasks to be assigned;

[0023] StepA2, through: Calculate the cost function for each robot to complete each task :

[0024] The cost function of a task is a mathematical expression used to measure the "cost" that each robot needs to pay to complete each task. is the distance between the robot and the task location, and its formula is: ;

[0025] Indicates the matching degree between the robot capability and the task requirement, which is obtained by multiplying the corresponding elements of the capability parameter vector and the task information vector and then summing them up;

[0026] The specific method is as follows:

[0027] Where k = 1, 2, ..., v;

[0028] StepA3. For each task, select The robot with the smallest value is assigned.

[0029] As a further solution of the present invention: the state monitoring method is as follows:

[0030] Step B1. Collect the robot's own status data in real time. The status data includes battery power, operating status of mechanical parts, and progress of the current task, and mark them as B in turn. i 、M i and P ij ;

[0031] The operating state includes a normal state and a fault state, and the operating state of the mechanical component is collected by assigning a value of 1 and a value of 0;

[0032] Step B2, compare the status data of different attributes with their corresponding preset status threshold data:

[0033] The state threshold data includes the minimum power value BY corresponding to the battery power and the execution progress ratio threshold PY;

[0034] If the battery power is lower than the minimum power value BY, the charging task allocation signal is triggered, and the robot is then assigned to the nearest charging station;

[0035] If the running status of the mechanical component is 0, the robot is marked as faulty, and its current task is suspended and reallocated to other robots;

[0036] If the progress of the current task is greater than the execution progress ratio threshold PY, the task execution status is re-evaluated and adjusted, including reallocating tasks or adjusting path planning;

[0037] Among them, when an object moves in two-dimensional or three-dimensional space, the velocity component refers to the size of each component vector obtained by decomposing the velocity vector along the direction of the coordinate axis.

[0038] As a further solution of the present invention: the conflict management method is as follows:

[0039] StepC1, obtain the current position of the robot and its corresponding time node;

[0040] Then, the current position of the robot is marked as (x i，t0 ,y i，t0 ), then obtain the velocity components of the robot in the x-axis and y-axis directions and mark them as H ix and H iy ;

[0041] Among them, t0 is the time node corresponding to the current position of the robot, and t0 is the time node specified by the next time node of the time node corresponding to the current position of the robot:

[0042] Step C2: When the robot's speed is constant during movement, the formula is: Calculate the position of the robot at the specified time node t1 ;

[0043] StepC3, then pass: Calculate the distance between two different robots ;

[0044] Where, f=1, 2, ... n-1, and in the formula, i≠if;

[0045] Step C4: The distance between two different robots Compare with the preset safety distance:

[0046] when When the distance is less than or equal to the preset safety distance, it is determined that there is a conflict between robot i and robot if;

[0047] when When it is greater than the preset safety distance, it is determined that there is no conflict between robot i and robot if;

[0048] Among them, the safety distance is pre-set according to factors corresponding to the robot's physical size, movement speed, and environmental conditions.

[0049] As a further solution of the present invention: in Step C2, when the robot has acceleration during the motion process, the formula is: Calculate the position of the robot at the specified time node t1 ;

[0050] In the formula, and are the acceleration components of the robot in the directions of the axes and .

[0051] As a further solution of the present invention: in Step C4, there is a conflict between robot i and robot if, and then the task order of the robots is rearranged according to the task priorities corresponding to the two different robots. Specifically, if the task priority of robot i is higher than the task priority of robot if, robot if is stopped for TS seconds, and then robot i is allowed to complete the task first, and then robot if is allowed to continue to perform the task after TS seconds;

[0052] Among them, TS is a preset value, which is set according to the movement speed of robot if, that is, after TS seconds, the speed of robot i and robot if Greater than the preset safety distance.

[0053] A robot cluster management system, the system is used to execute a robot cluster management method, comprising:

[0054] The state monitoring module is used to collect the robot's own state data in real time, and compare the state data with the corresponding preset state threshold data, and determine whether the robot's current task execution state needs to be adjusted based on the comparison result;

[0055] The task assignment module is used to collect the current position, capability parameters and task information of each robot, calculate the cost function of each robot to complete each task, and then select the robot with the smallest cost function value for assignment;

[0056] The conflict management module is used to obtain the current position of the robot and the corresponding time node, and obtain the velocity components on the x and y axes. It then determines whether there is a conflict between the robots based on the obtained results, and rearranges the task order of the robots in conflict according to their task priorities.

[0057] Beneficial effects of the present invention:

[0058] Efficient task allocation: By collecting the robot's location, capability parameters, and information about the task to be assigned, and calculating the cost function to select the most suitable robot to perform the task, it is possible to achieve an accurate match between the task and the robot's capability, so that each task can be completed at the lowest cost, thereby improving the task execution efficiency of the entire cluster. This precise allocation method helps to make full use of robot resources and avoid resource waste. Whether it is a complex and diverse task scenario or a large number of robots involved, it can effectively coordinate and ensure the scientific and reasonable nature of task allocation.

[0059] Accurate status monitoring: Real-time collection of the robot's own status data such as battery power, mechanical component operation status, and task execution progress, and comparison with the corresponding thresholds. This allows the system to promptly detect situations such as low robot power, mechanical failure, or abnormal task execution. For example, when the battery power is low, the charging task is automatically assigned, when there is a mechanical failure, the task is suspended and reallocated, and when the execution progress is abnormal, the task is re-evaluated and adjusted, ensuring the stable operation of the robot, reducing task delays and losses caused by emergencies, and improving the reliability and continuous operation capability of the entire robot cluster.

[0060] Effective conflict management: Obtain the robot's position, velocity component, and related parameters under acceleration, accurately calculate the robot's position at different time nodes, and then determine whether there is a conflict between robots. When a conflict occurs, rearrange the task order according to the task priority, and avoid collisions and other conflicts between robots by reasonably pausing the operation of low-priority robots, ensuring the safety and orderliness of the robot cluster in a complex motion environment, maintaining the normal operation process of the entire cluster, ensuring the smooth progress of each task, and reducing equipment damage and task failures that may be caused by conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described below in conjunction with the accompanying drawings.

[0062] Figure 1 It is a system block diagram of a robot cluster management system of the present invention.

[0063] Figure 2 It is a flow chart of a robot cluster management method of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example 1: Please refer to Figure 1 and Figure 2 As shown, the present invention is a robot cluster management system, comprising:

[0066] The task assignment module is used to collect the current position, capability parameters and task information of each robot, calculate the cost function of each robot to complete each task, and then select the robot with the smallest cost function value for assignment;

[0067] The task allocation module works as follows:

[0068] StepA1, collect the current position, capability parameters and task information of each robot to be assigned;

[0069] in:

[0070] The robot's position information is defined as (x i ,y i ), which is obtained through its built-in positioning system;

[0071] Where i=1, 2, ...n, i represents the number of robots, and n represents the number of robots;

[0072] The robot's capability parameter is defined as C i , is a vector containing multiple sub-parameters, including but not limited to load capacity, specific task efficiency, etc., and they are marked as C i，1 , C i，2 , ...C i，v , where v represents the number of all sub-parameter vectors in the capability parameter;

[0073] The task information of the task to be assigned is defined as T j , which is also a vector containing multiple sub-parameters, including the task location, and is labeled as (x T，j ,y T，j ), which also includes but is not limited to task type, task priority, etc., and are marked as T j，1 、T j，2 ,……T j，v , where v represents the number of all sub-parameter vectors in the task information except the task location, j = 1, 2, ... m, j represents the number of tasks to be assigned, and m represents the number of tasks to be assigned;

[0074] In this embodiment, the number of all sub-parameter vectors in the task information except the task location is equal to the number of all sub-parameter vectors in the capability parameter;

[0075] StepA2, through: Calculate the cost function for each robot to complete each task :

[0076] The cost function of a task is a mathematical expression that measures the "cost" that each robot needs to pay to complete each task. It is used to compare the performance of different robots in completing the same task. By calculating the cost function value of each robot completing each task, the system can select the robot with the smallest cost function value for task assignment. This is like conducting a comprehensive evaluation, taking into account the position and capabilities of the robot and the adaptability of the task, so as to find the most suitable robot to complete the task, so as to achieve efficient and reasonable task allocation and improve the efficiency of the entire robot cluster in completing tasks. is the distance between the robot and the task location, and its formula is: ;

[0077] Indicates the matching degree between the robot capability and the task requirement, which is obtained by multiplying the corresponding elements of the capability parameter vector and the task information vector and then summing them up;

[0078] The specific method is as follows:

[0079] Where k = 1, 2, ..., v;

[0080] For example:

[0081] If the robot's capability parameter vector is Ci=(3,5);

[0082] Among them, the first parameter indicates that the load capacity is 3 units, and the second parameter indicates that the specific task efficiency is 5 units;

[0083] If the task information vector of the task to be assigned is Tj=(2,4);

[0084] Among them, the first parameter is the task demand weight related to load, which is 2, and the second parameter is the task demand weight related to efficiency, which is 4;

[0085] The matching degree is ;

[0086] In this way, the matching degree between the robot's capabilities and the task requirements can be quantified for use in the cost function calculation of the task allocation module, thereby allocating tasks more reasonably;

[0087] StepA3. For each task, select The robot with the smallest value is assigned.

[0088] This implementation collects the current position, capability parameters and task information of each robot to be assigned through the task assignment module, calculates the cost function of each robot to complete each task, and selects the robot with the smallest cost function value for assignment. The robot position and capability are considered to be compatible with the task, and the most suitable robot to complete the task can be found, thereby improving the efficiency of the entire robot cluster in completing the task. When calculating the cost function, the matching degree between the robot capability and the task requirements is quantified through a specific formula, and the matching degree is obtained by multiplying the corresponding elements of the robot capability parameter vector and the task information vector, so that tasks can be assigned more reasonably based on the actual matching situation between the robot and the task during the task assignment process.

[0089] Embodiment 2: As Embodiment 2 of the present invention, when the present application is implemented, compared with Embodiment 1, the technical solution of this embodiment is different from that of Embodiment 1 only in that this embodiment further includes:

[0090] The state monitoring module is used to collect the robot's own state data in real time, and compare the state data with the corresponding preset state threshold data, and determine whether the robot's current task execution state needs to be adjusted based on the comparison result;

[0091] The specific method is as follows:

[0092] Step B1. Collect the robot's own status data in real time. The status data includes battery power, operating status of mechanical parts, and progress of the current task, and mark them as B in turn. i 、M i and P ij ;

[0093] The operating state includes a normal state and a fault state, and the operating state of the mechanical component is collected by assigning a value of 1 and a value of 0;

[0094] Step B2, compare the status data of different attributes with their corresponding preset status threshold data:

[0095] The state threshold data includes the minimum power value BY corresponding to the battery power and the execution progress ratio threshold PY;

[0096] If the battery power is lower than the minimum power value BY, the charging task allocation signal is triggered, and the robot is then assigned to the nearest charging station;

[0097] If the running status of the mechanical component is 0, the robot is marked as faulty, and its current task is suspended and reallocated to other robots;

[0098] If the progress of the current task is greater than the execution progress ratio threshold PY, the task execution status is re-evaluated and adjusted, including reallocating tasks or adjusting path planning;

[0099] This embodiment can collect the robot's own status data in real time by setting up a status monitoring module, including battery power, operating status of mechanical parts, progress of current task execution, etc. By comparing these status data with the corresponding preset status threshold data, it can be timely determined whether the robot's current task execution status needs to be adjusted, thereby ensuring the stability and continuity of the robot's task execution. When it is detected that the battery power is lower than the minimum power value, the charging task allocation signal can be triggered and the robot can be assigned to the nearest charging station; when the operating status of the mechanical part is a fault state, its current task can be suspended and the task can be reallocated to other robots; when the progress of the current task is greater than the execution progress ratio threshold, the task execution status can be re-evaluated and adjusted, such as reallocating tasks or adjusting path planning, effectively responding to various abnormal situations that may affect task execution.

[0100] Embodiment 3: As the embodiment 3 of the present invention, when the present application is implemented, compared with the embodiments 1 and 2, the technical solution of this embodiment is to combine the solutions of the above-mentioned embodiments 1 and 2 for implementation. The difference between the technical solution of this embodiment and the embodiments 1 and 2 is that in this embodiment, it also includes:

[0101] The conflict management module is used to obtain the current position of the robot and the corresponding time node, and obtain the velocity components on the x and y axes, and then determine whether there is a conflict between the robots based on the obtained results, and rearrange the task order of the robots with conflicts according to their task priorities;

[0102] When an object moves in two-dimensional or three-dimensional space, the velocity component refers to the magnitude of each component vector obtained by decomposing the velocity vector along the coordinate axis direction;

[0103] The method is as follows:

[0104] StepC1, obtain the current position of the robot and its corresponding time node;

[0105] Then, the current position of the robot is marked as (x i，t0 ,y i，t0 ), then obtain the velocity components of the robot in the x-axis and y-axis directions and mark them as H ix and H iy ;

[0106] Among them, t0 is the time node corresponding to the current position of the robot, and t0 is the time node specified by the next time node of the time node corresponding to the current position of the robot:

[0107] Step C2: When the robot's speed is constant during movement, the formula is: Calculate the position of the robot at the specified time node t1 ;

[0108] When the robot has acceleration during motion, the formula is: Calculate the position of the robot at the specified time node t1 ;

[0109] In the formula, and are the acceleration components of the robot in the direction of the axis and axis;

[0110] StepC3, then pass: Calculate the distance between two different robots ;

[0111] Where, f=1, 2, ... n-1, and in the formula, i≠if;

[0112] Step C4: The distance between two different robots Compare with the preset safety distance:

[0113] when When the distance is less than or equal to the preset safety distance, it is determined that there is a conflict between robot i and robot j, and the task order of the robots is rearranged according to the task priorities corresponding to the two different robots;

[0114] The specific method is: if the task priority of robot i is higher than the task priority of robot if, then robot if will be stopped for TS seconds, then robot i will be allowed to complete the task first, and then robot if will continue to perform the task after TS seconds;

[0115] Among them, TS is a preset value. In this embodiment, it is set according to the movement speed of robot if, that is, after TS seconds, the speed of robot i and robot if Greater than the preset safety distance;

[0116] Among them, the safety distance is pre-set according to factors corresponding to the robot's physical size, movement speed, and environmental conditions;

[0117] when When it is greater than the preset safety distance, it is determined that there is no conflict between robot i and robot if.

[0118] The conflict management module in this embodiment can obtain the current position of the robot and the corresponding time node, and obtain the velocity components on the x and y axes, and determine whether there is a conflict between the robots through a series of calculations. When it is determined that there is a conflict, the task order is rearranged according to the task priorities corresponding to the two different robots, which effectively avoids the impact of conflicts such as mutual collisions on the robot during the movement process, thereby ensuring the orderliness of the robot cluster operation. According to the robot's task priority and the preset stop time TS and other parameters, when it is determined that there is a conflict, the robot's task execution order can be reasonably arranged, such as allowing the robot with a higher task priority to complete the task first, and then allowing the robot with a slightly lower priority to continue to perform the task, ensuring the efficiency and rationality of task execution, while taking into account the priority requirements of different tasks.

[0119] Embodiment 4: As the fourth embodiment of the present invention, when the present application is implemented specifically, compared with Embodiment 1, Embodiment 2 and Embodiment 3, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned Embodiment 1, Embodiment 2 and Embodiment 3.

[0120] This embodiment combines the solutions of Embodiment 1, Embodiment 2, and Embodiment 3 and integrates the functional features of the previous embodiments. It can not only achieve efficient and reasonable task allocation, but also monitor the robot status in real time and handle abnormal situations in a timely manner, and effectively avoid conflicts between robots and reasonably adjust the task sequence, thereby comprehensively improving the overall efficiency of the robot cluster management system and ensuring that the robot cluster can complete tasks stably and efficiently under various complex situations.

[0121] A robot cluster management method is implemented by a robot cluster management system, and the method comprises the following steps:

[0122] Step 1: Status monitoring:

[0123] Collect the robot's own state data in real time, and compare the state data with the corresponding preset state threshold data. According to the comparison result, determine whether the robot's current task execution state needs to be adjusted;

[0124] Step 2: Task allocation:

[0125] Collect the current position, capability parameters and task information of each robot to be assigned, then calculate the cost function of each robot to complete each task, and then select the robot with the smallest cost function value for assignment;

[0126] Step 3: Conflict Management:

[0127] Get the current position of the robot and the corresponding time node, and get the velocity components on the x and y axes. Then, determine whether there is a conflict between the robots based on the obtained results, and rearrange the task order of the robots with conflicts according to their task priorities.

[0128] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0129] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A robot cluster management method, characterized in that: include: The first step is to collect the robot's own state data in real time, and compare the state data with the corresponding preset state threshold data. According to the comparison result, it is determined whether the robot's current task execution state needs to be adjusted; The second step is to collect the current position, capability parameters and task information of each robot to be assigned, and then calculate the cost function of each robot to complete each task, and then select the robot with the smallest cost function value for assignment; The third step is to obtain the current position of the robot and the corresponding time node, and obtain the velocity components on the x and y axes. Then, based on the obtained results, determine whether there is a conflict between the robots, and rearrange the task order of the robots with conflicts according to their task priorities.

2. A robot cluster management method according to claim 1, characterized in that: The tasks are allocated as follows: StepA1, collect the current position, capability parameters and task information of each robot to be assigned; in: The robot's position information is defined as (x i ,y i ), which is obtained through its built-in positioning system; Where i=1, 2, ...n, i represents the number of robots, and n represents the number of robots; The robot's capability parameter is defined as C i , is a vector containing multiple sub-parameters, including load capacity, specific task efficiency, etc., which are marked as C i，1 , C i，2 , ...C i，v , where v represents the number of all sub-parameter vectors in the capability parameter; The task information of the task to be assigned is defined as T j , which is also a vector containing multiple sub-parameters, including the task location, and is labeled as (x T，j ,y T，j ), which also includes task type, task priority, etc., and is marked as T j，1 、T j，2 ,……T j，v , where v represents the number of all sub-parameter vectors in the task information except the task location, j = 1, 2, ... m, j represents the number of tasks to be assigned, and m represents the number of tasks to be assigned; StepA2, through: Calculate the cost function for each robot to complete each task : in, is the distance between the robot and the task location; Indicates the matching degree between the robot capability and the task requirement, which is obtained by multiplying the corresponding elements of the capability parameter vector and the task information vector and then summing them up; StepA3. For each task, select The robot with the smallest value is assigned.

3. A robot cluster management method according to claim 1, characterized in that: The status monitoring method is as follows: Step B1. Collect the robot's own status data in real time. The status data includes battery power, operating status of mechanical parts, and progress of the current task, and mark them as B in turn. i 、M i and P ij ; The operating state includes a normal state and a fault state, and the operating state of the mechanical component is collected by assigning a value of 1 and a value of 0; Step B2: Compare the status data of different attributes with their corresponding preset status threshold data, and determine whether the robot battery is low, whether the robot is faulty, and whether the robot's current execution task needs to be readjusted based on the comparison result.

4. A robot cluster management method according to claim 3, characterized in that: In Step B2, the state threshold data includes a minimum power value BY corresponding to the battery power and an execution progress ratio threshold PY; If the battery power is lower than the minimum power value BY, the charging task allocation signal is triggered, and the robot is then assigned to the nearest charging station; If the running status of the mechanical component is 0, the robot is marked as faulty, and its current task is suspended and reallocated to other robots; If the progress of the current task execution is greater than the execution progress ratio threshold PY, the task execution status is re-evaluated and adjusted.

5. A robot cluster management method according to claim 1, characterized in that: Conflict management methods are as follows: StepC1, obtain the current position of the robot and its corresponding time node; Then, the current position of the robot is marked as (x i，t0 ,y i，t0 ), then obtain the velocity components of the robot in the x-axis and y-axis directions and mark them as H ix and H iy ; Among them, the velocity component refers to the magnitude of each component vector obtained by decomposing the velocity vector along the coordinate axis direction; Among them, t0 is the time node corresponding to the current position of the robot, and t0 is the time node specified by the next time node of the time node corresponding to the current position of the robot: Step C2: When the robot's speed is constant during movement, the formula is: Calculate the position of the robot at the specified time node t1 ; StepC3, then pass: Calculate the distance between two different robots ; Where, f=1, 2, ... n-1, and in the formula, i≠if; Step C4: The distance between two different robots Compare with the preset safety distance, and determine whether there is a conflict between robot i and robot if based on the comparison result; Among them, the safety distance is pre-set according to factors corresponding to the robot's physical size, movement speed, and environmental conditions.

6. A robot cluster management method according to claim 5, characterized in that: The comparison method in StepC4 is as follows: when When the distance is less than or equal to the preset safety distance, it is determined that there is a conflict between robot i and robot if; when When it is greater than the preset safety distance, it is determined that there is no conflict between robot i and robot if.

7. A robot cluster management method according to claim 5, characterized in that: In StepC2, when the robot has acceleration during motion, the formula is: Calculate the position of the robot at the specified time node t1 ; In the formula, and are the acceleration components of the robot in the directions of the axes and .

8. A robot cluster management method according to claim 6, characterized in that: In Step C4, there is a conflict between robot i and robot if, and the task order of the robots is rearranged according to the task priorities of the two different robots. The specific method is: if the task priority of robot i is higher than the task priority of robot if, robot if is stopped for TS seconds, and then robot i is allowed to complete the task first, and then robot if is allowed to continue to perform the task after TS seconds; Among them, TS is a preset value, which is set according to the movement speed of robot if, that is, after TS seconds, the speed of robot i and robot if Greater than the preset safety distance.

9. A robot cluster management method according to claim 2, characterized in that: In StepA2, the distance formula between the robot and the task location is: .

10. A robot cluster management system, characterized in that: The system is used to execute a robot cluster management method as described in any one of claims 1-9.

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