Method and system for optimizing scheduling of robot handling between multiple production equipment

Abstract

The present application provides a method and system for optimizing the scheduling of robot handling between multiple production equipment. According to the collected status of each production equipment and the product models currently supported for processing, combined with the historical processing cycle time and equipment yield of the equipment collected and stored in the database, the input products to be processed are dynamically allocated to each device, and the end time of the current product processing of the equipment is estimated. Then, it is determined by a series of scheduling strategy algorithms, including: maximum coherent task strategy, nearest neighbor priority strategy, equipment processing output yield priority strategy, etc. Using a robot can effectively solve the problems of low utilization rate of robots and production equipment and inability to integrate multiple production equipment in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robot control, and in particular to a control method and system when multiple robots are working simultaneously. Background Art

[0002] With the popularization of robot applications, robots are increasingly being used in handling applications in production and manufacturing to replace manual handling.

[0003] At present, most robot handling adopts a fixed handling method of equipment docking point to equipment docking point, that is, the robot has a specific corresponding relationship with the equipment and the items to be handled, and is limited to the operation between two equipment docking points. This method has the following main problems: 1) Low utilization of robots and equipment; 2) The docking point is limited to the range of the robot's own movement, which will result in being limited to a single production equipment and cannot integrate more production equipment; 3) The handling task cannot be adjusted dynamically, which will make it impossible to solve the impact of a certain equipment failure, balance the production load of each equipment, and reduce the buffer time between handling tasks. Summary of the invention

[0004] The present application provides a technical solution for optimizing robot handling and scheduling among multiple production equipment.

[0005] To achieve the above objectives, the first aspect of the present invention is to provide a method for optimizing the scheduling of robot transportation between multiple production equipment, the method comprising:

[0006] Collect the working status of each production equipment and the output yield data of each production equipment;

[0007] The robot determines the production equipment that has completed the production and processing task, and finds the longest continuous transport chain among the production equipment that has completed the production and processing task;

[0008] If there is only one longest continuous transport chain that can complete the maximum coherent task strategy, the robot will work according to the longest continuous transport chain; if there is more than one longest continuous transport chain that can complete the maximum coherent task strategy, the distance length of each longest transport chain is calculated, and the longest transport chain with the shortest distance is selected;

[0009] If there is only one longest transport chain with the shortest distance, the robot will work according to the longest transport chain with the shortest distance; if there is more than one longest transport chain with the shortest distance, the robot will determine the highest output yield of each longest transport chain with the shortest distance, and select the longest transport chain with the shortest distance based on the highest output yield; the robot will select the longest transport chain with the shortest distance to work according to the highest output yield.

[0010] The second aspect of the present application is to provide a robot transport optimization scheduling system between multiple production equipment, including:

[0011] Data collection module: collects the working status of each production equipment and the output yield data of each production equipment; Maximum coherent task strategy judgment module: finds the longest continuous transport chain from the production equipment that completes the production and processing tasks;

[0012] Nearest neighbor priority strategy judgment module: calculate the distance length of each longest transport chain, and select the longest transport chain with the shortest distance from the longest continuous transport chain;

[0013] Yield priority strategy judgment module: judge the highest output yield of the longest transport chain with the shortest distance, and select the longest transport chain with the shortest distance with the highest output yield;

[0014] in,

[0015] If the maximum coherent task strategy judgment module determines that there is only one longest continuous transport chain that can complete the maximum coherent task strategy, the robot will work according to the longest continuous transport chain; if there is more than one longest continuous transport chain that can complete the maximum coherent task strategy, it will be called to calculate the distance length of each longest transport chain and select the longest transport chain with the shortest distance;

[0016] If the nearest neighbor priority strategy judgment module determines that the longest transport chain with the shortest distance is one item, the robot will work according to the longest transport chain with the shortest distance; if there is more than one longest transport chain with the shortest distance, the yield priority strategy judgment module will be called to determine the highest output yield of each longest transport chain with the shortest distance, and select the longest transport chain with the shortest distance according to the highest output yield; the robot will select the longest transport chain with the shortest distance to work according to the highest output yield.

[0017] In the above content of the present application, the multiple production equipment refers to at least 3 production equipment with different production and processing functions, more preferably at least 5 production equipment with different production and processing functions, and more preferably at least 8 production equipment with different production and processing functions.

[0018] In the above content of this application, the number of production equipment for each production and processing function can be one or more.

[0019] In the above content of this application, if there is more than one longest transport chain with the shortest distance for the highest output yield, the robot selects one of the longest transport chains with the shortest distance for the highest output yield to work.

[0020] In a preferred embodiment, the longest transport chain refers to the longest chain that the robot can use to complete continuous transport between various production equipments, that is, it can perform continuous transport between the largest number of production equipments.

[0021] In a preferred embodiment, the robot transport optimization and scheduling system between multiple production equipments further includes a storage device for storing product processing cycle time of each production equipment.

[0022] In a preferred embodiment, in a method for optimizing the scheduling of robot transportation between multiple production equipment, if all production equipment are in a working state, the production equipment that can complete the production and processing work the fastest is selected according to the product processing cycle time, and a virtual working state is generated for the selected production equipment as the working state for completing the production and processing task, which is applied to the judgment of the longest continuous transportation chain, thereby obtaining a virtual longest continuous transportation chain, and the robot runs to the starting position of the transportation task of the virtual longest continuous transportation chain, waiting for transportation.

[0023] To this end, in a preferred embodiment, the robot transport optimization scheduling system between multiple production equipments further includes:

[0024] The strategy module for completing the work quickly is used to select the production equipment that can complete the production and processing work the fastest.

[0025] If all production equipment are in working state, the completion measurement module will be called to screen out the production equipment that can complete the production and processing work the fastest according to the product processing cycle time, and generate a virtual working state for the screened production equipment as the working state for completing the production and processing task, which will be applied to the judgment of the longest continuous transport chain, thereby obtaining the virtual longest continuous transport chain. The robot runs to the starting position of the transport task of the virtual longest continuous transport chain and waits for transport.

[0026] In a preferred embodiment, when all production equipment on the virtual longest continuous transport chain completes its production and processing work, the actual working status of the equipment is collected, and the robot waiting for transport starts working.

[0027] In a preferred embodiment, the production equipment is arranged in at least two rows, and in the judgment of the longest conveying chain with the shortest distance, the robot stop points of the relative production equipment are set to overlap, that is, the distance is 0, and the distance between adjacent production equipment in the same row is 1.

[0028] The present application provides a method and system for optimizing the scheduling of robot handling between multiple production equipments, which collects, processes and persists data such as the working status, processing cycle time and yield rate of each production equipment in real time, provides a scheduling strategy function to analyze the data to make decisions, and issues handling tasks to the robot. Using one robot can effectively solve the problems in the prior art such as low utilization rate of robots and production equipment and inability to integrate multiple production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of the method for optimizing the scheduling of robot transportation between multiple production equipment for this application.

[0030] Figure 2 A schematic diagram of an application scenario of the method for optimizing the scheduling of robot transportation between multiple production equipments in this application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] Example 1

[0033] Reference Figure 1 , this application provides a method and system for optimizing the scheduling of robot handling between multiple production equipment. According to the collected status of each production equipment and the product models currently supported for processing, combined with the historical processing cycle time and equipment yield of the equipment collected and saved in the database, the input products to be processed are dynamically allocated to each equipment, and the end time of the current product processing of the equipment is estimated. Then, a series of scheduling strategy algorithms are used for judgment, including:

[0034] The maximum coherent task strategy (i.e. the longest continuous transport chain of transport points A->B, B->C, C->D…) reduces robot and equipment idleness and improves equipment utilization;

[0035] The nearest neighbor priority strategy (for example, the shortest distance algorithm Dijkstra can be used to calculate the transport starting point closest to the robot's current position) can reduce the robot's moving path and save time;

[0036] The equipment processing output yield rate priority strategy (historical yield rates are counted and sorted, and equipment with the same yield rate is sorted by number) is used to improve the output yield rate.

[0037] The specific steps include:

[0038] s1: Collect data such as the status of each device, product processing cycle time, output yield, etc., and save them persistently in the database.

[0039] s2: Apply the maximum coherent task strategy analysis to the collected data: determine the production equipment that completes the production and processing tasks, and find the longest continuous handling chain from the production equipment that completes the production and processing tasks. This application hopes to generate as many combined handling task solutions as possible, such as raw materials -> process A, process A completes the product -> process B, process B -> process C, ..., process N completes the product -> finished product output. This can reduce the idle time of robots and equipment and improve equipment utilization.

[0040] s3: Determine how many longest continuous transport chains there are, i.e., transport task combination schemes. If there is only one transport task combination scheme, dispatch the robot to execute the transport task of the scheme; if there are more than one transport task combination schemes with the same number of combinations, apply the nearest neighbor priority strategy, calculate the distance length of each longest transport chain, select the longest transport chain with the shortest distance, and select the execution that is closest to the robot's current position and the starting point of the plan's transport task, so as to reduce the robot's wasted time on moving;

[0041] s4, determine how many solutions with the shortest distance and the longest transport chain there are. If there is only one, dispatch the robot to execute the transport task of the solution. If there are still multiple solutions, apply the high output yield priority strategy, and try to improve the output yield according to the solution with the highest yield of the nearest equipment collected. If it still cannot be screened out, take the first one to execute.

[0042] s5: After the robot completes the transport task, it returns to s1 and continues the cycle.

[0043] Example 2

[0044] On the basis of Example 1, the method and system for optimizing the scheduling of robot transportation between multiple production equipment in the present application may also include a strategy that is about to be completed: that is, according to the statistical equipment product processing cycle time, the equipment that is about to complete the processing is calculated and screened out, a virtual equipment state is generated, and then the maximum coherent task strategy is applied for analysis; a virtual transportation task combination plan is obtained, and the robot is scheduled to the starting position of the transportation task in the transportation plan in advance to reduce the buffer time between transportation tasks; if there is only one transportation task combination plan, the robot is scheduled to execute the transportation task of the plan; if there are multiple transportation plans with the same number of combinations, the nearest neighbor priority strategy is applied to screen out the execution that is closest to the current position of the robot and the starting point of the plan's transportation task, reducing the waste of the robot's moving time; if there are still multiple plans, the high output yield priority strategy is applied to improve the output yield as much as possible based on the plan with the highest yield of the nearest equipment collected; if it still cannot be screened out, the first execution is taken.

[0045] In this way, the present application not only realizes dynamic scheduling of robot handling under real-time equipment conditions, but also considers predictions of upcoming situations and arranges the robot to arrive at the starting position of the handling task in advance.

[0046] The specific steps include:

[0047] s1: Collect data such as the status of each device, product processing cycle time, output yield, etc., and save them persistently in the database.

[0048] s2: Apply the maximum coherent task strategy analysis to the collected data: determine the production equipment that completes the production and processing tasks, and find the longest continuous handling chain from the production equipment that completes the production and processing tasks. This application hopes to generate as many combined handling task solutions as possible, such as raw materials -> process A, process A completes the product -> process B, process B -> process C, ..., process N completes the product -> finished product output. This can reduce the idle time of robots and equipment and improve equipment utilization.

[0049] s3: Determine how many longest continuous transport chains there are, i.e., transport task combination schemes. If it is 0, the equipment that is about to complete the processing is calculated and screened out based on the statistical equipment product processing cycle time, and a virtual equipment state is generated. Then, the maximum coherent task strategy analysis is applied to obtain a virtual transport task combination scheme as the transport task combination scheme of the longest continuous transport chain;

[0050] If there is only one transport task combination plan, the robot will be dispatched to execute the transport task of the plan; if there are more than one transport task combination plans with the same number of combinations, the nearest neighbor priority strategy will be applied to calculate the distance length of each longest transport chain, select the longest transport chain with the shortest distance, and select the execution closest to the robot's current position and the starting point of the plan's transport task, so as to reduce the waste of robot's moving time;

[0051] s4, determine how many solutions with the shortest distance and the longest transport chain there are. If there is only one, dispatch the robot to execute the transport task of the solution. If there are still multiple solutions, apply the high output yield priority strategy, and try to improve the output yield according to the solution with the highest yield of the nearest equipment collected. If it still cannot be screened out, take the first one to execute.

[0052] s5: After the robot completes the transport task, it returns to s1 and continues the cycle.

[0053] Example 3

[0054] Reference Figure 2In this embodiment, there are P0-P8 docking points, where P0 is the loading and unloading docking point, P1-P4 are the docking points of the production equipment group of process A, and P5-P8 are the docking points of the production equipment group of process B. The robot needs to transport the products from the loading and unloading storage area to the production equipment of process A and process B in sequence for processing, and then transport them back to the loading and unloading storage area after processing, until all the products in the loading and unloading area are processed. That is, the robot transportation docking order is: P0->P1 / P2 / P3 / P4->P5 / P6 / P7 / P8->P0.

[0055] For example, if device P1 is idle, device P2 is finished processing, and device P5 is finished processing, the conveying chain solution obtained in this embodiment includes:

[0056] a1: P0->P1: The robot moves products from the loading and unloading area to the P1 equipment for processing.

[0057] a2: P0->P2->P5->P0: The robot transports products from the loading and unloading area to the P2 equipment processing process A, the products processed by the P2 equipment are transported to the P5 equipment processing process B, and the products processed by the P5 equipment are transported to the loading and unloading area.

[0058] a3: P5->P0: The robot moves the products processed by P5 equipment to the loading and unloading area.

[0059] Use the "maximum coherent task strategy" to calculate the number of tasks in the solution and filter out the largest set of solutions, where there is only one solution (i.e., the second solution), and schedule the robot to execute the solution.

[0060] Example 4

[0061] Reference Figure 2 , taking the ground track and the double picking mechanism as an example, there are P0-P8 docking points in this embodiment, of which P0 is the loading and unloading docking point, P1-P4 are the docking points of the production equipment group of process A, and P5-P8 are the docking points of the production equipment group of process B. The robot needs to transport the products from the loading and unloading storage area to the production equipment of process A and process B in sequence for processing, and then transport them back to the loading and unloading storage area after processing, until all the products in the loading and unloading area are processed. That is, the robot transportation and docking order is: P0->P1 / P2 / P3 / P4->P5 / P6 / P7 / P8->P0.

[0062] When equipment P1 is idle, equipment P2 and P3 are finished, and equipment P5, P6 and P7 are finished. , the conveying chain scheme obtained in this embodiment includes:

[0063] a1: P0->P1: The robot moves products from the loading and unloading area to the P1 equipment for processing.

[0064] a2: P0->P2 / P3->P5 / P6 / P7->P0: The robot transports products from the loading and unloading area to the P2 / P3 equipment for processing process A, and the products processed by the P2 / P3 equipment are transported to the P5 / P6 / P7 equipment for processing process B, and the products processed by the P5 / P6 / P7 equipment are transported to the loading and unloading area.

[0065] a3: P5 / P6 / P7->P0: The robot moves the products processed by P5 / P6 / P7 equipment to the loading and unloading area.

[0066] The "maximum coherent task strategy" is used to calculate the number of tasks in the scheme, and the largest set of schemes is screened out, that is, the second scheme. Among them, the second node of the second scheme has devices P2 and P3 to choose from, and the third node has devices P5, P6 and P7 to choose from. After permutations and combinations, there are a total of 6 schemes with the same task length.

[0067] Assume that the overlap distance of relative stop points is 0, and the distance between adjacent stop points is 1. That is, the distance between P1 P5, P2P6, P3P7 and P4P8 is 0, the distance between adjacent P0, P1, P2, P3 and P4 is 1, and the distance between adjacent P1, P5, P6, P7 and P8 is also 1. Therefore, the selected solution is: P0->P2->P6->P0, and the robot is dispatched to execute the solution.

[0068] Example 5

[0069] Referring to Example 4, when equipment P1 is idle, equipment P2 and P3 are finished, equipment P5, P6 and P7 are finished, and the unprocessed products in loading and unloading area P0 are empty. , the conveying chain scheme obtained in this example includes:

[0070] a1: P2->P6->P0: The robot moves the products processed by P2 equipment to process B of P6 equipment, and the products processed by P6 equipment are moved to the loading and unloading area.

[0071] a2: P3->P7->P0: The robot moves the products processed by P3 equipment to process B of P7 equipment, and the products processed by P7 equipment are moved to the loading and unloading area.

[0072] Assuming that the current robot stops at a stop point among P0, P1, P2, P5 and P6, the "nearest neighbor first strategy" can be used to screen out the plan: P2->P6->P0, and schedule the robot to execute the plan.

[0073] Assuming that the current robot stops at a stop point among P3, P4, P7 and P8, the "nearest neighbor first strategy" can be used to screen out the plan: P3->P7->P0, and schedule the robot to execute the plan.

[0074] Assuming that P3 is replaced by P4 and P7 is replaced by P8, and the robot is currently parked at a stop point between P3 and P7, the screening results are 2 solutions: b1.P2->P6->P0; b2.P4->P8->P0. Then use the "yield priority strategy" to filter out the only solution by weighted multiplication of the equipment yield, and schedule the robot to execute the solution.

[0075] The specific embodiments of the invention are described in detail above, but they are only examples, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications and substitutions made to the present invention are also within the scope of the present invention. Therefore, the equalization changes and modifications made without departing from the spirit and scope of the present invention should be included in the present invention.

Claims

1. A method for optimizing the dispatching of robots for transporting between multiple production equipments, characterized in that: The method comprises: collecting the working status of each production equipment and the output yield data of each production equipment; The robot determines the production equipment that has completed the production and processing task, and finds the longest continuous transport chain among the production equipment that has completed the production and processing task; If there is only one longest continuous transport chain that can complete the maximum coherent task strategy, the robot will work according to the longest continuous transport chain; if there is more than one longest continuous transport chain that can complete the maximum coherent task strategy, the distance length of each longest transport chain is calculated, and the longest transport chain with the shortest distance is selected; If there is only one longest transport chain with the shortest distance, the robot will work according to the longest transport chain with the shortest distance; if there is more than one longest transport chain with the shortest distance, the robot will determine the highest output yield of each longest transport chain with the shortest distance, and select the longest transport chain with the shortest distance based on the highest output yield; the robot will select the longest transport chain with the shortest distance to work according to the highest output yield.

2. The method according to claim 1, characterized in that If there is more than one longest transport chain with the shortest distance that has the highest output yield, the robot selects one of the longest transport chains with the shortest distance that has the highest output yield to work on.

3. The method according to claim 1, characterized in that In the robot transportation optimization scheduling method between multiple production equipment, if all production equipment are in a working state, the production equipment that can complete the production and processing work the fastest is selected according to the product processing cycle time, and a virtual working state is generated for the selected production equipment as the working state for completing the production and processing task, which is applied to the judgment of the longest continuous transportation chain, thereby obtaining the virtual longest continuous transportation chain. The robot runs to the starting position of the transportation task of the virtual longest continuous transportation chain and waits for transportation.

4. The method according to claim 3, characterized in that When all the production equipment on the virtual longest continuous transport chain completes its production and processing work, the actual working status of the equipment is collected, and the robot waiting for transport starts working.

5. The method according to claim 1, characterized in that The production equipment is arranged in at least two rows. In the judgment of the longest conveying chain with the shortest distance, the robot stop points of the relative production equipment are set to overlap, that is, the distance is 0, and the distance between adjacent production equipment in the same row is 1.

6. A robot transport optimization scheduling system between multiple production equipments, characterized in that: include: Data collection module: collects the working status of each production equipment and the output yield data of each production equipment; Maximum coherent task strategy judgment module: finds the longest continuous transport chain from the production equipment that completes the production and processing tasks; Nearest neighbor priority strategy judgment module: calculate the distance length of each longest transport chain, and select the longest transport chain with the shortest distance from the longest continuous transport chain; Yield priority strategy judgment module: judge the highest output yield of the longest transport chain with the shortest distance, and select the longest transport chain with the shortest distance with the highest output yield; in, If the maximum coherent task strategy judgment module determines that there is only one longest continuous transport chain that can complete the maximum coherent task strategy, the robot will work according to the longest continuous transport chain; if there is more than one longest continuous transport chain that can complete the maximum coherent task strategy, it will be called to calculate the distance length of each longest transport chain and select the longest transport chain with the shortest distance; If the nearest neighbor priority strategy judgment module determines that the longest transport chain with the shortest distance is one item, the robot will work according to the longest transport chain with the shortest distance; if there is more than one longest transport chain with the shortest distance, the yield priority strategy judgment module will be called to determine the highest output yield of each longest transport chain with the shortest distance, and select the longest transport chain with the shortest distance according to the highest output yield; the robot will select the longest transport chain with the shortest distance according to the highest output yield.

7. The system according to claim 6, characterized in that The robot transport optimization scheduling system between multiple production equipments also includes a storage device for storing the product processing cycle time of each production equipment.

8. The system according to claim 7, characterized in that The robot transport optimization and scheduling system between multiple production equipments also includes: The strategy module for completing the work quickly is used to select the production equipment that can complete the production and processing work the fastest. If all production equipment are in working state, the completion measurement module will be called to screen out the production equipment that can complete the production and processing work the fastest according to the product processing cycle time, and generate a virtual working state for the screened production equipment as the working state for completing the production and processing task, which will be applied to the judgment of the longest continuous transport chain, thereby obtaining the virtual longest continuous transport chain. The robot runs to the starting position of the transport task of the virtual longest continuous transport chain and waits for transport.

9. The system according to claim 8, characterized in that When all the production equipment on the virtual longest continuous transport chain completes its production and processing work, the actual working status of the equipment is collected, and the robot waiting for transport starts working.

10. The system according to claim 6, characterized in that The production equipment is arranged in at least two rows. In the judgment of the longest conveying chain with the shortest distance, the robot stop points of the relative production equipment are set to overlap, that is, the distance is 0, and the distance between adjacent production equipment in the same row is 1.

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