WO2022012267A1 - 一种五金柔性生产车间多加工机器人协作方法 - Google Patents

一种五金柔性生产车间多加工机器人协作方法 Download PDF

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WO2022012267A1
WO2022012267A1 PCT/CN2021/100899 CN2021100899W WO2022012267A1 WO 2022012267 A1 WO2022012267 A1 WO 2022012267A1 CN 2021100899 W CN2021100899 W CN 2021100899W WO 2022012267 A1 WO2022012267 A1 WO 2022012267A1
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processing
task
information
agv
processing robot
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PCT/CN2021/100899
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English (en)
French (fr)
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辛斌
鲁赛
窦丽华
陈杰
王晴
王淼
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北京理工大学
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Priority to US18/097,833 priority Critical patent/US20230229172A1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41815—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the cooperation between machine tools, manipulators and conveyor or other workpiece supply system, workcell
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291—Fleet control
    • G05D1/0297—Fleet control by controlling means in a control room
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/4155—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/4189—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system
    • G05B19/41895—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system using automatic guided vehicles [AGV]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0289—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling with means for avoiding collisions between vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32202—Integration and cooperation between processes
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32277—Agv schedule integrated into cell schedule
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32423—Task planning
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/40—Robotics, robotics mapping to robotics vision
    • G05B2219/40078—Sort objects, workpieces
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/50—Machine tool, machine tool null till machine tool work handling
    • G05B2219/50391—Robot
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/60—Electric or hybrid propulsion means for production processes

Definitions

  • the invention relates to the technical field of multi-agent collaboration, in particular to a multi-processing robot collaboration method in a hardware flexible production workshop.
  • the hardware industry is facing challenges such as product diversification, small batches, and rapid changes in market demand.
  • the traditional production mode has a low level of automation and is difficult to meet the needs of rapid production line switching in small batches and multiple batches. It is urgent to improve the level of automation and intelligence. level.
  • the modular hardware production workshop can well adapt to the changeable and flexible production needs.
  • the processing robot in the modular hardware workshop only needs to replace the tooling, jig and a small number of process modules to realize the rapid and flexible production of various types of multi-process products.
  • the collaborative technology for multi-processing robots mainly includes two types of control methods: centralized and distributed.
  • the centralized method mainly relies on the central management node with strong computing power to process a large amount of complex internal information in the workshop, and realizes it through flexible and efficient planning algorithms. Scheduling and control of all agents in the workshop; distributed methods mainly rely on the autonomous perception, communication and decision-making capabilities of intelligent processing robots. Through certain task allocation methods (such as methods based on market mechanisms), distributed automatic processing is realized in the workshop. efficient collaboration in the organization.
  • the above methods have their own advantages and disadvantages.
  • the centralized method relies too much on the central management node. Once the central management node is paralyzed, the workshop will not be able to continue processing tasks; while for the distributed method, although the dependence on the central management node is low, due to each Each agent can only obtain local information, and problems such as congestion and collision are prone to occur in the workshop.
  • the present invention provides a multi-processing robot collaboration method for a hardware flexible workshop, which can realize the assignment of workpiece processing tasks and the circulation of workpieces in different workstations, while avoiding large computational costs.
  • a multi-processing robot collaboration method in a hardware flexible production workshop which uses a processing robot collaboration system to achieve collaboration, and the multi-processing robot collaboration system uses a bus to aggregate the status information of each processing robot in a central management node in real time; each processing robot and AGV wirelessly Realize event-driven communication and information sharing under the local area network environment, and complete task assignment;
  • Step 1 generate a batch of workpiece processing tasks, including a variety of different types of workpieces
  • Step 2 the central management node assigns processing tasks, including the following steps:
  • Step 2.1 the processing task sender sends the workpiece processing task request to other processing robots in turn according to the task processing priority order of the task information to be processed through the communication network;
  • Step 2.2 after receiving the workpiece processing task request information, the processing robot predicts the completion time point of the corresponding task in the request information in combination with the task sequence in its current task list, and sends the estimated completion time of the task to the processing task sender;
  • Step 2.3 according to the feedback information of each processing robot, the sender of the processing task preferentially allocates the task package to the processing robot with a nearer expected completion time, and sends the corresponding confirmation information to the processing robot;
  • Step 2.4 after the processing robot receives the confirmation information, it inserts the task into the task list in the order of priority, and recalculates the expected completion time of each task; the processing robot that has not received the confirmation information maintains the original state;
  • step 3 the workpieces to be processed are called in sequence according to the priority order of the processing tasks, and the method for distributing the handling tasks includes the following steps:
  • Step 3.1 the processing robot reads the processing task in the task list, and sends the handling request information of the task to the surrounding AGV through the local communication network;
  • Step 3.2 after the idle AGV in the local communication network receives the handling request information, it calculates the estimated completion time of the handling task according to its own position, picking position and feeding position information, and sends the expected time as return information to the AGV. task sender;
  • step 3.3 the processing robot preferentially selects the AGV with the shortest completion time according to the response information fed back, and sends the confirmation information to the AGV;
  • Step 3.4 after the AGV receives the confirmation information, it transports the material to be processed from the processing robot's output buffer of the previous process to the current process's corresponding processing robot's in-feed buffer according to the task information; AGVs that have not received confirmation information within a certain period of time , will continue to wait to receive requests for other handling tasks;
  • Step 4 After the processing robot receives the workpiece to be processed, it starts to process the workpiece in the corresponding process. After completing the processing task of the process, the processing robot performs the next processing task assignment operation, including the following steps:
  • Step 4.1 determine whether all the processing steps of the workpiece have been completed. If it has been completed, perform step 5 to collect the AGV and transport it to the finished product area for storage, and end the processing task; otherwise, send the next processing of the workpiece to other surrounding processing robots. task request;
  • Step 4.2 the processing robot sends the workpiece processing task request to other processing robots in the workshop in turn according to the task processing priority order of the task information to be processed;
  • Step 4.3 after receiving the workpiece processing task request information, the processing robot capable of completing the processing task of the process, combined with the task sequence in its current task list, predicts the completion time point of the corresponding task in the request information, and predicts the completion of the task.
  • the time is sent to the task request sender;
  • Step 4.4 the sender assigns the task package to the processing robot with the shortest completion time according to the feedback information of each processing robot, and sends the corresponding confirmation information to the processing robot;
  • Step 4.5 after the processing robot receives the confirmation information, it will add the task to the task list in the order of priority and recalculate the expected completion time of each task, and go to step 3;
  • Step 5 after the processing robot has completed the processing of the last process of the workpiece, it is responsible for completing the assignment of the workpiece storage and handling tasks, including the following steps:
  • Step 5.1 the processing robot sends the workpiece storage and handling request to the surrounding AGV through the local communication network;
  • Step 5.2 after the idle AGV in the local communication network receives the handling request information, it calculates the estimated completion time of the handling task according to its own position, picking position and feeding position information, and sends the expected time as return information to the AGV. task sender;
  • Step 5.3 according to the response information fed back, the processing robot preferentially selects the AGV with the shortest completion time, and sends the confirmation information to the AGV;
  • step 5.4 after receiving the confirmation information, the AGV autonomously transports the completed workpiece to the finished product area according to the task information, and finally completes all the processing tasks of the workpiece.
  • multi-AGV path conflict resolution method including the following steps:
  • Step 6.1 during the traveling process of the AGV in the flexible production workshop, it shares its own location information, part of the path information and its own task priority information with the surrounding AGVs through the local communication network;
  • Step 6.2 after the AGV receives the information shared by the surrounding AGVs, it compares with its own location and path information to determine whether there will be a path conflict problem;
  • the centralized intervention scheduling method is used to discover and predict system conflicts and failures, including the following steps:
  • Step 7.1 the central management node collects the information from the workshop in real time through the industrial field bus, including the status information and environmental conditions of all processing robots and AGVs;
  • Step 7.2 carry out centralized analysis and processing on the information collected in step 7.1, and discover and predict the workshop failures that are occurring or may occur in the future, including the failure of processing robots in the workshop, obstacles in the workshop and traffic congestion;
  • Step 7.3 according to the results of step 7.2, the central management node adopts the scheduling strategy to adjust the current internal parameters, task list and current status information of each agent, so as to change the production operation status of the workshop to a minimum extent and relieve the current or possible future occurrence. Workshop failure.
  • the sender of the processing task is a central management node or a processing robot.
  • the method of the present invention fully utilizes the distributed collaboration method, and can realize the assignment of workpiece processing tasks and the circulation of workpieces in different workstations, while avoiding large computational costs.
  • the present invention adopts a multi-AGV path conflict resolution method to avoid possible collisions of the AGVs during the movement process.
  • the invention uses the method of centralized intervention and adjustment to discover and predict system conflicts and faults, and makes timely scheduling and adjustment, thereby improving the automation level and flexibility level of the hardware workshop.
  • Fig. 1 is the communication topology diagram of the hardware flexible workshop of the present invention.
  • FIG. 2 is a flow chart of the multi-processing robot collaboration according to the present invention.
  • Fig. 3 is the control structure diagram of the hardware flexible workshop of the present invention.
  • FIG. 4 is a simulation scene diagram of the hardware flexible workshop of the present invention.
  • FIG. 5 is a schematic diagram of solving the AGV path conflict according to the present invention.
  • FIG. 6 is a schematic diagram of solving AGV congestion according to the present invention.
  • the multi-processing robot cooperation method of the hardware flexible workshop proposed by the present invention takes the processing robot as the main initiator of the main processing tasks and handling tasks in the workshop, and realizes the rapid assignment and execution of tasks through communication and cooperation with various surrounding processing robots; comprehensive application Distributed collaboration and centralized intervention and deployment methods solve various possible faults and congestion problems in the workshop, and improve the flexibility and automation level of the system.
  • the present invention will focus on a modular flexible production workshop including a variety of processing robots and AGVs, and study the distributed task allocation methods for the above-mentioned various processing robots.
  • the application of the distributed collaboration method in the present invention will realize the flexible processing of multi-variety workpieces and the flexible flow of materials between processes, and greatly improve the production flexibility and expandability of the production workshop.
  • the communication topology diagram of the hardware flexible workshop of the present invention is shown in FIG. 1 .
  • the central management node is linked with other nodes through a communication network such as an industrial bus; each processing robot and the handling processing robot perform local self-organizing links through flexible communication methods to complete information exchange and sharing.
  • the hardware flexible workshop mainly includes three types of intelligent bodies with decision-making ability: central management nodes, processing robots and AGVs.
  • the central management node maintains real-time communication with the processing robot and AGV through communication technologies such as industrial fieldbus; the processing robot and AGV realize local low-frequency information exchange through Bluetooth or WLAN communication technology, which constitutes the information network architecture of the hardware flexible workshop. Provides a communication basis for machining tasks.
  • the multi-processing robot collaboration flow chart of the present invention is shown in Figure 2.
  • the multi-processing robot collaboration in the hardware workshop starts with the emergence of a new production task, and the multi-process processing of the workpiece is completed after each processing robot repeatedly performs the processing tasks, handling tasks and other steps.
  • the above-mentioned processing robots and the data links between them constitute a flexible hardware workshop that is compatible with centralized control and distributed collaborative work modes as shown in Figure 3.
  • the workshop takes local collaborative self-organizing behavior as the main behavior mode, including tasks assignment, various conflict fault resolution and other actions; secondly, through the central management node to supervise and manage the workshop production process from the upper level, supervise the working status of each processing robot and carry out Intervene appropriately to avoid massive congestion.
  • the processing robot and AGV have the capabilities of state perception, autonomous communication and autonomous decision-making.
  • the state perception capability refers to the ability to perceive and predict its own state, environmental state and current task completion;
  • the ability of autonomous decision-making refers to the ability to comprehensively analyze its own state and share information, and actively initiate related tasks or respond to task requests.
  • the simulation scene diagram of the hardware flexible workshop of the present invention is shown in Figure 4.
  • the hardware flexible workshop is organically composed of multiple modular processing units, AGVs and various corresponding supporting auxiliary equipment, which is realized by the flexible handling ability of AGV and the autonomous decision-making ability of processing robots. Efficient and flexible machining of workpieces.
  • the hardware flexible workshop can process 3 different workpieces (J1, J2, J3).
  • Each workpiece has 3 processes (P1, P2, P3) that need to be processed in sequence.
  • Table 1 there are 6 processing robots in the definition workshop, each processing robot is responsible for the processing of a certain process, and the processing time of the same process for different workpieces is different.
  • the specific parameters of the processing robot are shown in Table 2. ; Define that there are 4 homogeneous AGVs (A1, A2, A3, A4) in the workshop, each AGV can handle different workpiece raw materials, semi-finished products and finished products; there are still some processing tasks of other orders in the workshop that have not been completed.
  • a workpiece ID is defined here as Tn.
  • Each machining task can be represented by (Tn, Pm).
  • the multi-processing robot cooperation method of the present invention adopts a processing robot cooperation system to realize cooperation, and the communication strategy of the processing robot cooperation system meets the communication stability and rapidity requirements of various information exchange links of the multi-processing robot cooperation system.
  • the system aggregates the status information of each processing robot to the central management node in real time and at high speed, providing accurate and real-time information support for the centralized intervention scheduling method.
  • Each processing robot and AGV realize flexible event-driven self-organizing behavior in the wireless local area network environment.
  • the processing robots in the self-organizing area complete most tasks such as task allocation and conflict resolution through their own information sharing and information exchange.
  • It includes the processing task allocation method and the handling task allocation method; it can also include the multi-AGV path conflict resolution method and the centralized intervention scheduling method.
  • the processing task allocation method ensures that the raw material or semi-finished product enters the processing flow of the next process efficiently.
  • the handling tasks include handling tasks of raw materials, semi-finished products and finished products, and the distribution method of handling tasks ensures the efficient transfer of raw materials or semi-finished products after completing this process, and reduces the retention time of workpieces.
  • the multi-AGV path conflict resolution method avoids possible collisions of AGVs during movement.
  • the central management node uses the centralized intervention scheduling method to predict, intervene and manage the overall workshop production process from a global perspective.
  • Step 1 generate a batch of workpiece processing tasks, including a variety of different types of workpieces, as shown in Table 3;
  • step 2 the central management node distributes the processing tasks to the processing robots in the workshop in turn according to the priority of the processing tasks in the form of broadcasting.
  • the processing task allocation method mainly includes the following steps:
  • Step 2.1 (collection) the central management node sends the workpiece processing task request to the processing robot in the workshop in turn according to the task processing priority order of the task information to be processed;
  • Step 2.2 After the processing robot receives the workpiece processing task request information, it combines the task sequence in its current task list to predict the completion time point of the corresponding task in the request information, and sends the estimated completion time of the task to the central management node.
  • the central management node sends the processing tasks of (T11, P2) to all processing robots, and correspondingly, the processing robots M21 and M22 capable of processing technology P2 will complete the tasks and T11 workpieces according to the tasks to be completed in their current task sequence.
  • the processing time calculates the estimated time (T11, P2) and sends it to the central management node, as shown in Table 4,
  • step 2.3 (confirmation) the central management node preferentially assigns the task package to the processing robot with a shorter completion time point according to the feedback information of each processing robot, and sends the corresponding confirmation information to the processing robot. It is easy to get from Table 4 that the (T11, P2) machining task will be completed by M21;
  • Step 2.4 (Completion)
  • the processing robot After the processing robot receives the confirmation information, it inserts the task into the task list in the order of priority and recalculates the expected completion time of each task; the processing robot that has not received the confirmation information maintains the original state.
  • the task list before and after M21 receives task T11 is as follows:
  • Step 3 M21 calls the workpieces to be processed in sequence according to the priority order of the processing tasks, and the method for distributing the handling tasks includes the following steps:
  • Step 3.1 (collection) M21 reads the processing task in its own task list, and sends the handling request information of the task to the surrounding AGV through the local communication network;
  • Step 3.2 (response)
  • the idle AGV taking A2, A3, A4 as an example
  • the local communication network receives the handling request information, it calculates the handling task according to its own position, reclaiming position and feeding position and other information. Estimate the completion time, and send the expected time to M21 as return information. Now assume that the number of idle AGVs requested in the workshop and the expected completion time of the handling task are shown in the following table.
  • Step 3.3 (confirmation)
  • the processing robot preferentially selects the AGV with the shortest completion time according to the response information fed back, and sends the confirmation information to the AGV;
  • Step 3.4 (execute) After the AGV receives the confirmation information, it transports the material to be processed from the processing robot's output buffer of the previous process to the current process's corresponding processing robot's in-feed buffer according to the task information; no confirmation is received within a certain period of time The AGV of the information will continue to wait to receive requests for other handling tasks.
  • Step 4 After the processing robot receives the workpiece to be processed, it starts to process the workpiece in a corresponding process. After completing the processing task of the process, the processing robot performs the next processing task assignment operation, including the following steps:
  • Step 4.1 (judgment) to determine whether the processing workpiece has completed all the processing procedures, if it has been completed, perform step 5 to collect the AGV and transport it to the finished product area for storage, and end the processing task; otherwise, send the workpiece information to other surrounding processing robots.
  • step 4.1 (judgment) to determine whether the processing workpiece has completed all the processing procedures, if it has been completed, perform step 5 to collect the AGV and transport it to the finished product area for storage, and end the processing task; otherwise, send the workpiece information to other surrounding processing robots.
  • step 5 to collect the AGV and transport it to the finished product area for storage, and end the processing task; otherwise, send the workpiece information to other surrounding processing robots.
  • Step 4.2 (collection) the processing robot sends the workpiece processing task request to other processing robots in the workshop in turn according to the task processing priority order of the task information to be processed;
  • Step 4.3 (response) After the processing robot capable of completing the processing task of the process receives the workpiece processing task request information, it combines the task sequence in its current task list to predict the completion time point of the corresponding task in the request information, and assigns the task The estimated completion time of the task request is sent to the sender;
  • Step 4.4 (confirmation) the sender, according to the feedback information of each processing robot, preferentially assigns the task package to the processing robot with a shorter completion time point, and sends the corresponding confirmation information to the processing robot;
  • Step 4.5 (Completion) After the processing robot receives the confirmation message, it adds the task to the task list inserted in the order of priority and recalculates the expected completion time of each task, and goes to step 3.
  • Step 5 after the processing robot has completed the processing of the last process of the workpiece, it is responsible for completing the assignment of the workpiece storage and handling tasks, including the following steps:
  • Step 5.1 (collection) the processing robot sends the warehousing and handling request of the workpiece to the surrounding AGV through the local communication network;
  • Step 5.2 (response) After the idle AGV in the local communication network receives the handling request information, it calculates the expected completion time of the handling task according to its own position, reclaiming position and feeding position, and uses the expected time as The return information is sent to the task sender;
  • Step 5.3 (confirmation)
  • the processing robot preferentially selects the AGV with the shortest completion time according to the response information fed back, and sends the confirmation information to the AGV;
  • Step 5.4 (execution) After receiving the confirmation information, the AGV autonomously transports the completed workpiece to the finished product area according to the task information, and finally completes all the processing tasks of the workpiece.
  • each AGV and central management node in the workshop should perform steps 6 and 6 respectively. 7.
  • the task priority information can be used to alleviate the path conflicts through convenient local self-organization negotiation, as shown in Figure 5.
  • the following steps can be taken:
  • Step 6.1 (sharing) the AGV in the flexible production workshop will share its own location information, part of the path information and its own task priority information with the surrounding AGV through the local communication network during the traveling process;
  • Step 6.2 (prediction) After the AGV receives the information shared by the surrounding AGVs, it compares it with its own position and path information to determine whether there will be a path conflict problem;
  • Step 6.3 (processing) If there is a path conflict between itself and another AGV, first determine the priority order of each other's tasks, if its own priority is higher than the other, ignore the conflict; if its own priority is lower than the other, It is necessary to take evasive measures to give priority to the AGV with higher priority to pass the conflicting path.
  • the centralized scheduling method takes a long time to plan the production plan for all production tasks, and the production workshop with complex environment is prone to emergencies. Therefore, in the present invention, only centralized intervention is used. The method can predict, intervene and adjust the sudden situation in the workshop.
  • the central management node will perform the following monitoring and analysis steps,
  • Step 7.1 (monitoring) the central management node collects the status information and environmental conditions from all processing robots and AGVs in the workshop in real time through the industrial field bus;
  • Step 7.2 (analyze) Conduct centralized analysis and processing on the information collected in step 7.1, to discover and predict ongoing or future workshop failures, including processing robot failures in the workshop, sudden obstacles, vehicle congestion, etc.;
  • Step 7.3 (adjustment)
  • the central management node adopts an efficient scheduling strategy to reasonably adjust the current information such as the internal parameters, task list, and current status of each processing robot.
  • the production operation status of the workshop should be changed as little as possible.

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Abstract

本发明提供了一种五金柔性生产车间多机器人协作方法，能够实现工件加工任务的分配及工件在不同工位上的流转，同时避免较大的计算代价。本发明方法充分使用分布式协作的方法，针对现有技术状况，能够实现工件加工任务的分配及工件在不同工位上的流转，同时避免较大的计算代价。本发明采用多AGV路径冲突消解方法避免AGV在运动过程中可能发生的碰撞。本发明使用集中干预调节的方法，发现和预测系统冲突和故障问题，并做出及时调度和调整，提高五金车间的自动化水平和柔性水平。

Description

一种五金柔性生产车间多加工机器人协作方法 技术领域
本发明涉及多智能体协作技术领域，具体涉及一种五金柔性生产车间多加工机器人协作方法。
背景技术
当前五金行业均面临产品多样化、小批量、市场需求变化迅速等挑战，而传统的生产模式自动化水平低，难以应对小批量、多批次的快速产线切换需求，亟需提高自动化水平和智能化水平。模块化五金生产车间则能够很好地适应多变、灵活的生产需求。模块化的五金车间内的加工机器人只需更换工装、治具和少量的工艺模块，就可实现多类多工序产品的快速切线柔性生产。
在模块化车间的多加工机器人协作系统中，大规模加工机器人之间通信及协调方面也存在很多难题。目前面向多加工机器人协作技术，主要包括集中式和分布式两类控制方法，集中式方法主要依靠拥有较强计算能力的中心管理节点处理大量的繁杂的车间内部信息，通过灵活高效的规划算法实现对车间内部所有智能体的调度和控制；分布式方法主要依赖智能加工机器人的自主感知、通信和决策能力，通过一定的任务分配方法(如基于市场机制的方法)，在车间内部实现分布式自组织的高效协作。上述方法各有利弊，集中式方法过度依赖中心管理节点，一旦中心管理节点瘫痪，则车间将无法继续进行加工任务；而对于分布式方法，虽然对中心管理节点的依赖性较低，但由于每个智能体只能获得局部信息，车间内部容易出现拥堵，碰撞等问题。
目前，已有研究人员对车间加工机器人的任务分配方法进行了深入研究并已申请了大量的知识产权，但其中大部分研究是围绕同构自动导引小车 (Automated Guided Vehicle，简称AGV)研究其集中式的动态调度方法，部分虽涉及了分布式动态任务分配，但依然仅限于对AGV的任务分配,对于五金柔性车间中多品种、小批量产品的加工，复杂车间环境内容易出现的各种冲突和故障。
发明内容
有鉴于此，本发明提供了一种五金柔性车间多加工机器人协作方法，能够实现工件加工任务的分配及工件在不同工位上的流转，同时避免较大的计算代价。
为实现上述目的，本发明的技术方案如下，
一种五金柔性生产车间多加工机器人协作方法，采用加工机器人协作系统实现协作，所述多加工机器人协作系统利要总线将各加工机器人状态信息实时汇聚于中心管理节点；各加工机器人和AGV在无线局域网络环境下实现事件驱动的通信和信息共享，完成任务分配；
包括如下步骤：
步骤1，产生一个批次的工件加工任务，包含多种不同类型的加工工件；
步骤2，中心管理节点进行加工任务分配，包括如下步骤：
步骤2.1，加工任务发送方通过通信网络将待加工的任务信息按照任务加工优先级顺序，依次向其他加工机器人发送工件加工任务请求；
步骤2.2，加工机器人收到工件加工任务请求信息后，结合自身当前任务列表中的任务序列，预测请求信息中相应任务的完成时间点，并将该任务的预计完成时间发送给加工任务发送方；
步骤2.3，加工任务发送方根据各加工机器人的反馈的信息，优先将该任务包分配给预计完成时间点较近的加工机器人，并将对应的确认信息发送给该加工机器人；
步骤2.4，加工机器人收到确认信息后，将该任务按照优先级顺序插入到任 务列表中，并重新计算每个任务的预期完成时间；未收到确认信息的加工机器人维持原来状态；
步骤3，按照加工任务优先级顺序依次召集待加工工件，搬运任务分配方法包括如下步骤：
步骤3.1，加工机器人读取任务列表中的加工任务，并通过局部通信网络向周边AGV发送该任务的搬运请求信息；
步骤3.2，处于局部通信网络中的空闲AGV收到该搬运请求信息后，根据自身位置、取料位置和送料位置信息，计算该搬运任务的预计完成时间，并将该预期时间作为返回信息发给任务发送方；
步骤3.3，加工机器人根据反馈回来的响应信息，优先选择完成时间最短AGV，并将确认信息发送给该AGV；
步骤3.4，AGV收到确认信息后，根据任务信息将待加工物料从上一工序加工机器人出料缓冲区搬运至当前工序对应加工机器人入料缓冲区；在一定时间内未收到确认信息的AGV，将继续等待接收其他搬运任务的请求；
步骤4，加工机器人收到待加工工件后，开始对该工件进行相应工序的加工，加工机器人在完成该道工序加工任务之后，执行下一步的加工任务分配操作，包括如下步骤：
步骤4.1，判断加工工件是否已经完成了所有的加工工序，若已经完成则执行步骤5征集AGV搬运至成品区入库，结束加工任务；否则，向周围其他加工机器人发出该工件的下一工序加工任务请求；
步骤4.2，加工机器人将待加工的任务信息，按照任务加工优先级顺序，依次向车间内的其他加工机器人发送工件加工任务请求；
步骤4.3，有能力完成该工序加工任务的加工机器人收到工件加工任务请求信息后，结合自身当前任务列表中的任务序列，预测请求信息中相应任务的完成时间点，并将该任务的预计完成时间发送给任务请求发送方；
步骤4.4，发送方根据各加工机器人的反馈的信息，优先将该任务包分配给完成时间点最短的加工机器人，并将对应的确认信息发送给该加工机器人；
步骤4.5，加工机器人收到确认信息后，将该任务添加到按照优先级顺序插入到任务列表中并重新计算每个任务的预期完成时间，并转至步骤3；
步骤5，加工机器人完成了工件最后一道工序的加工后，负责完成该工件的入库搬运任务分配，包括如下步骤：
步骤5.1，加工机器人通过局部通信网络向周边AGV发出工件的入库搬运请求；
步骤5.2，处于局部通信网络中的空闲AGV收到该搬运请求信息后，根据自身位置、取料位置和送料位置信息，计算该搬运任务的预计完成时间，并将该预期时间作为返回信息发给任务发送方；
步骤5.3，加工机器人根据反馈回来的响应信息，优先选择完成时间最短的AGV，并将确认信息发送给该AGV；
步骤5.4，AGV收到确认信息后，根据任务信息自主地将已完成工件搬运至成品区，最终完成了该工件全部的加工任务。
其中，还包括多AGV路径冲突消解方法，包括如下步骤：
步骤6.1，柔性生产车间内的AGV在行进过程中，,通过局部通信网络与周边AGV共享自身的位置信息、部分路径信息以及自身任务优先级信息；
步骤6.2，AGV在收到周边AGV共享信息后，与自身的位置及路径信息作比较，判断是否会出现路径冲突问题；
若自身与另一个AGV出现路径冲突问题，则首先判断彼此任务优先级顺序，若自身优先级高于另一个，则忽略该冲突问题；若自身优先级低于另一个，则采取避让措施，令优先级较高的AGV通过存在冲突的路径。
其中，采用集中干预调度方法发现和预测系统冲突和故障问题，包括如下步骤：
步骤7.1，中心管理节点通过工业现场总线实时地收集来自车间内部的信息，包括所有加工机器人、AGV的状态信息及环境状况；
步骤7.2，对步骤7.1收集到的信息进行集中分析处理，发现和预测正在发生或未来可能发生的车间故障，包括车间内加工机器人故障、车间内障碍以及 交通拥堵；
步骤7.3，根据步骤7.2的结果，中心管理节点采用调度策略，对当前每个智能体内部参数、任务列表和当前状态信息进行调整，以最小幅度改变车间生产运行状态解除正在发生或未来可能发生的车间故障。
其中，所述加工任务发送方为中心管理节点或加工机器人。
有益效果，
本发明方法充分使用分布式协作的方法，针对现有技术状况，能够实现工件加工任务的分配及工件在不同工位上的流转，同时避免较大的计算代价。
本发明采用多AGV路径冲突消解方法避免AGV在运动过程中可能发生的碰撞。
本发明使用集中干预调节的方法，发现和预测系统冲突和故障问题，并做出及时调度和调整，提高五金车间的自动化水平和柔性水平。
附图说明
图1为本发明五金柔性车间通信拓扑图。
图2为本发明多加工机器人协作流程图。
图3为本发明五金柔性车间控制结构图。
图4为本发明五金柔性车间仿真场景图。
图5为本发明解决AGV路径冲突示意图。
图6为本发明解决AGV拥堵示意图。
具体实施方式
现结合附图对本发明做进一步详细描述。
本发明提出的五金柔性车间的多加工机器人协作方法以加工机器人为车间内主要加工任务和搬运任务的主导发起方，通过与周边各类加工机器人的交流 协作实现任务的快速分配和执行；综合运用分布式协作和集中式干预调配等方法，解决车间内多种可能发生的故障和拥堵问题，提高了系统的柔性化水平和自动化水平。具体地，本发明将围绕包括多种加工机器人和AGV的模块化柔性生产车间，研究上述各类加工机器人的分布式任务分配方法。本发明中分布式协作方法的应用，将实现多品种工件的灵活加工和工序间物料的柔性流转，极大提高生产车间的生产灵活性和可拓展性。
本发明五金柔性车间通信拓扑图如图1所示，存在具有通信决策能力的三种节点分别是中心管理节点、搬运加工机器人和加工机器人。其中，中心管理节点通过工业总线等通信网络与其他各节点链接；各加工机器人与搬运加工机器人通过灵活的通信方式进行局部自组织链接完成信息交互共享。五金柔性车间主要包括中心管理节点、加工机器人和AGV三类具有决策能力的智能体。其中，中心管理节点通过工业现场总线等通讯技术与加工机器人和AGV保持实时通讯；加工机器人与AGV通过蓝牙或WLAN通讯技术实现局部低频的信息交互，以此构成了五金柔性车间的信息网络架构，为加工任务提供通信基础。
本发明多加工机器人协作流程图如图2所示，五金车间多加工机器人协作由出现新的生产任务开始，经过各加工机器人反复执行加工任务、搬运任务等步骤，完成工件的多工序的加工。
上面所述各加工机器人及其之间的数据链路构成了如图3所示的能够兼容集中式控制和分布式协作工作模式的柔性五金车间。车间以局部的协作自组织行为为主要的行为方式，包含了任务分配、各类冲突故障消解等动作；其次通过中心管理节点从上层监督和管理车间生产过程，监督各加工机器人的工作状态并进行适当干预，避免出现大规模拥塞。所述加工机器人和AGV具有状态感知、自主通信和自主决策等能力，状态感知能力指能够对自身状态、环境状态 及当前的任务完成情况具有一定的感知和预测能力；自主通信能力指能够通过通信网络主动与周围其他加工机器人进行信息共享与交流的能力；自主决策能力指能够综合分析自身状态及共享信息，主动发起相关任务或响应任务请求的能力。
本发明五金柔性车间仿真场景图如图4所示，五金柔性车间以多个模块化加工单元、AGV及对应的各类配套辅助设备有机构成，通过AGV灵活的搬运能力及加工机器人自主决策能力实现工件的高效柔性加工。
如图4所示的五金柔性车间，共可加工3种不同的工件(J1,J2,J3)，每个工件分别有3道工艺(P1,P2,P3)需要依次加工，不同工件的加工工序如表1所示；定义车间内共有6台加工机器人，每台加工机器人负责某一工艺的加工，且对不同工件的相同的工艺的加工时间是不同的，加工机器人具体参数如表2所示；定义车间内共有4辆同构AGV(A1,A2,A3,A4)，每个AGV均能够搬运不同的工件原材料、半成品及成品；当前车间内部仍然存在部分其他订单的加工任务尚未完成。在此定义一个工件ID表示为Tn。每个加工任务可由(Tn,Pm)表示。
对车间内部工件和加工机器人关键参数列表如下，
表1工件类别及工序
工件种类 J1 J2 J3
加工工序 P1->P2->P3 P2->P1->P3 P1->P3->P2
表2加工机器人关键参数
Figure PCTCN2021100899-appb-000001
Figure PCTCN2021100899-appb-000002
本发明的多加工机器人协作方法采用加工机器人协作系统实现协作，所述加工机器人协作系统的通信策略满足多加工机器人协作系统各种信息交流环节的通信稳定性快速性要求，其中，多加工机器人协作系统在工业现场总线等技术的基础上，将各加工机器人状态信息实时高速的汇聚于中心管理节点，为集中干预调度方法提供准确实时的信息支持。各加工机器人和AGV在无线局域网络环境下实现灵活的事件驱动的自组织行为，自组织区域内部的加工机器人通过各自信息共享和信息交流，完成大部分的任务分配，冲突消解等任务。
包括加工任务分配方法和搬运任务分配方法；还可以包括多AGV路径冲突消解方法和集中干预调度方法。
加工任务分配方法确保原材料或半成品高效进入下一道工序的加工流程。
所述搬运任务包括原材料的搬运任务、半成品和成品的搬运任务，搬运任务的分配方法确保原材料或半成品在完成本道工序后的高效转运，减少工件的滞留时间。
多AGV路径冲突消解方法避免AGV在运动过程中可能发生的碰撞。
中心管理节点使用集中干预调度方法从全局的角度实现对整体车间生产过程的预测、干预和管理。
具体步骤如下：
步骤1，产生一个批次的工件加工任务，包含多种不同类型的加工工件，如表3所示；
表3订单中各工件参数信息
工件ID T11 T12 T13 T14 T15 T16 T17
工件种类 J2 J3 J3 J2 J1 J1 J2
优先级 7 6 5 4 3 2 1
步骤2，中心管理节点通过广播形式，按照加工任务的优先级大小，依次将这些加工任务分配给车间内的加工机器人，加工任务分配方法主要包括如下步骤：
步骤2.1，(征集)中心管理节点将待加工的任务信息，按照任务加工优先级顺序，依次向车间内的加工机器人发送工件加工任务请求；
步骤2.2，(响应)加工机器人收到工件加工任务请求信息后，结合自身当前任务列表中的任务序列，预测请求信息中相应任务的完成时间点，并将该任务的预计完成时间发送给中心管理节点。以工件T11为例，中心管理节点向所有加工机器人发送了(T11,P2)的加工任务，对应地能够加工工艺P2的加工机器人M21,M22将根据自身当下任务序列中要完成的任务及T11工件加工时间计算出(T11,P2)预计时间并发送给中心管理节点，如表4所示，
表4各加工机器人对(T11,P2)预计完成时间
加工机器人 M21 M22
对(T11,P2)任务的预计完成时间 84 91
步骤2.3，(确认)中心管理节点根据各加工机器人反馈的信息，优先将该任务包分配给完成时间点较短的加工机器人，并将对应的确认信息发送给该加工机器人。从表4容易得到，(T11,P2)加工任务将由M21完成；
步骤2.4，(完成)加工机器人收到确认信息后，将该任务按照优先级顺序插入到任务列表中并重新计算每个任务的预期完成时间；未收到确认信息的加工机器人维持原来状态。M21收到任务T11前后任务列表如下所示，
表5 M21任务列表更新示意
原M21任务列表 T1 T3 T2 T4 T5 T7 T8 T6 T9 T10    
优先级 8 8 6 6 6 5 4 4 3 1    
预计完成时间 72 80 84 88 90 98 106 110 114 122    
现M21任务列表 T1 T3 T11 T2 T4 T5 T7 T8 T6 T9 T10  
优先级 8 8 7 6 6 6 5 4 4 3 1  
预计完成时间 72 80 84 88 92 94 102 110 114 118 126  
步骤3，M21按照加工任务的优先级顺序依次召集待加工工件，搬运任务分配方法包括如下步骤：
步骤3.1，(征集)M21读取自身任务列表中的加工任务，并通过局部通信网络向周边AGV发送该任务的搬运请求信息；
步骤3.2，(响应)处于局部通信网络中的空闲AGV(以A2、A3、A4为例)收到该搬运请求信息后，根据自身位置、取料位置和送料位置等信息，计算该搬运任务的预计完成时间，并将该预期时间作为返回信息发给M21。现假定车间内收到请求的空闲AGV编号及对该搬运任务的预期完成时间如下表所示，
表6空闲AGV对T1搬运任务的完成时间
Figure PCTCN2021100899-appb-000003
步骤3.3，(确认)加工机器人根据反馈回来的响应信息，优先选择完成时间最短AGV，并将确认信息发送给该AGV；
步骤3.4，(执行)AGV收到确认信息后，根据任务信息将待加工物料从上一工序加工机器人出料缓冲区搬运至当前工序对应加工机器人入料缓冲区；在一定时间内未收到确认信息的AGV，将继续等待接收其他搬运任务的请求。
步骤4，加工机器人收到待加工工件后，开始对该工件进行相应工序的加工。加工机器人在完成该道工序加工任务之后，执行下一步的加工任务分配操作，包括如下步骤：
步骤4.1，(判断)判断该加工工件是否已经完成了所有的加工工序，若已经完成则执行步骤5征集AGV搬运至成品区入库，结束加工任务；否则，向周围其他加工机器人发出该工件的下一工序加工任务请求；
步骤4.2，(征集)加工机器人将待加工的任务信息，按照任务加工优先级顺序，依次向车间内的其他加工机器人发送工件加工任务请求；
步骤4.3，(响应)有能力完成该工序加工任务的加工机器人收到工件加工任务请求信息后，结合自身当前任务列表中的任务序列，预测请求信息中相应任务的完成时间点，并将该任务的预计完成时间发送给任务请求发送方；
步骤4.4，(确认)发送方根据各加工机器人的反馈的信息，优先将该任务包分配给完成时间点较短的加工机器人，并将对应的确认信息发送给该加工机器人；
步骤4.5，(完成)加工机器人收到确认信息后，将该任务添加到按照优先级顺序插入到任务列表中并重新计算每个任务的预期完成时间，并转至步骤3。
步骤5，加工机器人完成了工件最后一道工序的加工后，负责完成该工件的入库搬运任务分配，包括如下步骤：
步骤5.1，(征集)加工机器人通过局部通信网络向周边AGV发出工件的入库搬运请求；
步骤5.2，(响应)处于局部通信网络中的空闲AGV收到该搬运请求信息后，根据自身位置、取料位置和送料位置等信息，计算该搬运任务的预计完成时间，并将该预期时间作为返回信息发给任务发送方；
步骤5.3，(确认)加工机器人根据反馈回来的响应信息，优先选择完成时间最短的AGV，并将确认信息发送给该AGV；
步骤5.4，(执行)AGV收到确认信息后，根据任务信息自主地将已完成工件搬运至成品区，最终完成了该工件全部的加工任务。
在柔性加工车间运行过程中，多个AGV有可能因车间空间有限而出现路径冲突甚至是拥堵问题，为了避免这类问题的出现，车间内的各AGV和中心管理 节点应分别执行步骤6和步骤7。
当少数AGV出现路径冲突时，以任务优先级信息通过便捷的局部自组织协商即可缓解路径冲突，如图5所示，为了解决少量AGV如下所示的路径冲突问题，可以采取以下步骤，
步骤6.1，(共享)柔性生产车间内的AGV在行进过程中，会通过局部通信网络对向周边AGV共享自身的位置信息，部分路径信息以及自身任务优先级信息；
步骤6.2，(预测)AGV在收到周边AGV共享信息后，与自身的位置及路径信息作比较，判断是否会出现路径冲突问题；
步骤6.3，(处理)若自身与另一个AGV出现路径冲突问题，则首先判断彼此任务优先级顺序，若自身优先级高于另一个，则忽略该冲突问题；若自身优先级低于另一个，则需要采取避让措施，优先令优先级较高的AGV通过存在冲突的路径。
对于担任大量多品种工件加工任务的柔性生产车间，集中式调度的方法对所有的生产任务规划生产方案耗时长，且环境复杂的生产车间容易出现突发情况，因此本发明中仅仅通过集中式干预的方法对车间中突发的状况进行预测、干预和调节。中心管理节点将执行以下的监测和分析等步骤，
步骤7.1，(监测)中心管理节点通过工业现场总线实时地收集来自车间内部所有加工机器人、AGV的状态信息及环境状况；
步骤7.2，(分析)对步骤7.1收集到的信息进行集中分析处理，发现和预测正在发生或未来可能发生的车间故障，包括车间内加工机器人故障、突然出现的障碍、车辆拥堵等；
步骤7.3，(调节)根据步骤7.2的结果，中心管理节点采用高效的调度策略， 对当前每个加工机器人内部参数、任务列表、当前状态等信息进行合理地调整，在成功解除正在发生或未来可能发生的车间故障的前提下，以尽可能小幅度地改变车间生产运行状态。
比如当出现大规模的AGV任务拥塞时，局部协商很难完成路径冲突消解工作，为了解决这类拥塞情形，需要中心管理节点实时监督进行及时预测和干预，如图6所示。为了充分预测和干预AGV和加工机器人的运行情况，避免拥堵等类似问题出现。
综上所述，以上仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (4)

  1. 一种五金柔性生产车间多加工机器人协作方法，其特征在于，采用加工机器人协作系统实现协作，所述多加工机器人协作系统利用总线将各加工机器人状态信息实时汇聚于中心管理节点；各加工机器人和AGV在无线局域网络环境下实现事件驱动的通信和信息共享，完成任务分配；
    包括如下步骤：
    步骤1，产生一个批次的工件加工任务，包含多种不同类型的加工工件；
    步骤2，中心管理节点进行加工任务分配，包括如下步骤：
    步骤2.1，加工任务发送方通过通信网络将待加工的任务信息按照任务加工优先级顺序，依次向其他加工机器人发送工件加工任务请求；
    步骤2.2，加工机器人收到工件加工任务请求信息后，结合自身当前任务列表中的任务序列，预测请求信息中相应任务的完成时间点，并将该任务的预计完成时间发送给加工任务发送方；
    步骤2.3，加工任务发送方根据各加工机器人的反馈的信息，优先将该任务包分配给预计完成时间点较近的加工机器人，并将对应的确认信息发送给该加工机器人；
    步骤2.4，加工机器人收到确认信息后，将该任务按照优先级顺序插入到任务列表中，并重新计算每个任务的预期完成时间；未收到确认信息的加工机器人维持原来状态；
    步骤3，按照加工任务优先级顺序依次召集待加工工件，搬运任务分配方法包括如下步骤：
    步骤3.1，加工机器人读取任务列表中的加工任务，并通过局部通信网络向周边AGV发送该任务的搬运请求信息；
    步骤3.2，处于局部通信网络中的空闲AGV收到该搬运请求信息后，根据自身位置、取料位置和送料位置信息，计算该搬运任务的预计完成时间，并将该预期时间作为返回信息发给任务发送方；
    步骤3.3，加工机器人根据反馈回来的响应信息，优先选择完成时间最短AGV，并将确认信息发送给该AGV；
    步骤3.4，AGV收到确认信息后，根据任务信息将待加工物料从上一工序加工机器人出料缓冲区搬运至当前工序对应加工机器人入料缓冲区；在一定时间内未收到确认信息的AGV，将继续等待接收其他搬运任务的请求；
    步骤4，加工机器人收到待加工工件后，开始对该工件进行相应工序的加工，加工机器人在完成该道工序加工任务之后，执行下一步的加工任务分配操作，包括如下步骤：
    步骤4.1，判断加工工件是否已经完成了所有的加工工序，若已经完成则执行步骤5征集AGV搬运至成品区入库，结束加工任务；否则，向周围其他加工机器人发出该工件的下一工序加工任务请求；
    步骤4.2，加工机器人将待加工的任务信息，按照任务加工优先级顺序，依次向车间内的其他加工机器人发送工件加工任务请求；
    步骤4.3，有能力完成该工序加工任务的加工机器人收到工件加工任务请求信息后，结合自身当前任务列表中的任务序列，预测请求信息中相应任务的完成时间点，并将该任务的预计完成时间发送给任务请求发送方；
    步骤4.4，发送方根据各加工机器人的反馈的信息，优先将该任务包分配给完成时间点最短的加工机器人，并将对应的确认信息发送给该加工机器人；
    步骤4.5，加工机器人收到确认信息后，将该任务添加到按照优先级顺序插入到任务列表中并重新计算每个任务的预期完成时间，并转至步骤3；
    步骤5，加工机器人完成了工件最后一道工序的加工后，负责完成该工件的入库搬运任务分配，包括如下步骤：
    步骤5.1，加工机器人通过局部通信网络向周边AGV发出工件的入库搬运请求；
    步骤5.2，处于局部通信网络中的空闲AGV收到该搬运请求信息后，根据自身位置、取料位置和送料位置信息，计算该搬运任务的预计完成时间，并将该预期时间作为返回信息发给任务发送方；
    步骤5.3，加工机器人根据反馈回来的响应信息，优先选择完成时间最短的 AGV，并将确认信息发送给该AGV；
    步骤5.4，AGV收到确认信息后，根据任务信息自主地将已完成工件搬运至成品区，最终完成了该工件全部的加工任务。
  2. 如权利要求1所述的五金柔性生产车间多加工机器人协作方法，其特征在于，还包括多AGV路径冲突消解方法，包括如下步骤：
    步骤6.1，柔性生产车间内的AGV在行进过程中，,通过局部通信网络与周边AGV共享自身的位置信息、部分路径信息以及自身任务优先级信息；
    步骤6.2，AGV在收到周边AGV共享信息后，与自身的位置及路径信息作比较，判断是否会出现路径冲突问题；
    若自身与另一个AGV出现路径冲突问题，则首先判断彼此任务优先级顺序，若自身优先级高于另一个，则忽略该冲突问题；若自身优先级低于另一个，则采取避让措施，令优先级较高的AGV通过存在冲突的路径。
  3. 如权利要求1或2所述的五金柔性生产车间多加工机器人协作方法，其特征在于，采用集中干预调度方法发现和预测系统冲突和故障问题，包括如下步骤：
    步骤7.1，中心管理节点通过工业现场总线实时地收集来自车间内部的信息，包括所有加工机器人、AGV的状态信息及环境状况；
    步骤7.2，对步骤7.1收集到的信息进行集中分析处理，发现和预测正在发生或未来可能发生的车间故障，包括车间内加工机器人故障、车间内障碍以及交通拥堵；
    步骤7.3，根据步骤7.2的结果，中心管理节点采用调度策略，对当前每个智能体内部参数、任务列表和当前状态信息进行调整，以最小幅度改变车间生产运行状态解除正在发生或未来可能发生的车间故障。
  4. 如权利要求1所述的五金柔性生产车间多加工机器人协作方法，其特征在于，所述加工任务发送方为中心管理节点或加工机器人。
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