WO2017063453A1 - 一种工业机器人工艺云***及其工作方法 - Google Patents

一种工业机器人工艺云***及其工作方法 Download PDF

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Publication number
WO2017063453A1
WO2017063453A1 PCT/CN2016/096742 CN2016096742W WO2017063453A1 WO 2017063453 A1 WO2017063453 A1 WO 2017063453A1 CN 2016096742 W CN2016096742 W CN 2016096742W WO 2017063453 A1 WO2017063453 A1 WO 2017063453A1
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industrial robot
cloud
cloud server
working method
data
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PCT/CN2016/096742
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English (en)
French (fr)
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游玮
许礼进
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埃夫特智能装备股份有限公司
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Priority to JP2018516032A priority Critical patent/JP6615331B2/ja
Priority to US15/744,979 priority patent/US11119470B2/en
Priority to EP16854835.2A priority patent/EP3335842A4/en
Publication of WO2017063453A1 publication Critical patent/WO2017063453A1/zh

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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]
    • G05B19/4185—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 network communication
    • G05B19/41855—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 network communication by local area network [LAN], network structure
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • 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]
    • 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]
    • G05B19/4183—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 data acquisition, e.g. workpiece identification
    • 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]
    • G05B19/4185—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 network communication
    • G05B19/4186—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 network communication by protocol, e.g. MAP, TOP
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/40—Network security protocols
    • 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
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31422—Upload, download programs, parameters from, to station to, from server
    • 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
    • G05B2219/40383—Correction, modification program by detection type workpiece
    • 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]

Definitions

  • the invention relates to the technical field of industrial robot control and process operation, in particular to an industrial robot process cloud system and a working method thereof.
  • the present invention provides an industrial robot process cloud system and a working method thereof to solve the deficiencies in the prior art.
  • An industrial robot process cloud system includes an industrial robot control system integrating a human-machine interaction layer HMI, a motion planning control layer Montion, and a servo loop control layer Servo.
  • a cloud server that includes a process expert system.
  • the invention realizes the related functions of the process software package used by the traditional industrial robot to store and run the process expert system of the cloud server, does not occupy the hardware resources of the local control system of the industrial robot, has simple software structure, low cost, and relies on Smart and dynamic in the powerful hardware resources of the cloud server The establishment of an expert system.
  • the human-computer interaction layer HMI and the motion planning control layer Montion implement data interaction with the cloud server through a specific data interaction communication protocol by means of a network, and the human-computer interaction layer HMI inputs the job information and transmits it to the cloud server.
  • the cloud server After searching for the ready-made template program in the process expert system or performing similar comparison and inference calculation, the cloud server forms a specific robot operation program and downloads it into the industrial robot control system via the network.
  • the industrial robot control system integrates a robot sensing sensor layer sensor for sensing real-time data of the industrial robot, and the robot sensing sensor layer sensor includes a motor encoder and a current sensor, and transmits the final operational data of the industrial robot to the workpiece in batch operation.
  • the final running data also contains the underlying servo control layer Servo data.
  • the cloud server completes the optimization, learning and evolution of the process expert system.
  • the process expert system has real-time continuous learning and perfect functions, real-time update, so that users always get the latest process support, timeliness is strong, at the same time, the cloud server hardware resources will not have the same bottleneck as local hardware resources.
  • the process expert system includes a welding sub-cloud, a spray sub-cloud, a cutting sub-cloud, a scorpion cloud, an assembly sub-cloud, a smear cloud, and a grinding, polishing, deburring cloud, and the sub-cloud can be continuously increased according to the type of operation.
  • the invention carries out cloud storage of various processing types, utilizes network for sharing and real-time evolutionary learning, thereby facilitating the user to call the program closest to the real demand in real time, and solving the problem that the traditional industrial robot is inefficient in programming.
  • the network is a 3G/4G/5G mobile communication network or an Ethernet existing in the form of WIFI or physical connection, and adopts a common high-speed mobile communication network or Ethernet to satisfy the data interaction requirement of the present invention and ensure data transmission. Timeliness.
  • the present invention will be based on high speed network technology, and a process expert system with learning and evolution capabilities running on a cloud server is defined as a process cloud to cooperate with other parts of the present invention to form an industrial robot process cloud system.
  • a working method of an industrial robot process cloud system comprising the following steps:
  • the first step obtaining the 3D digital-analog input of the finished sample to the industrial robot control system, and passing Man-machine interaction layer HMI input processing parameters of industrial robots;
  • Step 2 The industrial robot control system uses the specific communication protocol to transmit the relevant data obtained in the first step to the cloud server;
  • the third step the cloud server downloads the operating program to the industrial robot control system through the network;
  • the fourth step after the field engineer simulates and confirms, controls the industrial robot to conduct trial production;
  • Step 5 Test the sample after the trial production to ensure that the sample meets the technical requirements
  • Step 6 Formal production after passing the test
  • Step 7 Upload the data collected by the industrial robots and sensors after the formal production process to the cloud server via the network;
  • Step 8 In the cloud server, the original downloaded robot operation program is compared with the actual officially produced robot operation program, and the online expert manually performs the process expert system correction and supplement or uses the intelligent algorithm such as deep learning to automatically complete the pair.
  • the data and rules of the process expert system are perfected, enabling the process expert system to complete learning and evolution.
  • the first step may be to directly obtain a three-dimensional digital model of the finished sample by computer aided design.
  • the first step may also be to obtain a three-dimensional digital model of the finished sample by three-dimensional scanning.
  • the processing parameters input in the first step include the material and the processing requirements of the workpiece.
  • the machining parameters can be input through the industrial robot human-machine interaction layer HMI by manual input.
  • the equipment property can also be automatically detected by the equipment instrument, and the processing parameters are input through the industrial robot human-machine interaction layer HMI.
  • the manner of automatically detecting the attribute of the workpiece by using the device instrument may be a method of reading a barcode detection or a method of reading an RFID detection.
  • the cloud server may be downloaded into the industrial robot control system by the online expert manual intervention program according to the workpiece information, and the solution and the operation program are stored in the cloud process expert system.
  • the cloud server can automatically search and calculate in the process expert system, and determine whether the search and the calculation are converged in real time. After convergence, the operating program is downloaded into the industrial robot control system through the network.
  • the data does not converge, it is manually intervened and adjusted by an online expert, that is, automatically The searched industrial robot operating program is corrected to converge the data.
  • the operating procedure of the industrial robot is corrected and adjusted by the offline engineer.
  • the data of the industrial robot entering the formal production process can be directly uploaded to the cloud server by the offline engineer.
  • the data collected by the industrial robots and sensors after entering the formal production process are automatically collected in real time and uploaded to the cloud server by means of the network.
  • the invention has the beneficial effects that the invention adopts cloud storage, cloud computing, big data mining and deep learning, realizes cloud processing on process experience of various industrial robot operation types, and uses high-speed mobile communication network or Ethernet for data processing.
  • Interactive and real-time evolutionary learning so that users can call the program closest to the real needs in real time, greatly improve the efficiency of production change, share a lot of useful industrial robot process experience, solve the traditional industrial robot dependence, and be subject to the final.
  • the user's own experience of robot operation and maintenance personnel and technicians effectively reduces the technical threshold and operation and maintenance cost of industrial robots.
  • the present invention utilizes a high-speed network to collect industrial robot operation data in real time, and uses data mining and cloud computing to complete the process.
  • the online learning and evolution of the expert system ensures that the user always receives the latest and optimal process support, avoiding the problems of poor timeliness, difficulty in updating, and low intelligence brought by the static process package of the traditional industrial robot; the present invention utilizes the cloud Hardware resource completion Knowledge of storage, computing, reduce local hardware industrial robot controller occupation, software architecture is simple, low cost, easy to standardize.
  • Figure 1 is a schematic diagram of the principle of the present invention
  • Figure 2 is a flow chart showing the operation of the first embodiment of the present invention.
  • Figure 3 is a flow chart showing the operation of the second embodiment of the present invention.
  • an industrial robot process cloud system includes an industrial robot control system integrating an operator interaction layer HMI, a motion planning control layer Montion, and a servo loop control layer Servo, and a cloud server including a process. expert system.
  • the invention realizes the related functions of the process software package used by the traditional industrial robot to store and run the process expert system of the cloud server, does not occupy the hardware resources of the local control system of the industrial robot, has simple software structure, low cost, and relies on The powerful hardware resources of the cloud server enable the construction of intelligent and dynamic expert systems.
  • the human-computer interaction layer HMI and the motion planning control layer Montion implement data interaction with the cloud server through a specific data interaction communication protocol by means of a network, and the human-computer interaction layer HMI inputs the job information and transmits it to the cloud server.
  • the cloud server After searching for the ready-made template program in the process expert system or performing similar comparison and inference calculation, the cloud server forms a specific robot operation program and downloads it into the industrial robot control system via the network.
  • the industrial robot control system integrates a robot sensing sensor layer sensor for sensing real-time data of the industrial robot, and the robot sensing sensor layer sensor includes a motor encoder and a current sensor, and transmits the final operational data of the industrial robot to the workpiece in batch operation.
  • the final running data also contains the underlying servo control layer Servo data.
  • the cloud server completes the optimization, learning and evolution of the process expert system.
  • the process expert system has real-time continuous learning and perfect functions, real-time update, so that users always get the latest process support, timeliness is strong, at the same time, the cloud server hardware resources will not have the same bottleneck as local hardware resources.
  • the process expert system includes a welding sub-cloud, a spray sub-cloud, a cutting sub-cloud, a scorpion cloud, an assembly sub-cloud, a smear cloud, and a grinding, polishing, deburring cloud, and the sub-cloud can be continuously increased according to the type of operation.
  • the invention carries out cloud storage of various processing types, utilizes network for sharing and real-time evolutionary learning, thereby facilitating the user to call the program closest to the real demand in real time, and solving the problem that the traditional industrial robot is inefficient in programming.
  • the network is a mobile communication network such as 3G, 4G, 5G or the like, or an Ethernet existing in the form of WIFI or physical connection, and the data interaction of the present invention can be satisfied by using an ordinary high-speed mobile communication network or Ethernet. Requirements, to ensure the timeliness of data transmission.
  • the present invention will be based on high speed network technology, and a process expert system with learning and evolution capabilities running on a cloud server is defined as a process cloud to cooperate with other parts of the present invention to form an industrial robot process cloud system.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • a working method of an industrial robot process cloud system includes the following steps:
  • the first step is to directly obtain the three-dimensional digital model of the finished sample through computer-aided design, or obtain the three-dimensional digital model of the finished sample through three-dimensional scanning, and obtain the three-dimensional digital model of the finished sample by manual input or equipment.
  • the instrument automatically detects the workpiece properties, that is, obtains the relevant information of the blank sample, and inputs the machining parameters through the human-machine interaction layer HMI of the industrial robot.
  • the processing parameters include the material and the processing requirements of the workpiece, and the method of automatically detecting the workpiece attribute by using the equipment instrument can be Read the barcode detection method or read the RFID detection method;
  • Step 2 The industrial robot control system uses the specific communication protocol to transmit the relevant data obtained in the first step to the cloud server;
  • the third step the cloud server downloads the operating program into the industrial robot control system according to the workpiece information, and the solution and the operating program are stored in the process expert system.
  • the fourth step after the field engineer simulates and confirms, controls the industrial robot to conduct trial production;
  • Step 5 Test the sample after the trial production to ensure that the sample meets the technical requirements. If the test fails, the offline robot will correct and adjust the operating procedures of the industrial robot;
  • Step 6 Formal production after passing the test
  • Step 7 Upload the data of the industrial robot entering the formal production process directly to the cloud server through the offline engineer, or automatically collect the data collected by the industrial robot and sensor after entering the formal production process, and upload it to the network through the network.
  • Cloud server
  • Step 8 In the cloud server, the original downloaded robot operation program is compared with the actual officially produced robot operation program, and the online engineer manually performs the process expert system correction and supplement or uses the intelligent algorithm such as deep learning to automatically complete the pair.
  • the data and rules of the process expert system are perfected, enabling the process expert system to complete learning and evolution.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • a working method of an industrial robot process cloud system includes the following steps:
  • the first step is to directly obtain the three-dimensional digital model of the finished sample through computer-aided design, or obtain the three-dimensional digital model of the finished sample through three-dimensional scanning, and obtain the three-dimensional digital model of the finished sample, and then input it into the industrial robot control system, adopting
  • the manual input method or the equipment instrument automatically detects the workpiece attributes, that is, the relevant information of the blank sample is obtained, and the processing parameters are input through the HMI of the industrial robot human-machine interaction layer.
  • the processing parameters include the material and the processing requirements of the workpiece, and are automatically detected by the equipment instrument.
  • the way of the workpiece attribute is the way of reading bar code detection or reading the way of RFID detection;
  • Step 2 The industrial robot control system uses the specific communication protocol to transmit the relevant data obtained in the first step to the cloud server;
  • the third step the cloud server searches and calculates in the process expert system, and judges whether the search and the calculation converge in real time. After convergence, the operating program is downloaded into the industrial robot control system through the network. If the data does not converge, the online expert Manual intervention and adjustment, that is, the industrial robot operating program obtained by the automatic search is corrected to make the data converge.
  • the fourth step after the field engineer simulates and confirms, controls the industrial robot to conduct trial production;
  • Step 5 Test the sample after the trial production to ensure that the sample meets the technical requirements. If the test fails, the offline robot will correct and adjust the operating procedures of the industrial robot;
  • Step 6 Formal production after passing the test
  • Step 7 Upload the data of the industrial robot entering the formal production process directly to the cloud server through the offline engineer, or automatically collect the data collected by the industrial robot and sensor after entering the formal production process, and upload it to the network through the network.
  • Cloud server
  • Step 8 In the cloud server, the original downloaded robot operation program is compared with the actual officially produced robot operation program, and the online engineer manually performs the process expert system correction and supplement or uses the intelligent algorithm such as deep learning to automatically complete the pair.
  • the data and rules of the process expert system are perfected, enabling the process expert system to complete learning and evolution.

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Networks & Wireless Communication (AREA)
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Abstract

一种工业机器人工艺云***，包括集成有人机交互层、运动规划控制层和伺服回路控制层的工业机器人控制***，以及云端服务器。云端服务器包括工艺专家***。人机交互层、运动规划控制层借助于网络，实现与云端服务器的数据交互。人机交互层输入作业信息后，传递给云端服务器，形成具体的机器人作业程序，并借助网络下载到工业机器人控制***内。该***能够对各种工业机器人作业类型的工艺经验进行云端处理，形成工艺专家***，利用网络进行数据交互和工艺专家***的实时进化学习。还公开了一种工业机器人工艺云***的工作方法。

Description

一种工业机器人工艺云***及其工作方法 技术领域
本发明涉及工业机器人控制和工艺作业技术领域，具体的说是一种工业机器人工艺云***及其工作方法。
背景技术
随着工业机器人技术的不断革新与发展，逐步取代了人工操作，极大的提高了生产效率和产品一致性。但是，传统的工业机器人在产品更换规格时，换产编程效率极低，且严重依赖于机器人使用方机器人操作工或维护工程师和工艺工程师的经验，后期机器人运维人员的成本投入较高；传统的工业机器人所使用的工艺软件包都是存储在机器人自身的控制器上的，占用的是工业机器人自身本地控制器的硬件资源，但是由于存在硬件瓶颈，机器人本地硬件资源无法完成大规模工艺数据采集、挖掘、工艺指令的计算、推理和工艺知识库的存储，工艺支持智能化和完整性较差，同时机器人相关工艺软件包需要定期更新，是静态的，无法做到实时更新，无法使用户使用最新、最优的工艺支持，时效性和客户体验较差；同时目前工业机器人软件架构复杂，无法做到标准化；硬件资源占用较大，成本较高。
发明内容
针对上述现有技术和架构的缺陷，本发明提出一种工业机器人工艺云***及其工作方法，以解决现有技术中所存在的不足。
一种工业机器人工艺云***，包括集成有人机交互层HMI、运动规划控制层Montion和伺服回路控制层Servo的工业机器人控制***。
还包括云端服务器，所述云端服务器包括工艺专家***。本发明将原本传统的工业机器人所使用的工艺软件包相关功能以存储和运行于云端服务器的工艺专家***进行实现，不占用工业机器人本地控制***的硬件资源，软件架构简单，成本较低，依托于云端服务器强大的硬件资源，实现了智能型和动态的 专家***的搭建。
所述人机交互层HMI、运动规划控制层Montion借助于网络，通过特定的数据交互通讯协议实现与云端服务器的数据交互，所述人机交互层HMI输入作业信息后，传递给云端服务器，经过云端服务器在工艺专家***内搜索现成的模板程序或者进行相似比对和推理计算后，形成具体的机器人作业程序，并借助网络下载到工业机器人控制***内。
所述工业机器人控制***内集成有用于感应工业机器人实时数据的机器人感应传感器层Sensor，机器人感应传感器层Sensor包含电机编码器和电流传感器，将工业机器人对工件进行批量作业时的最终的运行数据传递给云端服务器，最终的运行数据也含底层伺服控制层Servo数据，通过对与原始下载作业程序的对比和学习，云端服务器完成对工艺专家***的优化、学习和进化。工艺专家***具有实时的不断学习和完善功能，实时更新，让用户始终得到最新的工艺支持，时效性强，同时，云端服务器的硬件资源也不会像本地硬件资源一样存在瓶颈。采用本发明的技术方案后，能够大幅度提高工厂的换产编程效率，不依赖于和受制于用户自身的工业机器人运维人员和工艺人员的经验，降低工业机器人的使用成本。
所述工艺专家***包括焊接子云、喷涂子云、切割子云、码垛子云、装配子云、涂胶子云和打磨、抛光、去毛刺子云，后期根据作业类型的增多可以不断增加子云数量。本发明将各种加工类型的工艺进行云端存储，利用网络进行共享和实时进化学习，从而方便用户的实时调用最接近于真实需求的程序，解决了传统的工业机器人换产编程效率低下的问题。
所述的网络为3G/4G/5G移动通信网络或以WIFI、物理连接形式存在的以太网，采用普通的高速移动通信网络或以太网，即可满足本发明的数据交互要求，保证数据的传递时效性。
本发明将基于高速网络技术，在云端服务器运行的，具有学习和进化能力的工艺专家***定义为工艺云，以与本发明的其它部分配合，形成工业机器人工艺云***。
一种工业机器人工艺云***的工作方法，包括下列步骤：
第一步：获取成品样件的三维数模输入到工业机器人控制***，同时通过 工业机器人的人机交互层HMI输入加工参数；
第二步：工业机器人控制***利用特定的通讯协议将第一步得到的相关数据传递到云端服务器；
第三步：云端服务器通过网络将作业程序下载到工业机器人控制***内；
第四步：现场工程师模拟仿真及确认后，控制工业机器人进行试生产；
第五步：对试生产后的样件进行检测，以确保样件满足技术要求；
第六步：检测合格后进行正式生产；
第七步：将进入正式生产过程后的工业机器人及传感器收集的数据，借助网络上传到云端服务器；
第八步：在云端服务器中完成对原始下载的机器人作业程序与实际正式生产的机器人作业程序进行比对，由线上专家人工进行工艺专家***修正和补充或者利用深度学习等智能算法自动完成对工艺专家***的数据和规则完善，使得工艺专家***完成学习与进化。
所述第一步中可以是通过计算机辅助设计直接获取成品样件的三维数模。
所述第一步中也可以是通过三维扫描获取成品样件的三维数模。
所述第一步中输入的加工参数包括材质及工件加工工艺要求。
所述第一步中，可以采用手工输入的方式通过工业机器人人机交互层HMI输入加工参数。
所述第一步中，也可以采用设备仪器自动检测工件属性，并通过工业机器人人机交互层HMI输入加工参数。
所述采用设备仪器自动检测工件属性的方式可为读条码检测的方式或者读RFID检测的方式。
所述第三步中，可以是云端服务器根据工件信息，由线上专家人工介入生成作业程序下载到工业机器人控制***内，同时将该解决方案和作业程序存入云端工艺专家***。
所述第三步中，也可以是云端服务器在工艺专家***自动进行搜索和计算，实时判断搜索和计算是否收敛，收敛后，则通过网络将作业程序下载到工业机器人控制***内。
所述第三步中，若数据不收敛，则由线上专家人工干预和调整，即对自动 搜索得到的工业机器人作业程序进行修正，使数据收敛。
所述第五步中，若检测不合格，则通过线下工程师对工业机器人的作业程序进行修正和调整。
所述第七步中，可以通过线下工程师直接将进入正式生产过程后的工业机器人的数据上传到云端服务器。
所述第七步中，自动对进入正式生产过程后的工业机器人及传感器收集的数据进行实时采集，并借助网络上传到云端服务器。
本发明的有益效果是：本发明采用了云存储，云计算，大数据挖掘和深度学习，实现了对各种工业机器人作业类型的工艺经验进行云端处理，利用高速移动通信网络或以太网进行数据交互和实时进化学习，从而方便用户的实时调用最接近于真实需求的程序，极大的提高了换产效率，分享了大量有用的工业机器人工艺经验，解决了传统的工业机器人依赖、受制于最终用户自身的机器人运维人员和工艺人员的经验的问题，有效的降低了工业机器人使用技术门槛和运维成本；本发明利用高速网络实时采集工业机器人运行数据，利用数据挖掘和云计算完成对工艺专家***的在线学习和进化，从而能保证用户始终得到最新、最优的工艺支持，避免了传统工业机器人静态工艺包带来的时效性差、更新困难、智能化程度低等问题；本发明利用云端硬件资源完成工艺知识存储、计算，减少对工业机器人本地控制器的硬件占用，软件架构简单，成本较低，易于标准化。
附图说明
下面结合附图和实施例对本发明进一步说明。
图1为本发明的原理示意图；
图2为本发明的第一种实施例的工作流程图；
图3为本发明的第二种实施例的工作流程图。
具体实施方式
为了使本发明实现的技术手段、创作特征、达成目的与功效易于明白了解，下面对本发明进一步阐述。
如图1所示，一种工业机器人工艺云***，包括集成有人机交互层HMI、运动规划控制层Montion和伺服回路控制层Servo的工业机器人控制***，还包括云端服务器，所述云端服务器包括工艺专家***。本发明将原本传统的工业机器人所使用的工艺软件包相关功能以存储和运行于云端服务器的工艺专家***进行实现，不占用工业机器人本地控制***的硬件资源，软件架构简单，成本较低，依托于云端服务器强大的硬件资源，实现了智能型和动态的专家***的搭建。
所述人机交互层HMI、运动规划控制层Montion借助于网络，通过特定的数据交互通讯协议实现与云端服务器的数据交互，所述人机交互层HMI输入作业信息后，传递给云端服务器，经过云端服务器在工艺专家***内搜索现成的模板程序或者进行相似比对和推理计算后，形成具体的机器人作业程序，并借助网络下载到工业机器人控制***内。
所述工业机器人控制***内集成有用于感应工业机器人实时数据的机器人感应传感器层Sensor，机器人感应传感器层Sensor包含电机编码器和电流传感器，将工业机器人对工件进行批量作业时的最终的运行数据传递给云端服务器，最终的运行数据也含底层伺服控制层Servo数据，通过对与原始下载作业程序的对比和学习，云端服务器完成对工艺专家***的优化、学习和进化。工艺专家***具有实时的不断学习和完善功能，实时更新，让用户始终得到最新的工艺支持，时效性强，同时，云端服务器的硬件资源也不会像本地硬件资源一样存在瓶颈。采用本发明的技术方案后，能够大幅度提高工厂的换产编程效率，不依赖于和受制于用户自身的工业机器人运维人员和工艺人员的经验，降低工业机器人的使用成本。
所述工艺专家***包括焊接子云、喷涂子云、切割子云、码垛子云、装配子云、涂胶子云和打磨、抛光、去毛刺子云，后期根据作业类型的增多可以不断增加子云数量。本发明将各种加工类型的工艺进行云端存储，利用网络进行共享和实时进化学习，从而方便用户的实时调用最接近于真实需求的程序，解决了传统的工业机器人换产编程效率低下的问题。
所述的网络为3G、4G、5G等移动通信网络或以WIFI、物理连接形式存在的以太网，采用普通的高速移动通信网络或以太网，即可满足本发明的数据交互 要求，保证数据的传递时效性。
本发明将基于高速网络技术，在云端服务器运行的，具有学习和进化能力的工艺专家***定义为工艺云，以与本发明的其它部分配合，形成工业机器人工艺云***。
实施例一：
如图2所示，一种工业机器人工艺云***的工作方法，包括下列步骤：
第一步：通过计算机辅助设计直接获取成品样件的三维数模，或者通过三维扫描获取成品样件的三维数模，在获取成品样件的三维数模后，采用手工输入的方式或者采用设备仪器自动检测工件属性，即获取毛坯样件的相关信息，并通过工业机器人的人机交互层HMI输入加工参数，加工参数包括材质及工件加工工艺要求，采用设备仪器自动检测工件属性的方式可为读条码检测的方式或者读RFID检测的方式；
第二步：工业机器人控制***利用特定的通讯协议将第一步得到的相关数据传递到云端服务器；
第三步：云端服务器根据工件信息，由线上专家人工介入生成作业程序下载到工业机器人控制***内，同时将该解决方案和作业程序存入工艺专家***。
第四步：现场工程师模拟仿真及确认后，控制工业机器人进行试生产；
第五步：对试生产后的样件进行检测，以确保样件满足技术要求，若检测不合格，则通过线下工程师对工业机器人的作业程序进行修正和调整；
第六步：检测合格后进行正式生产；
第七步：通过线下工程师直接将进入正式生产过程后的工业机器人的数据上传到云端服务器，或者自动对进入正式生产过程后的工业机器人及传感器收集的数据进行实时采集，并借助网络上传到云端服务器；
第八步：在云端服务器中完成对原始下载的机器人作业程序与实际正式生产的机器人作业程序进行比对，由线上工程师人工进行工艺专家***修正和补充或者利用深度学习等智能算法自动完成对工艺专家***的数据和规则完善，使得工艺专家***完成学习与进化。
实施例二：
如图3所示，一种工业机器人工艺云***的工作方法，包括下列步骤：
第一步：通过计算机辅助设计直接获取成品样件的三维数模，或者通过三维扫描获取成品样件的三维数模，在获取成品样件的三维数模后，输入到工业机器人控制***，采用手工输入的方式或者采用设备仪器自动检测工件属性，即获取毛坯样件的相关信息，并通过工业机器人人机交互层HMI输入加工参数，加工参数包括材质及工件加工工艺要求，采用设备仪器自动检测工件属性的方式为读条码检测的方式或者读RFID检测的方式；
第二步：工业机器人控制***利用特定的通讯协议将第一步得到的相关数据传递到云端服务器；
第三步：云端服务器在工艺专家***进行搜索和计算，实时判断搜索和计算是否收敛，收敛后，则通过网络将作业程序下载到工业机器人控制***内，若数据不收敛，则由线上专家人工干预和调整，即对自动搜索得到的工业机器人作业程序进行修正，使数据收敛。
第四步：现场工程师模拟仿真及确认后，控制工业机器人进行试生产；
第五步：对试生产后的样件进行检测，以确保样件满足技术要求，若检测不合格，则通过线下工程师对工业机器人的作业程序进行修正和调整；
第六步：检测合格后进行正式生产；
第七步：通过线下工程师直接将进入正式生产过程后的工业机器人的数据上传到云端服务器，或者自动对进入正式生产过程后的工业机器人及传感器收集的数据进行实时采集，并借助网络上传到云端服务器；
第八步：在云端服务器中完成对原始下载的机器人作业程序与实际正式生产的机器人作业程序进行比对，由线上工程师人工进行工艺专家***修正和补充或者利用深度学习等智能算法自动完成对工艺专家***的数据和规则完善，使得工艺专家***完成学习与进化。
同时，将本发明与传统的工业机器人的具体使用情况进行了对比，如下表所示：
Figure PCTCN2016096742-appb-000001
Figure PCTCN2016096742-appb-000002
以上显示和描述了本发明的基本原理、主要特征和本发明的优点。本行业的技术人员应该了解，本发明不受上述实施例的限制，上述实施例和说明书中描述的只是本发明的原理，在不脱离本发明精神和范围的前提下，本发明还会有各种变化和改进，这些变化和改进都落入要求保护的本发明内。本发明要求保护范围由所附的权利要求书及其等效物界定。

Claims (20)

  1. 一种工业机器人工艺云***，包括集成有人机交互层HMI、运动规划控制层Montion和伺服回路控制层Servo的工业机器人控制***，其特征在于：
    还包括云端服务器，所述云端服务器包括工艺专家***；
    所述人机交互层HMI、运动规划控制层Montion借助于网络，通过特定的数据交互通讯协议实现与云端服务器的数据交互，所述人机交互层HMI输入作业信息后，传递给云端服务器，经过云端服务器在工艺专家***内搜索现成的模板程序或者进行相似比对和推理计算后，形成具体的机器人作业程序，并借助网络下载到工业机器人控制***内；
    所述工业机器人控制***内集成有用于感应工业机器人实时数据的机器人感应传感器层Sensor，将工业机器人对工件进行批量作业时的最终的运行数据传递给云端服务器，通过对与原始下载作业程序的对比和学习，云端服务器完成对工艺专家***的优化、学习和进化。
  2. 根据权利要求1所述的一种工业机器人工艺云***，其特征在于：所述工艺专家***包括焊接子云、喷涂子云、切割子云、码垛子云、装配子云、涂胶子云和打磨、抛光、去毛刺子云,后期根据作业类型的增多可以不断增加子云数量。
  3. 根据权利要求1所述的一种工业机器人工艺云***，其特征在于：所述的网络为3G移动通信网络、4G移动通信网络、5G移动通信网络或以WIFI、物理连接形式存在的以太网。
  4. 根据权利要求1所述的一种工业机器人工艺云***，其特征在于：所述机器人感应传感器层Sensor包含电机编码器和电流传感器。
  5. 根据权利要求1至4中任一项所述的一种工业机器人工艺云***的工作方法，其特征在于：包括下列步骤：
    第一步：获取成品样件的三维数模输入到工业机器人控制***，同时通过工业机器人的人机交互层HMI输入加工参数；
    第二步：工业机器人控制***利用特定的通讯协议将第一步得到的相关数据传递到云端服务器；
    第三步：云端服务器通过网络将作业程序下载到工业机器人控制***内；
    第四步：现场工程师模拟仿真及确认后，控制工业机器人进行试生产；
    第五步：对试生产后的样件进行检测，以确保样件满足技术要求；
    第六步：检测合格后进行正式生产；
    第七步：将进入正式生产过程后的工业机器人及传感器收集的数据，借助网络上传到云端服务器；
    第八步：在云端服务器中完成对原始下载的机器人作业程序与实际正式生产的机器人作业程序进行比对，完成对工艺专家***的数据和规则完善，使得工艺专家***完成学习与进化。
  6. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第一步中是通过计算机辅助设计直接获取成品样件的三维数模。
  7. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第一步中是通过三维扫描获取成品样件的三维数模。
  8. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第一步中输入的加工参数包括材质及工件加工工艺要求。
  9. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第一步中，采用手工输入的方式通过工业机器人人机交互层HMI输入加工参数。
  10. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第一步中，采用设备仪器自动检测工件属性，并通过工业机器人人机交互层HMI输入加工参数。
  11. 根据权利要求10所述的一种工业机器人工艺云***的工作方法，其特征在于：所述采用设备仪器自动检测工件属性的方式为读条码检测的方式。
  12. 根据权利要求10所述的一种工业机器人工艺云***的工作方法，其特征在于：所述采用设备仪器自动检测工件属性的方式为读RFID检测的方式。
  13. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第三步中，云端服务器根据工件信息，由线上专家人工介入生成作业程序下载到工业机器人控制***内，同时将解决方案和作业程序存入工艺专家***。
  14. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征 在于：所述第三步中，云端服务器在工艺专家***自动进行搜索和计算，实时判断搜索和计算是否收敛，收敛后，则通过网络将作业程序下载到工业机器人控制***内。
  15. 根据权利要求14所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第三步中，若数据不收敛，则由线上专家人工干预和调整，使数据收敛。
  16. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第五步中，若检测不合格，则通过线下工程师对工业机器人的作业程序进行修正和调整。
  17. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第七步中，通过线下工程师直接将进入正式生产过程后的工业机器人的数据上传到云端服务器。
  18. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第七步中，自动对进入正式生产过程后的工业机器人及传感器收集的数据进行实时采集，并借助网络上传到云端服务器。
  19. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第八步中，由线上专家人工进行工艺专家***修正和补充，完成对工艺专家***的数据和规则完善，使得工艺专家***完成学习与进化。
  20. 根据权利要求5所述的一种工业机器人工艺云***的工作方法，其特征在于：所述第八步中，利用深度学习智能算法自动完成对工艺专家***的数据和规则完善，使得工艺专家***完成学习与进化。
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