GB2590768A8 - Method for optimizing motion parameter of industrial robot for energy saving in big data environment - Google Patents

Method for optimizing motion parameter of industrial robot for energy saving in big data environment Download PDF

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Publication number
GB2590768A8
GB2590768A8 GB2016280.6A GB202016280A GB2590768A8 GB 2590768 A8 GB2590768 A8 GB 2590768A8 GB 202016280 A GB202016280 A GB 202016280A GB 2590768 A8 GB2590768 A8 GB 2590768A8
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United Kingdom
Prior art keywords
industrial robot
neural network
network model
energy consumption
big data
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GB2016280.6A
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GB202016280D0 (en
GB2590768B (en
GB2590768A (en
Inventor
Yan Jihong
Zhang Mingyang
Wang Pengxiang
Guo Chaozhong
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Harbin Institute of Technology
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Harbin Institute of Technology
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Publication of GB2590768A publication Critical patent/GB2590768A/en
Publication of GB2590768A8 publication Critical patent/GB2590768A8/en
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Classifications

    • 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
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • 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
    • 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
    • G05B2219/39—Robotics, robotics to robotics hand
    • G05B2219/39361—Minimize time-energy cost
    • 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
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/40—Minimising material used in manufacturing processes

Landscapes

  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Manipulator (AREA)
  • Feedback Control In General (AREA)

Abstract

A method for optimizing motion parameters of an industrial robot for energy saving in a big data environment comprises the following steps: step 1: determining parameters influencing the energy consumption of an industrial robot, and constructing an equation for a mathematical relationship between the energy consumption of the industrial robot and the influencing parameters; step 2: establishing a neural network model to describe the relationship between the energy consumption of the industrial robot and the influencing parameters; training inter-layer weights and thresholds of each layer of the established neural network model to obtain a trained neural network model; and step 3: optimizing the motion parameters of the industrial robot based on a genetic algorithm by taking the energy consumption of the industrial robot as an optimization target and using a non-optimized parameter as fixed input of the trained neural network model.
GB2016280.6A 2019-12-18 2020-10-14 Method for optimizing motion parameter of industrial robot for energy saving in big data environment Active GB2590768B (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911312024.XA CN110936382B (en) 2019-12-18 2019-12-18 Data-driven industrial robot energy consumption optimization method

Publications (4)

Publication Number Publication Date
GB202016280D0 GB202016280D0 (en) 2020-11-25
GB2590768A GB2590768A (en) 2021-07-07
GB2590768A8 true GB2590768A8 (en) 2021-07-21
GB2590768B GB2590768B (en) 2022-03-02

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ID=69911635

Family Applications (1)

Application Number Title Priority Date Filing Date
GB2016280.6A Active GB2590768B (en) 2019-12-18 2020-10-14 Method for optimizing motion parameter of industrial robot for energy saving in big data environment

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CN (1) CN110936382B (en)
GB (1) GB2590768B (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112597610B (en) * 2020-12-28 2024-02-13 优必康(青岛)科技有限公司 Optimization method, device and equipment for lightweight design of mechanical arm structure
CN113343391B (en) * 2021-07-02 2024-01-09 华电电力科学研究院有限公司 Control method, device and equipment for scraper material taking system
CN114253279B (en) * 2021-10-26 2024-02-02 西北工业大学 Underwater glider motion planning method considering ocean current environment
CN113954077B (en) * 2021-11-15 2023-03-24 天津大学 Underwater swimming mechanical arm trajectory tracking control method and device with energy optimization function
CN114030008B (en) * 2021-11-24 2023-08-22 浙江大学 Industrial robot practical training energy consumption measurement method based on data driving
CN114770499B (en) * 2022-03-30 2023-09-12 清华大学 Efficient modeling prediction method and device for energy consumption of industrial robot
CN117444985B (en) * 2023-12-20 2024-03-12 安徽大学 Mechanical arm trolley control method and system

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101847009B (en) * 2010-05-28 2011-12-14 广东工业大学 Biped robot gait energy efficiency optimization method
US9630318B2 (en) * 2014-10-02 2017-04-25 Brain Corporation Feature detection apparatus and methods for training of robotic navigation
CN106777527A (en) * 2016-11-24 2017-05-31 上海市特种设备监督检验技术研究院 Monkey operation energy consumption analysis method based on neural network model
CN108237531B (en) * 2016-12-26 2021-07-13 电子科技大学中山学院 Humanoid robot gait self-learning control method
EP3396598A3 (en) * 2018-05-21 2018-12-12 Erle Robotics, S.L. Method and user interface for managing and controlling power in modular robots and apparatus therefor
CN108920863B (en) * 2018-07-20 2021-02-09 湖南大学 Method for establishing energy consumption estimation model of robot servo system

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Publication number Publication date
CN110936382A (en) 2020-03-31
GB202016280D0 (en) 2020-11-25
GB2590768B (en) 2022-03-02
CN110936382B (en) 2020-08-25
GB2590768A (en) 2021-07-07

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