[go: up one dir, main page]

Liu et al., 2018 - Google Patents

Serocs: Safe and efficient robot collaborative systems for next generation intelligent industrial co-robots

Liu et al., 2018

View PDF
Document ID
15054330303409820831
Author
Liu C
Tang T
Lin H
Cheng Y
Tomizuka M
Publication year
Publication venue
arXiv preprint arXiv:1809.08215

External Links

Snippet

Human-robot collaborations have been recognized as an essential component for future factories. It remains challenging to properly design the behavior of those co-robots. Those robots operate in dynamic uncertain environment with limited computation capacity. The …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • 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
    • 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
    • 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
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • 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], 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
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • 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
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • 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
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric

Similar Documents

Publication Publication Date Title
Xu et al. Compare contact model-based control and contact model-free learning: A survey of robotic peg-in-hole assembly strategies
Cheng et al. Towards efficient human-robot collaboration with robust plan recognition and trajectory prediction
Liu et al. Serocs: Safe and efficient robot collaborative systems for next generation intelligent industrial co-robots
Ghadirzadeh et al. A sensorimotor reinforcement learning framework for physical human-robot interaction
de Souza et al. Reconfigurable grasp planning pipeline with grasp synthesis and selection applied to picking operations in aerospace factories
Hart et al. Learning generalizable control programs
Jin et al. Task learning, intent prediction, and adaptive blended shared control with application to excavators
Chryssolouris et al. Artificial intelligence in manufacturing equipment, automation, and robots
Wu et al. A framework of robot skill learning from complex and long-horizon tasks
Zieliński et al. A systematic method of designing control systems for service and field robots
Hudson et al. Model-based autonomous system for performing dexterous, human-level manipulation tasks
Dong et al. Learning and recognition of hybrid manipulation motions in variable environments using probabilistic flow tubes
Escobar-Naranjo et al. Applications of Artificial Intelligence Techniques for trajectories optimization in robotics mobile platforms
Ghalyan Force-Controlled Robotic Assembly Processes of Rigid and Flexible Objects
Liu et al. Robot safe interaction system for intelligent industrial co-robots
Flowers et al. A spatio-temporal prediction and planning framework for proactive human–robot collaboration
Elguea-Aguinaco et al. A Review on Reinforcement Learning for Motion Planning of Robotic Manipulators
Proia Control techniques for collaborative and cooperative robotic systems
Zhou et al. A cooperative shared control scheme based on intention recognition for flexible assembly manufacturing
Muthusamy et al. Decentralized approaches for cooperative grasp planning
Khotsianivskyi et al. Robotic manipulator motion planning method development using neural network-based intelligent system.
Oikonomou et al. Task driven skill learning in a soft-robotic arm
Chen et al. Enabling robot to assist human in collaborative assembly using convolutional neural networks
Shaarawy et al. Multi-robot vision-based task and motion planning for ev battery disassembly and sorting
Hou et al. A data-driven approach for motion planning of industrial robots controlled by high-level motion commands