robotics is spearheading smart production – but we need to ... - …€¦ · robotics is...
TRANSCRIPT
Robotics is spearheading Smart Production –but we need to deliver, too
30 July 2018
SDU ROBOTICS
Christian Schlette
ICINCO 2018Porto, Portugal29 – 31 July 2018
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Robot simulation and robot control*sigh* … my first “digital twin”
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Overview
TechnologiesFrom robot control and simulation
to “digital twins” and Smart Production
Transfer, trust and agilityTrying harder to bridge the gap between academia and industry
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Technologies
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Robotcontrol
Robotsimulation
Ethernet communication
System componentsRobot simulation, monitoring and control
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Simulation-based system developmentMockup for optimization of approach trajectories for DLR
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Robotcontrol
Robotsimulation
Sensorsimulation
Multi agent system control
Ethernet communication
System components…plus sensor simulation and MAS control
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Simulation-based system developmentR&D and operator training for the state of North-Rhine Westfalia (NRW)
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Robotcontrol
Robotsimulation
Sensorsimulation
Multi agent system control
Ethernet communication
Database technologies
Virtual Reality
System componentsDB technologies and VR to pull in, manage and visualize large environments
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Simulation-based system developmentVirtual commissioning for micro-optical assembly stations
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Smart Production in contextIoT shapes “Smart X”
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Source: Bosch Rexroth 2016
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Breaking existing hierarchiesSmart Production redefines horizontal and vertical integration schemes
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Source: PlattformIndustrie
4.0 2016
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Data-oriented view on digital twinsaka “Administrative shell”
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DigitalTwin
<Private>
DigitalTwin
<Public>
Physicalsystem
Data
Control
Public datasphere
Priv
ate
data
sphe
reProcess program
Process parameters
Process statusQuality report
• Order• Customer requirements
• Production progress• Product location
••
••
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Design
Design Operation / Maintenance
Operation / Maintenance
Production
Product
Productionsystem
Production-oriented view on digital twinsOptimization potentials based on digital twins
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System-oriented view on digital twinsresp. digital twins in robotics
Outputs
Target environment
Target system
InputsProcess
Outputs
Target environment
Target system
InputsProcess
Virtual system Physical system
Simulation & Planning
Databases(a-priori & in-situ)
System
Environment
exteroceptive sensor data
proprioceptive sensor data
control
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Optimization of equipment posese.g. camera and robot positioning in ReconCell
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Optimization of layup strategies Piston motion patterns in FlexCell
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Details on our approach in FlexCell
Ole W. Nielsen, Christian Schlette and Henrik G. Petersen:Fast and Simple Model for Free Hanging, Pre-impregnated Carbon Fibre MaterialProc. of the 15th Int. Conf. on Informatics in Control, Automation and Robotics (ICINCO),Vol. 1, pp. 7, 2018.
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Simulation of other automated componentse.g. vibratory bowl feeders
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NEW: Human-robot collaboratione.g. assembly of injection molds for LEGO
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Transfer, trust and agility
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I4.0-Lab and I4.0-LSOur complementary I4.0 initiatives for different audiences
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I4.0-Lab and I4.0-LSOur initiative to start a “reactor” with companies
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We need to improve - trust and agility
• Realistic results and outcomes• Continuous, reliable partnerships• Rapid and robust developments• Clear IPR management• Clear administrative processes• Short response times
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Thank your for your attention
Christian SchletteAssociate Professor
SDU ROBOTICSThe Mærsk Mc-Kinney Møller Institute (MMMI)University of Southern Denmark (SDU)
[email protected] +45 6550 7916M +45 9350 7377