Robotics is spearheading Smart Production – but we need to ... - …€¦ · Robotics is...

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)

chsch@mmmi.sdu.dkT +45 6550 7916M +45 9350 7377