Roboter lernen sehen und selbst zu navigieren - Chancen und Herausforderungen autonomer Roboter für die Arbeits- und Alltagswelt.
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1 Shaping the future Roboter lernen sehen und selbst zu navigieren - Chancen und Herausforderungen autonomer Roboter für die Arbeits- und Alltagswelt. Roland Siegwart, ETH Zurich Industrie Digitale Chancen im Facility Management und der Baubranche Ittigen, 20. Oktober, 2016 Roland Siegwart
2 Personal Computer 1981 Technologies Disrupting Services digitalization / industry 4.0 Internet / Information Mobile Devices / Connectivity 2016 Robotics / IoT Cyber-Physical Systems Roland Siegwart
3 Robotics today (Changan-Ford China ) Roland Siegwart industrial robots sold in in China
4 Smart and adaptive, connected, human-friendly exponential Next generation of Robots mobile, smart, connected, adaptive and closer to humans increase of complexity Industrial Robots Service and Personal Robots Cyborgs YuMi Roland Siegwart
5 Fascinating Robots Complex machines OceanOne Humanoid Underwater Robot, Stanford University SpotMini electic quadruped, Boston Dynamics DARPA Robotics Challenge , Team NEDO-JSK, Japan 12 x original speed!! Roland Siegwart
6 Service Robots Key Directions Robots that can dealing with uncertain and partially available information Robots that see, feel and understand their environment 50x speed Robots with torque and force control for tactile interaction ( soft robots ) Robots with intuitive human-machine interfaces Robots that learn and adapt every day Roland Siegwart
7 @ ETH Institute of Robotics and Intelligent Systems Prof. Dr. Roland Siegwart Mission and Dedication To create intelligent robots and systems that operate autonomously in complex and dynamic environments. Research Focus ~ 40 researcher (PhDs, Postdocs, ) ~ 60 master students ~ 10 startups Body Design Brain Navigation Novel robot concepts that are best adapted for ground, air, or water based applications. New algorithms for perception, localization, abstraction, mapping, and path planning that will enable autonomous operation in challenging environments. Roland Siegwart
8 Ultimate Rolling Robots designed by students rezero (2010) the ball balancing robot BeachBot (2014, with Disney) the beach artist Vertigo (2015 with Disney) the ultimate wall climber Scalevo (2015) the stair-climbing wheelchair Roland Siegwart
9 Walking Robots serial elastic actuation ALOF (2008) the versatile walker StarlETH (2010) the quadruped with serial elastic actuation AnyMal (2015) the ultimate quadruped Prof. Marco Hutter Roland Siegwart
10 Flying Robots new ways of flying OS4 (2003) pioneering quadrotors Reely (2009 with Disney) the flying reel Skye (2012 with Disney) the omnidirectional blimp PacFlyer/wingtra (2013) the VTOL UAV Roland Siegwart
11 ? Mobile Robot Navigation (Brain) Localization, Mapping and Planning Roland Siegwart
12 Seeing Laser-based 3D mapping Roland Siegwart
13 Seeing Visual-Inertial Motion Estimation Roland Siegwart
14 Intelligent Smartphone perceiving and understanding the environment phab2pro Lenovo Roland Siegwart
15 Real-time On-board Visual-Inertial Navigation Roland Siegwart
16 Flying Robots EU-Projects Roland Siegwart
17 UAV collision avoidance and path planning Real time 3D mapping (on-board) optimal path planning considering localization uncertainties Proto 2 Proto 1 Proto 3 Roland Siegwart
18 UAV facade scanning and 3D reconstruction Enhanced teleoperation or autonomous operation Visual-inertial localization for optimal 3D reconstruction Proto 1 Proto 2 Proto 3 Roland Siegwart
19 Collaborative Visual-Inertial Navigation in collaboration with Prof. Marco Hutter Roland Siegwart
20 Applications Roland Siegwart
21 Complexity of Services Tactile Manipulation Mobile Manipulation Advanced Interaction Autonomous Navigation Actions from simple motion to complex interaction Robotics Roadmap Toys Household assistant Industrial services Tour-Guides Transportation logistics Tactile manufacturing Vacuum cleaning Agriculture robots Household universal Search and rescue All-terrain navigation Construction robotics Industrial inspection Autonomous car industrial sites Autonomous car freeway 2010 Autonomous car urban Static Structured, 2D Static Unstructured, 3D Dynamic Structured, 2D Dynamic Unstructured, Dynamic 3D Static Environment - from static 2D grid maps to 3D cognitive maps Semantics dynamic 2030 Roland Siegwart
22 Zurich Area a melting pot for robotics technology Initiatives Spin-offs (*ASL) Industrial Collaborations (ASL) * * * Center for Learning Systems EU-Projects ASL * * * * Roland Siegwart
23 ASL Team Industrial Partners Funding Agencies Current and former ASL Members (2016) Roland Siegwart
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