Robots Leaving the Production Halls Opportunities and Challenges
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1 Shaping the future Robots Leaving the Production Halls Opportunities and Challenges Prof. Dr. Roland Siegwart APAC INNOVATION SUMMIT 17 Hong Kong Science Park Science, June 5, 2017 Roland Siegwart
2 Technologies disrupting services digitalization / industry Personal Computer Internet information sharing Smart Phones permanent connectivity IoT / Cyber-Physical Systems / Robots Data World is connecting with physically exponential increase of complexity Roland Siegwart
3 Robotics today Industrial Robots (Changan-Ford China ) Roland Siegwart industrial robots sold in in China
4 Robotics tomorrow Service Robots outside the production halls Roland Siegwart SpotMini electic quadruped, Boston Dynamics
5 Service Robots Key Challenges Robots that can dealing with uncertain and partially available information Robots that see, feel and understand their environment Robots with torque and force control for tactile interaction ( soft robots ) 50x speed Robots with intuitive human-machine interfaces Robots that learn and adapt every day Roland Siegwart
6 @ 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 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
7 Service Robots designed for challenging tasks BeachBot (with Disney) developed by students the beach artist Vertigo developed by students the ultimate wall climber Roland Siegwart
8 Service Robots designed for challenging tasks wingtra developed by students the VTOL UAV 81 hours non-stop in summer m, 6.2 kg Roland Siegwart
9 Service Robots designed for challenging tasks Scewo developed by students the stair-climbing wheelchair AnyMal soft interaction with the environment the ultimate quadruped Roland Siegwart
10 Seeing Laser-based 3D mapping Mapping & Localization scan t 1 scan matching scan t 2 Zukunft der Robotik
11 Seeing Visual-Inertial Motion Estimation Image 1 Image 2 Zukunft der Robotik
12 OKVIS Open Keyframe-based Visual-Inertial SLAM (tight coupling of vision and IMU) Cost Reprojection errors (weighted) IMU terms i: camera index; k: camera frame index; j: landmark index. Roland Siegwart
13 OKVIS in Action Roland Siegwart
14 ROVIO Robust Visual Inertial Odometry robo-centric representation EKF based IMU-Vision fuses projected intensity errors (instead of reprojection errors) Procedure feature detection & image patch is extracted Derivation of an intensity based error terms dimension reduction of error term by QR-decomposition directly used as Kalman filter innovation Roland Siegwart
15 ROVIO Robust Visual Inertial Odometry [M. Bloesch et al (2015). Robust Visual Inertial Odometry Using a Direct EKF-Based Approach, IROS] Roland Siegwart
16 Autonomous Flying Robots Visual-Inertial Sensor Combined High- and Low-level Control and processing power Robotic arm Versatile algorithm deployment Motion Estimation SLAM Dense 3D reconstruction Path-planning algorithms Obstacle Avoidance Roland Siegwart
17 UAV vision only navigation Vision-inertial navigation (one camera and IMU, GPS denied) Fully autonomous with on-board computing Scale estimation Feature-based visual SLAM robust against lighting changes and large scale changes Proto 2 Proto 1 Proto 3 Roland Siegwart
18 UAV collision avoidance and path planning Real time 3D mapping (on-board) optimal path planning considering localization uncertainties Roland Siegwart
19 Collaborative Visual-Inertial Navigation in collaboration with Prof. Marco Hutter Roland Siegwart
20 Collaborative Visual-Inertial Navigation teach and repeat Roland Siegwart
21 Collaborative Transportation master-slave control Roland Siegwart
22 Complexity of Services Tactile Manipulation Mobile Manipulation Advanced Interaction Autonomous Navigation Actions from simple motion to complex interaction Robotics Roadmap Toys Logistic assistant Industrial services Tour-Guides Transportation logistics Tactile manufacturing Vacuum cleaning Agriculture robots Household universal Search and rescue All-terrain navigation Construction robotics Autonomous car urban Industrial inspection Autonomous car industrial sites Autonomous car freeway 2010 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
23 Switzerland a melting pot for robotics technology Initiatives Spin-offs (*ASL) Industrial Collaborations (ASL) * * * Center for Learning Systems ielab * * * * Roland Siegwart
24 Tanks to: ASL Team Industrial Partners Funding Agencies Current and former ASL Members (2016) Roland Siegwart
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