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Impulse-PresentationABB Robotics, 5.12.2008
AIS / ICIAR 2010 Int. Conference on Autonomous and Intelligent Systems,Int. Conference on Autonomous and Intelligent Systems
Int. Conference on Image Analysis and RecognitionJune 21-23, 2010, Povoa de Varzim, Portugal
Roland SiegwartA t S t L bAutonomous Systems Lab
ETH Zurich
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Introduction and Challenge
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◦ “Seeing, Feeling and Understanding” the World Robots on Wheels◦ Tour-guide robotg◦ Rolling on a ball◦ Autonomous driving in cities◦ Rough terrain locomotiong
Robots on Legs◦ Quadruped running
Robots in the Air Robots in the Air◦ Micro helicopters◦ Solar airplane
Robots in the Water Robots in the Water◦ The robot tuna◦ Traversing the ocean
Conclusion
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Conclusion
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Theodore Von Karman the famous
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o Theodore Von Karman, the famous aerodynamicist, once observed that:◦ Science is the study of the world as it is.
i i i h i f h ld f◦ Engineering is the creation of the world of tomorrow.
Science is basically "passive“ observation of the universe as it exists, to generate knowledge.
Engineering is making use of that knowledge to meet human needs by creating machines meet human needs by creating machines, systems, processes, and technologies that have not previously existed.
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- Perception with multiple senses
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Reasoning about a situation Dealing with the real world
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o Dealing with the real world
Cognitive systems have to interpret situations based on uncertain and only partially available informationon uncertain and only partially available information
The need ways to learn functional and contextual information (models / semantics / understanding)information (models / semantics / understanding)
Probabilistic Reasoning
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Perception and models (“understanding”) are strongly linked
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What is the What is the difference
in brightness?in brightness?
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Tactility, key for controlling the real world
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Courtesy of Albu-Schaeffer & Hirzinger, DLR, Germany
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It takes us around 14 years to learn holding a glass with an optimal force
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Places / Situations
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Servicing / Reasoning•Functional / Contextual Relationships of Objectson
Objects
Servicing / Reasoning Relationships of Objects• imposed• learned• spatial / temporal/semanticnf
orm
atio
Doors, Humans, Coke bottle, car , …
•Models / Semantics• imposed
Interaction
• spatial / temporal/semantic
essi
ng In
FeaturesLines, Contours, Colors, Phonemes, …
• imposed• learned
Com
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R D t
•Models• imposedl d
Navigation
usin
g &
C
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Raw DataVision, Laser, Sound, Smell, …
• learnedFu
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Facts and Figures (May 15 – October 20, 2002)
◦ Fully autonomous navigation and interaction in human cluttered environment environment
◦ 11 robots◦ 12 hours per day◦ 159 days of operation ◦ 159 days of operation ◦ Operational time: 13,313 hours ◦ Number of visitors: 686,000◦ Total travel distance: 3 315 km
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◦ Total travel distance: 3,315 km ◦ navigation reliability nearly 100%
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Functional Design
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o g Humanoid appearance only if it is necessary for the functionality
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Facial expressions(Eye and eyebrow
Face tracking
LED t i
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LED matrix
Speech synthesis
Simple speech recognition
p y
Input buttons
Obstacle avoidancePath planning
Localization
Feature extractionp g
Multi robot coordinationPeople tracking
On-board computer
Tactile sensors
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On-board computerBatteries Bumpers
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56 s
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Wheel design adopted from Kumagai & Ochiai, Tohoku Gakuin Universtity, Japan
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Planar Model3D Model
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Zur Anzeige wird der QuickTime™ Dekompressor „“
benötigt.
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Up to 17° tilt angle Up to 3 5 m/s
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ZürichIn collaboration with University of Freiburg
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Motion is estimated by tracking salient points
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sensitive to outliers se s e o ou e sthat must be removed
Immagine 1 Immagine 2
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Numb. of iterations = 1 The most efficient algorithm
f i tli for removing outliers Runs to 800 fps!
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[Scaramuzza et al., ICRA’09] [Scaramuzza et al., ICCV’09]
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The 1-point method works optimally when the camera is on wheel axle
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However, when the cameras has an off-set something very special happens!y p pp
We can estimate the Absolute Scale from a single camera![Scaramuzza et al ICCV’09]
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[Scaramuzza et al., ICCV’09]
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The video shows a 3 Km path recovered using only point p g y pfeatures and the car speed for the scale
In the real case the algorithm works even faster than this video (without feature extraction) thanks to the usage of
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video (without feature extraction) thanks to the usage of vehicle motion model
ETH-ASL, EUROPA Kick-off, 23.03.2009
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Lowenstrasse, Zurich
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Passive locomotion concept 6 wheels
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o 6 wheels◦ two boogies on each side◦ one fixed wheel in the rear
f h l i h i ◦ one front wheel with spring suspension
length: 60 cmg height: 20 cm
Characteristics◦ highly stable in rough terrain◦ overcomes obstacles up to overcomes obstacles up to
2 times its wheel diameter
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Comparison of Concepts◦ CRAB (sim & HW)
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front
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CRAB RCL-E MER MER
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FWD BWD
Max. [-] 0.64 0.95 0.57 1.0
Max. T [Nm] 6.0 7.3 6.7 8.9
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RCL-C
Crab-ETH
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Spirit and Opportunity Robots on Mars
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Better tractive performance Lower total motion resistance
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Courtesy of DLR Köln
Total sinkage
[mm]
Wheel deflection
[mm]
Max. soil
slope [°]
Required wheel output torque [Nm]
Combined output
power (6 wheels) [W]
Required input
power [W]
[Nm]
Rigid wheelD=35 cm, b=15 cm,
h i ht 3 4 i 10 %
45.8 - 13.9 13.87 10.6 25.2
grouser height=3.4 cm, i=10 %
Flexible wheelD=35 cm, b=15 cm, grouser height=0 1 cm pressure
12.9 12.8 13.9 6.17 4.7 11.2
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grouser height=0.1 cm, pressure on rigid ground=5 kPa, i=10 %
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‘stiff’ robots:are built to do exactly
what they are told to what they are told to build rigid with strong
structural dampingp gand using high-gain position control
d i d suppress undesired dynamics
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Introduction Method Results Conclusion
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‘stiff’ robots: might kill people i ht kill b t might kill robots kill energy
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Introduction Method Results Conclusion
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‘soft’ robots:
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collisions & impactscollisions & impactscan better handle
uncertaintiescan temporarily
store energy
McGuigan & Wilson, 2003 – J. Exp. Bio.
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Introduction Method Results Conclusion
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Introduction Method Results Conclusion
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‘soft’ robots:
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collisions & impactscollisions & impactscan better handle
uncertaintiescan temporarily
store energyR d k Reduce peak power
McGuigan & Wilson, 2003 – J. Exp. Bio.
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Introduction Method Results Conclusion
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Introduction Method Results Conclusion
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Introduction Method Results Conclusion
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The perfect actuator…p o ides high po e /to q e at a
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o ◦ provides high power/torque at a low weight
◦ is (sometimes) back drivable is (sometimes) back drivable ◦ has a very low reflected inertia◦ can recover negative work◦ is efficient and easy to control◦ …
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Simulation and reality
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Access to environments where no human or other vehicles gets
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Reducing the risk for the environment
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Wing loading [N/m2]
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All flying objects follows the square-cube law of scaling:
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3W L mg b W: weight2S b
/W S b1
S: wing area
ht[N
]
1
31/W S k W
Wei
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Possible Wing loading as function of weight
1/ 3/ 47W S W
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Tennekes H (1996) The Simple Science of Flight, From Insects to Jumbo Jets, MIT Press, Cambridge
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Smaller and smaller◦ Innovative control
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◦ Design optimization◦ System leval design and ◦ System leval design and
integration
OS4 CoaX muFly
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70 cm650 g
10 cm50 g
30 cm200 g
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General visual control framework:
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1) Start Phase 2) Init Phase 3) Flight Phase
Take off(without map)
Initializationof vSLAM framework
Navigation using vSLAM(without map) framework
(baseline & scale)
artificial l d k
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landmark
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vSLAM algorithm (G. Klein [ISMAR 2007]):
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Tracking and mapping in separate threads
Use key frames instead of every camera frame to build map
Small and static environments
State of the art: Running in real time on a PC dual core 2GHz
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gWin/Mac/Linux), open source
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Feature based visual 3D mapping and autonomous navigation
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Develop & realize an autonomous solar powered micro glider
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altit de)altitude)
Atmospheric Density ◦ ~1/80 compared with earth1/80 compared with earth
Gravity◦ ~1/3 compared with earth ?
Solar Energy◦ ~1/2 compared with earth
? Targeted Payload◦ 0.5 Kg◦ Lightweight sensors and scientific instruments
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g g◦ Atmosphere, magnetic field study
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Based on Mass & Power Balance◦ Need for precise scaling laws
( d l )
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Airplane PartsS l ll• Solar cells
• Battery• Airframe Aerodynamic & Conditions• …
Total mass
Aerodynamic & Conditions Power for level Flight
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Influence of battery technology on flight altitude on Earth
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3.2 m
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Continuous flight successfully demonstration on
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June 20 to 21, 2008 - 27 hours flight
Main Characteristics◦ 3 2 m wing span◦ 3.2 m wing span◦ 2.4 kg total weight◦ 1.2 kg of battery
P 12W ( itho t pa load)◦ PLeveled ~ 12W (without payload)◦ Very stable, even at high speeds◦ Maximum power point 91-92 % efficiency 7 grams
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ZürichAutonomous Systems Lab
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Technical Details◦ Design inspired by Tuna
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6 Segments, 5 actuated joints
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Skin
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Crossing the Atlantic
www.ssa.ethz.ch
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Technical Details Technical Details◦ Very innovative design of rig◦ Length: 4m◦ Width: 1.6m◦ Over all height: 8.5m◦ Draught: 2mg◦ Weight: 530kg◦ Solar power and fuel-cells
Investment Investment◦ 15’000 Person hours◦ CHF 128‘000 cash sponsoring
CHF 70‘000 t i l i
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◦ CHF 70‘000 material sponsoring
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Entertainment Industrial Transportation
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Driver Support Systems The coffee servantNesspresso / Bluebotics Switzerland
Construction, mining Farming Rescuing, fire fighting, surveillance
Nesspresso / Bluebotics, Switzerland
Rescuing, fire fighting, surveillance Industrial services
Health and elderly care Services in private and public places
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ETH President greeting ASIMOV, Honda Inc.
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“Seeing, Feeling and Understanding” the world are key issues in robotics
Places / Situations
R i
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Models that reflect functionalities of bj t till i i
Objects
Models / SemanticsInteraction
Reasoning Understanding
objects are still missing
Intelligence starts with the design Raw Data
Features
ModelsNavigation
Intelligence starts with the designof the most appropriate system
d d i h dl b Bad designs can hardly be compensated by control and intelligence
"One should keep things as simple as possible but not simpler!"
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possible - but not simpler! (A. Einstein)
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WWW: www.asl.ethz.ch
YouTube Channel: YouTube Channel: http://www.youtube.com/profile?user=aslteam&view=videos
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