Design of Intelligent
Robotics
René van de Molengraft December, 6th, 2010
Theme
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Mission
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• Help elderly people to stay in their homes
Research challenges
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Learning
• Low-level:
3D-sensing, world modeling, grasping, locomotion • High-level:
cognition, planning, coordination of multiple robots
Approach
• Mixed Model/Data-based • Use priors
Robocup@Home
RoboCup use cases
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Midsize league soccer
• Scenario: structured, but highly dynamic • Low-level: world modeling, skills
• High-level: strategy, coordination
@Home league
• Scenario: static, but highly unstructured
• Low-level: 3D-sensing, object recognition, grasping • High-level: cognition, learning
RoboEarth Project
• Four-year funding from European Commission (FP7)
• Starting date: December 1st, 2009
• Consortium consisting of:
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Motion Control
• Performance • 90% feedforward • 10% feedback PAGE 8 12-03-2010• Humans in day2day tasks
• 90% feedforward – learning and memorization
Industrial robots
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• Structured environment
• Same scene, same task
Domestic and care robots
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• How to deal with a complex world?
• Changing scenes
RoboEarth
• How to deal with a complex world?
• Changing scenes
• Many possible scenarios
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• RoboEarth – learning and memorization
• Share any reusable knowledge between robots
RoboEarth
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Safety and risks
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• Consistency of data in RoboEarth • Who is allowed to sharing what? • Use existing protocols (HTTPS)
• Protect “personal” data (compare Streetview) • Certified users
• Log and analyse usage
• Liability in case of malfunction?
• Autonomous operation • Who is responsible?
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• “By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team”
• Joint development of technology in robotics and artificial intelligence
• Since 1997
/ faculteit werktuigbouwkunde
• Founded spring 2005 • 20 active members
• 2nd place World Cup 2008/2009/2010
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• fully autonomous
• vision and localisation • fast and safe
• ball-handling (don’t clamp!) • ball-kick
• team-play
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• design a team of
autonomous soccer robots
• robot is of DH/CS type • team is of DH/DS type • soccer = strategy + skills
/ faculteit werktuigbouwkunde • robustness • performance • time-to-next-tournament • safety • maintenance • • cost
Design criteria
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• FIFA rules
• additional Robocup rules • size, weight, color
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• manage complexity • keep it simple • hierarchy
• make choices explicit • use models
• create robustness • redundancy
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team
robot robot robot
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• localisation • soccer skills
• intercept, dribble, kick • soccer roles
• attacker, defender, goalkeeper • soccer tactics
• pass, assist, play system
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• low level (hardware) functions • drive
• kick
• ball handling
• sense environment
• (high) level (sofware) functions • localisation
• skills
• roles and strategy
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System architecture
/ faculteit werktuigbouwkunde • which sensors? • vision • odometry • accelerometers • laser • hardware or software? • cpu power • sensor fusion
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• vision
• number of cameras, type of camera, resolution,
omnivision, mirror,
construction, acquisition,
feature extraction, modelfit, object tracking etc.
/ faculteit werktuigbouwkunde • Interdisciplinary • mechanics • electronics • software • Approach • model-based design
(simulation and prototyping ) • early integration
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• hardware
• omnivision camera+mirror • electronic compass
• odometry with motor encoders • software
• white-line detection • orientation calibration • sensor fusion
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Evolution and revolution
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Devbot0 2005 Model 2006 Model 2007 Model 2008 Model 2009
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?
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• 5 agents: distributed system • agents must be self-reliant
• minimise interaction/sync • maximise cooperation
• aggressive environment
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• April 2011, German Open, Magdeburg • July 2011, World Cup, Istanbul
• Dedicated goal keeper
• Robustified ball handling • Passing during gameplay