© sebastian thrun, cmu, 20001 16-899c statistical techniques in robotics sebastian thrun and...
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© sebastian thrun, CMU, 2000 1
16-899C Statistical Techniques In Robotics
Sebastian Thrun and Geoffrey GordonCarnegie Mellon University
www.cs.cmu.edu/~thrunwww.cs.cmu.edu/~ggordon
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notes
Pointer to Larry’s material
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Administrative Information
Sebastian Thrun [email protected] Geoffrey Gordon [email protected] Web: http://www.cs.cmu.edu/~16899 Email list: [email protected] Time: Mon/Wed, 10:30-11:50am Location: NSH 3302 TA: n/a Appointments: send Email!
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Goals
Enable you to program robots and embedded systems in a robust fashion
Enable you to understand the intrinsic assumptions in your robot software
Enable you to pursue original research in probabilistic robotics
Sway you into joining a young and fascinating research field: probabilistic robotics
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What this course is not
Intro to robotics Little work Low on math
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Course Schedule
LocalizationSept 4-16
MappingSept 30-Oct 16
Decision MakingOct 21-30
Multi-AgentNov 4-11
Advanced PerceptionNov 13-25
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What You Should Do
Think
Think differently
Be critical
Come up with Original Research
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What Is A Good Project
Mine Mapping Multi-Agent Control
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Requirements
In teams of three:• Warm-up project (mobile robot localization)• Written assignment(s)• Research Project
Class Presence: all but two sessions (send me email) Quizzes (all but at most two) No exams
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Your next tasks
Check out Web site• Read assigned paper• Download map+sensor data and program robot
localization algorithm
Send Sebastian mail with your name and names of team mates (for warm-up project)
Come to class on Sept 9th (10:30am-11:50am)
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Five Sources of Uncertainty
EnvironmentDynamics
RandomAction Effects
SensorLimitations
InaccurateModels
ApproximateComputation
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Trends in Robotics
Reactive Paradigm (mid-80’s)• no models• relies heavily on good sensing
Probabilistic Robotics (since mid-90’s)• seamless integration of models and sensing• inaccurate models, inaccurate sensors
Hybrids (since 90’s)• model-based at higher levels• reactive at lower levels
Classical Robotics (mid-70’s)• exact models• no sensing necessary
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Rhino
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Minerva
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The CMU/Pitt Nursebot Initiative
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People Detection
Mike Montemerlo
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Learning Models of People
Maren Bennewitz
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3D Mapping Result
With: Christian Martin
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Multi-Robot Exploration
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Mine Mapping (brand new)
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What are interesting problems?
Mapping, automatic, manual, guided? Probabilistic localization, landmarks?, odometer!, Route planning, collision avoidance Mine Mapping?
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How can we solve them?
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Where Am I/?
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Nature of Sensor Data: Uncertainty
Odometry Data Range Data
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Warm-Up Assignment: Localization,Due Sept 23
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Warm-Up Assignment: Localization
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Warm-Up Assignment: Localization