Lecture times: MW 4:00-5:15PM
Teaching Assistant: Yadamala, Ugandhi
Place: Faner 1004
Join our googlegroup (Used for discussions, announcements, clarifications, excluding file posting feature): http://groups.google.com/group/cs404-siu-s11
Last Updated: February 24, 2011
Course Description:
This course is a comprehensive introduction to modern robotics with an emphasis on autonomous mobile robotics. Fundamentals of sensors and actuators as well as algorithms for top level control are discussed. Multi-robotics and human-robot interaction issues are explored. A term project is an integral part of this course. Class lectures will closely track outline of the course textbook. Lectures and exams are theoretical. Class project is hands on, pragmatic and research oriented.
For a more in depth orientation read my orientation. Teaching this broad in one course is challenging and requires compromises along countless spectrums and constraints, read about them in Tradeoff.
Projects: All options will be fully discussed in class. 40% of grade
A- Software track (default): A distributed system of search and rescue is discussed in class. Here is a summary:
1. There is an unfolding disaster such as an earth quake, fire, or
bombs in an area such as a small, indoor office building or an outdoor
drive-in theater. A number of people or animals will be in danger.
Your system user can alter the disaster setup and effects
parametrically.
2. There are multiple robots that the user will deploy to conduct
search and rescue. Types and numbers of robots and how they work
together will be user determined. You can choose the robot type. They
can be all the same or you may allow several types.
3. Allow for the user to change the disaster and robot interaction
details to see how that changes the results.
I recommend using one of the following platforms but others are
allowed.
(a) http://ccl.northwestern.edu/netlogo/
(b) http://msdn.microsoft.com/library/bb648760
B- Hardware track (optional) :
Students will use a physical robot kit to replicate a recently published work.
C- Language track (optional)
Students design and implement a programming language for autonomous robot control.
Pre-requisites:
CS 330 with a grade of C or better or graduate standing. Seek permission of instructor if you need clarifications. Please Be aware that this is a programming intensive and a hands on course, requiring nontrivial programming skills.
Graduate and undergraduate students are welcome.
Objectives:
You are expected to know or learn programming language on your own.
Grading: Class project at 40%, and two exams worth 15% each, HWs 30%
HWs: 5 HWs at up to 6 points each. All details are given in class.
Class participation and attendance will help determine borderline grades.
Week 1 |
Introduction, Syllabus, Orientation |
|
---|---|---|
Week 2 |
Locomotion, architecture, Potential Fields |
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Week 3 |
Motion Planning, Navigation, Bug AlgorithmsRoadmaps... |
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Week 4 |
Motion Planning, Navigation, Bug Algorithms, |
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week 5 | Mapping |
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Week 6 |
|
Mapping |
Week 7 |
Exam I on 3-2-11 |
Mapping |
Week 8 |
Probabilistic-robotics, Filters, Fuzzy logic Navigation |
|
Week 9 |
Spring break |
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Week 10 |
MDP, POMDP, HMM |
|
... |
||
... |
||
Week ? |
Exam II TBD |
Recommended textbook:
Howard Choset, et. al. 2005. Principles of Robot Motion: Theory, Algorithms, and Implementations, The MIT Press, ISBN-10: 0262033275.
Supplemental Reading:
The following links and papers are limited to education purposes by our local students. All rights remain with original sources. This list is updated as needed.
I - Good Old Fashioned Robotics
1. Principles of Robot Locomotion
2. Rodney A. Brooks' original subsumption paper
3. The Subsumption Architecture
4. Henry Hexmoor's High Level Control Loop
5. Potential Fields Tutorial
6. Howard Choset's textbook Chapter 2 slides (Bug Algorithms)
7. Howard Choset's textbook Chapter 5 slides
8. Roadmap slides
II- State of the Art in Robotics
9. Probabilistic-robotics
10. Bayesian Filters for Location Estimation
11. The Scalar Kalman Filter
12. A Fuzzy logic based Navigation System for a Mobile Robot
13. Hexmoor's Fuzzy logic
14. Affect based Computing
15. Robin Murphy's Emotion-based Control
16. Brick and Mortar
17. Markov Decision Processes
18. POMDP FAQ
19. Jong and Stone paper: RL and MDP
20. Hagit Shatkay on HMMs
21. Andrew Moore's HMM Tutorial Slides
III- New Frontiers
22. Coverage and Search
23. Multirobotics
24. Diana Spears' Swarms paper
25. Alife
Reference Sources:
1. S. Thrun, W. Burgard, D. Fox, 2005. Probabilistic Robotics, MIT press
2. S. LaValle, 2006. Planning Algorithms, Cambridge University Press.
3. R. Arkin, 1998. Behavior-Based Robotics, MIT press
4. G. Bekey, 2005. Autonomous Robots, MIT press
5. G. Dudek, 2005. Computational Principles of Mobile Robotics, Cambridge university press
6. Jones, Flynn, 1998. Mobile Robots: Inspiration to Implementation, AK Peters.
Last updated: January February 18, 2010