Instructor: Dr. Henry Hexmoor

Spring 2013
Instructor office hours: M/W 9:00AM-3:00PM

Lecture times: MW 4:00-5:15PM
Place: Faner 2127 (Lab) and Faner 1326 (Lecture)

For course content visit here with details as instructed in class.

Last Updated: October 9, 2012

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- Hands on track: We will use the BoeBot kits in the laboratory. Students will use a physical robot kit to replicate a recently published work as a homework activity for 10%. Remainder of activities will be to program robots for an organizd, collective action such as pushing people out of a dangerous area. A few robots will be equipped with basic scanning capability. Sound will be used for communication. Ultrasound, laser, and GPS will be used for collision avoidance.

B- Theoretical track: Explore and develop novel human-robot interaction concepts discussed in class. Browse recent HRI conferences.

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:

To have a rich understanding of robotic platforms and their limitations
To be able to program mobile robots
To be able to design automation solutions using mobile robots

You are expected to know or learn programming language on your own.

Grading: Class project at 40%, a traditional in-class exam worth 20%, and a take home report worth 20%.

Take home Report (20 points): This report is a commentary/rebuttal on manuscript written by me. Details will be discussed in class.

Special Activity: Opportunities for earning extra points (e.g., writing a user's manual for microcontroller) will also be discssed in class.

Class participation and attendance will help determine borderline grades.

**Important Dates (Dates will be determined in class and this page is periodically updated)**
Week 1		Introduction, Syllabus, Orientation
Week 2		Locomotion, architecture, Potential Fields
Week 3		Motion Planning, Navigation, Bug AlgorithmsRoadmaps...
Week 4		Motion Planning, Navigation, Bug Algorithms,
week 5		Mapping
Week 6		Mapping
Week 7	Exam I on 3-2-11	Mapping
Week 8		Probabilistic-robotics, Filters, Fuzzy logic Navigation
Week 9	Spring break
Week 10		MDP, POMDP, HMM
...
...
Week ?	Exam II TBD

Textbook: Hexmoor, 2013. Essential Principles fo Autonomous Robotics, Morgan and Claypool publishers.

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