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This is an introductory course presenting a series of algorithms related to the representation and use of geometric models acquired from sensor data. Course topics include: basic sensing and estimation techniques, geometric model representations, and motion planning algorithms. The course will also discuss applications in diverse areas such as mobile systems, robot manipulation, and medicine.
This course is intended for advanced undergraduates and first year graduate students. I assume students have a rudimentary understanding of linear algebra, calculus, and are able to program in some type of structured language. There will be four homework projects and two exams. Grading will be approximately 50% on the homework assignments and 50% on the exams.
| Meetings: | Tuesday and Thursday , 12-1:15, Shaffer 300 |
| Professor: | Greg Hager |
| E-mail: | hager at cs dot jhu dot edu |
| Office: | CSEB 121, Homewood Campus |
| Office hours: | By appointment |
Grading will be approximately 50% on the homework assignments and 50% on the exams.
Homeworks are due on by start of class on Thursday. Late homework is frowned upon. 10% of the possible grade is deducted for each day late. If there is ever a situation which prohibits you from turning in your homework on time, you must alert the Office of Student Affairs because I will check with them to verify the claims.
Honor Code
Above all, you must not misrepresent someone else's work as your own. You can avoid this in two ways:
Naturally, even if you give appropriate credit, you will only receive credit
for your original work, so for this class you should stick with option #1.
All cases of confirmed cheating/plagiarism will be reported to the Student Ethics Board.
Please read the Computer
Science Academic Integrity Code.
(Primary) Principles of Robot Motion (Choset et. al.), MIT Press, 2005
(Additional online resources and photocopied readings will be made available.)
| Week | Topic | Suggested Readings | Notes | Assignment |
| One | Basic concepts in robotics | Course Syllabus, Chap. 1, Appendices | Notes | |
| Two | Bug Algorithms and related sensor processing and estimation | Chap. 2 | Notes | Homework 1 |
| Three | Kinematics and Configuration Spaces | Chap 3 | Notes | |
| Four | Kinematics and Configuration Spaces | Chap 3 | Review Questions | |
| Five | Potential methods | Chap 4 | Notes | |
| Six | Potential Methods/Roadmaps | Chap 4, Chap 5, Chap 6 | Notes (5) Notes (6) | Assignment 2 |
| Seven | Review/Exam | |||
| Eight | Sample-based Path Planning | Chap 7 |
Hager Notes Erion notes (1) |
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| Nine | Sample-Based Path-Planning | Chap 7 |
Erion notes (2) Erion notes (3) |
Homework 3 |
| Ten | Localization: Kalman Filters | Chap 8 | Notes | |
| Eleven | Localization: KF/Bayesian Methods | Chap 9 | ||
| Twelve | Localization: Bayesian Methods | Chap 9 | Notes | Homework 4 |
| Thirteen | Wrap Up | Cowan Paper Cowan Notes |