Open to Graduate and Advanced Undergraduate Students
Class Location: Carnegie 106
Class Time: 10:00 – 11:50 - Mondays and Thursdays
Instructor: Jeff Trinkle
Office: MRC 330c
Office Hours: regular hours to be determined, otherwise, meetings will be by appointment
Email: trinkle +AT+ gmail.com
Prerequisites: Robotics I or permission of instructor.
Other useful background: linear algebra, Matlab
Description: This course introduces methods that leverage the basic analysis techniques learned in Robotics I to develop numerical and algorithmic techniques needed to endow robots with the “intelligence” to devise strategies to solve problems they will encounter. Once these abilities are sufficiently well developed, robots will become safe and autonomous, thus paving the way for pervasive personal robots. Topics include: grasp analysis; physical simulation; configuration and state space representations; planning methods including cell decomposition, roadmap methods, and rapidly-exploring random trees; and other current topics.
Steve M. LaValle, Planning Algorithms, Cambridge University Press, 2006. Available free at http://planning.cs.uiuc.edu/.
Other useful texts:
(1) Prattichizzo and Trinkle, "Grasping," in Springer Handbook of Robotics, Siciliano and Khatib, editors, Springer-Verlag, 2008.
(2) Peter Corke, Robotics, Vision, and Control: Fundamental Algorithms in Matlab, Springer Tracts in Advanced Robotics, 2011.
(3) R. Murray, Z. Li, and S. Sastry, A Mathematical Introduction to Robotic Manipulation, CRC Press, 1994.
(4) Howie Choset, et al, Principles of Robot Motion: Theory, Algorithms, and Implementations, MIT Press, 2005.
(5) M.T. Mason, Mechanics of Robot Manipulation, MIT Press, 2001.
Programming assignments will be done in Matlab to take advantage of the robotics and computer vision toolboxes, so we can focus on understanding higher-level robotics problems rather than coding. Peter Corke's book, listed above, is fully integrated with the toolboxes and serves as an excellent reference manual for the toolboxes and the basics of robotics and computer vision.
Schedule: Lectures, Assignments, Due Dates
RPI Matlab Simulator: Developed in RPI's CS Robotics Lab.
Schunk Powerball Light-weight Arm: Should arrive in the CS Robotics Lab in early February.
Barrett Technologies: Maker of the Barrett Arm and Hand in the CS Robotics Lab.