Robotics

Course Description

In this graduate class, you will learn about the theory and implementations on manipulator and mobile robots. Evaluation will be based on a final exam, attendance, homeworks and project.

Tentative List of Topics

  • History, growth; Robot applications, Basic Definitions
  • Kinematics- coordinate transformations, DH parameters
  • Forward kinematics,
  • Inverse Kinematics
  • Jacobians
  • Force Analysis (Static and Dynamic)
  • Trajectory Planning
  • Mobile Robots and Behaviorist Planning
  • Path Planning: A*, Dijkstra, Voronoi, etc.
  • Probabilistic Robotics
  • Bayesian Filtering (Kalman and Particle Filtering)
  • Simultaneous Localization and Mapping
  • Project Presentations
  • Introduction to Reinforcement Learning (MDP, POMDP)

Prerequisites

Basic Python. There are no other formal prerequisites, but knowledge of probability theory and linear algebra is encouraged.

Grading

Project(s): 45%
Final exam: 30%
~5 Hws: 25%
A student will get an F1 if s/he does not submit at least 60% Hws.

Textbook

You can use the following books:
Introduction to Robotics: Mechanics and Control, by John Craig, Pearson. (3rd or 4th Edition).
Other references
Probabilistic Robotics, S. Thrun, W. Burgard, and D. Fox. MIT Press, Cambridge, MA, 2005.
Planning Algorithms, Steven M. LaValle. Cambridge University Press.
Programming Robots with ROS: A Practical Introduction to the Robot Operating System. Quigley, Gerkey, & Smart, O’Reilley, 2015.

Expectations

You can expect me to come to class on time, clearly communicate, give you feedback on a timely manner, adjust lecture material based on performance on presentations and homeworks. I can expect you to come to class on time, be attentive and engaged in class, take notes and ask questions when something is not clear, spend an adequate amount of time on the class each week (at least 3 hours), spend 60-80 hours on your class.