Introduction to Robotics

Course Description

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

Tentative List of Topics

• Week 1: History, growth; Robot applications, Basic Definitions
• Week 2: Mobile Robots and Behaviorist Planning
• Week 3: Path Planning: A*, Dijkstra, Voronoi, etc.
• Week 4-5: Probabilistic Robotics
• Week 6-7: Simultaneous Localization and Mapping
• Week 8: Bayesian Filtering (Kalman and Particle Filtering)
• Week 9: Kinematics- coordinate transformations, DH parameters
• Week 10: Forward kinematics,
• Week 11: Inverse Kinematics
• Week 12: Jacobians
• Week 13-14: Force Analysis (Static and Dynamic)

Prerequisites

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

Grading

Midterm: 40%
Final exam: 50%
Attendance: 10%

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.