Instructor: Ramviyas N. Parasuraman

Time and Location of Lectures:

Monday (2:30-3:20 PM), Tuesday & Thursday (2:00-3:15 PM), Boyd #222

Office Hours and Location

Boyd 803, Tuesday & Thursday 1-2 PM or by an email appointment  

Course Overview

Students completing this course will gain an understanding of the hardware and software involved in robotics, with a focus on programming algorithms. Students will learn various mathematical and statistical models, associated algorithms, and their implementations, which are making modern-day robotics possible. They will understand the past, present and future of robotics, and the main challenges that make robotics difficult.

Textbook: "Introduction to Autonomous Mobile Robots" by Roland Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, MIT Press (2nd Ed. 2011)
Reference book: "Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox. MIT Press (2005)

Note: We will follow the structure and content of the EdX course on Autonomous Mobile Robots and heavily use their slides in this class (all copyrights of the slides belong to the authors and EdX). It's an excellent course from the ETH Zurich team. The idea is that the student always has an additional venue for learning and reference.

Course Topics

The course topics presented here is a general plan for the course; deviations announced to the class by the instructor may be necessary.
I. Overview of Robotics: Introduction, Robot Hardware, Robotic Software Architectures, Probability Theory, Field Applications
II. Robotic Perception: Range Finders and Camera models
III. Robotic Motion: Kinematics, Velocity Motion Model, Odometry Motion Model, Motion and Maps
IV. Localization: State Estimation under Uncertainty, Filters: Bayes, Kalman, Extended Kalman, and Monte Carlo, Localization methods: Markov, Extended Kalman Filter, Particle Filter, Monte Carlo framework, etc.
V. Mapping: Occupancy Grid Mapping, Learning Inverse Measurement Model, Simultaneous Localization and Mapping (SLAM) solutions

The course is composed of both theoretical and practical components. There will be four assignments during this course and each assignment will have theoretical questions and practical programming problems. Likewise, the midterm and final exam has written quizzes and programming projects. Therefore, it is necessary to learn and perform well in both theoretical and practical parts of this course to pass the course.

Grading