Autonomous Robotic Systems

Catalog description

This course is an in-depth study of state-of-the-art technologies and methods of autonomous robotic systems. The course consists of two components: lectures on theory, and course projects. Lectures will draw from textbook of probabilistic robotics and current research literature with several reading discussion classes. The primary topics include mathematic foundation of Bayes filter and Gaussian filters, robot motion and perception, simultaneous localization and mapping (SLAM), and autonomous navigation. In the project component of this class, students will do computer simulation of SLAM algorithms and/or implement algorithms on mobile devices or robotic platforms.