CS 547: Sensing and Planning in Robotics

Fall 2005

Wednesday 3:30 - 6:30 p.m. in ZFS 352

This class focuses on modern techniques, based largely on probability theory, to solve problems in mobile robotics. A running theme throughout the class is that robotics involves uncertainty at several different levels. The machinery of estimation theory and probability theory (Bayes filters), has been applied with great success to cope with uncertainty in sensing and actuation. The class will cover the relevant theory and applications to problems in robot localization, mapping, exploration, tracking etc. If time permits we will also cover some advanced topics such as extensions to multi-robot systems.

READ THIS CAREFULLY IF YOU PLAN TO TAKE THE CLASS

The treatment in class will be mathematical. Students are expected to know elementary probability theory, calculus, and linear algebra at the undergraduate level. There is no formal prerequisite for the class (don't believe what the catalogue says). If you want to register, and the office won't let you, send email to the instructor. Based on the mathematical treatment presented in class, students will be expected to complete a fairly sizeable class project. Most students will be expected to use a standard robot simulator for the project. There are limited opportunities for advanced students to use state-of-the-art robots (thanks to a generous grant from Intel Corporation). In order to complete the project, students will need to have a reasonable background in programming. (Typical projects are expected to run into a few thousand lines of code). All the software tools used in the class run on Linux. Students are expected to be reasonably competent in programming under Linux (or some other Unix flavor).

This page was created by Gaurav Sukhatme and is maintained by him. Last updated 11/22/05