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EECS568 Mobile Robotics: Methods and Algorithms

Instructions for running our provided virtual machine, setting up your own, or setting up your computer to run course code can be found on the SoftwareSetup page.


Theory and application of probabilistic techniques for autonomous mobile robotics. This course will present and critically examine contemporary algorithms for robot perception (using a variety of modalities), state estimation, mapping, and path planning. Topics include Bayesian filtering; stochastic representations of the environment; motion and sensor models for mobile robots; algorithms for mapping, localization, planning and control in the presence of uncertainty; application to autonomous marine, ground, and air vehicles.

We will assume students have a basic familiarity with Linear Algebra and Probability. A few review sessions will be offered at the beginning of the term for Linear Algebra.

We will also assume students are proficient in Java. Those with strong backgrounds in C or C++ should be able to pick up what they need, but this course is not suitable for students who lack this background.

Important Dates

Quiz 1. Wed., October 3

10a-11a (in class) pdf

Midterm 1. Wed., October 10

10a-12p (in class)

Quiz 2. Wed., November 21

10a-11a (in class) pdf

Midterm 2. Wed., November 28

10a-12p (in class)

Final Project Showcase. Mon., December 10

10a-1p Tishman Hall.

Important note: Please notice the links in the sidebar. Assignments and course information are available there.