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EECS568 Mobile Robotics: Methods and Algorithms

  • Materials from the Vis tutorial can be donwloaded from this page: Vis_Tutorial
  • Instructions for setting up a virtual machine or configuring your linux distribution can be found here: Software_Setup


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

Midterm 1. October 26

10a-12p (in class) or 7-9p, TBD

Midterm 2. December 5

10a-12p (in class) or 7-9p, TBD

Final Project Showcases

10a-12p December 12 in Tishman Hall.

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