2.166 Probabilistic Techniques for Mobile Robotics


Class Info

Theory and application of probabilistic techniques for autonomous mobile robotics. Topics include probabilistic state estimation and decision making for mobile robots; stochastic representations of the environment; dynamic models and sensor models for mobile robots; algorithms for mapping and localization; planning and control in the presence of uncertainty; cooperative operation of multiple mobile robots; mobile sensor networks; application to autonomous marine (underwater and floating), ground, and air vehicles.

This class has 6.041 as a prerequisite.

2.166 will not be offered this semester. It will be available in the Spring semester, and will be instructed by J. J. Leonard.

Lecture occurs 3:00 PM to 5:00 PM on Mondays and Wednesdays in 35-308.

This class counts for a total of 12 credits.

In the Spring 2016 Subject Evaluations, 2.166 was rated 5.6 out of 7.0. You can find more information at the What is Duckietown? site or on the 2.166 Stellar site.

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