2.166 Autonomous Vehicles
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 6.041B 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.
This class counts for a total of 12 credits. This is a graduate-level class.
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