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Notes

  • 1. Introduction
  • 2. Probabilities
  • 3. Robot Environment Interaction
  • 4. Localization
  • 5. Discrete Bayes Filters
  • 6. Robot Motion
  • 7. Sensor Models
  • 8. Grid Localization
  • 9. Monte-Carlo Localization
  • 10. Markov Decision Process
  • 11. Policies and Value Function
  • 12. Dynamic Programming
  • 13. Partially Observable Markov Decision Process

Assignments

  • 1. Localization in a Maze
  • 2. Monte-Carlo Localization in a Maze

Project

  • Project

Course outline

  • Computer Science 529 – Mobile Robotics
CSCI 529
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