CSE 571
Ai-Based Mobile Robotics
Credits
4.0
Lead Instructor
Dieter Fox
Textbook
Probabilistic Robotics and various research papers
Course Description
Overview of mobile robot control and sensing. Behavior-based control, world modeling, localization, navigation, and planning Probabilistic sensor interpretation, Bayers filters, particle filters. Projects: Program real robots to perform navigation tasks.
Prerequisites
CSE major and CSE 473, or permission of instructor.
CE Major Status
None
Course Objectives
- Introduce various techniques for probabilistic state estimation and discuss their application to problems such as robot localization, mapping, and manipulation.
- Provide a problem-oriented introduction to relevant machine learning and computer vision techniques.
ABET Outcomes
No outcomes registered
Course Topics
- Overview of mobile robotics (hardware, software architectures, sensors)
- Probabilistic models of sensing and acting
- Bayesian state estimation and filtering: Kalman filters (extended, unscented), particle filters, dynamic Bayesian networks
- Robot localization
- Map building / SLAM
- Kinect cameras for mapping, modeling, and recognition
- Markov decision processes (MDPs, POMDPs, reinforcement learning)
- Additional estimation and control topics (manipulation, object recognition and modeling, ...)