This course introduces students to foundational mathematical models and algorithms used to implement intelligent behavior in autonomous robots, such as autonomous vehicles, drones, industrial robots, and medical robots. Course material will draw from the following topics:

Modeling and representation. 2D/3D transformations, 2D/3D geometry, forward and inverse kinematics, motion representations, configuration space.
Motion planning and control. Motion planning, task planning, feedback control, optimal and model predictive control.
Perception. Uncertainty modeling, state estimation, visual sensors, 3D mapping, calibration, some computer vision.
Software and hardware system integration. Simulation software, visualization and GUIs, distributed system middleware, performance evaluation.

The content of this course will consist of lectures, homework assignments, and simulation-based programming assignments.  Programming will be in the Python language.

This course can be considered as an advanced version of ECE470 / ME445 (Introduction to Robotics) that is intended for graduate students. The breadth of the material is similar, but this course will approach selected topics in greater technical depth and rigor. By the end of this course, students should be better prepared to understand academic papers and implement state-of-the-art methods used in robotics.

Time and Location

09:30AM – 10:45AM Mondays and Wednesdays

2039 Campus Instructional Facility

Textbook and readings

Most readings will be in the online Robotic Systems book draft. Some readings will be excerpted from the following online texts:

Lynch, K.M. and Park, F.C. Modern Robotics. Cambridge University Press, 2017.
Murphy, K.P. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023.
Choset, Lynch, Hutchinson, Kantor, Burgard, Kavraki, and Thrun.  Principles of Robot Motion: Theories, Algorithms, and Implementations.  MIT Press, Boston, 2005.
Murray, Li, and Sastry. A Mathematical Introduction to Robotic Manipulation.  CRC Press, 1994.
LaValle, S.  Planning Algorithms.  Cambridge University Press, 2006.
Szeleski, R. Computer Vision: Algorithms and Applications.
O’Kane.  A Gentle Introduction to ROS. 2014. 

Course Content

Course content is available to enrolled students through Canvas.

A tentative schedule can be found here.