Introduction to Robotics

Course description

Robotics is a rapidly growing field with applications including drones, autonomous vehicles, and home assistants. This course will provide an introduction to the fundamental theoretical and algorithmic principles behind modern robotic systems. The course will also allow students to get hands-on experience through project-based assignments with the Crazyflie quadrotor. For the final project, students train a vision-based navigation policy using imitation learning. Course topics include:

• Motion Planning
• Feedback Control
• State estimation, localization, and mapping
• Machine learning for robotics
• Broader topics: Robotics and the law, ethics, and economics

This course is aimed at undergraduate students (primarily juniors and seniors). The graduate-level track (ROB 549) is aimed at first-year PhD students.

Note: this course website is the public-facing version; Princeton students enrolled in the course should use Canvas. This website provides access to course materials including lecture videos, notes, slides, assignments, and the final project (see below).

Instructor

Anirudha Majumdar

Mechanical & Aerospace Engineering, Princeton University

Acknowledgements

A number of course staff members have contributed greatly to the development of this course over the years. I am particularly grateful to Jon Prevost, Vincent Pacelli, Julienne LaChance, Alec Farid, Meghan Booker, David Snyder, Allen Ren, Eric Lepowsky, Alexandra Bodrova, and Nate Simon.

Lecture recordings were done by the Princeton Broadcast Center.

Reference textbooks

• D. Gammelli, J. Lorenzetti, K. Luo, G. Zardini, M. Pavone, Principles of Robot Autonomy.
• S. M. LaValle, Planning Algorithms.
• S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics.

Course prerequisites

Multivariable calculus, linear algebra, basic probability, basic differential equations, some programming experience (this course uses Python).

Hardware

The assignments for the course (provided below) include theory, programming, and hardware implementation components. For the hardware, we use the Crazyflie drone from Bitcraze. This is a lightweight drone with open-source software. The motion planning and feedback control hardware assignments can be completed with the following parts list:

• Crazyflie 2.1 (brushless) drone
• Flow deck v2
• Crazyflie PA radio

For the final project, students train a vision-based navigation policy using imitation learning. We attach cameras to the drones, which transmit images in real-time to a receiver unit plugged into to a laptop. Completing the final project requires the following additional parts:

• Digital FPV camera
• Long pins