Welcome#
Welcome to the course of Modeling and Control of Robotics (MAE 547, Fall 2023).
Course objectives#
This course covers theory and methods for modeling and control of robots. Homogeneous transforms, direct and inverse kinematics, Jacobian, statics, dynamics, and control of robotic arms will be discussed.
Recommended textbooks#
Robotics: Modeling, Planning and Control by Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo, Springer, 2009
Modern Robotics Mechanics, Planning, and Control by Kevin M. Lynch and Frank C. Park, Cambridge University Press, 2017
About this online note#
This note is available online as an open-source resource. The majority of its contents, including figures, are directly adapted from Siciliano et al.’s book and a few from Lynch’s book. The purpose of these notes is to provide a concise material specifically tailored for course students. Simultaneously, it aims to benefit general students interested in robotics by offering a quick and accessible source for learning, reference, and review, eliminating the need to read an entire book. Nevertheless, I highly encourage everyone to explore the original books by Siciliano et al. and Lynch when time permits.
About Me#
I am an Assistant Professor in the Ira A. Fulton Schools of Engineering at Arizona State University. This is my webpage: https://wanxinjin.github.io/
About my IRIS Lab#
The Intelligent Robotics and Interactive Systems (IRIS) Lab focues on:
Human robot alignment: We develop methods to empower a robot with the ability to efficiently understand and be understood by human users through a variety of physical interactions. We explore how robots can aptly respond to and collaborate meaningfully with users.
Contact-rich robot manipulation: We leverage physical principles to derive representations/models that enable robots to interact efficiently with their surroundings. We develop algorithms to enable robots manipulate objects through contact efficiently and robustly.
Fundamental methods in robotics: We focus on developing fundamental theories and algorithms for achieving efficient, safe, and robust robot intelligence. Our methods lie at the intersection of model-based (control and optimization) and data-driven approaches, harnessing the complementary benefits of both.
The IRIS lab is actively looking for robotics enthusiasts to join our team. If you’re interested in becoming a part of our lab, please submit your application here: Join IRIS Lab.