Welcome#
Welcome to the course of Modeling and Control of Robotics (MAE 547, Fall 2024).
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. PI of Intelligent Robotics and Interactive Systems (IRIS) Lab. This is the lab webpage: https://irislab.tech/
About IRIS Lab#
The Intelligent Robotics and Interactive Systems (IRIS) Lab focues on:
Human-autonomy alignment: We develop certifiable, efficient, and empowering methods to enable robots to align their autonomy with human users through various natural interactions.
Contact-rich dexterous manipulation: We develop efficient physics-based representations/modeling, planning/control methods to enable robots to gain dexterity through frequently making or breaking contacts with objects.
Fundamental computational methods: We develop fundamental algorithms for efficient, safe, and robust robot intelligence, by harnessing the complementary benefits of model-based and data-driven approaches.
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.