Underactuated Robots

Prof. Leonardo Lanari and Prof. Giuseppe Oriolo

Dipartimento di Ingegneria Informatica, Automatica e Gestionale

Sapienza Università di Roma

NOTICE: The 2017/18 UR course will start one week later. The first class will be on March 6 at 14:00.

Information

schedule | 26 Feb - 31 May 2018; Tue 14-00-16:00, Thu 14:00-18:00, room A4 |

office hours | Thu 14:00-16:00, room A211, DIAG, Via Ariosto 25 |

lanari [at] diag [dot] uniroma1 [dot] it; oriolo [at] diag [dot] uniroma1 [dot] it |

Audience

This 3-credits module is part of Elective in Robotics, a 4-module course offered to students
of the Master in Artificial Intelligence and Robotics at Sapienza
University of Rome. It can also be taken by students of the Master in
Control Engineering as one of the two modules of Control Problems in Robotics.

Objective

The
course focuses on underactuation as a pervasive principle in advanced
robotic systems (flexible robots, gymnast robots, humanoids, flying
robots), and presents a review of modeling and control methods for
underactuated robots.

Syllabus (preliminary)

The first part of the course will consider methods that decompose the system dynamics into actuated and unactuated components, and then apply methods from nonlinear and geometric control. Such methods include partial feedback linearization, energy-based methods (which often exploit passivity properties), backstepping, and geometric control techniques that exploit differential flatness.

The second part of the course will consider optimization-based methods. At their core, these algorithms rely on numerical optimization algorithms, and they are viable for systems with many degrees of freedom. These methods often exploit classical results from optimal control, including the Hamilton-Jacobi-Bellman equation and Pontryagin's maximum principle, along with methods from linear quadratic regulator theory for cases in which local linearization is feasible.

It is assumed that students taking this course are familiar with classical algorithms for path planning (e.g., notions of configuration space, and sampling-based planning algorithms such as PRM and RRT), concepts of stability for nonlinear systems (mainly Lyapunov theory), and robot dynamics and control (including state-space control, feedback linearization).

Material (will be added during the course)

Grading

Any student who has attended at least 2/3 of the lectures can pass this module by either giving a presentation
on a certain topic (based on technical papers) or developing a small
project (typically involving simulations). For more details, see the
main pages of Elective in Robotics or Control Problems in Robotics.

Master Theses at the Robotics Laboratory

Master Theses on the topics studied in this course are available at the DIAG Robotics Lab. More information can be found here.

Questions/comments: oriolo [at] diag [dot] uniroma1 [dot] it