Prof. Seth Hutchinson
Department of Electrical and Computer Engineering
University of Illinois
IMPORTANT: If you plan to attend this module please inform me at lanari@diag.uniroma1.it. This is essential for organization purposes.
Information
temporary webpage (during the course this will be the webpage updated by Prof. Seth Hutchinson)
| schedule |
28 Nov - 21 Dec 2016
Mon 15:45-17:15 (in A7)
Wed 15:45-17:15 (in A7)
Fri 14:00-15:30 (in A7)
|
| office hours |
|
| e-mail |
seth [at] illinois
[dot] edu |
Audience
This is one of the 4 modules of Elective in Robotics in 2016/2017,
offered to students
of the Master in Artificial Intelligence and Robotics (MARR) at
Sapienza University of Rome.
Objective
The
course presents the basic methods for analyzing and controlling
underactuated mechanical systems.
Syllabus
(preliminary)
In
this class, we will consider the problems of motion planning and
control of underactuated systems. Underactuated robots are those for
which the number of actuators is strictly less than the number of
degrees of freedom. Classical examples with a small number of degrees
of freedom include robot arms that contain unactuated joints (e.g., the
Acrobot or Pendubot), robot arms with flexible joints, the cart-pole
system, and the Furuta pendulum. In addition to these basic systems,
most all robots that are capable of locomotion are underactuated. This
includes legged robots, flying robots, swimming robots, snake-like
robots. For such robots, there is no actuator that can directly control
the position and velocity of the center of mass. In the case of bipedal
locomotion, the state of a humanoid's center of mass can only be
controlled indirectly, using impact forces that occur at footfalls. For
aerial vehicles such as quadrotors or fixed wing aircraft, the position
of the center of mass is again indirectly controlled, in this case
through the use of aerodynamic forces. This latter class of systems is
typically far more complex than the classical examples.
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 will be 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
Grading
After
attending the course, students should either give a presentation
on a certain topic (based on technical papers) or develop a small
project (typically involving simulations). For more details, see the
main page for Elective in Robotics.
Master
Theses at the Robotics Laboratory
Master
Theses on the topics studied in this course are available at the Robotics Laboratory.
More information can be found here.
Questions/comments: lanari [at] diag [dot] uniroma1 [dot] it