IcETRAN 2022, Section RO (Robotics)
INVITED PAPER:
Robust Control and Estimation of Soft Robots
Adriano Fagiolini, Università degli Studi di Palermo, Palermo, Italia, fagiolini@unipa.it
Abstract:
This talk will address robust and adaptive estimation and control of soft robots. In particular, it will discuss how adaptive control techniques, developed over the past decades, can be applied and adapted to cope with the peculiarities of soft robot models. The discussion will continue by presenting a possible line of research aimed at developing solutions for simultaneous position and stiffness control in soft robot joints and their possible applications.
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Adriano Fagiolini is an Assistant Professor at the University of Palermo, Italy. He received an
M.S. degree in Computer Science Engineering in 2004 and a Ph.D. degree in Robotics and Automation in 2009 from the University of Pisa. He has been a Visiting Researcher at the Department of Energy, IUT Longwy Universite de Lorraine (France) in 2019, at the School of Physics and Engineering, University of the South Pacific (Fiji), in 2017 and 2018, and at the Department of Mechanical Engineering, the University of California at Riverside, in 2015 and 2017. He enrolled in the Summer Student Programme at the European Center for Nuclear Research (CERN), Geneva, in 2002, and in the International Curriculum Option of Doctoral studies in Hybrid control for complex, distributed, and heterogeneous embedded systems, in 2007. In 2008, he led the team of the University of Pisa during the first European Space Agency’s Lunar Robotics Challenge, which resulted in a second–place prize for the team. He was one of the recipients of the IEEE ICRA’s Best Manipulation Paper Award in 2005.
He is an Associate Editor of the IEEE Robotics and Automation Letters (RA–L) since 2018 and
of the IEEE International Conference on Robotics and Automation (ICRA) and IEEE/RSJ International Conference on Intelligent Robots (IROS) since 2017. He is a member of the IFAC
Technical Committee on Control and Education since 2020. His main research interests are in soft robotics, learning methods for robotics, self–driving racecars, distributed algorithms for consensus on set–valued Boolean information, and data clustering and estimation.
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