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Thisbook focuses on two challenges posed in robot control by the increasingadoption of robots in the everyday human environment: uncertainty and networkedcommunication. PartI of the book describes learning control to address environmental uncertainty.Part II discusses state estimation, active sensing, and complex scenarioperception to tackle sensing uncertainty. Part IIIcompletes the book with control of networked robots and multi-robot teams.
Each chapter features in-depth technical coverage and case studieshighlighting the applicability of the techniques, with real robots or insimulation. Platforms include mobile ground, aerial, and underwater robots, aswell as humanoid robots and robot arms. Source code and experimental data areavailable at http://extras.springer.com.
The text gathers contributions from academic and industry experts,and offers a valuable resource for researchers or graduate students in robotcontrol and perception. It also benefits researchers in related areas, such ascomputer vision, nonlinear and learning control, and multi-agent systems.
List of contents
From the Contents: Part I Learning Control in Unknown Environments.- Robot Learning for Persistent Autonomy.- The Explore-Exploit Dilemma in Nonstationary Decision Making under Uncertainty.- Part II Dealing with Sensing Uncertainty.- Observer Design for Robot Manipulators via Takagi-Sugeno Models and Linear Matrix Inequalities.- Part III Control of Networked and Interconnected Robots.- Vision-based quadcopter navigation in structured environments.
About the author
Lucian
Busoniu received the M.Sc. degree (valedictorian) from the Technical University
of Cluj-Napoca, Romania, in 2003 and the Ph.D. degree (cum laude) from the
Delft University of Technology, the Netherlands, in 2009. He has held research
positions in the Netherlands and France, and is currently an associate
professor with the Department of Automation at the Technical University of
Cluj-Napoca. His fundamental interests include planning-based methods for
nonlinear optimal control, reinforcement learning and dynamic programming with
function approximation, and multiagent systems; while his practical focus is
applying these techniques to robotics. He has coauthored a book and more than
50 papers and book chapters on these topics. He was the recipient of the 2009
Andrew P. Sage Award for the best paper in the IEEE Transactions on Systems,
Man, and Cybernetics.
Levente Tamas received the M.Sc. (valedictorian) and the Ph.D.
degree in electrical engineering from TechnicalUniversity of Cluj-Napoca,
Romania, in 2005 and 2010, respectively. He took part in several postdoctoral
programs dealing with 3D perception and robotics, the most recent one spent at
the Bern University of Applied Sciences, Switzerland. He is currently with the
Department of Automation, Technical University of Cluj-Napoca, Romania. His
research focuses on 3D perception and planning for autonomous mobile robots,
and has resulted in several well ranked conference papers, journal articles, and
book chapters in this field.
Summary
This
book focuses on two challenges posed in robot control by the increasing
adoption of robots in the everyday human environment: uncertainty and networked
communication. Part
I of the book describes learning control to address environmental uncertainty.
Part II discusses state estimation, active sensing, and complex scenario
perception to tackle sensing uncertainty. Part III
completes the book with control of networked robots and multi-robot teams.
Each chapter features in-depth technical coverage and case studies
highlighting the applicability of the techniques, with real robots or in
simulation. Platforms include mobile ground, aerial, and underwater robots, as
well as humanoid robots and robot arms. Source code and experimental data are
available at http://extras.springer.com.
The text gathers contributions from academic and industry experts,
and offers a valuable resource for researchers or graduate students in robot
control and perception. It also benefits researchers in related areas, such as
computer vision, nonlinear and learning control, and multi-agent systems.
