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Handling uncertainty and networked structure in robot control

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Handling uncertainty and networked structure in robot control/ edited by Lucian Busoniu, Levente Tamas.
other author:	Busoniu, Lucian.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xxviii, 388 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Robots - Control systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-26327-4
ISBN:	9783319263274

Handling uncertainty and networked structure in robot control
Handling uncertainty and networked structure in robot control[electronic resource] /edited by Lucian Busoniu, Levente Tamas. - Cham :Springer International Publishing :2015. - xxviii, 388 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.422198-4182 ;. - Studies in systems, decision and control ;v. 2. .

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.

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.

ISBN: 9783319263274

Standard No.: 10.1007/978-3-319-26327-4doiSubjects--Topical Terms:

557477
Robots
--Control systems.

LC Class. No.: TJ211.35

Dewey Class. No.: 629.892

Handling uncertainty and networked structure in robot control
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505 0 $a 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.
520 $a 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.
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