Handling Uncertainty and Networked Structure in Robot Control
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Beschreibung
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.
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.
Presents in-depth coverage of a range of algorithms, which readers can implement and modify for their own systems Demonstrates practical investigations and case studies that highlight the applicability of the techniques Describes a selection of modern methods, offering readers up-to-date means of tackling uncertainty and network structure in robot control Provides additional electronic material, such as source code and experimental data Includes supplementary material: sn.pub/extras
Autor*in
Lucian Bușoniu
Themen in »Handling Uncertainty and Networked Structure in Robot Control«
Autonomous Mobile Robots Learning-based Control Networked Multi-robot Systems Networked Single-robot Systems Perception in Complex Systems State Estimation