Título: |
Distributed Autonomous Robotic Systems The 11th International Symposium edited by M. Ani Hsieh, Gregory Chirikjian. |
Otros autores: |
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Ani Hsieh, M, ed.
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Chirikjian, Gregory, ed.
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Editado por: |
Springer Berlin Heidelberg :;Imprint: Springer,
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Año de publicación: |
2014.
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Lugar de publicación: |
Berlin, Heidelberg :
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Descripción física: |
xxii, 450 p. : il. |
ISBN: |
9783642551468
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Colección: |
Springer Tracts in Advanced Robotics,
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Materias: |
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Artificial Intelligence (incl. Robotics). |
Engineering. |
Automation. |
Robotics. |
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Notas: |
Coordination for Perception, Coverage, and Tracking -- Task Allocation and Coordination Strategies -- Modular Robots and Novel Mechanisms and Sensors -- Formation Control and Motion Planning for Robot Teams -- Learning, Adaptation, and Cognition in Many Robot Systems.-. |
Sumario: |
Distributed robotics is a rapidly growing and maturing interdisciplinary research area lying at the intersection of computer science, network science, control theory, and electrical and mechanical engineering. The goal of the Symposium on Distributed Autonomous Robotic Systems (DARS) is to exchange and stimulate research ideas to realize advanced distributed robotic systems. This volume of proceedings includes 31 original contributions presented at the 2012 International Symposium on Distributed Autonomous Robotic Systems (DARS 2012) held in November 2012 at the Johns Hopkins University in Baltimore, MD USA. The selected papers in this volume are authored by leading researchers from Asia, Europa, and the Americas, thereby providing a broad coverage and perspective of the state-of-the-art technologies, algorithms, system architectures, and applications in distributed robotic systems. The book is organized into five parts, representative of critical long-term and emerging research thrusts in the multi-robot community: Coordination for Perception, Coverage, and Tracking; Task Allocation and Coordination Strategies; Modular Robots and Novel Mechanisms and Sensors; Formation Control and Planning for Robot Teams; and Learning, Adaptation, and Cognition for Robot Teams. |
URL: |
http://dx.doi.org/10.1007/978-3-642-55146-8
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