TY  - BOOK
AU  - Galindo,Cipriano
AU  - Fernández-Madrigal,Juan-Antonio
AU  - Gonzáez,Javier
ED  - SpringerLink (Online service)
TI  - Multiple Abstraction Hierachies for Mobile Robot Operation in Large Environments
T2  - Studies in Computational Intelligence,
SN  - 9783540726906
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Engineering mathematics
KW  - Automation and Robotics
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
N1  - A Robotic Future -- Multi-Hierarchical, Symbolic Representation of the Environment -- Application of Hierarchical Models to Mobile Robot Task Planning -- Mobile Robot Operation with Multi-Hierarchies -- Automatic Learning of Hierarchical Models -- Implementation and Experiences on a Real Robot
N2  - This book focuses on the efficient performance of mobile robots through the use of multi-hierarchical symbolic representations of the environment. A mobile robot intended to perform deliberative actions must possess some symbolic representation of its workspace, but such representations of real environments usually become so large that should be conveniently arranged in order to facilitate and, in some cases, make possible their use. Apart from the drawback of dealing with large amounts of information, other problems stand out when using symbolic representations. One is keeping the model properly optimized with respect to the robot tasks while maintaining it coherent with reality. Another one is the creation (or modification) of symbols from sensorial data. This book addresses all these issues considering symbolic multi-hierarchical structures. Such structures, based on the concept of abstraction, allow a robot to speed up its operation in large environments. Practical solutions tested on real robots (for instance, a robotic wheelchair for elder people) are provided. The book is intended for PhD students and in general for robotics, computer science, and artificial intelligence researchers
UR  - http://dx.doi.org/10.1007/978-3-540-72690-6
ER  -