Predictive Autonomous Robot Navigation: POMDPs for robot navigation with integrated human motion prediction - Couverture souple

Foka, Amalia

 
9783659880070: Predictive Autonomous Robot Navigation: POMDPs for robot navigation with integrated human motion prediction
Couverture souple
ISBN 10 : 3659880078 ISBN 13 : 9783659880070
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

A primary goal in robotics research is to provide means for mobile platforms to perform autonomously within their environment. Depending on the task at hand, autonomous performance can be defined as the execution by the robot, without human intervention, of certain navigational tasks. Commonly addressed navigation tasks include the localization, mapping, path planning and obstacle avoidance tasks. A probabilistic framework for the navigation tasks of localization, path planning and obstacle avoidance in dynamic environments is presented based on the Partially Observable Markov Decision Process (POMDP) model. POMDPs have the major shortcoming of their extreme computational complexity and hence they have been mainly used in robotics as high level path planners only. An hierarchical representation of POMDPs is introduced specifically designed for the autonomous robot navigation problem and termed as the Robot Navigation-Hierarchical POMDP (RNHPOMDP). Integration of human motion prediction into the navigation model is utilized with two kinds of prediction: short-term and long-term prediction. The book should be useful to students in Robotics utilizing POMDPs.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

A primary goal in robotics research is to provide means for mobile platforms to perform autonomously within their environment. Depending on the task at hand, autonomous performance can be defined as the execution by the robot, without human intervention, of certain navigational tasks. Commonly addressed navigation tasks include the localization, mapping, path planning and obstacle avoidance tasks. A probabilistic framework for the navigation tasks of localization, path planning and obstacle avoidance in dynamic environments is presented based on the Partially Observable Markov Decision Process (POMDP) model. POMDPs have the major shortcoming of their extreme computational complexity and hence they have been mainly used in robotics as high level path planners only. An hierarchical representation of POMDPs is introduced specifically designed for the autonomous robot navigation problem and termed as the Robot Navigation-Hierarchical POMDP (RNHPOMDP). Integration of human motion prediction into the navigation model is utilized with two kinds of prediction: short-term and long-term prediction. The book should be useful to students in Robotics utilizing POMDPs.

Biographie de l'auteur

Amalia Foka, Assistant Professor on Computer Science Applications for Arts, School of Fine Arts, University of Ioannina, Greece: Studied Computer Systems Engineering (BEng) and Advanced Control (MSc) at UMIST, UK. She completed her PhD in the area of robotics at the Computer Science Department, University of Crete, Greece.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
3659880078
ISBN 13 
9783659880070
Reliure
Broché
Nombre de pages
136
Coordonnées du fabricant
non disponible
Personne responsable
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