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

Foka, Amalia

Softcover

ISBN 10: 3659880078 ISBN 13: 9783659880070

Publisher: 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.

About this title

About the Author

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.

Edition details

• Publisher: LAP LAMBERT Academic Publishing
• Publication date: 2016
• Language: English
• ISBN 10: 3659880078
• ISBN 13: 9783659880070
• Binding: Paperback
• Number of pages: 136