Authors:
- Provides an overview of robotic perception systems and how human behavior has been a challenge for robotic researchers
- Introduction to the use of probabilistic tools to implement robotic perception, adding to it working examples and case studies
- Focuses on multisensory perception and the action-perception loop addressing the topic of probabilistic approaches to robotics
Part of the book series: Springer Tracts in Advanced Robotics (STAR, volume 91)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (9 chapters)
-
Front Matter
-
Probabilistic Modelling for Robotic Perception
-
Front Matter
-
-
Probabilistic Approaches for Robotic Perception in Practice
-
Front Matter
-
-
Back Matter
About this book
This book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot? How should a robotic system perceive, infer, decide and act efficiently? These are two of the challenging questions robotics community and robotic researchers have been facing.
The development of robotic domain by the 1980s spurred the convergence of automation to autonomy, and the field of robotics has consequently converged towards the field of artificial intelligence (AI). Since the end of that decade, the general public’s imagination has been stimulated by high expectations on autonomy, where AI and robotics try to solve difficult cognitive problems through algorithms developed from either philosophical and anthropological conjectures or incomplete notions of cognitive reasoning. Many of these developments do not unveil even a few of the processes through which biological organisms solve these same problems with little energy and computing resources. The tangible results of this research tendency were many robotic devices demonstrating good performance, but only under well-defined and constrained environments. The adaptability to different and more complex scenarios was very limited.
In this book, the application of Bayesian models and approaches are described in order to develop artificial cognitive systems that carry out complex tasks in real world environments, spurring the design of autonomous, intelligent and adaptive artificial systems, inherently dealing with uncertainty and the “irreducible incompleteness of models”.
Authors and Affiliations
-
ISR – DEEC, Instituto de Sisteasm e Robotica, Coimbra, Portugal
João Filipe Ferreira
-
Departamento de Engenharia, Electrotécnica e Computadores, Instituto de Sisteasm e Robotica, Universidade de Coimbra, Coimbra, Portugal
Jorge Miranda Dias
Bibliographic Information
Book Title: Probabilistic Approaches to Robotic Perception
Authors: João Filipe Ferreira, Jorge Miranda Dias
Series Title: Springer Tracts in Advanced Robotics
DOI: https://doi.org/10.1007/978-3-319-02006-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-02005-1Published: 11 September 2013
Softcover ISBN: 978-3-319-03289-4Published: 25 August 2015
eBook ISBN: 978-3-319-02006-8Published: 30 August 2013
Series ISSN: 1610-7438
Series E-ISSN: 1610-742X
Edition Number: 1
Number of Pages: XXIX, 242
Topics: Robotics and Automation, Artificial Intelligence, Cognitive Psychology, Image Processing and Computer Vision, Signal, Image and Speech Processing