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Springer Tracts in Advanced Robotics

Probabilistic Approaches to Robotic Perception

Authors: Ferreira, João Filipe, Dias, Jorge Miranda

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  • 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
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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”.

Table of contents (9 chapters)

Table of contents (9 chapters)
  • Fundamentals of Bayesian Inference

    Pages 3-36

    Ferreira, João Filipe (et al.)

  • Representation of 3D Space and Sensor Modelling Within a Probabilistic Framework

    Pages 37-69

    Ferreira, João Filipe (et al.)

  • Bayesian Programming and Modelling

    Pages 71-102

    Ferreira, João Filipe (et al.)

  • Hierarchical Combination of Bayesian Models and Representations

    Pages 103-119

    Ferreira, João Filipe (et al.)

  • Bayesian Decision Theory and the Action-Perception Loop

    Pages 121-145

    Ferreira, João Filipe (et al.)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-02006-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.99
price for USA in USD
  • ISBN 978-3-319-02005-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $139.99
price for USA in USD
  • ISBN 978-3-319-03289-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Probabilistic Approaches to Robotic Perception
Authors
Series Title
Springer Tracts in Advanced Robotics
Series Volume
91
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-02006-8
DOI
10.1007/978-3-319-02006-8
Hardcover ISBN
978-3-319-02005-1
Softcover ISBN
978-3-319-03289-4
Series ISSN
1610-7438
Edition Number
1
Number of Pages
XXIX, 242
Topics