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© 2014

Probabilistic Approaches to Robotic Perception

Benefits

  • 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

Book

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 91)

Table of contents

  1. Front Matter
    Pages 1-24
  2. Probabilistic Modelling for Robotic Perception

    1. Front Matter
      Pages 1-1
    2. João Filipe Ferreira, Jorge Dias
      Pages 3-36
    3. João Filipe Ferreira, Jorge Dias
      Pages 71-102
    4. João Filipe Ferreira, Jorge Dias
      Pages 103-119
    5. João Filipe Ferreira, Jorge Dias
      Pages 121-145
    6. João Filipe Ferreira, Jorge Dias
      Pages 147-167
  3. Probabilistic Approaches for Robotic Perception in Practice

    1. Front Matter
      Pages 169-169
    2. João Filipe Ferreira, Jorge Dias
      Pages 185-226
    3. João Filipe Ferreira, Jorge Dias
      Pages 227-232
  4. Back Matter
    Pages 233-241

About this book

Introduction

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”.

Keywords

Ambiguity Bayesian Approach Multisensory Fusion Plausible Reasoning Robotic Perception Robotics Uncertainty

Authors and affiliations

  1. 1.ISR – DEECInstituto de Sisteasm e RoboticaCoimbraPortugal
  2. 2.Departamento de Engenharia, Electrotécnica e ComputadoresInstituto de Sisteasm e Robotica, Universidade de CoimbraCoimbraPortugal

Bibliographic information

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