Context Classifier for Service Robots

Tiago Ferreira, Fabio Miranda, Pedro Sousa, Jose Barata, Joao Pimentao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper a context classifier for service robots is presented. Independently of the application, service robots need to have the notion of their context in order to behave appropriately. A context classification architecture that can be integrated in service robots reliability calculation is proposed. Sensorial information is used as input. This information is then fused (using Fuzzy Sets) in order to create a knowledge base that is used as an input to the classifier. The classification technique used is Bayes Networks, as the object of classification is partially observable, stochastic and has a sequential activity. Although the results presented refer to indoor/outdoor classification, the architecture is scalable in order to be used in much wider and detailed context classification. A community of service robots, contributing with their own contextual experience to dynamically improve the classification architecture, can use cloud-based technologies.

Original languageEnglish
Title of host publicationDoCEIS 2015
Subtitle of host publicationTechnological Innovation for Cloud-Based Engineering Systems
EditorsLM CamarinhaMatos, TA Baldissera, G DiOrio, F Marques
Place of PublicationCham
PublisherSPRINGER-VERLAG BERLIN
Pages196-203
Number of pages8
ISBN (Electronic)978-3-319-16766-4
ISBN (Print)978-3-319-16765-7
DOIs
Publication statusPublished - 2015
Event6th IFIPWG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS) - Costa de Caparica, Portugal
Duration: 13 Apr 201515 Apr 2015

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSPRINGER-VERLAG BERLIN
Volume450
ISSN (Print)1868-4238

Conference

Conference6th IFIPWG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS)
CountryPortugal
CityCosta de Caparica
Period13/04/1515/04/15

Keywords

  • Context
  • Service robots
  • Reliability
  • Fuzzy sets
  • Bayes networks
  • SYSTEM
  • RECOGNITION

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