Context Classifier for Service Robots

  • Tiago FerreiraEmail author
  • Fábio Miranda
  • Pedro Sousa
  • José Barata
  • João Pimentão
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 450)


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.


Context Service robots Reliability Fuzzy sets Bayes networks 


  1. 1.
    Dey, A.K., Abowd, G.D.: Towards a Better Understanding of Context and Context-Awareness. Handheld Ubiquitous Comput. 304–307 (1999)Google Scholar
  2. 2.
    Dey, A.K.: Understanding and Using Context. Pers. Ubiquitous Comput. 5, 4–7 (2001)CrossRefGoogle Scholar
  3. 3.
    Haghighi, P., Krishnaswamy, S., Zaslavsky, A., Gaber, M.M.: Reasoning about context in uncertain pervasive computing environments. Smart Sens. Context 5279, 112–125 (2008)CrossRefGoogle Scholar
  4. 4.
    Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6, 161–180 (2010)CrossRefGoogle Scholar
  5. 5.
    Zadeh, L.: Fuzzy Sets. Inf. Control. 8, 338–353 (1965)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Russell, S., Norvig, P., Canny, J., Malik, J., Edwards, D.: Artificial intelligence: a modern approach (2009)Google Scholar
  7. 7.
    Quigley, M., Conley, K.: ROS: an open-source Robot Operating System. … open source …. (2009)Google Scholar
  8. 8.
    Kehoe, B., Sachin, P.: A Survey of Research on Cloud Robotics and Automation. IEEE Trans. Autom. Sci. Eng. 1–11 (2010)Google Scholar
  9. 9.
    Miranda, F., Ferreira, T.: Review on Context Classification in Robotics. Rough Sets Intell. …. (2014)Google Scholar
  10. 10.
    Varvadoukas, T., Giannakidou, E., Gomez, J.V., Mavridis, N.: Indoor Furniture and Room Recognition for a Robot Using Internet-Derived Models and Object Context. Front. Inf. Technol. 122–128 (2012)Google Scholar
  11. 11.
    Wang, E., Kim, Y.S., Kim, H.S., Son, J.H., Lee, S., Suh, I.H.: Ontology Modeling and Storage System for Robot Context Understanding. Knowledge-Based Intell. Inf. Eng. Syst. 3683, 922–929 (2005)Google Scholar
  12. 12.
    Choi, J., Park, Y., Lim, G., Lee, S.: Ontology-Based Semantic Context Modeling for Object Recognition of Intelligent Mobile Robots. Recent Prog. Robot. Viable Robot. Serv. to Hum. 370, 399–408 (2008)Google Scholar
  13. 13.
    Wibisono, W., Zaslavsky, A., Ling, S.: Improving situation awareness for intelligent on-board vehicle management system using context middleware. IEEE Intell. Veh. Symp. 1109–1114 (2009)Google Scholar
  14. 14.
    Nienhiiser, D., Gumpp, T., Zollner, J.M.: A Situation Context Aware Dempster-Shafer Fusion of Digital Maps and a Road Sign Recognition System. Intell. Veh. Symp. 2009 IEEE. 1401–1406 (2009)Google Scholar
  15. 15.
    Schlenoff, C., Messina, E.: A robot ontology for urban search and rescue. In: Proc. 2005 ACM Work. Res. Knowl. Represent. Auton. Syst., KRAS 2005, pp. 27–34 (2005)Google Scholar
  16. 16.
    Trovato, G., Zecca, M., Kishi, T., Endo, N., Hashimoto, K., Takanishi, A.: Generation of Humanoid Robot’s Facial Expressions for Context-Aware Communication. Int. J. Humanoid Robot. 10, 23 (2013)CrossRefGoogle Scholar
  17. 17.
    Mou, W., Chang, M., Liao, C.: Context-aware assisted interactive robotic walker for Parkinson’s disease patients. Intell. Robot. Syst. 329 – 334 (2012)Google Scholar
  18. 18.
    Yi, C., Suh, I.H., Lim, G.H., Choi, B.-U.: Bayesian robot localization using spatial object contexts. In: 2009 IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 3467–3473 (2009)Google Scholar
  19. 19.
    Provine, R., Uschold, M., Smith, S., Stephen, B., Schlenoff, C.: Observations on the use of ontologies for autonomous vehicle navigation planning. Rob. Auton. Syst. (2004)Google Scholar
  20. 20.
    Mastrogiovanni, F., Sgorbissa, A., Zaccaria, R.: Context assessment strategies for Ubiquitous Robots. In: 2009 IEEE Int. Conf. Robot. Autom. pp. 2717–2722 (2009)Google Scholar
  21. 21.
    Hristoskova, A.: E., C., Veloso, M., De, F.: Heterogeneous Context-Aware Robots Providing a Personalized Building Tour. Int. J. Adv. Robot. Syst. 10, 13 (2013)Google Scholar
  22. 22.
    Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., Scuse, D.: WEKA Manual for Version 3-7-11. (2014)Google Scholar
  23. 23.
    Zhou, P., Zheng, Y., Li, Z., Li, M., Shen, G.: IODetector: A generic service for indoor outdoor detection. … Embed. Netw. Sens. …. (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Tiago Ferreira
    • 1
    Email author
  • Fábio Miranda
    • 2
  • Pedro Sousa
    • 2
  • José Barata
    • 2
  • João Pimentão
    • 2
  1. 1.Holos SACaparicaPortugal
  2. 2.Faculdade de Ciências e Tecnologia – UNL CaparicaCaparicaPortugal

Personalised recommendations