Hostname: page-component-74656f9884-54qdv Total loading time: 0 Render date: 2026-11-10T10:39:16.308Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic training method applied to a WiFi+ultrasound POMDP navigation system

Published online by Cambridge University Press:  09 March 2009

M. Ocaña ,
L. M. Bergasa ,
M. A. Sotelo ,
R. Flores ,
D. F. Llorca  and
D. Schleicher
M. Ocaña*
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain.
L. M. Bergasa
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain.
M. A. Sotelo
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain.
R. Flores
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain.
D. F. Llorca
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain.
D. Schleicher
Affiliation:
Department of Electronics, Escuela Politécnica Superior, University of Alcalá, Campus Universitario s/n, 28871 Alcalá de Henares, Madrid, Spain.
*
*Corresponding author. E-mail: mocana@depeca.uah.es
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot position. The method has been tested in a real environment using two commercial Pioneer 2AT robotic platforms in the premises of the Department of Electronics at the University of Alcalá. Some experimental results and conclusions are presented.

Keywords

WiFi+Ultrasound robot navigation systemWiFi signal strength localization systemPartially Observable Markov Decision Process

Information

Type
Article
Information
Robotica , Volume 27 , Issue 7 , December 2009 , pp. 1049 - 1061
Copyright
Copyright © Cambridge University Press 2009

References

1.López, M. E., Bergasa, L. M., Barea, R. and Escudero, M. S., “A navigation system for assistant robots using visually augmented POMDPs,” Autonom. Rob. 19 (1), 77–87 (2005).Google Scholar
2.Sotelo, M. A., Ocaña, M., Bergasa, L. M., Flores, R., Marrón, M. and García, M. A., “Low level controller for a POMDP based on WiFi observations,” Rob. Autonom. Syst. 55 (2), 132–145 (2007).CrossRefGoogle Scholar
3.Cox, I., “Blanche-an experiment in guidance and navigation of an autonomous robot vehicle,” IEEE Trans. Rob. Automat. 7 (2), 193–204 (1991).Google Scholar
4.Want, R., Hopper, A., Falco, V. and Gibbons, J., “The active badge location system,” ACM Trans. Info. Syst. 10, 91–102 (1992).Google Scholar
5.Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M. and Shafer, S., “Multi-Camera Multi-Person Tracking for Easy Living,” Proceedings of Third IEEE International Workshop on Visual Surveillance (IEEE Computer Society, Washington, DC, 2002) pp. 3–10.Google Scholar
6.Priyantha, N. B., Chakraborthy, A. and Balakrishnan, H., “The Cricket Location Support System,” Proceedings of the Sixth ACM MobiCom (ACM New York, 2002) pp. 155–164.Google Scholar
7.Barber, R., Mata, M., Boada, M. J. L., Armingol, J. M. and Salichs, M. A., “A Perception System based on Laser Information for Mobile Robot Topologic Navigation,” Proceedings of 28th Annual Conference of the IEEE Industrial Electronics Society (IEEE Industrial Electronics Society, Sevilla, Spain, 2002) pp. 2779–2784.Google Scholar
8.Bose, A. and Foh, C. H., “A Practical Path Loss Model for Indoor WiFi Positioning Enhancement,” Proceedings of the International Conference on Information, Communications and Signal Processing ICICS'07 (Singapore, 2007) pp. 1–5.Google Scholar
9.Otsason, V., Accurate Indoor Localization Using Wide GSM Fingerprinting Master's Thesis (University of Tartu, 2005).Google Scholar
10.Bahl, P. and Padmanabhan, V. N., “RADAR: A, In-building RF-based User Location and Tracking System,” Proceedings of the IEEE Infocom 2000, vol. 2 (IEEE Computer and Communications Societies, Tel-Aviv, Israel, 2000) pp. 775–784.Google Scholar
11.Ladd, A., Bekris, K., Rudys, A., Marceu, G., Kavraki, L. and Wallach, D., “Robotics Based Location Sensing Using Wireless Ethernet,” Proceedings of the International Conference on Mobile Computing and Networking (ACM New York, NY, USA, Atlanta, GA, 2002) pp. 227–238.Google Scholar
12.Howard, A., Siddiqi, S. and Sukhatme, G. S., “An Experimental Study of Localization Using Wireless Ethernet,” Proceedings of the International Conference on Field and Service Robotics Lake Yamanaka, Japan (2003).Google Scholar
13.LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B., “PlaceLab: Device Positioning Using Radio Beacons in the Wild”, In: Pervasive Computing, Lecture Notes in Computer Science, vol. 3468 (Springer-Verlag, Berlin, Germany, 2005) pp. 116–133.CrossRefGoogle Scholar
14.Enge, P. and Misra, P., “Special Issue on GPS: The Global Positioning System”, Proceedings of the IEEE (IEEE, Piscataway, NJ, Jan. 1999) pp. 3–15.Google Scholar
15.Matellán, V., Cañas, J. M. and Serrano, O., “WiFi localization methods for autonomous robots,” Robotica 24 (4), 455–461 (2006).Google Scholar
16.Ocaña, M., WiFi Global Localization System applied to a Semiautonomous Robot Navigation System Ph.D. Thesis (Department of Electronics, Polytechnic School, University of Alcala, Spain, 2005).Google Scholar
17.Lopez, E., Barea, R., Bergasa, L. M. and Escudero, M. S., “A human-robot cooperative learning system for easy installation of assistant robots in new working environments,” J. Intell. Rob. Syst. 40, 233–265 (2004).Google Scholar
18.López, M. E., Global Navigation System Based on Partially Markov Decision Process. Application in an Assistant Robot Ph.D. Thesis (Department of Electronics, Polytechnic School, University of Alcalá, Spain, 2004).Google Scholar
19.Simmons, R., Coodwin, R., Haigh, K. Z., Koenig, S. and O'Sullivan, J., “A Layered Architecture for Office Delivery Robots,” Proceedings of the First International Conference on Autonomous Agents (Agents'97) (ACM Press, Marina del Rey, CA, 1997) pp. 245–252.CrossRefGoogle Scholar
20.Konolige, K., Saphira Robot Control Architecture (SRI International, 2002).Google Scholar
21.Youssef, M. and Agrawala, A., “Small-Scale Compensation for WLAN Location Determination Systems,” Proceedings of the 2003 ACM workshop on Wireless security (2003) pp. 11–20.Google Scholar