Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T13:01:53.877Z Has data issue: false hasContentIssue false
  • English
  • Français

A hybrid approach to fast and accurate localization for legged robots

Published online by Cambridge University Press:  01 November 2008

Renato Samperio ,
Huosheng Hu ,
Francisco Martín  and
Vicente Matellán
Renato Samperio*
Affiliation:
Department of Computing & Electronic Systems, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom
Huosheng Hu
Affiliation:
Department of Computing & Electronic Systems, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom
Francisco Martín
Affiliation:
Robotics Lab-GSyC, DITTE-ESCET-URJC, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
Vicente Matellán
Affiliation:
Robotics Lab-GSyC, DITTE-ESCET-URJC, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper describes a hybrid approach to a fast and accurate localization method for legged robots based on Fuzzy-Markov (FM) and Extended Kalman Filters (EKF). Both FM and EKF techniques have been used in robot localization and exhibit different characteristics in terms of processing time, convergence, and accuracy. We propose a Fuzzy-Markov–Kalman (FM–EKF) localization method as a combined solution for a poor predictable platform such as Sony Aibo walking robots. The experimental results show the performance of EKF, FM, and FM-EKF in a localization task with simple movements, combined behaviors, and kidnapped situations. An overhead tracking system was adopted to provide a ground truth to verify the performance of the proposed method.

Keywords

Legged robotsrobot localisationExtended Kalman filtersFuzzy-Markov methods
Type
Article
Information
Robotica , Volume 26 , Issue 6 , November 2008 , pp. 817 - 830
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Baltzakis, H. and Trahanias, P., “Hybrid Mobile Robot Localization Using Switching State-Space Models,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC, USA (2002) pp. 366–373.Google Scholar
2.Buschka, P., Saffiotti, A. and Wasik, Z., “Fuzzy Landmark-Based Localization for a Legged Robot,” Proceedings of IEEE Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kagawa University, Takamatsu, Japan, (2000) pp. 1205–1210.Google Scholar
3.Duckett, T. and Nehmzow, U., “Performance Comparison of Landmark Recognition Systems for Navigating Mobile Robots,” Proceedings of 17th National Conference on Artificial Intelligence (AAAI-2000), AAAI Press/The MIT Press (2000) pp. 826–831.Google Scholar
4.Fox, D., Burgard, W., Dellaert, F. and Thrun, S., “Monte Carlo Localization: Efficient Position Estimation for Mobile Robots,” Proceedings of AAAI/IAAI, Orlando, Florida, USA, (1999), pp. 343–349.Google Scholar
5.Fox, D., Burgard, W. and Thrun, S., “Markov Localization for Reliable Robot Navigation and People Detection,” In Sensor Based Intelligent Robots, (Christens, H. I. et al. , ed), Dagstuhl Castle, Germany, (1998), pp. 1–20.Google Scholar
6.Gutmann, J.-S., “Markov–Kalman Localization for Mobile Robots,” International Conference on Pattern Recognition (2), Quebec, Canada, (2002), pp. 601–604.Google Scholar
7.Gutmann, J.-S., Burgard, W., Fox, D. and Konolige, K., “An Experimental Comparison of Localization Methods,” Proceedings of IEEE/RSJ International Conference on Intelligen Robots and Systems (Oct. 13–17, 1998) vol. 2, pp. 736–743.Google Scholar
8.Hatice, K., Celik, B. and Akin, H. L., “Comparison of localization methods for a robot soccer team,” Int. J. Adv. Robot. Syst. 3 (4), 295–302 (2006).Google Scholar
9.Herrero-Pérez, D., Artínez Barberá, H. M. and Saffiotti, A., “Fuzzy Self-Localization Using Natural Features in the Four-Legged League,” In RoboCup 2004: Robot Soccer World Cup VIII (Nardi, D., Riedmiller, M. and Sammut, C., eds.) (LNAI. Springer-Verlag, Berlin, DE, 2004). Online at http://www.aass.oru.se/~asaffio/. pp. 110–121.Google Scholar
10.Jensfelt, P., Austin, D., Wijk, O. and Andersson, M., “Feature Based Condensation for Mobile Robot Localization,” Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, USA, (2000) pp. 2531–2537.Google Scholar
11.Kiriy, E. and Buehler, M., “Three-state extended kalman filter for mobile robot localization,” Tech. Rep. TR-CIM 05.07, McGill University, Montreal, Canada, April 2002.Google Scholar
12.Kristensen, S. and Jensfelt, P., “An Experimental Comparison of Localisation Methods,” Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, Navada, USA, (Oct. 27–31, 2003) pp. 992–997.Google Scholar
13.Tanaka, K., Kimuro, Y., Okada, N. and Kondo, E., “Global Localization with Detection of Changes in Non-Stationary Environments,” Proceedings of IEEE International Conference on Robotics and Automation (Apr. 26–May 1, 2004) pp. 1487–1492.Google Scholar
14.Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A., Dellaert, F., Fox, D., Hahnel, D., Rosenberg, C., Roy, N., Schulte, J. and Schulz, D. “Probabilistic algorithms and the interactive museum tour-guide robot Minerva,” Int. J. Robot. Res. 19 (11), 972–999 (2000).CrossRefGoogle Scholar
15.Thrun, S., Fox, D., Burgard, W. and Dellaert, F., “Robust Monte Carlo localization for mobile robots,” J. Artificial Intell. 128 (1–2), 99–141 (2001).CrossRefGoogle Scholar