Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-07T23:43:21.676Z Has data issue: false hasContentIssue false
  • English
  • Français

Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots

Published online by Cambridge University Press:  01 May 2009

Amitava Chatterjee
Amitava Chatterjee*
Affiliation:
Jadavpur University, Electrical Engineering Department, Kolkata, West Bengal 700032, India E-mail: achatterjee@ee.jdvu.ac.in
Get access Rights & Permissions [Opens in a new window]

Summary

The present paper proposes a successful application of differential evolution (DE) optimized fuzzy logic supervisors (FLS) to improve the quality of solutions that extended Kalman filters (EKFs) can offer to solve simultaneous localization and mapping (SLAM) problems for mobile robots and autonomous vehicles. The utility of the proposed system can be readily appreciated in those situations where an incorrect knowledge of Q and R matrices of EKF can significantly degrade the SLAM performance. A fuzzy supervisor has been implemented to adapt the R matrix of the EKF online, in order to improve its performance. The free parameters of the fuzzy supervisor are suitably optimized by employing the DE algorithm, a comparatively recent method, popularly employed now-a-days for high-dimensional parallel direct search problems. The utility of the proposed system is aptly demonstrated by solving the SLAM problem for a mobile robot with several landmarks and with wrong knowledge of sensor statistics. The system could successfully demonstrate enhanced performance in comparison with usual EKF-based solutions for identical environment situations.

Keywords

Extended Kalman filterFuzzy supervisorDifferential evolutionSensor statisticsSLAM problem
Type
Article
Information
Robotica , Volume 27 , Issue 3 , May 2009 , pp. 411 - 423
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.Dissanayake, M. W. M. G., Newman, P., Clark, S. and Durrant-Whyte, H. F., “A solution to the simultaneous localization and map building (SLAM) problem,” IEEE Tran. Rob. Automat. 17 (5), 229–241 (Jun. 2001).Google Scholar
2.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges,” Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico (2003).Google Scholar
3.Smith, R. and Cheeseman, P., “On the representation and estimation of spatial uncertainty,” Int. J. Rob. Res. 5 (4), 56–68 (1986).CrossRefGoogle Scholar
4.Bailey, T., Mobile Robot Localization and Mapping in Extensive Outdoor Environments PhD Thesis (University of Sydney, 2002).Google Scholar
5.Davison, A. J. and Murray, D. W., “Simultaneous localization and map-building using active vision,” IEEE Tran. Pattern Anal. Mach. Intell. 24 (7), 865–880 (Jul. 2002).CrossRefGoogle Scholar
6.Guivant, J. and Nebot, E., “Optimization of the simultaneous localization and map-building algorithm and real-time implementation,” IEEE Tran. Rob. Automat. 17 (3), 242–257 (Jun. 2001).Google Scholar
7.Mehra, R. K., “On the identification of variances and adaptive Kalman filtering,” IEEE Tran. Automat. Control AC-15 (2), 175–184 (1970).Google Scholar
8.Fitzgerald, R. J., “Divergence of the Kalman filter,” IEEE Tran. Automat. Control AC-16 (6), 736–747 (1971).Google Scholar
9.Kobayashi, K., Cheok, K. C., Watanabe, K. and Munekata, F., “Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique,” IEEE Tran. Indus. Electron. 45 (3), 510–518 (Jun. 1998).Google Scholar
10.Loebis, D., Sutton, R., Chudley, J. and Naeem, W., “Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system,” Control Eng. Pract. 12, 1531–1539 (2004).CrossRefGoogle Scholar
11.Storn, R., “On the usage of differential evolution for function optimization” Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), Berkley, USA, 19–22 June 1996, pp. 519–523.CrossRefGoogle Scholar
12.Storn, R. and Price, K., “Minimizing the real functions of the ICEC'96 contest by differential evolution” IEEE Conference on Evolutionary Computation, Nagoya, Japan, 1996, pp. 842–844.CrossRefGoogle Scholar
13. Available online. http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm. Last accessed 24-June-2008.Google Scholar
14. Available online. http://www.icsi.berkeley.edu/~storn/code.html. Last accessed 24-Jun-2008.Google Scholar
15.Clerc, M. and Kennedy, J., “The particle swarm-explosion, stability and convergence in a multidimensional complex space,” IEEE Tran. Evol. Comput. 6 (1), 58–73 (Feb. 2002).CrossRefGoogle Scholar
16.Shi, Y. and Eberhart, R. C., “Empirical study of particle swarm optimization,” Proceedings of the IEEE Congress on Evolutionary Computation (1999), pp. 1945–1950.Google Scholar