Autonomous Robots

, Volume 24, Issue 4, pp 349–364 | Cite as

A bacterial colony growth algorithm for mobile robot localization

Article

Abstract

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons.

The Bacterial Colony Growth Algorithm (BCGA) provides two different levels of modeling: a background level that carries on the multi-hypothesis and a foreground level that identifies the best hypotheses according to an exchangeable strategy. Experiments, carried out on the robot ATRV-Jr manufactured by iRobot, show the effectiveness of the proposed BCGA.

Keywords

Mobile robot Localization Species evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transaction on Signal Processing, 50(2), 174–188. CrossRefGoogle Scholar
  2. Austin, D. J., & Jensfelt, P. (2000). Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization. In Proceedings of the 2000 IEEE international conference on robotics and automation. Google Scholar
  3. Burgard, W., Fox, D., Hanning, D., & Schmidt, T. (1996). Estimating the absolute position of a mobile robot using position probability grids. In Proceedings of the fourteenth national conference on artificial intelligence (pp. 896–901). Google Scholar
  4. Burgard, W., Derr, A., Fox, D., & Cremers, A. B. (1998). Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach. In Proceedings of the international conference on intelligent robot and systems. Google Scholar
  5. Burrage, K., & Burrage, P. M. (2003). Numerical methods for stochastic differential equations with applications. SIAM: Philadelphia. Google Scholar
  6. Doucet, A. (1997). Monte Carlo methods for Bayesian estimation of hidden Markov models. Applications to radiation signals. PhD thesis, Univ. Paris-Sud, Orsay. Google Scholar
  7. Fox, D., Burgard, W., & Thrun, S. (1999). Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11, 391–427. MATHGoogle Scholar
  8. De Freitas, N., Doucet, A., & Gordon, N. J. (2001). Sequential Monte Carlo methods in practice. Berlin: Springer. MATHGoogle Scholar
  9. Gasparri, A., Panzieri, S., Pascucci, F., & Ulivi, G. (2007). A spatially structured genetic algorithm on complex networks for robot localization. In Proceedings of the IEEE international conference on robotics and automation. Rome, Italy. Google Scholar
  10. Jensfelt, P., & Kristensen, S. (2001). Active global localization for a mobilt robot using multiple hypothesis tracking. IEEE Transaction on Robotics and Automation, 17(5), 748–760. CrossRefGoogle Scholar
  11. Kalman, R. (1960). A new approach to linear filtering and prediction problems. Transactions ASME Journal of Basic Engineering, 82, 35–44. Google Scholar
  12. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE international conference on neural networks (pp. 1942–1948). Google Scholar
  13. Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational & Graphical Statistics, 5(1), 1–25. CrossRefMathSciNetGoogle Scholar
  14. Malthus, T. (1798). An essay on the principle of population. London: Johnson, in St. Paul’s Church-Yard. Google Scholar
  15. Moravec, H. P., & Elfes, A. (1985). High resolution maps from wide angle sonar. In Proceedings of the IEEE international conference on robotics and automation (pp. 116–121). Google Scholar
  16. Parrott, D., & Li, X. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Transactions on Evolutionary Computation, 440–458. Google Scholar
  17. Schnell, S., & Turner, T. E. (2004). Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Progress in Biophysics and Molecular Biology, 235–260. Google Scholar
  18. Verhulst, P. F. (1845). Recherches matematiques sur la loi d’accroissement de la population. Noveaux Memories de l’Academie Royale des Sciences et Belles-Lettres de Bruxelles, 18(1), 1–45. Google Scholar
  19. Volterra, V. (1931). Variations and fluctuations of the number of individuals in animal species. In Animal ecology. New York: McGraw-Hill. Google Scholar
  20. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1, 80–83. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Dipartimento di Informatica e AutomazioneRomaItaly

Personalised recommendations