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.
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References
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.
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.
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).
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.
Burrage, K., & Burrage, P. M. (2003). Numerical methods for stochastic differential equations with applications. SIAM: Philadelphia.
Doucet, A. (1997). Monte Carlo methods for Bayesian estimation of hidden Markov models. Applications to radiation signals. PhD thesis, Univ. Paris-Sud, Orsay.
Fox, D., Burgard, W., & Thrun, S. (1999). Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11, 391–427.
De Freitas, N., Doucet, A., & Gordon, N. J. (2001). Sequential Monte Carlo methods in practice. Berlin: Springer.
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.
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.
Kalman, R. (1960). A new approach to linear filtering and prediction problems. Transactions ASME Journal of Basic Engineering, 82, 35–44.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE international conference on neural networks (pp. 1942–1948).
Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational & Graphical Statistics, 5(1), 1–25.
Malthus, T. (1798). An essay on the principle of population. London: Johnson, in St. Paul’s Church-Yard.
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).
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.
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.
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.
Volterra, V. (1931). Variations and fluctuations of the number of individuals in animal species. In Animal ecology. New York: McGraw-Hill.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1, 80–83.
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Gasparri, A., Prosperi, M. A bacterial colony growth algorithm for mobile robot localization. Auton Robot 24, 349–364 (2008). https://doi.org/10.1007/s10514-007-9076-1
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DOI: https://doi.org/10.1007/s10514-007-9076-1