A bacterial colony growth algorithm for mobile robot localization
- 168 Downloads
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.
KeywordsMobile robot Localization Species evolution
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- Burrage, K., & Burrage, P. M. (2003). Numerical methods for stochastic differential equations with applications. SIAM: Philadelphia. Google Scholar
- 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
- 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
- Kalman, R. (1960). A new approach to linear filtering and prediction problems. Transactions ASME Journal of Basic Engineering, 82, 35–44. Google Scholar
- 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
- Malthus, T. (1798). An essay on the principle of population. London: Johnson, in St. Paul’s Church-Yard. Google Scholar
- 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
- 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
- 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
- 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
- Volterra, V. (1931). Variations and fluctuations of the number of individuals in animal species. In Animal ecology. New York: McGraw-Hill. Google Scholar