Abstract
A new algorithm based on evolutionary computation concepts is presented in this paper. This algorithm is a non linear evolutive filter known as the Evolutive Localization Filter (ELF) which is able to solve the global localization problem in a robust and efficient way. The proposed algorithm searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information up to date. The population evolves by using the log-likelihood of each candidate pose according to the observation and the motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The algorithm has been tested on a mobile robot equipped with a laser range finder to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arras, K., Castellanos, J.A., and Siegwart, R. Feature-based multi-hypothesis localization and tracking for mobile robots using geometric constraints. In: Proc. of the Int. Conference on Robotics and Automation ICRA-02, pp. 1371–1377. Washington DC, USA (2002)
Austin, D.J., Jensfelt, P. Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization. In: Proc. of the Int. Conference on Robotics and Automation ICRA-00, pp. 1036–1041. San Francisco, CA, USA (2000)
Burgard, W., Fox, D., Henning, D., Schmidt, T. Estimating the absolute position of a mobile robot using position probability drids. In: Proc. of the National Conference on Artificial Intelligence AAAI-96, pp. 896–901. Potland, Oregon, USA (1996)
Cox I.J. (1991) Blanche-An Experiment in guidance and navigation of an autonomous robot vehicle. IEEE Trans. Robotics Autom. 7, 193–204
Cox I.J., Leonard J.J. (1994) Modeling a dynamic environment using a Bayesian multi hypothesis approach. Artif. Int. 66, 311–344
Crowley, J.L. World modelling and position estimation for a mobile robot using ultrasonic ranging. In: IEEE International Conference on Robotics and Automation. Scottsdale, AZ (1989)
Dellaert, F., Fox, D., Burgard, W., Thrun, S. Monte Carlo Localization for Mobile Robots. In: Proceedings of the 1999 International Conference on Robotics and Automation, pp. 1322–1328 (1999)
Doucet A. (1998) On sequential simulation-based methods for Bayesian filtering. Technical Report CUED/FINFENG/TR 31’, Cambridge University, Dept of Engineering, Cambridge, UK
Fox D., Burgard W., Thrun S. (1999) Markov localization for mobile robots in dynamic environments. J. Artif. Int. Res. 11, 391–427
Garrido, S., Moreno, L., Salichs, M.A. Non linear on line identification of dynamic systems with restricted genetic optimization. In: Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, EUFIT, pp. 423–428 (1998)
Garrido, S., Moreno, L. Learning adaptive parameters with restricted genetic optimization. In: Bio-inspired Applications of Connectionism: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN2001, pp. 612–620. Springer-Verlag (2001)
Goldberg, D.E. Genetic algorithm in Search, Optimization, and Machine Learning. Addison Wesley Publishing Company (1989)
He J., Kang L. (1999) On the convergence rates of genetic algorithms. Theor Comput. Sci. 229, 23–39
Jensfelt, P., Wijk, O., Austin, D.J., Andersson, M. Experiments on augmenting condensation for mobile robot localization. In: Proc. of the Int. Conference on Robotics and Automation ICRA-00, pp. 2518–2524. San Francisco, CA, USA (2000)
Jensfelt P. (2001) Approaches to mobile robot localization in indoor environments. Doctoral Thesis, Royal Institute of Technology, Sweden
Jensfelt, P., Kristensen, S. Active global localisation for a mobile robot using multiple hypothesis tracking. In: Proc. IJCAI Workshop on Reasoning with Uncertainty in Robot Navigation, pp. 13–22. Stockholm, Sweden (1999)
Kalos, M.H., Whitlock, P.A. Monte Carlo Methods, Vol. I: Basics. John Wiley and Sons (1986)
Liu J., Chen R. (1998) Sequential Monte Carlo methods for dynamic systems. J. Amer. Statis. Assoc. 93, 1032–1044
Leonard, J.J., Durrant-White H.F. Directed Sonar Sensing for Mobile Robot Navigation. Kluwer Academic Publishers (1992)
Lenser, S., Veloso, M. Sensor resetting localization for poorly modelled mobile robots. In: Proc. IEEE International Conference on Robotics and Automation (ICRA-2000). San Francisco, CA (2000)
Storn, R., Price, K. Differential Evolution–A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, March (1995)
Thrun S. (1998) Bayesian landmark learning for mobile robot localization. Mach. Learn. 33(1): 41–76
Thrun S., Fox D., Burgard W., Dellaert F. (2001) Robust Monte Carlo localization for mobile robots. Artif. Int. 128, 99–141
Reuter, J. Mobile robot self-localization using PDAB. In: Proc. IEEE International Conference on Robotics and Automation (ICRA-2000). San Francisco, CA (2000)
Roumeliotis, S.I., Bekey, G.A. Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization. In: Proc. IEEE International Conference on Robotics and Automation (ICRA-2000). pp. 2985–2992. San Francisco (2000)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Moreno, L., Garrido, S. & Blanco, D. Mobile Robot Global Localization using an Evolutionary MAP Filter. J Glob Optim 37, 381–403 (2007). https://doi.org/10.1007/s10898-006-9054-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-006-9054-8