Position estimation in mobile robots using particle filters
DOI:
https://doi.org/10.33571/rpolitec.v13n25a8Keywords:
Mobile robot, particle filter, Monte Carlo methods, stochastic filter, Bayesian filterAbstract
This works presents an approach to solve the problem of controlling differential motion mobile robots with odometry techniques, trajectory tracking algorithms based on A*, control by pure persecution and state estimation using particles filters to calculate the robot location. The paper is accompanied by a series of simulation results that verify the proper functioning of the proposed methodology.
Article Metrics
Abstract: 1047 HTML (Español (España)): 442 PDF (Español (España)): 510 XML (Español (España)): 44PlumX metrics
References
Dudek, G. y Jenkin, M., Computational principles of mobile robotics. Cambridge University Press, 2010.
Siegwart, R., Nourbakhsh, I. R., y Scaramuzza, D. Introduction to autonomous mobile robots. MIT press, 2011.
García Caicedo, J. M. Navegación de un robot móvil sobre terreno irregular con contacto de su brazo con el suelo [Master Thesis]. Medellín, Colombia: Universidad de Antioquia, 2012.
Rekleitis, I. M. A particle filter tutorial for mobile robot localization. Tech. Rep. TR-CIM- 04-02. Montreal, Canada. Centre for Intelligent Machines, McGill University, 2004.
Chen, Z. Bayesian filtering: From Kalman filters to particle filters, and beyond. Statistics 182 (1), 1-69, 2003.
Kalman, R. E. A new approach to linear filtering and prediction problems. Journal of basic Engineering, 82, 35 - 45, 1960.
Kalman, R. E. y Bucy, R. S.. New results in linear filtering and prediction theory. Journal of basic engineering, 83, 95 - 108, 1961.
Simon, D. Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons, 2006.
Gordon, N. J., Salmond, D. J., y Smith, A. F. Novel approach to nonlinear/non-gaussian bayesian state estimation. IEEE Proceedings Radar and Signal Processing, 140, 107–113, 1993.
Doucet, A. y Johansen, A. M. A Tutorial on Particle filtering and smoothing: Fiteen years later. The Oxford handbook of nonlinear filtering, 656–705, 2011.
Arulampalam, M. S., Maskell, S., N. Gordon, y Clapp, T. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions Signal Processing, 50, 174 - 188, 2002.
R. R. Luque. Localización multirrobot basada en filtro de partículas [Ph.D. Thesis]. Madrid España: Universidad de Alcalá, 2006.
Cook, G. Mobile robots: navigation, control and remote sensing. John Wiley & Sons, 2011.
S. Thrun, W. Burgard, and D. Fox. Probabilistic robotics. MIT press, 2005.
López, D. G., Aldeguer, R. R., & Ruiz, F. E. Aplicación del muestreo bayesiano en robots móviles: estrategias para localización y estimación de mapas del entorno. Alicante, España: Universidad de Alicante, 1999.
Lee, D. Curso de Coursera: Robotics, Estimation and Learning. Disponible en https://www.coursera.org [consultado el 26 de marzo de 2017].
Thrun, S. Particle Filters in Robotics. Proceedings of Uncertainty in AI, 1, 511 - 518, 2002.
Algarabia. Circunferencia osculatriz. Disponible en https://commons.wikimedia.org/ [consutado el 26 de marzo de 2017].
Baturone, A. O. Robótica: manipuladores y robots móviles. Marcombo, 2005.
Siciliano, B., Sciavicco, L., Villani, L., y Oriolo, G. Robotics: modelling, planning and control. Springer Science & Business Media, 2010.
Dissanayake, M., Newman, P., Clark, S., Durrant-Whyte, H. F., y Csorba, M. A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation, 17, 229 - 241, 2001.
Durrant-Whyte, H. y Bailey T. Simultaneous localization and mapping: part I. Robotics & Automation Magazine, 99 - 106, 2006.
Published
How to Cite
Issue
Section
License
_
