Minimizing of Target Localization Error using Multi-robot System and Particle Filters
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Minimizing of Target Localization Error using Multi-robot System and Particle Filters

Authors: Jana Puchyova

Abstract:

In recent years a number of applications with multirobot systems (MRS) is growing in various areas. But their design is in practice often difficult and algorithms are proposed for the theoretical background and do not consider errors and noise in real conditions, so they are not usable in real environment. These errors are visible also in task of target localization enough, when robots try to find and estimate the position of the target by the sensors. Localization of target is possible also with one robot but as it was examined target finding and localization with group of mobile robots can estimate the target position more accurately and faster. The accuracy of target position estimation is made by cooperation of MRS and particle filtering. Advantage of usage the MRS with particle filtering was tested on task of fixed target localization by group of mobile robots.

Keywords: Multi-robot system, particle filter, position estimation, target localization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062738

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References:


[1] J. E. Handschin and D. Q. Mayne, "Monte Carlo Techniques to Estimate the Conditional Expectation in Multi-stage Non-linear Filtering," International Journal of Control 9(5), pp. 547-559, 1969.
[2] N. J. Gordon, D. J. Salmond and A. F. M. Smith, "Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation," IEEE Proc.-F, vol. 140, no. 2, pp. 107-113, 1993.
[3] A. Doucet, J. F. G. Freitas and N. Gordon, Sequential Monte Carlo Methods In Practice. New York: Springer-Verlag, 2001.
[4] M. S. Arulampalam, S. Maskell, N. Gordon, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, February 2002.
[5] M. Kulich and M. Saska, Vybran'a t'emata z mobiln'─▒ robotiky. 2011.
[6] P. ˇSevˇc'ık and O. Kov'aˇr, "Very efficient exploitation of FPGA block RAM memories in the complex digital system design," Journal of information, control and management systems, vol. 8, no. 4 spec. iss., pp. 403-414, 2010.
[7] P. G. Jayasekara, L. Palafox, T. Sasaki, H. Hashimoto and B. H. Lee, "Simultaneous localization assistance for multiple mobile robots using particle filter based target tracking," 5th International Conference on Information and Automation for Sustainability (ICIAFs), pp. 469-474, 2010.
[8] A. Howard, "Multi-robot Simultaneous Localization and Mapping using Particle Filters," Robotics: Science and Systems Conference, pp. 201- 208, 2005.
[9] R. Havangi, M. A. Nekoui and M. Teshnehlab, "A Multi Swarm Particle Filter for Mobile Robot Localization," IJCSI International Journal of Computer Science Issues, vol. 7, Issue 3, no. 2, pp. 15-22, May 2010.
[10] S. Thrun, D. Fox, W. Burgard and F. Dellaert, "Robust monte carlo localization for mobile robots," Artificial Intelligence, 128(1-2), 2000.
[11] L. Carlone, M. K. Ng, J. Du, B. Bona and M. Indri, "Rao-Blackwellized article Filters Multi Robot SLAM with Unknown Initial Correspondences and Limited Communication," IEEE International Conference on Robotics and Automation, Alaska, USA, pp. 243-249, May 2010.
[12] D. Schulz, W. Burgard D. Fox and A. B. Cremers, "Tracking Multiple Moving Targets with a Mobile Robot using Particle Filters and Statistical Data Association," Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea, May 2001.
[13] M. K. Pitt and N. Shephard, "Filtering via Simulation: Auxiliary Particle Filters," Journal of the American Statistical Association, vol. 94, No. 446., pp. 590-599, 1999.
[14] J. L. Blanco-Claraco, Contributions to Localization, Mapping and Navigation in Mobile Robotics. PhD Thesis, 2009.
[15] B. Ristic, S. Arulampalm and N. Gordon, Beyond the Kalman filter: particle filters for tracking applications. Boston, Ma., London: Artech House, 2004.
[16] J. P'uchyov'a, "Exploration algorithm with shortened return for group of mobile robots," MEMSTECH 2012: Perspective technologies and methods in MEMS design, pp. 77-80, 2012.
[17] http://mrpt.org/