Minimizing of Target Localization Error using Multi-robot System and Particle Filters
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.
[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/
[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/
@article{"International Journal of Information, Control and Computer Sciences:55427", author = "Jana Puchyova", title = "Minimizing of Target Localization Error using Multi-robot System and Particle Filters", 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.", volume = "7", number = "6", pages = "771-5", }
