Simultaneous Localization and Mapping for Robot Based Multi-Agent System

Xiao Kun Leng; Xin Wei Wang; Song Hao Piao

doi:10.4028/www.scientific.net/AMM.556-562.2248

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Simultaneous Localization and Mapping for Robot...

Simultaneous Localization and Mapping for Robot Based Multi-Agent System

Abstract:

Applications on Multi-agent system have been widely studied recently. The positioning of Robotic system is to estimate the position and posture and accurate position estimation. FastSLAM is a SLAM algorithm based on particle filtering, which can perform positioning fast and has been widely applied. This paper applied the genetic particle filtering into SLAM problem to optimize the SLAM algorithm. We present the algorithms based on genetic particle filtering which can obviously reduce the number of particles needed in FastSLAM. The experimental results show that the improvement measures can effectively improve the performance of the algorithm, so that it enables them to maintain a reliable positioning.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

2248-2251

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.2248

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Online since:

May 2014

Authors:

Xiao Kun Leng, Xin Wei Wang, Song Hao Piao*

Keywords:

Genetic Particle Filter, Multi-Agent System (MAS), Robot, SLAM

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* - Corresponding Author

References

[1] J. Castellanos, J. Neira and O. Strauss, in: Detecting high level features for mobile robot localization, Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, (1996).

DOI: 10.1109/mfi.1996.572237

Google Scholar

[2] M. Batke and L.S. Gurvit: IEEE Transactions on Robotics and Automation Vol. 13(2) (1997), p.251.

Google Scholar

[3] J.J. Leonard and H.F. Durrant-Whyte: IEEE Transactions on Robtics and Automation Vol. 7(2) (1991) p.376.

Google Scholar

[4] D. Fox, W. Burgard and S. Thrun: Journal of Artificail Intelligence Research Vol. 11(1), (1999) p.391.

Google Scholar

[5] I. Arasaratnam, S. Haykin and R.J. Ellion: Proceedings of the IEEE Vol. 95(5) (2007), p.953.

Google Scholar

[6] S.I. Roumeliotis and G.A. Bekey, in: Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization, Proceedings of IEEE International Conference On Robotics and Automation. San Francisco, California (2000).

DOI: 10.1109/robot.2000.846481

Google Scholar