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http://dx.doi.org/10.6109/jicce.2012.10.4.390

Automatic Mutual Localization of Swarm Robot Using a Particle Filter  

Lee, Yang-Weon (Department of Information and Communication Engineering, Honam University)
Abstract
This paper describes an implementation of automatic mutual localization of swarm robots using a particle filter. Each robot determines the location of the other robots using wireless sensors. The measured data will be used for determination of the movement method of the robot itself. It also affects the other robots' self-arrangement into formations such as circles and lines. We discuss the problem of a circle formation enclosing a target that moves. This method is the solution for enclosing an invader in a circle formation based on mutual localization of the multi-robot without infrastructure. We use trilateration, which does require knowing the value of the coordinates of the reference points. Therefore, specifying the enclosure point based on the number of robots and their relative positions in the coordinate system. A particle filter is used to improve the accuracy of the robot's location. The particle filter is operates better for mutual location of robots than any other estimation algorithms. Through the experiments, we show that the proposed scheme is stable and works well in real environments.
Keywords
Particle filter; Swarm robot; Tracking;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Y. W. Lee, "Development of tracking filter for the location awareness of moving objects in ubiquitous computing," International Journal of Maritime Information and Communication Sciences, vol. 6, no. 1, pp. 86-90, 2008.
2 A. Farinelli, L. Iocchi, and D. Nardi, "Multirobot systems: a classification focused on coordination," IEEE Transactions on Systems, Man, and Cybernetics Part B, vol. 34, no. 5, pp. 2015- 2028, 2004.   DOI   ScienceOn
3 L. E. Parker, "ALLIANCE: an architecture for fault tolerant multirobot cooperation," IEEE Transactions on Robotics and Automation, vol. 14, no. 2, pp. 220-240, 1998.   DOI   ScienceOn
4 K. P. Sycara, "Multiagent system," AI Magazine, vol. 19, no. 2, pp. 79-92, 1998.
5 D. S. Huang, H. H. S. Ip, K. C. K. Law, and Z. Chi, "Zeroing polynomials using modified constrained neural network approach," IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 721-732, 2005.   DOI   ScienceOn
6 C. G. Kang and D. H. Kim, "Designing of dynamic sensor networks based on meter-range swarming flight type air nodes," International Journal of Maritime Information and Communication Sciences, vol. 9, no. 6, pp. 625-628, 2011.
7 Y. W. Lee, "Implementation of code generator of particle filter," International Journal of Maritime Information and Communication Sciences, vol. 8, no. 5, pp. 493-497, 2010.
8 J. Borenstein, H. R. Everett, and L. Feng, "Where am I? Sensors and methods for mobile robot positioning," The University of Michigan, Ann Arbor: MI, Technical Report, 1996.
9 L. E. Navarro-Serment, C. J. J. Paredis, and P. Khosla, "A beacon system for the localization of distributed robotic teams," in Proceedings of the International Conference on Field and Service Robotics, Pittsburgh: PA, pp. 232-237, 1999.
10 S. Simic and S. Sastry, "Distributed localization in wireless ad hoc networks," Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, Technical Report UCB/ERL M02/26, 2002.
11 J. Liu and J. Wu, Multi-Agent Robotic Systems, Boca Raton, FL: CRC Press, 2001.
12 Y. W. Lee, "Automation of an interactive interview system by hand gesture recognition using particle filter," International Journal of Maritime Information and Communication Sciences, vol. 9, no. 6, pp. 633-636, 2011.