Simultaneously Localization and Mapping (SLAM) in mobile robots is an issue which is of utmost importance to Robotic Science researchers. The goal is to determine the position of intelligent mobile robots and their navigation to provide the overview map of the environment when no knowledge of it exists. So far, various methods have been presented in group robots to solve the SLAM problem. After about six years of research on mobile robots in Yazd Robotic Association and of applying available algorithms on rescue real robots, we will review major approaches on a robot and will introduce a new method that has been applied on two platforms of manual and autonomous robots in IranOpen 2011 competitions. We provide a method based on ROA-BlackWelized Particles filter (RBPF) algorithm and Forgetting curve in Multi robots which, compared to the limitations of collecting environmental data in the past two years, has provided satisfactory results. In the end, we simulate the consequence method in order to provide the robot’s overview and local maps from the environment in PlayerStage software.
[1] Jensfelt,P., Wijk,O., Austin,D., Andersson,M., 2000. Experiments on augmenting condensation for mobile rbot localization. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, April 2000.
[2] Fox,D., Burgard,W., Dellaert,F., Thrun,S., 1999. Monte Carlo localization: Ecient position estimation for mobile robots. In: Proc. Of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, Florida.
[3] Wolf,J., Burgard,W., Burkhardt,H., 2002. Robust visionbased localization for mobile robots using an image retrieval system based on invariant features. In: Proc. of the International Conference on Robotics and Automation (ICRA).
[4] Howard,A., Mataric,M.J., Sukhatme,G.S., 2002. Localization for mobile robot teams: A distributed mle approach. In: Proc.ofthe 8-th Interna-tional Symposium in Experimental Robotics (ISER'02), Sant' Angelo d'Ischia, Italy.
[5] Menegatti,E., Zoccarato, M., Pagello,E., Ishiguro, H., 2004. Image-based monte-carlo localisation with omnidirectional images, Robotics and Autonomous Systems.
[6] Burgard,D., Fox,D., Hennig,T., Schmidt , 1996 . Estimating the absulute position of a mobile robot using position probability grids. In: Proc. of the National Conference on Artificial Intelligence (AAAI).
[7] Fox,D., Burgard,W., Kruppa,W., Thrun, S., 2000. A probabilistic approach to collaborative multi-robot localization. Autonomous Robotics, Special Issue on Heterogeneous Multi-Robot Systems, 8(3),pp.325-344 .
[8] Milford,M., Wyeth,G. , 2010. Hybrid robot control and SLAM for persistent navigation and mapping, Robotics and Autonomous Systems Journal,vol 58, pp. 1096-1104 .
[9] Roumeliotis,SJ., Bekey,G.A. , 2000. Collective localization: A distributed kalman filter approach. In: Proc. of the IEEE International Conference on Robotics and Automation, volume 2, pp 1800-1087.
[10] Kurazume,R., Hirose,S., 2000. An experimental study of a cooperative positioning system, Autonomous Robots, 8(1):pp.43-52.
[11] Cadena,C., Neira,J., 2010. SLAM in O(log n) with the Combined Kalman-Information Filter, Robotics and Autonomous Systems Journal,vol 58.pp. 1207–1219.
[12] Thrun,S., 2001. A probabilistic online mapping algorithm for teams of mobile robots., International Journal of Robotics Research, 20(5).
[13] Carpenter,J., Cli_ord,P., Fernhead,P., 1997. An improved particle filter for non-linear problems. Technical report, Department of Statistics, University of Oxford.
[14] Burgard,W., Fox,D., Jans,H., Matenar,C., Thrun,S., 1999. Sonar-based mapping of large-scale mobile robot environments using em. , In: ICML '99: Proceedings of the Sixteenth International Conference on Machine Learning, pp 67-76, San Francisco, CA, USA.
Copyright: © 2013 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
