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Localization of the Wheeled Mobile Robot Based on Multi-Sensor Data Fusion

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EN
Abstrakty
EN
The paper presents a method of localization of a mobile robot which relies on aggregation of data from several sensors. A review of the state of the art regarding methods of localization of ground mobile robots is presented. An overview of design of the four-wheeled mobile robot used for the research is given. The way of representation of robot environment in the form of maps is described. The localization algorithm which uses the Monte Carlo localization method is described. The simulation environment and results of simulation investigations are discussed. The measurement and control equipment of the robot is described and the obtained results of experimental investigations are presented. The obtained results of simulation and experimental investigations confirm the validity of the developed robot localization method. They are the foundation of further research, where additional sensors supporting the localization process could be used.
Twórcy
autor
  • Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, 02-486, Poland
autor
  • Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, 02-486, Poland
Bibliografia
  • [1] Siegwart R., Introduction to Autonomous Mobile Robots, MIT Press, 2004.
  • [2] Cox I. J., Wilfong G. T., ed., Autonomous Robot Vehicles, New York, NY, USA: Springer-Verlag New York, Inc., 1990. DOI: 10.1007/978-1-4613-8997-2.
  • [3] Fukuda T., Ito S., Oota N., Arai F., Abe Y., Tanaka K., Tanaka Y., „Navigation system based on ceiling landmark recognition for autonomous mobile robot”. In: International Conference on Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON ’93, vol. 3, 1993, 1466–1471. DOI: 10.1109/IECON.1993.339287.
  • [4] Hertzberg J., Kirchner F., „Landmark-based autonomous navigation in sewerage pipes”. In: , Proceedings of the First Euromicro Workshop on Advanced Mobile Robot, 1996, 68–73. DOI: 10.1109/EURBOT.1996.551883.
  • [5] Jaroszek P., Globalne planowanie ścieżki (Global Path Planning), Bachelor of Engineering Thesis, Warsaw University of Technology, Warsaw, 2012. (In Polish)
  • [6] Bartoszek J., Trojnacki M., Bigaj P., „Simulation of semiautonomy mode for ibis mobile robot with analysis of sensor failure tolerance”, Journal of Automation, Mobile Robotics, & Intelligent System., Vol. 5, No. 4, ss. 3–10, 2011.
  • [7] Borenstein J., Navigating Mobile Robots: Systems and Techniques. Wellesley, Mass: A K Peters Ltd, 1996.
  • [8] Yamauchi B., Schultz A., Adams W., „Mobile robot exploration and map-building with continuous localization”. In: 1998 IEEE International Conference on Robotics and Automation. Proceedings, 1998, vol. 4, 3715–3720. DOI: 10.1109/ROBOT.1998.681416.
  • [9] H. M. Choset, Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press, 2005.
  • [10] Burgard D., Fox, D. Hennig, i T. Schmidt, „Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids”, 1996. .
  • [11] Cassandra A., Kaelbling L. P., Kurien J., „Acting under uncertainty: discrete Bayesian models for mobile-robot navigation”, w Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems ’96, IROS 96, 1996, vol. 2, 963–972.
  • [12] Kalman R., „A New Approach to Linear Filtering and Prediction Problems”, Trans. ASME – J. Basic Eng., no. 82 (Series D), 1960, 35–45.
  • [13] Thrun S., „Bayesian Landmark Learning for Mobile Robot Localization”, Machine Learning,vol 33, no. 1, October, 1998, 41–76.
  • [14] Burgard W., Derr A., Fox D., Cremers A., „Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach”. In:1998 IEEE/RSJ International Conference on Intelligent Robots and Systems,. Proceedings, vol. 2, 730–735. DOI: 10.1109/IROS.1998.727279.
  • [15] Thrun S., Fox D., Burgard W., Dellaert F., Robust Monte Carlo Localization for Mobile Robots, Artificial Intelligence, vol. 128, no. 1–2, 2001, 99–141. DOI: 10.1016/S0004-3702(01)00069-8.
  • [16] Bedkowski J., Maslowski A., De Cubber G., „Real time 3D localization and mapping for USAR robotic application”, Industrial Robot: An International Journal, vol. 39, no. 5, 2012, 464–474.
  • [17] Mandic D. P., Obradovic D., Kuh A., et al. „Data Fusion for Modern Engineering Applications: An Overview”. In: Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005, ed. W. Duch, J. Kacprzyk, E. Oja, and S. Zadrożny, Springer Berlin Heidelberg, 2005, 715–721.
  • [18] „PIAP – producer of EOD equipment, EOD robots and surveillance robots”. [Online]. http://antiterrorism.eu/en/.
  • [19] Trojnacki M., Dynamics modeling of wheeled mobile robots, PIAP Publ. House, Warsaw 2013.
  • [20] Russell S. J., Norvig P., Artificial Intelligence: A Modern Approach, Prentice Hall, 2010.
  • [21] Robert C. P., Introducing Monte Carlo Methods with R, 2010 ed., New York: Springer Verlag, 2009. DOI: 10.1007/978-1-4419-1576-4.
  • [22] Perski A., Wieczynski A., Baczynska M., et al., „Odbiorniki GNSS w praktyce inżynierskiej. Badania stacjonarne”, Pomiary Automatyka Robot., vol. 17, no. 4, 2013, 64–77. (In Polish)
Typ dokumentu
Bibliografia
Identyfikator YADDA
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