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Tytuł artykułu

Designing Vision Based Autonomous Docile-X Mobile Robot for Real-time Application to Soccer Behaviors

Autorzy
Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper aims at developing a real-time, robust, and reliable behaviors for an omnidirectional soccer robot, can be used in crowded dynamically-changing environments. The soccer robot system consists of highly coordinated operations and movements so as to fulfill specific objectives, even under unfavorable situations. The associated issues are position control, velocity control and sensing information in addition to the need for imitating the human-like decision. The proposed method considers not only the kinematics of the robot but also its dynamics. Moreover, a control structure is designed and several behaviors for a soccer robot are proposed. Image processing, recognition and target following algorithm are illustrated through experiments.
Twórcy
autor
  • Divison of Theory of Machines and Robots, Warsaw University of Technology, 00-665 Warsaw, ul. Nowowiejska 24, Poland
autor
  • Department of Computer science and Engineering, Central University of Rajasthan, 305-801 Ajmer, Rajasthan, India
Bibliografia
  • [1] Kitano H., Tambe M., Stone P., et al., “The Robo-Cup Synthetic Agent Challenge 97”. In: Robo- Cup-97: Robot Soccer World Cup I, 1998, chapter 4, 45–50. DOI: 10.1007/3-540-64473-3_49.
  • [2] Asada M., Kuniyoshi Y., Drogoul A., et al. , “The RoboCup Physical Agent Challenge: Phase I (Draft)”. In: Proc. of the 1st International Workshop on RoboCup, 1997, 51–56.
  • [3] Chen Ch-H., Cheng Ch., Page D., Koschan A., Abidi M., “Tracking a moving object with real time system”. In: Proc. of Industrial Robot: An International Journal, vol. 33, no. 6, 2006, 460–468.DOI: 10.1108/01439910610705635. [4] Novak G., Bais A., Mahlknecic S., “Simple stereo vision system for real-time object recognition for an autonomous mobile robot”. In: 2nd IEEE International Conference on Computational Cybernetics, ICCC 2004, 213–216. DOI: 10.1109/ICCCYB.2004.1437710.
  • [5] Neves A. J. R., A. J. Pinho, Martins D. A., Cunha B., An efficient omnidirectional vision system for soccer robots: From calibration to object detection, 2010, 957–4158. DOI: 10.1016/j.mechatronics. 2010.05.006.
  • [6] Docile-X documentation, www.jayrobotix.co.in.
  • [7] Kuo Ch.-H., Yang Ch.-M., Yang F.-Ch., “Development of Intelligent Vision Fusion Based Autonomous Soccer Robot”. In: Proceedings of the 2005 IEEE International Conference on Mechatronics, July 10–12, 2005, Taipei, Taiwan, 124–129.
  • [8] Papageorgiou C.P., Oren M., Poggio T., “A general framework for object detection”. In: 1998 IEEE 6th International Conference on Computer Vision, 1998, 555–562.
  • [9] Cheng G., Zelinsky A., “Real-time vision processing for a soccer playing mobile robot”. In: RoboCup-97: Robot Soccer World Cup I, 1998, 144–155.
  • [10] Sahota M.K., Mackworth A.K, Kingdon S.J., Barman R.A., “Real-time control of soccer playing robots using off-board vision: The dynamic test bed”. In: IEEE Proc. Int. Conf. on Robots and Automation, Japan, 1995, 3690–3693.
  • [11] Archibald J.K., Beard R.W., “Competitive robot soccer: A design experience for graduate students”. In: Proceedings of the 32nd Annual Frontiers in Education, vol. 3 , 2002, Boston, MA, USA, November, 2002, pp. F3D14–19.
  • [12] Dutta V., Sharma K., Gahalot D., “Performance comparison of hard and soft approaches for document clustering”, International Journal of Computer Application, vol. 41, no. 7, March 2012, 0975–8887. DOI: doi.org/10.5120/5557-7632.
  • [13] Dutta V., Nishtha Kesswani N., “Fuzzy Clustering and Fuzzy Merging (FCFM): A Unique model for document clustering”, CSI Journal of Ccomputing, vol. 1, no. 3, 2012, 46–51.
  • [14] Freeston L.. Applications of the Kalman filter algorithm to robot localization and world modeling. Technical report, University of Newcastle, Journal of Automation, Mobile Robotics & Intelligent Systems VOLUME 8, N° 4 2014 50 Articles
  • NSW, Australia. 2002.
  • [15] Hargrave P. J., “A tutorial introduction to Kalman filtering”. In: IEE Colloquium onKalman Filters: Introduction, Applications and Future Developments, IET, 1989, 1–6 .
  • [16] Arulampalam M. S., Maskell S., Gordon N., “Clapp T., A tutorial on particle filters for online nonlinear/ nonGaussian Bayesian tracking”, IEEE Transactions on Signal Processing, vol. 50, no. 2, 2002, 174–188.
  • [17] https://www.robomatter.com (omnidirectional wheel of Figure 1-a ).
  • [18] Siegwart R., Nourbakhsh I. R., Davide Scaramuzza D., Introduction to Autonomous Mobile Robots, 2nd edition 2011, Massachusetts Institute of Technology. ISBN 978-0-262-01535-6.
  • [19] Siegwart R., Nourbakhsh I. R., Introduction to Autonomous Mobile Robots, 1st edition, Massachusetts Institute of Technology, 2004. ISBN 0-262-19502-X.
  • [20] Goris K., Autonomous Mobile Robot: Mechanical Design, Doctoral Thesis, Vrije University of Brussel, Belgium, 2004–2005.
  • [21] Wahab M. N. A., Sivadev N., Sundaraj K., “Target distance estimation using monocular vision system for mobile robot”. In: 2011 IEEE conference on Open Systems (ICOS), 2011, 11–15.
  • [22] Zieliński C., “Formal approach to the design of robot programming frameworks: the behavioral control case”, Bulletin of the Polish Academy of Sciences – Technical Sciences, vol. 53, no.1, 2005, 57–67.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f048aa57-4720-4710-8879-a38ee7a87796
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