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PYTHEAS : an integrated robotic system with autonomous navigation capabilities

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we present PYTHEAS, an integrated robotic software system that supports autonomous navigation capabilities. These include localization, workspace mapping, path planning and tracking, and obstacle avoidance. PYTHEAS enables mapping of an unknown indoor environment by exploiting sensory information extracted from a laser scanner. Based on this acquired environment representation, the system is able to navigate autonomously in the mapped workspace, avoiding at the same time dynamic obstacles, such as moving persons or other objects. The developed competences are coupled in an integrated system, which can be controlled through a user-friendly interface over the web. Experimental results demonstrate the ability of the developed system to map complicated environments and support navigation in dynamic worlds.
Twórcy
autor
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion, Crete,Greece
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion, Crete,Greece
autor
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion, Crete,Greece
autor
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion, Crete,Greece
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
autor
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion, Crete,Greece
autor
  • Department of Computer Science University of Crete Heraklion, Crete, Greece
  • Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion, Crete,Greece
Bibliografia
  • [1 ] Castellanos J.A., Martinez J.M., Neira J., Tardos J.D., Simultaneous map building and localization for mobile robots: A multi-sensor fusion approach, In Intl. Conf. Robotics and, Autornation, pp. 1244-1249,1998.
  • [2 ] Moravec H., Elfes A., High-resolution maps from wide angle sonar, IEEE International Conference on Robotics and Autornation, pp 116-121, St. Louis, MO, 1985
  • [3 ] Kuipers B., Byun Y.T., A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations, Journal of Robotics and Autonomous Systems, 1991, 8, pp.47-63.
  • [4 ] Mataric M.J., A distributed model for mobile robot environment-learning and navigation, Master’s thesis, MITm Cambridge, MA, January 1990, also available as MIT Al Lab Tech Report AITR-1228.
  • [5 ] Cassandra A., Kaelbling L., Kurien .1., Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation, In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS ‘96), (1996).
  • [6 ] Simmons R,., Koenig S., Probabilistic robot navigation in partially observable environments, In Proc. Intl. Joint Conf. on Artificial Intelligence (IJCAI ‘95), pp.1080-1087, Montreal, Canada, 1995.
  • [7 ] Thrun S., Learning metric-topological maps for indoor mobile robot navigation, Artificial Intelligence, 1999, (1), pp.21-71.
  • [8 ] Latombe J.C., Robot motion planning, vol. Fourth Printing 1996, Kluwer Academic Publishers, Boston, MA, 1991.
  • [9 ] Fox D., Burgard W., Thrun S., Controlling synchro-drive robots with the dynamic window approach to collision avoidance, In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, 1996.
  • [10 ] Fox D., Burgard W., Thrun S., The dynamie window approach to collision avoidance, IEEE Robotics and Automation Magazine, to appear.
  • [11] Borenstein J., Koren Y., Real-time obstacle avoidance for mobile robots, IEEE Trans. Systems, Man and Cybernetics, Vol. 19, No. 5, pp 1179-1187.
  • [12] Borenstein J., Koren Y., The Vector Field Histogram - fast obstacle avoidance for Mobile Robots, IEEE Journal Robotics and Autom., 1991, Vol.7, No.3, pp. 278-288.
  • [13] Thrun S., Bucken A., Learning maps for indoor mobile robot navigation, Tech.Rep. TR CMU-CS-96-12R Carnegie Mellon University, Pittsburgh, PA 15213, April 1996.
  • [14] Fox D., Bnrgard W., Thrun S., Markov icalization for mobile robots in dynamic environments, Journal of Artificial Intellience Research, 1999, 11, pp.391-427.
  • [15] Cassandra A.R., Kaelbling L.P., Kurien J., Acting under uncertainty: discrete Bayesian models for mobile-robot navigation, In Proc. 7EE/RSJ Intl. Conf. on Intelligent Robots and Systems, (IROS 96), pp.963-972, 1996.
  • [16] Cormen T., Leiserson C., Rivest R., Introduction to algorithms. MIT Pres, 1990
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT2-0001-1472
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