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Map construction and localization using Lego Mindstorms NXT

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EN
Abstrakty
EN
Maps are very useful for understanding unknown places before visiting them as maps represent spatial relationships between various objects in a region. Using robots for map construction is an important field these days as robots can reach places which may be inaccessible to human beings. This paper presents a method to use the data obtained from a single ultrasonic sensor mounted on a robot, to construct a map and localize the robot within that map. Map of the previously unknown environment is created with the help of a mobile robot, built using Lego Mindstorms NXT assembled in a modified TriBot configuration. The robot is equipped with an ultrasonic sensor and is controlled from a computer system running a MATLAB program, which communicates with the NXT over a USB or Bluetooth connection and performs complex calculations that are not possible for the NXT itself. After the map construction, the robot finds its position in the map by using a particle filter. Implementation has been done in MATLAB programming environment using RWTH – Mindstorms NXT Toolbox and has been successfully tested for map construction of a room and localization within that room with the use of a TriBot.
Twórcy
autor
  • Department of Computer Science, Faculty of Mathematical Sciences, New Academic Block, University of Delhi, Delhi- 110007, India
autor
  • Department of Computer Science, Faculty of Mathematical Sciences, New Academic Block, University of Delhi, Delhi- 110007, India
Bibliografia
  • [1] Burguera A., González Y.’ Oliver G., “Mobile Robot Localization Using Particle Filters and Sonar Sensors”, Advances in Sonar Technology, In-Tech:Vienna, Austria, 2009, Chapter 10, pp. 213–232.
  • [2] Adiprawita W., Ahmad A. S., Sembiring J., Trilaksono, B. R., “New Resampling Algorithm for ParticleFilter Localization for Mobile Robot with3 Ultrasonic Sonar Sensors”, In: Proceedings of International Conference on Electrical Engineering and Informatics, Bandung, Indonesia, July 17–19, 2011, pp. 1431–1436.
  • [3] Burguera A., González Y., Oliver G., “Sonar Sensor Models and Their Application to Mobile Robot Localization”, Sensors, vol. 9, 2009, pp. 10217–10243.
  • [4] Thrun S., “Particle Filters in Robotics”, In: Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence (UAI), Edmonton, Alberta, Canada, August 1–4, 2002, pp. 511–518.
  • [5] Howell J., Donald B.R., “Practical Mobile Robot Self-Localization”, In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, USA, April 24–28, 2000, vol. 4, pp. 3485–3492.
  • [6] Yamauchi B., Schultz A., Adams W., “Mobile Robot Exploration and Map-Building with Continuous Localization”, In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Leuven, Belgium, May 16–20, 1998, vol. 4,pp. 3715–3720.
  • [7] Varveropoulos V., “Robot Localization and Map Construction Using Sonar Data”, The Rossum Project: 2000. Available online: http://www.rossum.sourceforge.net/papers/Localization (accessed on 17 January 2012).
  • [8] Howard A., “Multi-robot Simultaneous Localization and Mapping using Particle Filters”, Int. J. Robot. Res., vol. 25, 2006, pp. 1243–1256.
  • [9] RWTH - Mindstorms NXT Toolbox, RWTH Aachen University, 2010. Available online: http://www.mindstorms.rwth-aachen.de/trac/ wiki (accessed on 18 August 2011).
  • [10] Fox D., Burgardy W., Dellaerta F., Thrun S., “Monte Carlo Localization: Efficient Position Estimation for Mobile Robots”, In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, Orlando, FL, USA, July 18–22, 1999,pp. 343–349.
  • [11] Artificial Intelligence (CS373) Programming a Robotic Car, Udacity, 2012. Available online: http://www.udacity.com/overview/Course/cs373 (accessed on 26 February 2012).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e5ad3d90-e497-4420-92e5-84e60a19515c
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