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Journal of Intelligent and Robotic Systems

, Volume 46, Issue 3, pp 221–243 | Cite as

A Neural-based Model for Fast Continuous and Global Robot Location

  • Álvaro Sánchez Miralles
  • Miguel Ángel Sanz Bobi
Article

Abstract

One of the problems in the field of mobile robotics is the estimation of the robot position in an environment. This paper proposes a model for estimating a confidence interval of the robot position in order to compare it with the estimation made by a dead-reckoning system. Both estimations are fused using heuristic rules. The positioning model is very valuable in estimating the current robot position with or without knowledge about the previous positions. Furthermore, it is possible to define the degree of knowledge of the robot previous position, making it possible to adapt the estimation by varying this knowledge degree. This model is based on a one-pass neural network which adapts itself in real time and learns about the relationship between the measurements from sensors and the robot position.

Key words

first location problem location mobile robot neural network 

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References

  1. 1.
    Armingol, J.M.: Localización geométrica de robots móviles autónomos. PhD Thesis. Universidad Carlos III de Madrid. Leganes, 1997. In SpanishGoogle Scholar
  2. 2.
    Arras, O.K., Tomatis, N.: Improving Robustness and Precision in Mobile Robot Localization by Using Laser Ranger Finding and Monocular Vision, 3rd European Workshop on Advanced mobile Robots, 1999Google Scholar
  3. 3.
    Betke, M., Gurvits, L.: Mobile Robot localization using landmarks. IEEE Trans. Robot. Autom. 13(2), 251–263 (1997)CrossRefGoogle Scholar
  4. 4.
    Borenstein, J., Everett, B., Feng, L.: Navigating Mobile Robots: Systems and Techniques. A.K. Peters, Ltd., Wellesley, MA (1996)zbMATHGoogle Scholar
  5. 5.
    Bulata, H., Devy, M.: Incremental Construction of a Landmark-based and topological model of indoor environments by a mobile robot. In: Proceedings of the IEEE International Conference on Robotics and Automation, 1996Google Scholar
  6. 6.
    Burgard, W., Fox, D., Henning, D., Schmidt, T.: Estimating the absolute position of a mobile robot using position probability grids. 14th National Conf. on Artificial Intelligence 2, 896–901 (1996)Google Scholar
  7. 7.
    Burgard, W., Derr, A., Fox, D., Cremers, A.B.: Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach. IROS98 Int. Conf. on Intelligent Robots and Systems, pp. 730–735 (1998)Google Scholar
  8. 8.
    Burgard, W., Cremers, A.B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S.: Experiences with an interactive museum tour-guide robot. Artif. Intell. 114(1–2), 3–55 (1999)CrossRefzbMATHGoogle Scholar
  9. 9.
    Cahut, L., Valvanis, K.F., Deliç, H.,: Sonar resolution based environment mapping. IEEE Int. Conf. on Robotics and Automation, pp. 2541–2547 (May 1998)Google Scholar
  10. 10.
    Castellanos, J.A.: Mobile Robot Localization and Map Building: A Multisensor Fusion Approach. Kluwer, pp. 224 (Mar 2000)Google Scholar
  11. 11.
    Chong, K.S., Kleeman, L.: Sonar based map building for mobile robot. IEEE Int Conf. on Robotics and Automation, pp. 1700–1705 (April 1997)Google Scholar
  12. 12.
    Cox, I.J.: Blanche: An autonomous robot vehicle for structured environments. IEEE Trans. Robot. Autom. 2, 978–982 (April 1988)Google Scholar
  13. 13.
    Cox, I.J.: Blanche – An experiment in Guidance and navigation of and Autonomous Mobile Robot. IEEE Trans. Robot. Autom. 7(3), 193–204 (1991)CrossRefGoogle Scholar
  14. 14.
    Crowley, J.L., Wallner, F., Schiele, B.: Positioning estimation using principal components of range data. ICRA98, Int. Conf. on Robotics and Automation, pp. 3121–3128 (1998)Google Scholar
  15. 15.
    Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229–242 (June 2001)CrossRefGoogle Scholar
  16. 16.
    Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press (2000)Google Scholar
  17. 17.
    Einsele, T.: Real-time self-location in unknown indoor environments using a panorama laser range finder. IEEE Int. Conf. on Intelligent Robots and Systems, pp. 697–702 (1997)Google Scholar
  18. 18.
    Fenwick, J.W., Newman, P.M., Leonard, J.J.: Cooperative concurrent mapping and localization. Int. Conf. Robot. Autom. 2, 1810–1817 (May 2002)Google Scholar
  19. 19.
    Forsberg, J., Larsson, U., Wernersson, A.: On mobile robot navigation in cluttered rooms using the range weighted hough transform. IEEE Robot. Autom. Soc. Mag. 2(1), 18–26 (March 1995)CrossRefGoogle Scholar
  20. 20.
    Fox, D., Burgard, W., Dellaert, F., Thurn, S.: Monte Carlo Localization: Efficient Position Estimation for mobile Robots. 16th National Conf. on Artificial Intelligence (1999)Google Scholar
  21. 21.
    Freund, E., Dierks, F.: Map-Based Free Navigation for Autonomous Vehicles. Int. J. Syst. Sci. 27(8), 753–770 (1996)CrossRefzbMATHGoogle Scholar
  22. 22.
    Gerecke, U., Sharkey, N., Sharkey, A.: Reliable robot localization with an ensemble approach. In: Proceedings of the Second International Symposium on Robotics and Automation (ISRA-2000), Monterrey, Mexico, pp. 515–520, 2000Google Scholar
  23. 23.
    Gerecke, U., Sharkey, N.E., Sharkey A.J.C.: Common evidence vectors for self-organized ensemble localization. Neurocomputing 55(3/4), 499–519 (Oct 2003)Google Scholar
  24. 24.
    Guivant, J., Nebot, E., Baiker, S.: Autonomous navigation and map building using laser range sensors in outdoor applications. J. Robot. Syst. 17(10), 565–583 (Oct 2000)CrossRefzbMATHGoogle Scholar
  25. 25.
    Guivant, J.E., Nebot, E.M.: Optimization of the simultaneous localization and map-building algorithm for real-time implementation. IEEE Trans. Robot. Autom. 17(3), 242–258 (June 2001)CrossRefGoogle Scholar
  26. 26.
    Guivant, J., Nebot, E.: Improving computational and memory requirements of simultaneous localization and map building algorithms. Int. Conf. Robot. Autom. pp. 2731–2736 (2002)Google Scholar
  27. 27.
    Gutierrez-Osuna, R., Janet Jason, A., Luo Ren, C.: Modeling ultrasonic range sensors for localization of autonomous mobile robots. Trans. Ind. Electron. 45(4), 654–662 (Aug 1998)CrossRefGoogle Scholar
  28. 28.
    Hasselblad, V.: Estimation of parameters for a mixture of normal distribution. Technometrics 8(3), 431–445 (August 1966)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Jensfelt, P., Wijk, J., Austin, D.J., Magnus, A.: Experiments on augmenting condensation for mobile robot localization. IEEE Trans. Robot. Autom. 17(5), 748–760 (Oct 2001)CrossRefGoogle Scholar
  30. 30.
    Jensfelt, P., Kristensen, S.: Active global localization for a mobile robot using multiple hypothesis tracking. IEEE Trans. Robot. Autom. 17(5), 748–761 (Oct 2001)CrossRefGoogle Scholar
  31. 31.
    Kaelbling, L., Cassandra, A., Kurien, J.: Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. IEEE Int. Conf. on Intelligent Robots and Systems (1996)Google Scholar
  32. 32.
    Leonard, J.J., Durrant-Whyte, H.F.: Simultaneous map building and localization for an autonomous robot. IEEE Int. Conf. on Intelligent Robots and Systems, pp. 1442–1447 (1991)Google Scholar
  33. 33.
    Montemerlo, M., Thrun, S.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. In AAAI 2002Google Scholar
  34. 34.
    Muñoz, A.J., Gonzalez, J.: Two-dimentional landmark-based position estimation from a single image. IEEE Trans. Robot. Autom. 16(5), 542–552 (Oct 2000)Google Scholar
  35. 35.
    Newman, P., Leonard, J., Tardos, J.D., Neira, J.: Explore and return: experimental validation of real-time concurrent mapping and localization. Proc. IEEE Int. Conf. on Robotics and Automation, pp. 1802–1809 (2002)Google Scholar
  36. 36.
    Reina. A.J.: Navegación de robots móbiles mediante escaner láser radial. Tesis doctoral. Universidad de Malaga, 2001Google Scholar
  37. 37.
    Sánchez, A., Sanz Bobi, M.A. Real Time Dynamic Ellipsoidal Neural Network (RTDENN). Int. Conf. on signal processing, robotics and automation (ISPRA), Jun 2002Google Scholar
  38. 38.
    Sánchez, A.: Environment modelling for autonomous robots using artificial intelligence techniques. In Spanish. PhD Universidad Pontificia Comillas. Spain, Dec 2002Google Scholar
  39. 39.
    Sánchez, A., Sanz Bobi, M.A.:Global path planning in Gaussian probabilistic maps. J. Intell. Robot. Syst. 40(1), 89–102 (May 2004)CrossRefGoogle Scholar
  40. 40.
    Sarabia, A.: Introducción a la estadística. Ediciones ICAI, 1984. In SpanishGoogle Scholar
  41. 41.
    Schiele, B., Crowley, J.L.: A comparison of position estimation techniques using occupancy grids. In: Proc of the IEEE International Conference on Robotics and Automation, pp. 1628–1634 (1994)Google Scholar
  42. 42.
    Schultz, A., Adams, W., Yamauchi, B.: Integrating exploration, localization, navigation and planning with a common representation. Auton. Robots. 6(3), 293–308 (May 1999)CrossRefGoogle Scholar
  43. 43.
    Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)CrossRefGoogle Scholar
  44. 44.
    Tardos, J.D., Neira, J., Newman, P., Leonard, J.: Robust mapping and localization in indoor environments using sonar data. Int. J. Rob. Res. 21(4) (2002)Google Scholar
  45. 45.
    Thrun, S., Burgard, B., Fox, D.: A probabilistic approach to concurrent mapping and localization for mobile robots. Mach. Learn. Auton. Robots. 31(5), 1–25 (1998)Google Scholar
  46. 46.
    Thurn, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. IEEE Int. Conf. on Robotics and Automation, pp. 321–328 (April 2000)Google Scholar
  47. 47.
    Townsend, N.W., Tarassenko, L.: Neural networks for mobile robot localisation using infra-red range sensing. Neural Comput. Appl. 8(2), 114–135 (1999)CrossRefGoogle Scholar
  48. 48.
    Vlassis, N., Kröse, B.: Robot environment modeling via principal component regression. IROS99 IEEE Int. Conf. on Intelligent Robots and Systems, pp. 677–682 (1999)Google Scholar
  49. 49.
    Zhang, L., Ghosh, B.K.: Line segment based map building and localization using 2D laser rangefinder. IEEE Int. Conf. on Robotics and Automation, pp. 2538–2543 (April 2000)Google Scholar
  50. 50.
    Zunino, G., Christensen, H.I.: Simultaneous localization and mapping in realistic environments. SLAM Summer School 2002. StockholmGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2006

Authors and Affiliations

  • Álvaro Sánchez Miralles
    • 1
  • Miguel Ángel Sanz Bobi
    • 1
  1. 1.Escuela Técnica Superior de Ingeniería- ICAI, Instituto de Investigación TecnológicaUniversidad Pontificia ComillasMadridSpain

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