Experimental Validation of an Evolutionary Method to Identify a Mobile Robot’s Position

  • Angel Kuri-Morales
  • Ignacio Lopez-Peña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)


A method to determine the position of a mobile robot using machine learning strategies was introduced in [1]. The method raises the possibility to decrease the size of database that holds the images that describe an area where a robot will localize itself. The present work does a statistical validation of the approach by calculating the Hamming and Euclidean distances between all the images using on the one hand all their pixels and on the other hand the reduced set of pixels obtained by the GA as described in [1]. To perform the analysis, a new series of images were taken from a specific position at several angles in both horizontal (pan) and vertical (tilt). These images were compared using two different measures: a) the Hamming distance and b) the Euclidean distance to determine how similar are one from another.


Genetic Algorithms Multi-objective Optimization Machine Learning 


  1. 1.
    Kuri-Morales, A., Lopez, J.I.: A Novel Method to Determine a Robot’s Position Based on Machine Learning Strategies. In: 10th Mexican International Conference on Artificial Intelligence (MICAI), November 26-December 4, pp. 97–101 (2011), doi:10.1109/MICAI.2011.41Google Scholar
  2. 2.
    Margrity, B., Leonid, G.: Mobile Robot Localization Using Landmarks. IEEE Transactions on Robotics and Automation 13(2), 251–263 (1997)CrossRefGoogle Scholar
  3. 3.
    Boley, D.L., Steinmetz, E.S., Sutherland, K.T.: Robot localization from landmarks using recursive total least squares. In: Proceedings of Robotics and Automation, vol. 2, pp. 1381–1386 (1996)Google Scholar
  4. 4.
    Kim, S.J., Kim, B.K.: Dynamic localization based on EKF for indoor mobile robots using discontinuous ultrasonic distance measurements. In: 2010 International Conference on Control Automation and Systems (ICCAS), October 27-30, pp. 1912–1917 (2010)Google Scholar
  5. 5.
    Wu, H., Qin, S.-Y.: A new method of distance estimation for robot localization in real environment based on manifold learning. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, November 2-4, vol. 2, pp. 585–590 (2007), doi:10.1109/ICWAPR.2007.4420737Google Scholar
  6. 6.
    Mitchell, M.: An Introduction to Genetic Algorithms. Theoretical Foudations of Genetic Algorithms, ch. 4, pp. 96–99. MIT Press (1998)Google Scholar
  7. 7.
    References Portable Pixel Map specification,
  8. 8.
    Zopounidis, C., Pardalos, P.M. (eds.): Handbook of Multicriteria Analysis, Applied Optimization. On Multi-Objective Evolutionary Algorithms, vol. 103, ch. 10, pp. 287–310. Springer, Heidelberg (2010), doi:10.1007/978-3-540-92828-7_10Google Scholar
  9. 9.
    Kuri-Morales, A.: The Application of Genetic Algorithms to the Evaluation of Software Reliability, pp. 100–120. IGI Global, Chis, M. (ed.) (2010)Google Scholar
  10. 10.
    Hamming, R.W.: Error Detecting and Error Correcting Codes. Bell System Technical Journal 26(2), 147–160 (1950)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Angel Kuri-Morales
    • 1
  • Ignacio Lopez-Peña
    • 2
  1. 1.Departamento de ComputaciónInstituto Tecnológico Autónomo de MéxicoMexico CityMexico
  2. 2.Posgrado en Ciencia e Ingeniería de la ComputaciónIIMAS – UNAMMexico CityMexico

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