single-jc.php

JACIII Vol.11 No.6 pp. 688-700
doi: 10.20965/jaciii.2007.p0688
(2007)

Paper:

Multiresolutional Fusion of Perceptions Applied to Robot Navigation

Özer Ciftcioglu, Michael S. Bittermann, and I. Sevil Sariyildiz

Chair Design & Informatics, Delft University of Technology, Berlageweg 1, 2628 CR Delft, The Netherlands

Received:
January 16, 2007
Accepted:
March 20, 2007
Published:
July 20, 2007
Keywords:
perceptual robotics, robot navigation, multiresolution, Kalman filtering, information fusion
Abstract

Visual perception-based autonomously moving virtual agent in virtual reality as a counterpart of an actual robot moving with a given dynamics is investigated. The visual perception is mathematically modelled as a probabilistic process obtaining and interpreting visual information from an environment. The perception obtained in the form of measurements in 2D is used for perceptual robot navigation. By means of this twofold gain is obtained; while the autonomous robot is navigated, it is equipped with some human-like behaviour, thereby dealing with complexity and environmental dynamics. The visual data is processed in a multiresolutional form via wavelet transform and optimally estimated via extended Kalman filtering in each resolution level and the outcomes are fused for improved estimation of the trajectory. The perceptual robotics experiments are carried out in virtual reality for the demonstration of the feasibility of the investigations in this domain. The computer experiments are carried out with perception measurement data, and the sensor/data fusion experiments are carried out by means of simulation. The improvement on the trajectory estimation by means of sensor/data fusion is demonstrated. The research is connected to building technological robotics, where some form of perceptual intelligence, like reaction to moving objects around, is required during operation.

Cite this article as:
Özer Ciftcioglu, Michael S. Bittermann, and I. Sevil Sariyildiz, “Multiresolutional Fusion of Perceptions Applied to Robot Navigation,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.6, pp. 688-700, 2007.
Data files:
References
  1. [1] J. Bigun, “Vision with direction,” Springer Verlag, 2006.
  2. [2] D. Marr, “Vision,” Freeman, 1982.
  3. [3] T. A. Poggio, V. Torre, and C. Koch, “Computational vision and regularization theory,” Nature, Vol.317, pp. 314-319, 1985.
  4. [4] J. J. Gibson, “The Ecological Approach to Visual Perception,” Lawrence Erlbaum Associates, Publishers, 1986.
  5. [5] M. T. Turvey and R. E. Shaw, “Ecological foundations of cognition. I: Symmetry and specificity of animal-environment systems,” Journal of Consciousness Studies, Vol.6, pp. 95-110, 1999.
  6. [6] R. E. Shaw and M. T. Turvey, “Ecological foundations of cognition. II: Degrees of freedom and conserved quantities in animalenvironment systems,” Journal of Consciousness Studies, Vol.6, pp. 111-124, 1999.
  7. [7] A. Cowey and E. T. Rolls, “Human cortical magnification factor and its relation to visual acuity,” Experimental Brain Research, Vol.21, pp. 447-454, 1974.
  8. [8] R. Hecht-Nielsen, “The mechanism of thought,” presented at IEEE World Congress on Computational Intelligence WCCI 2006, Int. Joint Conf. on Neural Networks, Vancouver, Canada, 2006.
  9. [9] J. G. Taylor, “Neural networks of the brain: Their analysis and relation to brain images,” presented at Int. Joint Conf. on Neural Networks IJCNN 2005, Montreal, Canada, 2005.
  10. [10] D. H. Hubel, “Exploration of the primary visual cortex 1955-78 (Nobel Lecture),” Nature, Vol.299, pp. 515-524, 1982.
  11. [11] T. N. Wiesel, “Postnatal development of the visual cortex and the influence of environment (Nobel Lecture),” Nature, Vol.299, pp. 583-591, 1982.
  12. [12] J. K. O’Regan, H. Deubel, J. J. Clark, and R. A. Rensink, “Picture changes during blinks: looking without seeing and seeing without looking,” Visual Cognition, Vol.7, pp. 191-211, 2000.
  13. [13] R. A. Rensink, J. K. O’Regan, and J. J. Clark, “To see or not to see: The need for attention to perceive changes in scenes,” Psychological Science, Vol.8, pp. 368-373, 1997.
  14. [14] L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.20, pp. 1254-1259, 1998.
  15. [15] A. M. Treisman and G. Gelade, “A feature-integration theory of attention,” Cognitive Psychology, Vol.12, pp. 97-136, 1980.
  16. [16] A. L. Yarbus, “Eye Movements and Vision,” New York: Plenum Press, 1967.
  17. [17] E. H. Adelson and J. R. Bergen, “The Plenoptic function and the elements of early vision,” in Computational Models of Visual Processing, M. Landy and J. A. Movshon (Eds.), Cambridge: MIT Press, pp. 3-20, 1991.
  18. [18] T. V. Papathomas, C. Chubb, A. Gorea, and E. Kowler, “Early Vision and Beyond,” MIT Press, 1995.
  19. [19] L. van Hemmen, J. Cowan, and E. Domany, “Models of Neural Networks IV: Early Vision and Attention,” Springer, 2001.
  20. [20] P. Herrero, C. Greenhalgh, and A. d. Antonio, “Modelling the sensory abilities of intelligent virtual agents,” Autonomous Agents and Multi-Agent Systems, Vol.11, pp. 361-385, 2005.
  21. [21] Ö. Ciftcioglu, M. S. Bittermann, and I. S. Sariyildiz, “Towards computer-based perception by modeling visual perception: a probabilistic theory,” presented at 2006 IEEE Int. Conf. on Systems, Man, and Cybernetics, Taipei, Taiwan, 2006.
  22. [22] M. Beetz, T. Arbuckle, T. Belker, A. B. Cremers, D. Schulz, M. Bennewitz, W. Burgard, D. Hähnel, D. Fox, and H. Grosskreutz, “Integrated, plan-based control of autonomous robots in human environments,” IEEE Intelligent Systems Vol.16, pp. 56-65, 2001.
  23. [23] G. Oriolio, G. Ulivi, and M. Vendittelli, “Real-time map building and navigation for autonomous robots in unknown environments,” IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybernetics, Vol.28, pp. 316-333, 1998.
  24. [24] M. Wang and J. N. K. Liu, “Online path searching for autonomous robot navigation,” presented at IEEE Conf. on Robotics, Automation and Mechatronics, Singapore, 2004.
  25. [25] E. Ahle and D. Söffker, “A concept for a cognitive-oriented approach to build autonomous systems,” presented at 2005 IEEE Int. Conf. on Systems, Man and Cybernetics, Big Island, Hawaii, 2005.
  26. [26] E. Ahle and D. Söffker, “A cognitive-oriented architecture to realize autonomous behaviour – part II: Application to mobile robots,” presented at 2006 IEEE Conf. on Systems, Man, and Cybernetics, Taipei, Taiwan, 2006.
  27. [27] E. Ahle and D. Söffker, “A cognitive-oriented architecture to realize autonomous behaviour – part I: Theoretical background,” presented at 2006 IEEE Conf. on Systems, Man, and Cybernetics, Taipei, Taiwan, 2006.
  28. [28] C. Burghart, R. Mikut, R. Stiefelhagen, T. Asfour, H. Holzapfel, P. Steinhaus, and R. Dillmann, “A cognitive architecture for a humanoid robot: A first approach,” presented at 2005 5th IEEE-RAS Int. Conf. on Humanoid Robots, Tsukuba, Japan, 2005.
  29. [29] R. Garcia-Martinez and D. Borrajo, “An integrated approach of learning, planning, and execution,” Journal of Intelligent and Robotic Systems, Vol.29, pp. 47-78, 2000.
  30. [30] D. Söffker, “From human-machine-interaction modeling to new concepts constructing autonomous systems: A phenomenological engineering-oriented approach,” Journal of Intelligent and Robotic Systems, Vol.32, pp. 191-205, 2001.
  31. [31] B. Adams, C. Breazeal, R. A. Brooks, and B. Scassellati, “Humanoid robots: a new kind of tool,” Intelligent Systems and Their Applications, IEEE [see also IEEE Intelligent Systems], Vol.15, pp. 25-31, 2000.
  32. [32] A. Papoulis, “Probability, Random Variables and Stochastic Processes,” New York: McGraw-Hill, 1965.
  33. [33] Ö. Ciftcioglu, M. S. Bittermann, and I. S. Sariyildiz, “Studies on visual perception for perceptual robotics,” presented at ICINCO 2006 – 3rd Int. Conf. on Informatics in Control, Automation and Robotics, Setubal, Portugal, 2006.
  34. [34] T. T. J. M. Peeters and Ö. Ciftcioglu, “Statistics on exponential averaging of periodograms,” IEEE Trans. on Signal Processing, Vol.43, pp. 1631-1636, 1995.
  35. [35] L. Hong, “Multiresolutional filtering using wavelet transform,” IEEE Trans. on Aerospace and Electronic Systems, Vol.29, pp. 1244-1251, 1993.
  36. [36] B. D. O. Anderson and J. B. Moore, “Optimal Filtering,” Englewood Cliffs, New Jersey: Prentice-Hall, 1979.
  37. [37] R. G. Brown, “Introduction to Random Signal Analysis and Kalman Filtering,” New York: John Wiley & Sons, 1983.
  38. [38] A. H. Jazwinski, “Stochastic Processes and Filtering Theory,” New York Academic Press, 1970.
  39. [39] P. S. Maybeck, “Stochastic Models, Estimation and Control, Vol II,” New York: Academic Press, 1982.
  40. [40] P. S. Maybeck, “Stochastic Models, Estimation and Control, Vol I,” New York: Academic Press, 1979.
  41. [41] S. Mallat, “A Wavelet Tour of Signal Processing,” New York: Associated Press, 1999.
  42. [42] S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.11, pp. 674-693, 1989.
  43. [43] D. B. Percival and A. T. Walden, “Wavelet Methods for Time Series Analysis,” Cambridge University Press, 2000.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021