Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-27T04:20:07.928Z Has data issue: false hasContentIssue false
  • English
  • Français

Entropy Flow-Aided Navigation

Published online by Cambridge University Press:  26 November 2010

He Deng ,
Chao Pan ,
Tongxin Wen  and
Jianguo Liu
He Deng
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
Chao Pan
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
Tongxin Wen
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
Jianguo Liu*
Affiliation:
(National Key Laboratory of Science and Technology, Huazhong University, Wuhan, PRC)
*
(Email: jgliu@ieee.org)
Get access Rights & Permissions [Opens in a new window]

Abstract

Integrating the visual navigation mechanism of flying insects with a nonlinear Kalman filter, this paper proposes a novel navigation algorithm. New concepts of entropic map and entropy flow are presented, which can characterize topographic features and measure changes of the image respectively. Meanwhile, an auto-selecting algorithm of assessment threshold is proposed to improve computational accuracy and efficiency of global motion estimation. The simulation results suggest that the navigation algorithm can perform real-time rectification of the missile's trajectory well, and can reduce the cost of the missile's hardware.

Keywords

entropy flowoptical flownavigation
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 1 , January 2011 , pp. 109 - 125
Copyright
Copyright © The Royal Institute of Navigation 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Farrell, J. L. (1976). Integrated Aircraft Navigation. New York: Academic.Google Scholar
[2]Enns, R. and Morrell, D. (1995). Terrain-aided navigation using the viterbi algorithm. Journal of Guidance, Control and Dynamics, 18, 1444–1449.Google Scholar
[3]Bertoni, G., Borghi, D. and Zanzi, M. (1998). The GPS integrity problem. Proceeding of the 14 thIFAC Symposium on Automatic Control in Aerospace, Soeul, Korea.Google Scholar
[4]Esch, H. E. and Burns, J. E. (1996). Distance estimation by foraging honeybees. Journal of Experimental Biology, 199, 155–162.CrossRefGoogle ScholarPubMed
[5]Reisenman, C., Haag, J. and Borst, A. (2003). Adaptation of response transients in fly motion vision. I: Experiments. Vision Research, 43, 1291–1307.CrossRefGoogle ScholarPubMed
[6]Collett, T. S., Dillmann, E., Giger, A. and Wehner, R. (1992). Visual landmarks and route following in desert ants. Journal of Comparative Physiology A, 170, 435–442.CrossRefGoogle Scholar
[7]Wehner, R. (2003). Desert ant navigation: how miniature brains solve complex tasks. Journal of Comparative Physiology A, 189, 579–588.CrossRefGoogle ScholarPubMed
[8]Srinivasan, M. V., Zhang, S. W., Lehrer, M. and Collett, T. S. (1996). Honeybee navigation en route to the goal: visual flight control and odometer. Journal of Experimental Biology, 199, 237–244.CrossRefGoogle Scholar
[9]O′Carroll, D. C., Bidwell, N. J., Laughlin, S. B. and Warrant, E. J. (1996). Insect motion detectors matched to visual ecology. Nature, 382, 63–66.CrossRefGoogle ScholarPubMed
[10]Srinivasan, M. V., Lehrer, M., Kirchner, W. H. and Zhang, S. W. (1991). Range perception through apparent image speed in freely flying honeybees. Visual Neuroscience, 6, 519–535.CrossRefGoogle ScholarPubMed
[11]Baird, E., Srinivasan, M. V., Zhang, S., Lamont, R. and Cowling, A. (2006). Visual control of flight speed and height in honeybee. Lecture Notes in Artificial Intelligence, 4095, 40–51.Google Scholar
[12]Franceschini, N., Ruffier, F. and Serres, J. (2007). A bio-inspired flying robot sheds light on insect piloting abilities. Current Biology, 17, 329–335.CrossRefGoogle ScholarPubMed
[13]von Frisch, K. (1967). The dance language and orientation of honeybees. Cambridge: Belknap.Google Scholar
[14]David, C. T. (1982). Compensation for height in the control of groundspeed by Drosophila in a new, “Barber's Pole” wind tunnel. Journal of Comparative Physiology A, 147, 485–493.CrossRefGoogle Scholar
[15]Baird, E., Srinivasan, M. V., Zhang, S. W. and Cowling, A. (2005). Visual control of flight speed in honeybees. Journal of Experimental Biology, 208, 3895–3905.CrossRefGoogle ScholarPubMed
[16]Esch, H. E., Zhang, S. W., Srinivasan, M. V. and Tautz, J. (2001). Honeybee dances communicate distances measured by optic flow. Nature, 411, 581–583.Google Scholar
[17]Shannon, C. E. (1948). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5, 3–55.CrossRefGoogle Scholar
[18]Shiozacki, A. (1986). Edge extraction using entropy operator. Computer Vision, Graphics and Image Processing archive, 36, 1–9.CrossRefGoogle Scholar
[19]Si, A., Srinivasan, M. V. and Zhang, S. (2003). Honeybee navigation: properties of the visually driven ‘odometer’. Journal of Experimental Biology, 206, 1265–1273.CrossRefGoogle ScholarPubMed
[20]Horn, B. and Schunck, B. (1981). Determining optical flow. Artificial Intelligence, 17, 185–203.CrossRefGoogle Scholar
[21]Lucas, B. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the 7 thInternational Joint Conference on Artificial Intelligence, Canada.Google Scholar
[22]Barron, J. L., Fleet, D. J. and Beauchemin, S. S. (1994). Performance of optical flow techniques. International Journal of Computer Vision, 12, 43–77.CrossRefGoogle Scholar
[23]Fleet, D. J. and Jepson, A. D. (1990). Computation of component image velocity from local phase information. International Journal of Computer Vision, 5, 77–104.CrossRefGoogle Scholar
[24]Galvin, B., McCane, B., Novins, K., Mason, D. and Mills, S. (1998). Recovering motion fields: an analysis of eight optical flow algorithms. Proceedings of 1998 British Machine Vision Conference, Southampton, England.Google Scholar
[25]Beauchemin, S. S. and Barron, J. L. (1995). The computation of optical flow. ACM Computing Surveys, 27, 433–467.Google Scholar
[26]Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME – Journal of Basic Engineering, 82, 35–45.Google Scholar
[27]Pitman, G. R. (1962). Inertial Guidance. New York: Academic.Google Scholar