Autonomous Robots

, Volume 9, Issue 1, pp 7–16 | Cite as

Cooperative Navigation of Micro-Rovers Using Color Segmentation

  • Jeff Hyams
  • Mark W. Powell
  • Robin Murphy
Article

Abstract

This paper addresses position estimation of a micro-rover mobile robot (called the “daughter”) as a larger robot (the “mother”) tracks it through large spaces with unstructured lighting. Position estimation is necessary for localization, where the mother extracts the relative position of the daughter for mapping purposes, and for cooperative navigation, where the mother controls the daughter in real-time. The approach taken is to employ the Spherical Coordinate Transform color segmenter developed for medical applications as a low computational and hardware cost solution. Data was collected from 50 images taken in five types of lighting: fluorescent, tungsten, daylight lamp, natural daylight indoors and outdoors. The results show that average pixel error was 1.5, with an average error in distance estimation of 6.3 cm. The size of the error did not vary greatly with the type of lighting. The segmentation and distance tracking have also been implemented as a real-time tracking system. Using this system, the mother robot is able to autonomously control the micro-rover and display a map of the daughter's path in real-time using only a Pentium class processor and no specialized hardware.

color segmentation cooperative navigation visual tracking mobile robots 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ballard, D.H., and Brown, C.M. 1982. Computer Vision, Prentice-Hall, Inc.: Englewood Cliffs, NJ.Google Scholar
  2. Besl, P.J. 1988. Active, optical range imaging sensors. Machine Vision and Applications, 1: 127–152.Google Scholar
  3. Blitch, J.G. 1995. AI applications for robot assisted urban search and rescue. The AI Exchange, Fall, 9: 15–20.Google Scholar
  4. Firby, J.R., Prokopowicz, P.N., and Swain, M.J. 1998. The animate agent architecture. In Artificial Intelligence and Mobile Robots, D. Kortenkamp, R.P. Bonasso, and R. Murphy (Eds.), MIT Press.Google Scholar
  5. Hong, W.J. and Slotine, J.J.E. 1995. Experiments in hand-eye coordination using active vision. In Proceedings of the Fourth International Symposium on Experimental Robotics, ISER'95, 30 June–2 July, Stanford, California.Google Scholar
  6. Horswill, I. 1998. The polly system. Artificial Intelligence and Mobile Robots, D. Kortenkamp, R.P. Bonasso, and R. Murphy (Eds.), MIT Press.Google Scholar
  7. Miller, D.P. and Wright, A. 1995. Autonomous spacecraft docking using multi-color targets. In Proceedings of the Sixth Topical Meeting on Robotics, Monterey, California.Google Scholar
  8. Murphy, R. 1998. Coordination and control of sensing for mobility using action-oriented perception. In Artificial Intelligence and Mobile Robots, D. Kortenkamp, R.P. Bonasso, and R. Murphy (Eds.), MIT Press.Google Scholar
  9. Murphy, R.R., Ausmus, M., Bugajska, M., Ellis, T., Johnson, T., Kelley, N., Kiefer, J., and Pollock, L. 1999. Marsupial-like mobile robot societies. Agents 99.Google Scholar
  10. Newton Cognachrome Vision System. http://www.newtonlabs.comGoogle Scholar
  11. Powell, M. and Murphy, R. 1999. Position estimation of micro-rovers using a spherical coordinate transform color segmenter. CVPR 99: Workshop on Perception for Mobile Agents.Google Scholar
  12. Sargent, R., Bailey, B., Witty, C., and Wright, A. 1997. The importance of fast vision in winning the first micro-robot world cup soccer tournament. Robotics and Autonomous Systems, 21(2): 139–147.Google Scholar
  13. Sargent, R., Bailey, B., Witty, C., and Wright, A. 1997. Dynamic object capture using fast vision tracking. AI Magazine, 18(1): 65–72.Google Scholar
  14. Umbaugh, Scott E.C. 1998. Computer Vision and Image Processing, Prentice-Hall: Englewood Cliffs, NJ.Google Scholar
  15. Veloso, M., Stone, P., and Han, K. 1998. RoboCup-97 small-robot world champion team. AI Magazine, 19(3): 61–69.Google Scholar
  16. Verri, A. and Torre, V. 1986. Absolute depth estimate in stereopsis. J. Opt. Soc. Am. A, 3(3): 297–299.Google Scholar
  17. Volpe, R., Litwin, T., and Matthies, L. 1995. Mobile robot localization by remote viewing of a colored cylinder. In Proceedings IROS'95, vol. 1, pp. 257–263.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Jeff Hyams
    • 1
  • Mark W. Powell
    • 2
  • Robin Murphy
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  2. 2.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  3. 3.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA

Personalised recommendations