Skip to main content
Log in

Scene Reconstruction and Robot Navigation Using Dynamic Fields

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In this paper, we present an approach to autonomous robot navigation in an unknown environment. We design and integrate algorithms to reconstruct the scene, locate obstacles and do short-term field-based path planning. The scene reconstruction is done using a region matching flow algorithm to recover image deformation and structure from motion to recover depth. Obstacles are located by comparing the surface normal of the known floor with the surface normal of the scene. Our path planning method is based on electric-like fields and uses current densities that can guarantee fields without local minima and maxima which can provide solutions without the need of heuristics that plague the more traditional potential fields approaches. We implemented a modular distributed software platform (FBN) to test this approach and we ran several experiments to verify the performance with very encouraging results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion. Intl' J.of Computer Vision,2:283–310.

    Google Scholar 

  • Arkin, R.C. 1989. Motor schema-based mobile robot navigation. International Journal of Robotics Research, 8(4):92–112.

    Google Scholar 

  • Barron, J.L., Fleet, D.J., and Beauchemin, S.S. 1994. Performance of optical flow techniques. Int'l Journal of Computer Vision, 12:43–77.

    Google Scholar 

  • Chapuis, R., Potelle, A., Brame, J.L., and Chausse, F. 1995. Realtime vehicle trajectory supervision on the highway. The InternationalJournal of Robotics Research, 14:531–542.

    Google Scholar 

  • Chuang, J.H. 1993. Potential-based modeling of three dimensional workspace for obstacle avoidance. In IEEE International Conferenceon Robotics & Automation, Vol. 3, pp. 19–24.

    Google Scholar 

  • Dickmanns, E.D., Mysliwetz, B., and Christians, T. 1990. An integrateds patio-temporal approach to automatic visual guidance ofautonomous vehicles. IEEE Transaction on System, Man, and Cybernetics, 20(6):1273–1990.

    Google Scholar 

  • Feder, H.J.S. and Slotine, J.-J. 1997. Real-time path planning using harmonic potentials. In Dynamic Environments, Proc. IEEEConference on Robotics and Automation, pp. 874–881.

  • Ferrell, Cynthia. 1995. A comparison of three insect-inspired locomotion controllers. Robotics and Autonomous Systems,16:135–159.

    Google Scholar 

  • Horn, B.K.P. 1986. Robot Vision, McGraw-Hill Book Company: New York.

    Google Scholar 

  • Horn, B.K.P. and Schunck, B.G. 1981. Determining optical flow. Artificial Intelligence, 17:185–204.

    Google Scholar 

  • Hwang, Y.K. and Ahuja, N. 1992. A potential field approach to path planning. IEEE Transactions on Robotics and Automation,8(1):23–32.

    Google Scholar 

  • Real World Interface, Inc. 1990. G96 Sonar Board Guide to Operations Version 1.1, Real World Interface, Inc.: Dublin.

    Google Scholar 

  • RealWorld Interface, Inc. 1994. B12 Base Manual Version 2.4, Real World Interface, Inc.: Dublin.

    Google Scholar 

  • Jenkin, M.R.M. et al. 1993. Global navigation for ARK. In Proc.IEEE/RSJ, IROS, Yokohama, Japan, pp. 2165–2171.

    Google Scholar 

  • Khatib, O. 1986. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research,5(1):90–98.

    Google Scholar 

  • Kim, J.O. and Khosla, P. 1991. Real-time obstacle avoidance using harmonic potential function. In Proc.IEEE Conference on Robotics and Automation, pp. 790–796.

  • Klein, R. 1989. Concrete and Abstract Voronoi Diagrams, Springer-Verlag: Berlin.

    Google Scholar 

  • Langer, D., Rosenblatt, J.K., and Hebert, M. 1994. A behavior-based system for off-road navigation. IEEE Transactions on Robotics and Automation, 10(6):776–783.

    Google Scholar 

  • Lucas, B. 1984. Generalized image matching by the method of differences. Ph.D. Dissertation, Dept. of Computer Science, Carnegie Mellon University.

    Google Scholar 

  • Marco, D.B., Healey, A.J., and McGhee, R.B. 1996. Autonomous underwater vehicles: Hybrid control of mission and motion. Autonomous Robots, 3:169–186.

    Google Scholar 

  • Matthies, Larry. 1992. Stereo vision for planetary rovers: Stochastic modeling to near real time implementation. International Journal of Computer Vision, 8(1):71–91.

    Google Scholar 

  • Moravec, H.P. 1977. Towards automatic visual obstacle avoidance. In Proc.5th IJCAI, IJCAI-77, Cambridge, Massachusetts, p. 584.

  • Okui, Takao and Shinoda, Yoshiaki. 1996. An outdoor robots system for autonomous mobile all purpose platform. Robotics and Autonomous Systems, 17:99–106.

    Google Scholar 

  • Pratt, W.K. 1991. Digital Image Processing, Wiley: New York.

    Google Scholar 

  • Reid, M.B. 1993. Path planning using optically computed potential fields. IEEE International Conference on Robotics & Automation, Vol. 2, pp. 295–300.

    Google Scholar 

  • Singh, L., Stephanou, H., and Wen, J. 1996. Real-time motion control with circulatory fields. In Proc.IEEE Conference on Robotics and Automation, pp. 2737–2742.

  • Spetsakis, M.E. and Aloimonos, J. 1988. Optimal estimation of structure from motion from point correspondences in two frames. In Proc.ICCV, Tampa, Florida.

  • Spetsakis, Minas. 1994. MediaMath: A research environment for vision research. In Vision Interface, Banf: Alberta, pp. 118–126.

    Google Scholar 

  • Stoer, J. and Bulirsch, R. 1980. Introduction to Numerical Analysis, Springer-Verlang.

  • Weber, J. and Malik, J. 1993. Robust computation of optical flow in a multi-scale differential framework. In ICCV, pp. 231–236.

  • Weisbin, C.R., Montemerlo, M., and Whittaker, W. 1993. Evolving directions in NASA's planetary rover requirements and technology. Robotics and Autonomous Systems, 11:3–11.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wong, B., Spetsakis, M. Scene Reconstruction and Robot Navigation Using Dynamic Fields. Autonomous Robots 8, 71–86 (2000). https://doi.org/10.1023/A:1008992902895

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008992902895

Navigation