Autonomous Robots

, Volume 6, Issue 3, pp 293–308 | Cite as

Integrating Exploration, Localization, Navigation and Planning with a Common Representation

  • Alan C. Schultz
  • William Adams
  • Brian Yamauchi
Article

Abstract

Two major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments, and the view of integration as a basic research issue. Where reasonable, we try to use the same representations to allow different components to work more readily together and to allow better and more natural integration of and communication between these components. In this paper, we describe our most recent work in integrating mobile robot exploration, localization, navigation, and planning through the use of a common representation, evidence grids.

mobile robots localization planning navigation exploration evidence grids integration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bauer, R. 1995. Active manoeuvres for supporting the localisation process of an autonomous mobile robot. Robotics and Autonomous Systems, 16:39–46.Google Scholar
  2. Borenstein, J. and Koren, Y. 1991. The vector field histogram—fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation, 7(3):278–288.Google Scholar
  3. Chenavier, F. and Crowley, J. 1992. Position estimation for a mobile robot using vision and odometry. In Proc. IEEE Int. Conf. on Robotics and Automation, IEEE: Nice, France, pp. 2588–2592.Google Scholar
  4. Graves, K., Adams, W., and Schultz, A. 1997. Continuous localization in changing environments. In Proc. of the 1997 IEEE Int. Symp. on Computational Intelligence in Robotics and Automation, IEEE: Monterey, CA, pp. 28–33.Google Scholar
  5. Horn, J. and Schmidt, M. 1995. Continuous localization of a mobile robot based on 3D-laser-range-data, predicted sensor images, and dead-reckoning. Robotics and Autonomous Systems, 14:99–118.Google Scholar
  6. Hughes, K. and Murphy, R. 1992. Ultrasonic robot localization using Dempster-Shafer theory. In 1992 SPIE Stochastic Methods in Signal Processing and Computer Vision, invited session on applications for vision and robotics.Google Scholar
  7. Hughes, K., Tokuta, A., and Ranganathan, N. 1992. Trulla: An algorithm for path planning among weighted regions by localized propagations. In Proc. of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, Raleigh, NC, pp. 469–475.Google Scholar
  8. Koenig, S. and Simmons, R. 1998. Xavier: A robot navigation architecture based on partially observable Markov decision process models. In Artificial Intelligence and Mobile Robots, Kortenkamp, Bonasso, and Murphy (Eds.), AAAI Press, pp. 91–122.Google Scholar
  9. Lee, W. 1996. Spatial semantic hierarchy for a physical robot. Ph.D. Thesis, Department of Computer Sciences, The University of Texas, Austin.Google Scholar
  10. Leonard, J., Durrant-Whyte, H., and Cox, I. 1992. Dynamic map building for an autonomous mobile robot. The International Journal of Robotics Research, 11:286–298.Google Scholar
  11. Lu, F. and Milios, E. 1994. Robot pose estimation in unknown environments by matching 2-D range scans. In Proc. IEEE Computer Vision and Pattern Recognition, IEEE: Seattle, pp. 935–938.Google Scholar
  12. Mataric, M. 1992. Integration of representation into goal-driven behavior-based robots. IEEE Transactions on Robotics and Automation, 8(3):304–312.Google Scholar
  13. Moravec, H. and Elfes, A. 1985. High resolution maps from wide angle sonar. In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 116–121.Google Scholar
  14. Moravec, H.P. 1988. Sensor fusion in evidence grids for mobile robots. AI Magazine, 61–74.Google Scholar
  15. Murphy, R., Hughes, K., and Noll, E. 1996. An explicit path planner to facilitate reactive control and terrain preferences. In Proc. of the 1996 IEEE International Conf. on Robotics and Automation, pp. 2067–2072.Google Scholar
  16. Schiele, B. and Crowley, J. 1994. A comparison of position estimation techniques using occupancy grids. Robotics and Autonomous Systems, 12:163–171.Google Scholar
  17. Schultz, A. and Adams, W. 1998. Continuous localization using evidence grids. In Proc. of the 1998 IEEE Int. Conf. on Robotics and Automation, Leuven, Belgium, pp. 2833–2839.Google Scholar
  18. Thrun, S. 1998. Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99:21–71.Google Scholar
  19. Thrun, S. and Bücken, A. 1996. Integrating grid-based and topological maps for mobile robot navigation. In Proc. of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, OR, pp. 944–950.Google Scholar
  20. Yamauchi, B. 1996. Mobile robot localization in dynamic environments using dead reckoning and evidence grids. Proc. IEEE Int. Conf. on Robotics and Automation, IEEE: New York, NY, pp. 1401–1406.Google Scholar
  21. Yamauchi, B. 1997. A frontier-based approach for autonomous exploration. In Proc. of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151.Google Scholar
  22. Yamauchi, B. 1998. Frontier-based exploration using multiple robots. In Proc. of the Second International Conference on Autonomous Agents (Agents'98), Minneapolis, MN.Google Scholar
  23. Yamauchi, B., Schultz, A., and Adams, W. 1998. Mobile robot exploration and map-building with continuous localization. In Proc. of the 1998 IEEE International Conference on Robotics and Automation, Leuven, Belgium, pp. 3715–3720.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Alan C. Schultz
    • 1
  • William Adams
    • 1
  • Brian Yamauchi
    • 1
  1. 1.Naval Research LaboratoryNavy Center for Applied Research in Artificial Intelligence (NCARAI)USA

Personalised recommendations