Probabilistic robotics

ABSTRACT

Planning and navigation algorithms exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.

REFERENCES

• 1: M. W. M. G. Dissanayake , P. Newman , Hugh F. Durrant-Whyte , Steve Clark , M. Csorba, An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building, The Sixth International Symposium on Experimental Robotics VI, p.265-274, March 26-28, 1999
• 2: Doucet, A., de Freitas, J., and Gordon, N., Eds. Sequential Monte Carlo Methods in Practice. Springer Verlag, New York, 2001.
• 3: Hugh F. Durrant-Whyte, An autonomous guided vehicle for cargo handling applications, International Journal of Robotics Research, v.15 n.5, p.407-440, Oct. 1996 [doi>10.1177/027836499601500501]
• 4: Yufeng Liu , Rosemary Emery , Deepayan Chakrabarti , Wolfram Burgard , Sebastian Thrun, Using EM to Learn 3D Models of Indoor Environments with Mobile Robots, Proceedings of the Eighteenth International Conference on Machine Learning, p.329-336, June 28-July 01, 2001
• 5: Maybeck, P. Stochastic Models, Estimation, and Control, Vol. 1. Academic Press, Inc., New York, 1979.
• 6: McLachlan, G. and Krishnan, T. The EM Algorithm and Extensions. Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., New York, 1997.
• 7: Illah R. Nourbakhsh , Alvaro Soto , Judith Bobenage , Sebastien Grange , Roland Meyer , Ron Lutz, An effective mobile robot educator with a full-time job, Artificial Intelligence, v.114 n.1-2, p.95-124, Oct. 1999 [doi>10.1016/S0004-3702(99)00027-2]
• 8: Roy, N. and Thrun, S. Coastal navigation with mobile robot. In Proceedings of the Conference on Neural Information Processing Systems (Denver, Dec.). MIT Press, Cambridge, MA, 1999.
• 9: Reid G. Simmons , David Apfelbaum , Wolfram Burgard , Dieter Fox , Mark Moors , Sebastian Thrun , Håkan L. S. Younes, Coordination for Multi-Robot Exploration and Mapping, Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, p.852-858, July 30-August 03, 2000
• 10: Sondik, E. The Optimal Control of Partially Observable Markov Processes. Ph.D. thesis, Stanford University, 1971.
• 11: Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A., Dellaert, F., Fox, D., Haehnel, D., Rosenberg, C., Roy, N., Schulte, J., and Schulz, D. Probabilistic algorithms and the interactive museum tour-guide robot Minerva. Int. J. Robo. Res. 19, 11 (2000), 972-999.
• 12: Williams, S., Dissanayake, G., and Durrant-Whyte, H. Towards terrainaided navigation for underwater robotics. Adv. Robo. 15, 5 (2001).

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.9 Robotics

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS

General Terms:
Algorithms