Constraint Based World Modeling

• Autorzy: Göhring D , Mellmann H. , Gerasymova K. , Burkhard H-D.
• Języki publikacji: EN

Abstrakty

EN

Common approaches for robot navigation use Bayesian filters like particle filters, Kalman filters and their extended forms. We present an alternative and supplementing approach using constraint techniques based on spatial constraints between object positions. This yields several advantages. The robot can choose from a variety of belief functions, and the computational complexity is decreased by efficient algorithms. The paper investigates constraint propagation techniques under the special requirements of navigation tasks. Sensor data are noisy, but a lot of redundancies can be exploited to improve the quality of the result. We introduce two quality measures: The ambiguity measure for constraint sets defines the precision, while inconsistencies are measured by the inconsistency measure. The measures can be used for evaluating the available data and for computing best fitting hypothesis. A constraint propagation algorithm is presented.

Słowa kluczowe

EN

robot navigation; constraints

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2008
• Tom: Vol. 85, nr 1-4
• Strony: 123--137
• Opis fizyczny: bibliogr. 11 poz., wykr.

Twórcy

autor

Göhring D

autor

Mellmann H.

autor

Gerasymova K.

autor

Burkhard H-D.

• Humboldt-Universität zu Berlin, Institut für Informatik, Unter den Linden 6, D-10099 Berlin,

Bibliografia

• [1] http://www.ki.informatik.hu-berlin.de, 2007.
• [2] http://www.robocup.org, 2007.
• [3] E. Davis. Constraint propagation with interval labels. Artificial Intelligence, 32, 1987.
• [4] D. Göhring, K. Gerasymova, and H.-D. Burkhard. Constraint based world modeling for autonomous robots. 2007. Proceedings of the CS&P 2007.
• [5] F. Goualard and L. Granvilliers. Controlled propagation in continuous numerical constraint networks. ACM Symposium on Applied Computing, 2005.
• [6] G. Grisetti, C. Stachniss, S. Grzonka, and Burgard. A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. In RSS, Atlanta, GA, USA, 2007. Accepted for publiction.
• [7] J.-S. Gutmann, W. Burgard, D. Fox, and K. Konolige. An experimental comparison of localization methods. In Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 1998.
• [8] M. Jüngel. Memory-based localization. 2007. Proceedings of the CS&P 2007.
• [9] R. Kalman. A new approach to linear filtering and prediction problems. Transactions of the ASME - Journal of Basic Engineering, 82:35-45, 1960.
• [10] E. Olson, J. Leonard, and S. Teller. Fast iterative alignment of pose graphs with poor initial estimates. In International Conference on Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE, 2006.
• [11] A. Stroupe, M. Martin, and T. Balch. Distributed sensor fusion for object position estimation by multi-robot systems. In A. Bredenfeld, A. Jacoff, I. Noda, and Y. Takahashi, editors, Proceedings of the 2001 IEEE International Conferenceon Robotics and Automation (ICRA-01), Lecture Notes in Artificial Intelligence, pages 154-165. Springer,2001.