Abstract
In this paper we propose a novel, hybrid path planning system based on an extended A*-method in combination with special RBF-networks. The output of the A*-method, a set of classified cells, is used to train two variants of RBF-networks. Global RBF-networks (GRBF-networks) represent a wide area around the optimal path and generate smooth paths. Local RBF-networks (LRBF-networks) represent a small area around the optimal path and guarantee an obstacle-free “tube” surrounding this path. GRBF- and LRBF-networks are tested in different 3D- and 6D-scenarios.
Parts of this work have been supported by the Federal Ministry for Education, Science, Research and Technology (BMBF).
Preview
Unable to display preview. Download preview PDF.
References
Kaspar Althoefer and Guido Bugmann. Planning and learning goal-directed sequences of robot-arm movements. In Proceedings of the International Conference on Artificial Neural Networks (ICANN'95) Paris, volume 1, pages 449–454, 1995.
Thomas Frontzek. Entwicklung eines neuronalen Bahnplaners mit heuristischer Vorverarbeitung. Master thesis, University of Bonn, Dept. Computer Science, 1996.
David Gelperin. On the optimality of A*. Artificial Intelligence, 8:69–76, 1977.
P. E. Hart, N. J. Nilsson, and B. Raphael. A formal basis for the heuristic determination of minimum cost paths. In IEEE Transactions on Systems, Man, and Cybernetics, volume 2, pages 100–107, 1968.
Tomaso Poggio and Federico Girosi. A theory of networks for approximation and learning. A.I. Memo No. 1140, MIT, 1989.
Charles W. Warren. Fast path planning using modified A* method. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 662–667, 1993.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Frontzek, T., Goerke, N., Eckmiller, R. (1997). A hybrid path planning system combining the A*-method and RBF-networks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020251
Download citation
DOI: https://doi.org/10.1007/BFb0020251
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63631-1
Online ISBN: 978-3-540-69620-9
eBook Packages: Springer Book Archive