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Towards Learning Path Planning for Solving Complex Robot Tasks

2001

Conference Paper

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For solving complex robot tasks it is necessary to incorporate path planning methods that are able to operate within different high-dimensional configuration spaces containing an unknown number of obstacles. Based on Advanced A*-algorithm (AA*) using expansion matrices instead of a simple expansion logic we propose a further improvement of AA* enabling the capability to learn directly from sample planning tasks. This is done by inserting weights into the expansion matrix which are modified according to a special learning rule. For an examplary planning task we show that Adaptive AA* learns movement vectors which allow larger movements than the initial ones into well-defined directions of the configuration space. Compared to standard approaches planning times are clearly reduced.

Author(s): Frontzek, T. and Lal, TN. and Eckmiller, R.
Journal: Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna
Pages: 943-950
Year: 2001
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Proceedings of the International Conference on Artificial Neural Networks (ICANN’2001) Vienna

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{1938,
  title = {Towards Learning Path Planning for Solving Complex Robot Tasks},
  author = {Frontzek, T. and Lal, TN. and Eckmiller, R.},
  journal = {Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna},
  pages = {943-950},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2001}
}