Generation of Paths in a Maze using a Deep Network without Learning
Machine Learning
2023-03-15 v1 Machine Learning
Abstract
Trajectory- or path-planning is a fundamental issue in a wide variety of applications. Here we show that it is possible to solve path planning for multiple start- and end-points highly efficiently with a network that consists only of max pooling layers, for which no network training is needed. Different from competing approaches, very large mazes containing more than half a billion nodes with dense obstacle configuration and several thousand path end-points can this way be solved in very short time on parallel hardware.
Cite
@article{arxiv.2004.00540,
title = {Generation of Paths in a Maze using a Deep Network without Learning},
author = {Tomas Kulvicius and Sebastian Herzog and Minija Tamosiunaite and Florentin Wörgötter},
journal= {arXiv preprint arXiv:2004.00540},
year = {2023}
}