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arXiv · 2004.00540

Generation of Paths in a Maze using a Deep Network without Learning

Tomas Kulvicius·Sebastian Herzog·Minija Tamosiunaite·Florentin Wörgötter
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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.

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Tomas Kulvicius, Sebastian Herzog, Minija Tamosiunaite, Florentin Wörgötter. 2020-04-01. Generation of Paths in a Maze using a Deep Network without Learning. https://doi.org/10.1109/tnnls.2021.3089023

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