GAN Path Finder: Preliminary Results

  • Natalia SobolevaEmail author
  • Konstantin Yakovlev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11793)


2D path planning in static environment is a well-known problem and one of the common ways to solve it is to (1) represent the environment as a grid and (2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being – to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.


Path planning Machine learning Convolutional neural networks Generative adversarial networks 



This work was supported by the Russian Science Foundation (Project No. 16-11-00048).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.Federal Research Center “Computer Science and Control” of Russian Academy of SciencesMoscowRussia

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