Advertisement

GAN Path Finder: Preliminary Results

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

Abstract

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.

Keywords

Path planning Machine learning Convolutional neural networks Generative adversarial networks 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

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

References

  1. 1.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)CrossRefGoogle Scholar
  2. 2.
    Ebendt, R., Drechsler, R.: Weighted A* search - unifying view and application. Artif. Intell. 173(14), 1310–1342 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Eitel, A., Hauff, N., Burgard, W.: Learning to singulate objects using a push proposal network. In: Proceedings of the International Symposium on Robotics Research (ISRR 2017), Puerto Varas, Chile (2017)Google Scholar
  4. 4.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  5. 5.
    Finn, C., Levine, S.: Deep visual foresight for planning robot motion. In: Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), pp. 2786–2793 (2017)Google Scholar
  6. 6.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680 (2014)Google Scholar
  7. 7.
    Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: Draw: a recurrent neural network for image generation. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) (2015)Google Scholar
  8. 8.
    Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 5769–5779. Curran Associates Inc., Red Hook (2017)Google Scholar
  9. 9.
    Gupta, S., Davidson, J., Levine, S., Sukthankar, R., Malik, J.: Cognitive mapping and planning for visual navigation. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (2017)Google Scholar
  10. 10.
    Harabor, D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI 2011), pp. 1114–1119 (2011)Google Scholar
  11. 11.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)CrossRefGoogle Scholar
  12. 12.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976 (2017)Google Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2012)CrossRefGoogle Scholar
  14. 14.
    Lee, L., Parisotto, E., Chaplot, D.S., Xing, E., Salakhutdinov, R.: Gated path planning networks. In: Proceedings of the 25th International Conference on Machine Learning (ICML 2018), pp. 2953–2961 (2018)Google Scholar
  15. 15.
    Likhachev, M., Stentz, A.: R* search. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-2008) (2008)Google Scholar
  16. 16.
    Mirowski, P., et al.: Learning to navigate in cities without a map. In: Advances in Neural Information Processing Systems, pp. 2419–2430 (2018)Google Scholar
  17. 17.
    Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR abs/1411.1784 (2014). http://arxiv.org/abs/1411.1784
  18. 18.
    Panov, A.I., Yakovlev, K.S., Suvorov, R.: Grid path planning with deep reinforcement learning: preliminary results. Procedia Comput. Sci. 123, 347–353 (2018)CrossRefGoogle Scholar
  19. 19.
    Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2536–2544 (2016)Google Scholar
  20. 20.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  21. 21.
    Schleich, D., Klamt, T., Behnke, S.: Value iteration networks on multiple levels of abstraction. In: Proceedings of Robotics: Science and Systems (RSS-2019) (2019)Google Scholar
  22. 22.
    Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Trans. Comput. Intell. AI Games 4(2), 144–148 (2012)CrossRefGoogle Scholar
  23. 23.
    Tamar, A., Wu, Y., Thomas, G., Levine, S., Abbeel, P.: Value iteration networks. In: Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 2154–2162 (2016)Google Scholar
  24. 24.
    Thrun, S.: Learning occupancy grid maps with forward sensor models. Auton. Robots 15(2), 111–127 (2003)CrossRefGoogle Scholar
  25. 25.
    Zhu, Y., et al.: Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), pp. 3357–3364 (2017)Google Scholar

Copyright information

© 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

Personalised recommendations

Cite paper

Buy options