Deep net route generation faster than a bullet

Darrell L. Young; Chris Eccles

doi:10.1117/12.2559743

21 April 2020 Deep net route generation faster than a bullet

Darrell L. Young, Chris Eccles

Proceedings Volume 11398, Geospatial Informatics X; 1139808 (2020) https://doi.org/10.1117/12.2559743
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Recent breakthroughs in deep net processing have shown the ability to compute solutions to physics-based problems such as the three-body problem many orders-of-magnitude times faster. In this paper, we show how a deep autoencoder, trained on paths generated using a dynamical, physics-based model can generate comparable routes much faster. The autogenerated routes have all the properties of a physics-based model without the computational burden of explicitly solving the dynamical equations. This result is useful for planning and multi-agent reinforcement learning simulation purposes. In addition, the fast route planning capability may prove useful in real time situations such as collision avoidance or fast dynamic targeting response.

Citation Download Citation

Darrell L. Young and Chris Eccles "Deep net route generation faster than a bullet", Proc. SPIE 11398, Geospatial Informatics X, 1139808 (21 April 2020); https://doi.org/10.1117/12.2559743

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