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

Author Affiliations +

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE