Neural network based trajectory optimization for unmanned aerial vehicles

Brian R. Geiger, Joseph F. Horn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

A neural network approximation to direct trajectory optimization methods is presented. The method uses neural networks to approximate the dynamics and objective equations integrated over a given time interval. The trajectory is then built recursively and treated as a nonlinear programming problem. The method is compared to a direct collocation method as well as more recent pseudospectral methods and shows competitive results while being computationally faster. In addition, a neural network provides a continuously differentiable function approximation which may be advantageous when a discontinuous objective function is used in a nonlinear solver. A surveillance trajectory planning problem for an unmanned aerial vehicle is given as an example application and results are presented for all three methods.

Original languageEnglish (US)
Title of host publication47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781563479694
DOIs
StatePublished - Jan 1 2009

Publication series

Name47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition

All Science Journal Classification (ASJC) codes

  • Space and Planetary Science
  • Aerospace Engineering

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    Geiger, B. R., & Horn, J. F. (2009). Neural network based trajectory optimization for unmanned aerial vehicles. In 47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition [2009-0054] (47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition). American Institute of Aeronautics and Astronautics Inc.. https://doi.org/10.2514/6.2009-54