Trajectory generation using deep neural network

Toshinobu Watanabe, Eric N. Johnson

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

Abstract

This paper introduces the state-of-art trajectory generation technique by the Deep Neural Network. This technique is one of the supervised learning methods. A certain trajectory generation technique provides the optimal solution to the neural network. The neural network studies how to generate a trajectory from training data. The first survey verifies the possibility for the Deep Neural Network to be able to create the trajectory, which the Artificial Potential Field method makes under obstacle presence field. Since the output of the neural network is heuristic, it is not optimal and may violate a constraint. Therefore, we inform the method that a trajectory generation method uses the result, which the neural network output, as an initial guess.

Original languageEnglish (US)
Title of host publicationAIAA Information Systems-AIAA Infotech at Aerospace
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Edition209989
ISBN (Print)9781624105272
DOIs
Publication statusPublished - Jan 1 2018
EventAIAA Information Systems-AIAA Infotech at Aerospace, 2018 - Kissimmee, United States
Duration: Jan 8 2018Jan 12 2018

Publication series

NameAIAA Information Systems-AIAA Infotech at Aerospace, 2018
Number209989

Other

OtherAIAA Information Systems-AIAA Infotech at Aerospace, 2018
CountryUnited States
CityKissimmee
Period1/8/181/12/18

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All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Industrial and Manufacturing Engineering

Cite this

Watanabe, T., & Johnson, E. N. (2018). Trajectory generation using deep neural network. In AIAA Information Systems-AIAA Infotech at Aerospace (209989 ed.). (AIAA Information Systems-AIAA Infotech at Aerospace, 2018; No. 209989). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2018-1893