Learning optimal guidance behavior in unknown environments within receding horizon planning

Abhishek Verma, Berenice F Mettler May

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

1 Scopus citations

Abstract

Aerospace Engineering and Mechanics, University of Minnesota Minneapolis, Minnesota, USA This paper describes a guidance algorithm for autonomous operation in partially known environments. The emphasis of the paper is enabling learning within a receding horizon trajectory optimization framework. The information acquired from an exteroceptive sensor is assimilated into a spatial value function. This setup has the advantage that the system learns information directly relevant to optimal guidance and control behavior and enables efficient trajectory-planning in unknown or partially known environments. The system's performance is demonstrated using successive runs in high-fidelity indoor simulations.

Original languageEnglish (US)
Title of host publication70th American Helicopter Society International Annual Forum 2014
PublisherAmerican Helicopter Society
Pages2158-2168
Number of pages11
ISBN (Print)9781632666918
StatePublished - Jan 1 2014
Event70th American Helicopter Society International Annual Forum 2014 - Montreal, QC, Canada
Duration: May 20 2014May 22 2014

Publication series

NameAnnual Forum Proceedings - AHS International
Volume3
ISSN (Print)1552-2938

Other

Other70th American Helicopter Society International Annual Forum 2014
CountryCanada
CityMontreal, QC
Period5/20/145/22/14

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  • Cite this

    Verma, A., & Mettler May, B. F. (2014). Learning optimal guidance behavior in unknown environments within receding horizon planning. In 70th American Helicopter Society International Annual Forum 2014 (pp. 2158-2168). (Annual Forum Proceedings - AHS International; Vol. 3). American Helicopter Society.