Vision-based optimal landing on a moving platform

Takuma Nakamura, Stephen Haviland, Dmitry Bershadsky, Eric N. Johnson

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

6 Scopus citations


This paper describes a vision-based control architecture designed to enable autonomous landing on a moving platform. The landing trajectory is generated by using the receding-horizon differential dynamic programming (DDP), an optimal control method. The trajectory generation is aided by the output of a vision-based target tracking system. The vision system uses multiple extended Kalman filters which allows us to estimate the position and heading of the moving target via the observed locations. The combination of vision-based target tracking system and the receding-horizon DDP gives an unmanned aerial vehicle the capability to adaptively generate a landing trajectory against tracking errors and disturbances. Additionally, by adding the exterior penalty function to the cost of the DDP we can easily constrain the trajectory from collisions and physically infeasible solutions. We provide key mathematics needed for the implementation and share the results of the image-in-the-loop simulation and flight tests to validate the suggested methodology.

Original languageEnglish (US)
Title of host publication72nd American Helicopter Society International Annual Forum 2016
Subtitle of host publicationLeveraging Emerging Technologies for Future Capabilities
PublisherAmerican Helicopter Society
Number of pages11
ISBN (Electronic)9781510825062
StatePublished - 2016

Publication series

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

All Science Journal Classification (ASJC) codes

  • General Engineering


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