Title:
Vision-Based Optimal Landing On a Moving Platform
Vision-Based Optimal Landing On a Moving Platform
Author(s)
Nakamura, Takuma
Haviland, Stephen
Bershadsky, Dmitry
Johnson, Eric N.
Haviland, Stephen
Bershadsky, Dmitry
Johnson, Eric N.
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Abstract
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.
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Date Issued
2016-05
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Text
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Proceedings