MAV Stabilization using Machine Learning and Onboard Sensors

In many situations, Miniature Aerial Vehicles (MAVs) are limited to using only on-board sensors for navigation. This limits the data available to algorithms used for stabilization and localization, and current control methods are often insufficient to allow reliable hovering in place or trajectory f...

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Bibliographic Details
Main Authors Yosinski, Jason, Bills, Cooper
Format Journal Article
LanguageEnglish
Published 20.02.2012
Subjects
Computer Science - Artificial Intelligence
Computer Science - Robotics
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Summary:In many situations, Miniature Aerial Vehicles (MAVs) are limited to using only on-board sensors for navigation. This limits the data available to algorithms used for stabilization and localization, and current control methods are often insufficient to allow reliable hovering in place or trajectory following. In this research, we explore using machine learning to predict the drift (flight path errors) of an MAV while executing a desired flight path. This predicted drift will allow the MAV to adjust it's flightpath to maintain a desired course.
DOI:10.48550/arxiv.1202.4465