MAV Stabilization using Machine Learning and Onboard Sensors

Jason Yosinski; Cooper Bills

MAV Stabilization using Machine Learning and Onboard Sensors

Robotics 2012-02-22 v1 Artificial Intelligence

Authors: Jason Yosinski , Cooper Bills

Abstract

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.

Keywords

unmanned aerial vehicle uav tracking quadrotor control

Cite

@article{arxiv.1202.4465,
  title  = {MAV Stabilization using Machine Learning and Onboard Sensors},
  author = {Jason Yosinski and Cooper Bills},
  journal= {arXiv preprint arXiv:1202.4465},
  year   = {2012}
}

Comments

9 pages, 7 figures

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