MAV Stabilization using Machine Learning and Onboard Sensors
This addresses stabilization issues for MAVs in navigation-limited scenarios, but appears incremental as it builds on existing control methods.
The researchers tackled the problem of MAV stabilization with limited onboard sensors by using machine learning to predict drift, enabling adjustments to maintain desired flight paths.
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.