MAV Stabilization using Machine Learning and Onboard Sensors

TL;DR

This paper explores using machine learning with onboard sensors to predict and correct MAV drift, enhancing stabilization and navigation capabilities in sensor-limited environments.

Contribution

It introduces a novel approach of applying machine learning to predict MAV drift using onboard sensors for improved flight control.

Findings

Machine learning models can effectively predict MAV drift.

Predicted drift enables better trajectory correction.

Enhanced stabilization in sensor-limited MAVs.

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.