An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles

Doctoral thesis (2018)

Authors

Copyright: © 2018 J. Junell
DOI: https://doi.org/10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294
Reinforcement Learning Transfer learning Micro Aerial Vehicle Hierarchical Reinforcement Learning Quadrotor Policy Iteration State Abstraction
More Info
expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The use of Micro Aerial Vehicles (MAVs) in practical applications, to solve real-world problems, is growing in demand as the technology becomes more widely known and accessible. Proposed applications already span a wide berth of fields like military, search and rescue, ecology, artificial pollinators, and more. As compared to larger Unmanned Aerial Systems (UAS), MAVs are specifically desirable for applications which take advantage of their small size or light weight – whether that means being discreet, having insect-like maneuverability, operating in small spaces, or being more inherently safe with respect to injury towards people. In some cases, MAVs work under conditions where autonomy is needed. The small size of MAVs and the desire for autonomy combine to create a demanding set of challenges for the guidance, navigation, and control (GNC) of these systems. Limitations of on-board sensors, difficulties in modeling their complex and often time varying dynamics, and limited on-board computational resources, are just a few examples of the challenges facing MAV autonomy...