AirGuardian : a parallel autonomy approach to self-flying planes
Name
1192561427-MIT.pdf
Size
6.81 MB
Format
Adobe PDF
Checksum (MD5)
d80f0b1182420c2c17249675dbc757fa
Author(s)
Knapp, Alexander W.
Advisor(s)
Daniela Rus.
Alternative Title
Parallel autonomy approach to self-flying planes
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
In this paper we lay the foundation for a fixed-wing parallel autonomy aircraft system in which both the autonomous component of the aircraft and the pilot jointly fly the plane resulting in an overall safer and more effective flying experience. Experimenting with both imitation learning and reinforcement learning we develop the fully autonomous flight component of this system through the case study of flight within a canyon. Our strategy includes vision-based learning with both camera and depth map inputs to create an end-to-end learning platform for aircraft control. Using a yaw based controller and reinforcement learning, we are able to demonstrate stable flight in a unknown canyon environments that exhibit unexpected hazards such as wind gusts and terrain changes.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 95-97).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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