Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV

Liam J. Fletcher; Robert J. Clarke; Thomas S. Richardson; Mark Hansen

doi:10.2514/6.2022-1288

Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV

Liam J. Fletcher, Robert J. Clarke, Thomas S. Richardson, Mark Hansen

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

2 Citations (Scopus)

Original language	English
Title of host publication	AIAA SciTech Forum 2022
Publisher	American Institution of Aeronautics and Astronautics
ISBN (Print)	9781624106316
DOIs	https://doi.org/10.2514/6.2022-1288
Publication status	Published - 29 Dec 2022
Event	2022 AIAA SciTech Forum - San Diego, United States Duration: 3 Jan 2022 → 7 Jan 2022

Publication series

Name	AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Conference

Conference	2022 AIAA SciTech Forum
Country/Territory	United States
City	San Diego
Period	3/01/22 → 7/01/22

Bibliographical note

Funding Information:
This work was part-funded by the Defence Science and Technology Laboratory (DSTL), Ministry of Defence. This work was partially supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory. The authors would like to thank all contributors to ArduPilot[18], and the pymavlink and stable-baselines3 [15] libraries, all of which have enabled this work. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/

Publisher Copyright:
© 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

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10.2514/6.2022-1288

https://arc.aiaa.org/doi/10.2514/6.2022-1288

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Cite this

Fletcher, L. J., Clarke, R. J., Richardson, T. S., & Hansen, M. (2022). Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV. In AIAA SciTech Forum 2022 Article AIAA 2022-1288 (AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022). American Institution of Aeronautics and Astronautics. https://doi.org/10.2514/6.2022-1288

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title = "Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV",

author = "Fletcher, {Liam J.} and Clarke, {Robert J.} and Richardson, {Thomas S.} and Mark Hansen",

note = "Funding Information: This work was part-funded by the Defence Science and Technology Laboratory (DSTL), Ministry of Defence. This work was partially supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory. The authors would like to thank all contributors to ArduPilot[18], and the pymavlink and stable-baselines3 [15] libraries, all of which have enabled this work. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/ Publisher Copyright: {\textcopyright} 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.; 2022 AIAA SciTech Forum ; Conference date: 03-01-2022 Through 07-01-2022",

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Fletcher, LJ , Clarke, RJ , Richardson, TS & Hansen, M 2022, Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV. in AIAA SciTech Forum 2022., AIAA 2022-1288, AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022, American Institution of Aeronautics and Astronautics, 2022 AIAA SciTech Forum, San Diego, United States, 3/01/22. https://doi.org/10.2514/6.2022-1288

Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV. / Fletcher, Liam J.; Clarke, Robert J.; Richardson, Thomas S. et al.
AIAA SciTech Forum 2022. American Institution of Aeronautics and Astronautics, 2022. AIAA 2022-1288 (AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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N1 - Funding Information: This work was part-funded by the Defence Science and Technology Laboratory (DSTL), Ministry of Defence. This work was partially supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory. The authors would like to thank all contributors to ArduPilot[18], and the pymavlink and stable-baselines3 [15] libraries, all of which have enabled this work. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/ Publisher Copyright: © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

PY - 2022/12/29

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PB - American Institution of Aeronautics and Astronautics

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