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Autonomous Vehicular Landings on the Deck of an Unmanned Surface Vehicle using Deep Reinforcement Learning

Published online by Cambridge University Press:  08 April 2019

Riccardo Polvara Open the ORCID record for Riccardo Polvara [Opens in a new window] ,
Sanjay Sharma Open the ORCID record for Sanjay Sharma [Opens in a new window] ,
Jian Wan ,
Andrew Manning  and
Robert Sutton
Riccardo Polvara*
Affiliation:
Lincoln Centre for Autonomous Systems Research, School of Computer Science, College of Science, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK
Sanjay Sharma
Affiliation:
Autonomous Marine Systems Research Group, School of Engineering, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK E-mails: [email protected], [email protected], [email protected], [email protected]
Jian Wan
Affiliation:
Autonomous Marine Systems Research Group, School of Engineering, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK E-mails: [email protected], [email protected], [email protected], [email protected]
Andrew Manning
Affiliation:
Autonomous Marine Systems Research Group, School of Engineering, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK E-mails: [email protected], [email protected], [email protected], [email protected]
Robert Sutton
Affiliation:
Autonomous Marine Systems Research Group, School of Engineering, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK E-mails: [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Autonomous landing on the deck of a boat or an unmanned surface vehicle (USV) is the minimum requirement for increasing the autonomy of water monitoring missions. This paper introduces an end-to-end control technique based on deep reinforcement learning for landing an unmanned aerial vehicle on a visual marker located on the deck of a USV. The solution proposed consists of a hierarchy of Deep Q-Networks (DQNs) used as high-level navigation policies that address the two phases of the flight: the marker detection and the descending manoeuvre. Few technical improvements have been proposed to stabilize the learning process, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Simulated studies proved the robustness of the proposed algorithm against different perturbations acting on the marine vessel. The performances obtained are comparable with a state-of-the-art method based on template matching.

Keywords

Deep reinforcement learningUnmanned aerial vehicleAutonomous agents
Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1867 - 1882
Copyright
© Cambridge University Press 2019 

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