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

R. Polvara, Sanjay Sharma, J. Wan, A. Manning, R. Sutton

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1867-1882
JournalRobotica
Volume37
Issue number11
DOIs
Publication statusPublished - 8 Apr 2019

Keywords

  • Deep reinforcement learning
  • Unmanned aerial vehicle
  • Autonomous agents

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