Autonomous Vehicular Landings on the Deck of an Unmanned Surface Vehicle using Deep Reinforcement Learning
- 8 April 2019
- journal article
- research article
- Published by Cambridge University Press (CUP) in Robotica
- Vol. 37 (11), 1867-1882
- https://doi.org/10.1017/s0263574719000316
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.Keywords
This publication has 31 references indexed in Scilit:
- Reinforcement learning in robotics: A surveyThe International Journal of Robotics Research, 2013
- Autonomous reinforcement learning with experience replayNeural Networks, 2013
- Autonomous landing at unprepared sites by a full-scale helicopterRobotics and Autonomous Systems, 2012
- A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural LandmarksJournal of Intelligent & Robotic Systems, 2009
- A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural LandmarksPublished by Springer Science and Business Media LLC ,2009
- Low-Cost Visual Tracking of a Landing Place and Hovering Flight Control with a MicrocontrollerJournal of Intelligent & Robotic Systems, 2009
- Efficient cooperative search of smart targets using UAV SwarmsRobotica, 2008
- Reinforcement learning of motor skills with policy gradientsNeural Networks, 2008
- Unmanned aircraft navigation for shipboard landing using infrared visionIEEE Transactions on Aerospace and Electronic Systems, 2002
- LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROLAsian Journal of Control, 1999