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Sim-to-Real quadrotor landing via sequential deep Q-Networks and domain randomization
- Source :
- Robotics, 9 (1), 8, Robotics, Volume 9, Issue 1, Robotics, Vol 9, Iss 1, p 8 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI, 2020.
-
Abstract
- The autonomous landing of an Unmanned Aerial Vehicle (UAV) on a marker is one of the most challenging problems in robotics. Many solutions have been proposed, with the best results achieved via customized geometric features and external sensors. This paper discusses for the first time the use of deep reinforcement learning as an end-to-end learning paradigm to find a policy for UAVs autonomous landing. Our method is based on a divide-and-conquer paradigm that splits a task into sequential sub-tasks, each one assigned to a Deep Q-Network (DQN), hence the name Sequential Deep Q-Network (SDQN). Each DQN in an SDQN is activated by an internal trigger, and it represents a component of a high-level control policy, which can navigate the UAV towards the marker. Different technical solutions have been implemented, for example combining vanilla and double DQNs, and the introduction of a partitioned buffer replay to address the problem of sample efficiency. One of the main contributions of this work consists in showing how an SDQN trained in a simulator via domain randomization, can effectively generalize to real-world scenarios of increasing complexity. The performance of SDQNs is comparable with a state-of-the-art algorithm and human pilots while being quantitatively better in noisy conditions.
- Subjects :
- Sim-to-Real
0209 industrial biotechnology
Control and Optimization
Computer science
lcsh:Mechanical engineering and machinery
Real-time computing
Sample (statistics)
02 engineering and technology
Task (project management)
Domain (software engineering)
020901 industrial engineering & automation
Artificial Intelligence
Component (UML)
0202 electrical engineering, electronic engineering, information engineering
Reinforcement learning
lcsh:TJ1-1570
deep reinforcement learning
business.industry
Mechanical Engineering
DATA processing & computer science
Robotics
H671 Robotics
aerial vehicles
020201 artificial intelligence & image processing
Artificial intelligence
ddc:004
G760 Machine Learning
business
Subjects
Details
- Language :
- English
- ISSN :
- 22186581
- Database :
- OpenAIRE
- Journal :
- Robotics, 9 (1), 8, Robotics, Volume 9, Issue 1, Robotics, Vol 9, Iss 1, p 8 (2020)
- Accession number :
- edsair.doi.dedup.....53aab5b6b2fcb5393439f6e202ea4d2a