Location of Repository

Vision-based landing of a simulated unmanned aerial vehicle with fast reinforcement learning

By Marwan Shaker, Mark N. R. Smith, Shigang Yue and Tom Duckett


Landing is one of the difficult challenges for an unmanned\ud aerial vehicle (UAV). In this paper, we propose a vision-based landing approach for an autonomous UAV using reinforcement learning (RL). The autonomous UAV learns the landing skill from scratch by interacting with the environment. The reinforcement learning algorithm explored and extended in this study is Least-Squares Policy Iteration (LSPI) to gain a fast learning process and a smooth landing trajectory. The proposed approach has been tested with a simulated quadrocopter in an extended version of the USARSim Unified System for Automation and Robot Simulation) environment. Results showed that LSPI learned the landing skill very quickly, requiring less than 142 trials

Topics: H670 Robotics and Cybernetics, H671 Robotics, G400 Computer Science
Year: 2010
DOI identifier: 10.1109/EST.2010.14
OAI identifier: oai:eprints.lincoln.ac.uk:3867

Suggested articles


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.