Shaker, Marwan and Smith, Mark N R and Yue, Shigang and Duckett, Tom (2010) Vision-based landing of a simulated unmanned aerial vehicle with fast reinforcement learning. In: International Symposium on Learning and Adaptive Behaviour in Robotics Systems (LAB-RS 2010), 6-7 September 2010, Canterbury, UK.

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Abstract

Landing is one of the difficult challenges for an unmanned 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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Landing is one of the difficult challenges for an unmanned 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.
Keywords:	Reinforcement learning, Landing of Unmanned Aerial Vehicle
Subjects:	H Engineering > H670 Robotics and Cybernetics H Engineering > H671 Robotics G Mathematical and Computer Sciences > G400 Computer Science
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Users 502410 not found.
Date Deposited:	18 Jan 2011 20:51
Last Modified:	13 Mar 2013 08:53
URI:	http://eprints.lincoln.ac.uk/id/eprint/3867

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