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

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.

Documents
PID1374145.pdf
[img]
[Download]
[img]
Preview
PDF
PID1374145.pdf - Whole Document

737Kb

Official URL: http://www.est-2010.info/download/EST%202010%20pre...

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:Also: Emerging Security Technologies (EST), 2010 International Conference on
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 Science > School of Computer Science
ID Code:3867
Deposited By:INVALID USER
Deposited On:18 Jan 2011 20:51
Last Modified:17 Feb 2014 16:24

Repository Staff Only: item control page