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

Shaker, Marwan, Smith, Mark N. R., 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.

Full content URL:

PID1374145.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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.

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
Related URLs:
ID Code:3867
Deposited On:18 Jan 2011 20:51

Repository Staff Only: item control page