This article presents the design of a reinforcement learning method based flight controller to enhance the qualities of image taken from an octorotor platform. Concerning the effect of a low resolution and a high blur rate of target images on feature extraction and target detection, we started by analyzing the relationship between these two kinds of image qualities and altitude and velocity of the octorotor. This leads to the generation of corresponding control commands. We then applied a reinforcement learning technique to automatically design the altitude and velocity controllers of the octorotor. The image analysis and the control command generation algorithms are successfully tested on the octorotor platform, and the controllers demonstrate a satisfactory performance in simulations.
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ASME 2018 Dynamic Systems and Control Conference
September 30–October 3, 2018
Atlanta, Georgia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5191-3
PROCEEDINGS PAPER
Image Quality-Driven Octorotor Flight Control via Reinforcement Learning Available to Purchase
Qiang Li
University of Central Florida, Orlando, FL
Yunjun Xu
University of Central Florida, Orlando, FL
Paper No:
DSCC2018-9039, V003T37A001; 8 pages
Published Online:
November 12, 2018
Citation
Li, Q, & Xu, Y. "Image Quality-Driven Octorotor Flight Control via Reinforcement Learning." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Atlanta, Georgia, USA. September 30–October 3, 2018. V003T37A001. ASME. https://doi.org/10.1115/DSCC2018-9039
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