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.
- Dynamic Systems and Control Division
Image Quality-Driven Octorotor Flight Control via Reinforcement Learning
- Views Icon Views
- Share Icon Share
- Search Site
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
Download citation file: