Real-Time Learning of Efficient Lift Generation on a Dynamically Scaled Flapping Wing Using Policy Search
Yagiz E. Bayiz, Long Chen, Shih-Jung Hsu, Pan Liu, Aaron N. Aguiles, Bo Cheng
Abstract
In this work, we present a successful application of a policy search algorithm to a real-time robotic learning problem, where the goal is to maximize the efficiency of lift generation on a dynamically scaled flapping robotic wing. The robotic wing has two degrees-of-freedom, i.e., stroke and pitch, and operates in a tank filled with mineral oil. For all experiments, the Reynolds number is maintained constant at 1000, where learning is performed for different prescribed stroke amplitudes to find the optimal wing pitching amplitude and the stroke-pitch phase difference that maximize the power loading (PL) of lift generation, a measure of aerodynamic efficiency. For the investigated stroke amplitude range (30°-90°), the efficiency is observed to increase with the stroke amplitude and the lift is mainly generated through the delayed stall, a quasi-steady aerodynamic mechanism. Furthermore, the wing rotation becomes more asymmetric with respect to stroke reversal as the stroke amplitude decreases, indicating an increased use of unsteady lift generation mechanisms at lower stroke amplitudes.
BibTeX
@inproceedings{icra2018_realtimelearning,
title = {Real-Time Learning of Efficient Lift Generation on a Dynamically Scaled Flapping Wing Using Policy Search},
author = {Yagiz E. Bayiz and Long Chen and Shih-Jung Hsu and Pan Liu and Aaron N. Aguiles and Bo Cheng},
booktitle = {ICRA 2018},
year = {2018}
}