Acquisition of flapping motion for follow-up flight

Abstract

We propose to apply machine learning to acquire the optimal flapping behavior for Micro Air Vehicle (MAV). In this paper, we create a model of dragonfly in a virtual physical environment and build a controller for the flapping motion using an artificial neural network. Through several simulation experiments, it is possible to verified that our proposed method acquires a controller for the target-tracking behavior.