Machine Learning Approach for Drone Perception and Control

Abstract

This study focuses on the application of machine learning and neural networks for the action selection and better understanding of the environment for controlling unmanned aerial vehicles, instead of explicit models to achieve the same task. Implementation of machine learning and deep learning algorithms such as non-linear regression were combined with neural networks to learn the system dynamics of a drone for the prediction of future states. Behavior cloning method is applied to mimic the actions of autopilot and comparative study of the decisions of autopilot and learned model were conducted in a simulated environment. The deep convolutional neural network was utilized for the visual perception task in the forest environment by detecting trees as obstacles. The prediction of future states and mimicking the autopilot actions were realized with relatively small error to the data from explicit model and the tree detection was successful even in the low sunlight condition.