Autonomous navigation of aerial vehicles by visual reference

Abstract

This paper presents the development of an autonomous navigation system for Unmanned Aerial Vehicles (UAVs) using visual reference. The proposal employs a Convolutional Neural Network (CNN) to classify traffic signal images, enabling UAVs to navigate evolving dynamic environments. This research involves the configuration of the Robot Operating System (ROS) for UAV communication, the implementing of a specialized CNN for image classification, and the integration of this network into the navigation system. Therefore, a system will be presented for image acquisition and UAV manipulation based on CNN outputs. We present experimental results specially designed to demonstrate the efficiency of the proposal, to validate the analysis and implementation.