UAV adaptive control based on AI to transfer the energy wirelessly.

Abstract

This thesis aims to configure a set-up for UAV self-controlling without human intervention and no prior information about the environment to recharge IoT devices wirelessly, so they continue to operate for as long as possible. IoT devices have become more popular and widespread, and their involvement is crucial in many fields. Also, they can provide a multitude of services to enhance human lives. As challenges, UAV is supposed to explore the environment to get more information about it continuously, and therefore we should carefully minimize its energy consumption to avoid its possible failure. As a solution, an AI-based algorithm called Q-learning is adopted to optimize the movement of the UAV and enhance the energy transfer toward the IoT devices.