Introduction

The logistics and transportation industries are going through a rapid change with a wide range of innovations across the world. The logistics consumers need for delivering their packages has increased drastically while they expect to get the parcels delivered as early as possible with less to no delivery charges. In recent years, many logistics companies have invested in designing the drones for package deliveries. The problems with such systems would be huge energy consumption, limited battery life, delivery time and drone collisions with obstacles or with each other. By utilizing the potential of Convolutional Neural Network especially RL and GAN, we plan to enhance the capability of UAVs to deliver freight in a faster, energy efficient and safer way. Usage of smart and self-learning UAVs will reduce a lot of dependency on using already busy roadways and fossil fuels. Air highways come with the advantage of reduced energy utilization, faster delivery, and ease of reaching remote or hard-to-reach places by land. Enhancing the cellular connectivity using the proposed two drone model (Delivery & Aerial base station drones) would dramatically increase the area of coverage and would enable the UAVs to travel to more remote areas.