Autonomous Aerial Delivery Vehicles, a Survey of Techniques on how Aerial Package Delivery is Achieved

Abstract

Autonomous aerial delivery vehicles have gained significant interest in the last decade. This has been enabled by technological advancements in aerial manipulators and novel grippers with enhanced force to weight ratios. Furthermore, improved control schemes and vehicle dynamics are better able to model the payload and improved perception algorithms to detect key features within the unmanned aerial vehicle's (UAV) environment. In this survey, a systematic review of the technological advancements and open research problems of autonomous aerial delivery vehicles is conducted. First, various types of manipulators and grippers are discussed in detail, along with dynamic modelling and control methods. Then, landing on static and dynamic platforms is discussed. Subsequently, risks such as weather conditions, state estimation and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorises the topic into two areas: drone operations and drone-truck collaborative operations.

Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure

“…It is also due to the fact that one of the most practical and certainly popular ways of image acquisition is the use of unmanned aerial vehicles (UAVs). Such devices have been used for the preparation of datasets, e.g., [ 26 , 40 , 42 , 43 , 44 , 46 , 48 , 51 , 52 ]. Among other things, they helped to provide a power infrastructure classification model using deep neural networks that achieved an overall F-score of 75% for multi-class classification scenarios and 88% for pylon identification [ 46 ].…”