Design, Implementation, and Applications of Fully Autonomous Aerial Systems

Abstract

Fully autonomous aerial systems (FAAS) combine edge and cloud hardware with UAVs and considerable software support to create self-governing systems. FAAS complete complicated missions with no human piloting by sensing and responding to their environment in real-time. FAAS require highly complex designs to function properly, including layers of on-board, edge, and cloud hardware and software. FAAS also necessitate complex software used for controlling low-level UAV actions, data collection and management, image processing, machine learning, mission planning, and high-level decision-making which must integrate across the compute hierarchy effectively to meet autonomy goals in real-time.

The complexity of even a relatively simple FAAS makes efficiency difficult to guar- antee. Efficiency, however, is paramount to the effectiveness of a FAAS. FAAS per- form missions in resource-scarce environments like natural disaster areas, crop fields, and remote infrastructure installations. These areas have limited access to computa- tional resources, network connectivity, and power. Furthermore, UAV battery lives are short, with flight times rarely exceeding 30 minutes. If FAAS are inefficiently designed, UAV may waste precious battery life awaiting further instructions from remote compute resources, delaying or precluding mission completion. For this rea- son, it is imperative that FAAS designers carefully choose or design edge hardware configurations, machine learning models, autonomy policies, and deployment models.

FAAS have the capability to revolutionize a number of industries, but much re- search must be done to facilitate their usability and effectiveness. In this dissertation, I outline my efforts toward designing and implementing FAAS that are efficient and effective. This dissertation will focus on the following five topics encompassing design, implementation, and applications of FAAS:

§1. Creation of new general and domain-specific machine learning algorithms and careful utilization of others

§2. Selection of hardware at all levels in the FAAS hierarchy

§3. Power and environmental awareness informing selection and switching of auton- omy policies, hardware devices, machine learning techniques and deployment characteristics.

§4. Online learning capabilities resilient to limited cloud access, network interruption, and power scarcity.

§5. Thorough applications which demonstrate the technological value of FAAS, drive adoption, and determine future research challenges.