Yong Pei, Ph.D. (Advisor); Jack S. Jean, Ph.D. (Committee Member); Mateen M. Rizki, Ph.D. (Committee Member); Ross McNutt, Ph.D. (Committee Member)
Doctor of Philosophy (PhD)
With the growing number of Unmanned Aircraft Systems, current network-centric architectures present limitations in meeting real-time and time-critical requirements. Current methods utilizing centralized off-platform processing have inherent energy inefficiencies, scalability challenges, performance concerns, and cyber vulnerabilities. In this dissertation, an adaptive, two-stage, energy-efficient, edge-centric architecture is proposed to address these limitations. A novel, edge-centric Sense-and-Avoidance architecture framework is presented, and a corresponding prototype is developed using commercial hardware to validate the proposed architecture. Instead of a network-centric approach, processing is distributed at the logical edge of the sensors, and organized as Detection and Classification Subsystems. Classical machine vision algorithms are used to detect and produce a region of interest. The region of interest is then segmented and fed to the Classification Subsystem to be classified using optimized neural networks. A compressed frame from the Detection Subsystem, along with the region of interest and classification results from the Classification Subsystem, can be sent to the Ground Control Station to produce an Artificial Intelligence enhanced view to increase operator comprehension. Experimentation and testing indicate this approach is feasible for real-time operations supporting throughput of at least three 4K frames per second. Additionally, on-platform detection and classification can occur without offloading large amounts of imagery to ground processing, thereby reducing unnecessary network transmissions and associated energy consumption. The sufficient processing frame rate effectively eliminates any hover during sensing processing, demonstrating how the architecture can reduce energy consumption for battery-powered electric unmanned aerial vehicles. This novel approach opens new opportunities to reduce power consumption in future electric transport systems and meet real-time, safety-critical requirements in Unmanned Aircraft Systems.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2021, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.