Graduate Studies, UNL

 

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

First Advisor

Justin Bradley

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

Brittany Duncan, Carl Nelson, Shaui Nie

Department

Computer Engineering

Date of this Version

2025

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College of the University of Nebraska in partial fulfillment of requirements for the degree Doctor of Philosophy (Ph.D.)

Major: Computer Engineering

Under the supervision of Professor

Lincoln, Nebraska, December 2025

Comments

Copyright 2025, the author. Used by permission

Abstract

Uncrewed Aerial Systems (UAS) have been integrated into a wide range of research and industrial applications, with growing interest in extending mission duration and spatial coverage through coordinated multi-UAS systems, or swarms. While swarming offers the potential for extended mission endurance and robustness through advanced path-planning, control, and estimation algorithms, significant challenges arise when implementing these methods on decentralized platforms composed of size, weight, and power-constrained (SWaP) vehicles. Limitations in onboard computational capacity and congested communication channels can break critical design-time assumptions, which at best, will degrade application quality of service, and at worst, destabilize the fleet through excessive delays and missed deadlines.

This research presents key breakthroughs in the design of SWaP-constrained UAS swarms for safe, efficient operation in outdoor, unpredictable environments. In this work, the limitations of SWaP-constrained vehicles are characterized through experimentation, and these observations motivate a co-regulated autonomy architecture for swarm control, navigation, and collision-avoidance that adaptively balances computational effort with the needs of the individual UAS and the swarm as a whole. Complementing this co-regulated architecture is a rate-adaptive real-time task model that further extends dynamic resource allocation for cyber-physical systems, ensuring that these adaptive behaviors may be safely implemented on constrained onboard hardware.

The resulting methodologies are validated through extensive experimentation on a UAS swarm platform, supported by a custom-built swarm toolchain for simulation, ground control, and communication. Data for this work was captured over the course of 300+ individual UAS flights that took place at Rodger's Memorial Farm, NE and the Joint Inter-agency Field Experimentation (JIFX) at Camp Roberts, CA, with several flights of 8 UAS flying simultaneously. The experimental results show that state-of-the-art swarming capabilities can be made tractable on SWaP-constrained platforms by embedding resource awareness and flexible quality-of-service into the system design, enabling more efficient and more resilient cyber-physical autonomy.

Advisor: Justin Bradley

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