Date of Award

Spring 2024

Access Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Aerospace Engineering


Aerospace Engineering

Committee Chair

Troy Henderson

First Committee Member

Riccardo Bevilacqua

Second Committee Member

Morad Nazari

Third Committee Member

Sirani Perera

Fourth Committee Member

Sean Phillips

College Dean

Jim Gregory


Resiliency in multi-agent system navigation is reliant on the inherent ability of the system to withstand, overcome, or recover from adverse conditions and disturbances. In large part, resiliency is achieved through reducing the impact of critical failure points to the success and/or performance of the system. In this view, decentralized multi-agent architectures have become an attractive solution for multi-agent navigation, but decentralized architectures place the burden of information acquisition directly on the agents themselves. In fact, the design of distributed estimators has been a growing interest to enable complex multi-sensor/multi-agent tasks. In such scenarios, it is important that each local estimator converges to the true global system state - a quality known as state omniscience. Most previous related work has focused on the design of such systems under varying assumptions on the graph topology with simplified information fusion schemes. Contrarily, this work introduces characterizations of state omniscience under generalized graph topologies and generalized information fusion schemes. State omniscience is discussed analogously to observability from classical control theory; and a collection of necessary and sufficient conditions for a distributed estimator to be state omniscient are presented. This dissertation discusses this phenomena in terms of an on-orbit scenarios dubbed the local catalog maintenance problem and the cooperative local catalog maintenance problem. The goal of each agent is to maintain their catalog of all bodies (objects and agents) within this neighborhood through onboard angles-only measurements and cooperative communication with the other agents. A central supervisor selects the target body for each agent, a local controller tracks the selected target body for each agent, and a local estimator coalesces both an agent's measurements and state estimates provided by neighboring agents within the communication graph. Numerical results are provided to demonstrate the supervisor's ability to select an appropriate target subject to an uncertainty threshold, the controller's ability to track the selected target, and the estimator's ability to maintain an accurate and precise estimate of each of the bodies in the local environment.

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