Date of Award

2026

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering (PhD)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Jeremy P. Bos

Committee Member 1

Darrell L. Robinette

Committee Member 2

Anthony J. Pinar

Committee Member 3

Bo Chen

Abstract

Through random sampling, sample-based path planners enable autonomous agents to quickly find paths without human intervention. However, due to the paths' randomness, sample-based path planners currently require additional verification, partially nullifying agents' ability to act autonomously. I set out to characterize this uncertainty so humans know what to expect from these path planners and know how to alter the path planner to desired specifications. To ensure the results are theoretical as well as practical, I first create a stochastic model of path length uncertainty using the trade-off between sampling time and optimality. By leveraging this model, my proposed algorithm reduces median path length by approximately 10% in higher-dimensional simulations without significantly reducing success rate. To generalize these results, I then include obstacles in my model. In obstacle-dense experiments, I see a further 5% decrease in median path length without significant decreases in success rate. Finally, using my model to accelerate parameter optimization, I create a closed-form distribution that directly models observable parameters (e.g. time and cost) using underlying path planner parameters. I show my approach optimizes parameters twice as fast as using the explicit sample-based path planner. By characterizing path length uncertainty in sample-based path planners, I reduce human oversight on autonomous path planning, minimizing lost time and exposure to danger.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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