Date of Award
8-2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Civil Engineering
Committee Chair/Advisor
Pamela Murray-Tuite
Committee Member
Matthias Schmid
Committee Member
Mashrur Ronnie Chowdhury
Abstract
This thesis addresses the complex challenge of path planning for Unmanned Ground Vehicles (UGVs) in areas where traditional navigation systems are inadequate, such as unstructured or off-road military zones. Recognizing the limitations of current path planning algorithms, which primarily focus on optimizing for the shortest path and often fail to account for variability and risks, this research proposes an enhanced Hyperstar algorithm. This approach not only considers the fastest route but also integrates maximum delays and visibility risks into its computation, ensuring a balance between swift mission completion and concealment from adversaries.
Utilizing terrain maps and incorporating uncertainties in map data through Monte Carlo simulations, the study evaluates the algorithm's effectiveness across various scenarios, including different levels of visibility risk, maximum delay, and bidirectional navigation challenges. The algorithm's adaptability is demonstrated through numerical examples and terrain tests, highlighting its ability to offer multiple route choices so the vehicle can adjust to environmental changes.
Recommended Citation
Afriyie, Israel, "Advancing Unmanned Ground Vehicle Path Planning With Quantified Map Uncertainty" (2024). All Theses. 4385.
https://open.clemson.edu/all_theses/4385