{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/26763"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/26763","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Stochastic Motion Planning for Applications in Subsea Survey and Area Protection","abstract":"This dissertation addresses high-level path planning and cooperative control for autonomous vehicles. The objective of our work is to closely and rigorously incorporate classication and detection performance into path planning algorithms, which is not addressed with typical approaches found in literature. We present novel path planning algorithms for two different applications in which autonomous vehicles are tasked with engaging targets within a stochastic environment. 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