Abstract
dc:description.abstractThis thesis implements a novel distributed, deterministic algorithm for as few unmanned agents as possible to detect and cover as many static targets of unknown location as possible. This algorithm, Pruning-Perception-Decision (PPD), strikes the balance of exploration versus exploitation by maximizing the number of targets covered by each agent. Agents can cover only one grouping of targets at a time and continue exploring until they find an uncovered target. In doing so, agents' search area is discretized into a grid, where the average percent coverage of each tile is monitored with respect to each agent's field-of-view. Once all agents are covering targets and the average area-coverage value stabilizes, PPD terminates. Alternately, if all targets are found and there exist additional explorer agents, PPD terminates when a time threshold is reached. Simulations show that implementing PPD results in faster convergence than the state-of-the-art by nearly an order of magnitude as well as improved target coverage. Additionally, results of a second demonstration suggest that PPD could be applied to targets appearing and disappearing.
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Aerospace Engineering
- Department dc:contributor.department
- Aerospace and Ocean Engineering
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2025
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Petsopoulos, Genevieve Marie
- Chair dc:contributor.committeechair
-
- Woolsey, Craig A.
- Committee members dc:contributor.committeemember
-
- Crandall, Kyle
- Artis, Harry Pat
Subjects
dc:subject × 5Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:41958
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/123883