Remote Operator Blended Intelligence System for Environmental Navigation and Discernment (RobiSEND)

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dc.contributor.authorGaines, Jonathan Ellioten
dc.contributor.committeechairWicks, Alfred L.en
dc.contributor.committeememberWoolsey, Craig A.en
dc.contributor.committeememberKurdila, Andrew J.en
dc.contributor.committeememberXuan, Jianhua Jasonen
dc.contributor.committeememberKochersberger, Kevin B.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T20:16:28Zen
dc.date.adate2011-10-03en
dc.date.available2014-03-14T20:16:28Zen
dc.date.issued2011-09-06en
dc.date.rdate2011-10-03en
dc.date.sdate2011-09-19en
dc.description.abstractMini Rotorcraft Unmanned Aerial Vehicles (MRUAVs) flown at low altitude as a part of a human-robot team are potential sources of tactical information for local search missions. Traditionally, their effectiveness in this role has been limited by an inability to intelligently perceive unknown environments or integrate with human team members. Human-robot collaboration provides the theory for building cooperative relationships in this context. This theory, however, only addresses those human-robot teams that are either robot-centered or human-centered in their decision making processes or relationships. This work establishes a new branch of human-robot collaborative theory, Operator Blending, which creates codependent and cooperative relationships between a single robot and human team member for tactical missions. Joint Intension Theory is the basis of this approach, which allows both the human and robot to contribute what each does well in accomplishing the mission objectives. Information processing methods for shared visual information and object tracking take advantage of the human role in the perception process. In addition, coupling of translational commands and the search process establish navigation as the shared basis of communication between the MRUAV and human, for system integration purposes. Observation models relevant to both human and robotic collaborators are tracked through a boundary based approach deemed AIM-SHIFT. A system is developed to classify the semantic and functional relevance of an observation model to local search called the Code of Observational Genetics (COG). These COGs are used to qualitatively map the environment through Qualitative Unsupervised Intelligent Collaborative Keypoint (QUICK) mapping, created to support these methods.en
dc.description.degreePh. D.en
dc.identifier.otheretd-09192011-195545en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09192011-195545/en
dc.identifier.urihttp://hdl.handle.net/10919/29032en
dc.publisherVirginia Techen
dc.relation.haspartGaines_Permission_Document.pdfen
dc.relation.haspartGaines_JE_D_2011.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecthuman-robot collaborationen
dc.subjectintelligent information gatheringen
dc.subjectoperator blendingen
dc.subjectperceptionen
dc.subjectintegrationen
dc.subjectrotorcraften
dc.subjectlocal searchen
dc.subjectunmanned systemsen
dc.titleRemote Operator Blended Intelligence System for Environmental Navigation and Discernment (RobiSEND)en
dc.typeDissertationen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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