Title page for ETD etd-09192011-195545

Type of Document Dissertation
Author Gaines, Jonathan Elliot
Author's Email Address gaines90@vt.edu
URN etd-09192011-195545
Title Remote Operator Blended Intelligence System for Environmental Navigation and Discernment (RobiSEND)
Degree PhD
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Wicks, Alfred L. Committee Chair
Kochersberger, Kevin Bruce Committee Member
Kurdila, Andrew J. Committee Member
Woolsey, Craig A. Committee Member
Xuan, Jianhua Jason Committee Member
  • human-robot collaboration
  • intelligent information gathering
  • operator blending
  • perception
  • integration
  • rotorcraft
  • local search
  • unmanned systems
Date of Defense 2011-09-06
Availability unrestricted
Mini 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.
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