Title:
Furthering human-robot teaming, interaction, and metrics through computational methods and analysis

dc.contributor.advisor Feigh, Karen M.
dc.contributor.author Ma, Mingyue (Lanssie)
dc.contributor.committeeMember Fong, Terry
dc.contributor.committeeMember Chernova, Sonia
dc.contributor.committeeMember Goel, Ashok
dc.contributor.committeeMember Fujimoto, Richard
dc.contributor.department Aerospace Engineering
dc.date.accessioned 2020-05-20T16:56:49Z
dc.date.available 2020-05-20T16:56:49Z
dc.date.created 2019-05
dc.date.issued 2019-03-29
dc.date.submitted May 2019
dc.date.updated 2020-05-20T16:56:49Z
dc.description.abstract Human-robot teaming is a complex design trade space with dynamic aspects and particulars. In order to support future day human-robot teams and scenarios, we need to assist team designers and evaluators in understanding core teaming components. This work is centered around teams that complete space missions and operations. The central scope and theme of this work target the way users should design, evaluate, and think about human-robot teams. This work attempts to do so by defining a framework, conceptual methodology, and operationalized metrics for human-robot teams. We begin by scoping and distilling common components from human-only teaming and human-robot teaming research based in areas such as human factors, cognitive psychology, robotics, and human-robot interaction. Taking these constructs, we derive a framework that describes and organizes the factors, as well as relationships between them. This work also presents a theoretical methodology to support designers to understand the impact teaming components have on expected interaction. This methodology is implemented for four case studies of distinct team types and scenarios including moving furniture, a SWAT team operation, a rover recon, and an in-orbit maintenance mission. After assessing various existing methodologies and perspectives, we derive metrics operationalized from work allocation. To test these learnings, this work modeled and simulated human-robot teams in action, specifically in an in-orbit maintenance scenario. In addition to analyzing simulation results given different team configurations, task allocations, and teamwork modes, a HITL experiment confirmed a human perspective of robotic team members. This experiment also refines the modeling of teams and validates our performance metrics. This dissertation makes the following contributions to the field of human-robot teaming and interaction: 1) Created a new comprehensive framework for human-robot teaming by combining key components of team design and interaction, 2) Developed a method to identify distinct archetypes of interaction in human-robot teams (and showed how they fit into a universal framework), 3) Derived metrics from the HRT framework to capture the teaming elements beyond performance and efficiency; operationalized the method and metrics in a computational framework for simulation and analysis, 4) Extended existing computational framework for function allocation to include the metrics, 5) Demonstrated the sensitivity of effective teams to attributes of both teamwork and taskwork.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/62655
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Human-robot teaming
dc.subject Human-robot interaction
dc.subject Work allocation
dc.subject Modeling
dc.subject Simulation
dc.title Furthering human-robot teaming, interaction, and metrics through computational methods and analysis
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Feigh, Karen M.
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication 43635977-32d3-4083-875f-9a9adff86a8f
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication f6a932db-1cde-43b5-bcab-bf573da55ed6
thesis.degree.level Doctoral
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