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The University of Texas at Austin

A mixed-initiative framework for multi-agent human-robot teams

Abstract

dc:description.abstract

Robots and autonomous systems are expanding into more complex, dynamic, and human-centered operating domains. In such domains, neither full autonomy nor full human control are ideal and the appropriate level of autonomy is contextually-dependent. Mixed-initiative systems enable flexible autonomy, making them ideal for complex operating environments. However, most mixed-initiative research does not consider multi-agent human-robot systems, and a multi-agent mixed-initiative robot control framework is not readily available. This project explores the requirements for a mixed-initiative human-robot control framework and proposes an initial design. The mixed-initiative framework was deployed onto a mobile robot, where three different agents could take, transfer, share, enable, or disable robot control. Compared to single-agent systems, the multi-agent mixed-initiative system completed a series of navigation tasks more quickly. The multi-agent system also completed navigation tasks that a single-agent autonomous system could not. Further design improvements and applications for mixed-initiative systems are discussed.

Degree

thesis:*
Name thesis:degree_name
Master of Science in Engineering
Level thesis:degree_level
Masters
Discipline thesis:degree_discipline
Mechanical Engineering
Grantor
The University of Texas at Austin
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Duncan, John Alexander
Advisor dc:contributor.advisor
  • Landsberger, Sheldon

Subjects

dc:subject × 6

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:repositories.lib.utexas.edu:2152/86978

Chain of custody

source
Harvested from
University of Texas
Base URL
repositories.lib.utexas.edu/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Duncan, John Alexander. A mixed-initiative framework for multi-agent human-robot teams. Masters thesis, The University of Texas at Austin, 2020. https://hdl.handle.net/2152/86978