Title: Trait-Based Modeling for Multi-Robot Coordination
Committee:
Dr. Sonia Chernova(Advisor) - School of Interactive Computing, Georgia Institute of Technology
Dr. Harish Ravichandar- School of Interactive Computing, Georgia Institute of Technology
Dr. Seth Hutchinson - School of Interactive Computing, Georgia Institute of Technology
Dr. Magnus Egerstedt- School of Engineering, University of California Irvine
Dr. Nicholas Roy - Department of Aeronautics & Astronautics, Massachusetts Institute of Technology

Abstract: Heterogeneous multi-agent systems offer the potential to solve complex problems in various domains, that would otherwise be infeasible for a single agent. To effectively deploy multi-robot teams, researchers need to reason about several interdependent problems at varying levels of abstraction. In particular, there are four important questions that heterogeneous multi-robot systems must address: task planning (what), motion planning (how), task allocation (who), and scheduling (when). These problems are complex, interconnected, and flexible to various team compositions and agent types. To solve such complex problems, researchers require accurate models of agent capabilities that help teams effectively leverage the individual agents' relative strengths. One methodology for modeling agents in a multi-agent team is through the use of traits (capabilities). This thesis examines the use of trait-based models for representing individual agents in the context of multi-agent teaming applications and how trait-based modeling can be leveraged to enable more robust and efficient solutions to multi-agent coalition formation.  Specifically, we examine how these techniques can be used in coalition formation algorithms to answer the four problems of task allocation, scheduling, motion planning, and task planning.