MEAM Ph.D. Thesis Defense: “Multi-Robot Coordination and Cooperation via Graph-Based Computation”

Multi-robot coordination and cooperation are critical behaviors that improve team performance and enable new tasks in application areas like autonomous construction, agriculture, and extended operation in large unknown regions. This dissertation examines these behaviors in the context of the multi-robot resource allocation problem, where robots must be allocated to regions of demand. In particular, we are interested in uncertainty-tolerant approaches that apply to large multi-robot teams. We introduce graph-based computation via Graph Neural Networks applied to the distributed multi-agent coverage control problem, which boasts improved performance and scalability by leveraging learned inter-agent communication strategies. We also introduce a graph-based modeling and planning approach to multi-robot task allocation in complex multi-task missions where coordination and cooperation are explicitly required. We expand this approach into an online setting that re-plans around task failures and unexpected observations. We demonstrate empirically that these modeling approaches and algorithms bring performance improvements that further the state of the art by leveraging the fundamental graph structure present in some multi-robot problems.