Trait-Based Modeling for Multi-Robot Coordination
Author(s)
Neville, Glen T.
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Abstract
Heterogeneous multi-agent systems offer the potential to solve complex problems in various domains, such as agriculture, military, assembly, and warehouse automation, that would otherwise be infeasible for a single agent. To effectively deploy heterogeneous multi-robot teams, research must address four problems at varying levels of abstraction: task planning (what), motion planning (how), task allocation (who), and scheduling (when). These problems are highly interdependent, and prior work has demonstrated that systems that exploit synergies between individual solutions to these problems can lead to more effective and efficient multi-robot coordination. 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 questions of task allocation, scheduling, motion planning, and task planning. We show that algorithms that leverage trait-based modeling of robots and tasks lead to effective and efficient coordination in heterogeneous multi-robot teams and outperform existing methods with respect to schedule makespan, allocation quality, and computational efficiency.
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Date
2022-10-17
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Dissertation