Extreme environments, such as those encountered in planetary exploration or disaster response, present complex, time-sensitive tasks with significant uncertainty. In such settings, heterogeneous teams of robots with diverse capabilities can offer more robust solutions. By efficiently coordinating robots with specialized skills, these teams can adapt to unknowns, enhance safety, and successfully execute complex tasks.

The challenge lies in coordinating these multi-agent teams and maximizing the unique strengths of each member to achieve a shared objective under harsh constraints and high costs of failure. Current strategies, however, are computationally intensive and often rely on expert oversight, posing a significant burden on practitioners in real-world deployment.

This thesis focuses on developing efficient collaboration strategies for coordinating heterogeneous robotic teams for information gathering tasks. The work addresses multi-agent coordination through three main elements: goal decomposition, robot-task allocation, and deployment decisions. We not only present methods that improve the operations of a team when used as individual modules, but also present an integrated pipeline that is a step towards autonomous heterogeneous multi-agent exploration.

The foundation of our approach is a novel spectral-based framework. This framework utilizes spectral decomposition to represent the diverse capabilities of agents and the characteristics of the environment compactly. This representation then serves as the basis for efficiently building and coordinating teams. The developed methods are rigorously evaluated using real-world datasets and expert feedback.