  We would like to enable a collaborative multiagent team to navigate at long
length scales and under uncertainty in real-world environments. In practice,
planning complexity scales with the number of agents in the team, with the
length scale of the environment, and with environmental uncertainty. Enabling
tractable planning requires developing abstract models that can represent
complex, high-quality plans. However, such models often abstract away
information needed to generate directly-executable plans for real-world agents
in real-world environments, as planning in such detail, especially in the
presence of real-world uncertainty, would be computationally intractable. In
this paper, we describe the deployment of a planning system that used a
hierarchy of planners to execute collaborative multiagent navigation tasks in
real-world, unknown environments. By developing a planning system that was
robust to failures at every level of the planning hierarchy, we enabled the
team to complete collaborative navigation tasks, even in the presence of
imperfect planning abstractions and real-world uncertainty. We deployed our
approach on a Clearpath Husky-Jackal team navigating in a structured outdoor
environment, and demonstrated that the system enabled the agents to
successfully execute collaborative plans.
