  Marsupial robot teams consist of carrier robots that transport and deploy
multiple passenger robots, such as a team of ground robots that carry and
deploy multiple aerial robots, to rapidly explore complex environments. We
specifically address the problem of planning the deployment times and locations
of the carrier robots to best meet the objectives of a mission while reasoning
over uncertain future observations and rewards. While prior work proposed
optimal, polynomial-time solutions to single-carrier robot systems, the
multiple-carrier robot deployment problem is fundamentally harder as it
requires addressing conflicts and dependencies between deployments of multiple
passenger robots. We propose a centralized heuristic search algorithm for the
multiple-carrier robot deployment problem that combines Monte Carlo Tree Search
with a dynamic programming-based solution to the Sequential Stochastic
Assignment Problem as a rollout action-selection policy. Our results with both
procedurally-generated data and data drawn from the DARPA Subterranean
Challenge Urban Circuit show the viability of our approach and substantial
exploration performance improvements over alternative algorithms.
