  We present a new algorithm for deploying passenger robots in marsupial robot
systems. A marsupial robot system consists of a carrier robot (e.g., a ground
vehicle), which is highly capable and has a long mission duration, and at least
one passenger robot (e.g., a short-duration aerial vehicle) transported by the
carrier. We optimize the performance of passenger robot deployment by proposing
an algorithm that reasons over uncertainty by exploiting information about the
prior probability distribution of features of interest in the environment. Our
algorithm is formulated as a solution to a sequential stochastic assignment
problem (SSAP). The key feature of the algorithm is a recurrence relationship
that defines a set of observation thresholds that are used to decide when to
deploy passenger robots. Our algorithm computes the optimal policy in $O(NR)$
time, where $N$ is the number of deployment decision points and $R$ is the
number of passenger robots to be deployed. We conducted drone deployment
exploration experiments on real-world data from the DARPA Subterranean
challenge to test the SSAP algorithm. Our results show that our deployment
algorithm outperforms other competing algorithms, such as the classic secretary
approach and baseline partitioning methods, and is comparable to an offline
oracle algorithm.
