  We present PLUMES, a planner to localizing and collecting samples at the
global maximum of an a priori unknown and partially observable continuous
environment. The "maximum-seek-and-sample" (MSS) problem is pervasive in the
environmental and earth sciences. Experts want to collect scientifically
valuable samples at an environmental maximum (e.g., an oil-spill source), but
do not have prior knowledge about the phenomenon's distribution. We formulate
the MSS problem as a partially-observable Markov decision process (POMDP) with
continuous state and observation spaces, and a sparse reward signal. To solve
the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with
continous-observation Monte Carlo Tree Search to efficiently localize and
sample from the global maximum. In simulation and field experiments, PLUMES
collects more scientifically valuable samples than state-of-the-art planners in
a diverse set of environments, with various platforms, sensors, and challenging
real-world conditions.
