  This paper considers centralized mission-planning for a heterogeneous
multi-agent system with the aim of locating a hidden target. We propose a mixed
observable setting, consisting of a fully observable state-space and a
partially observable environment, using a hidden Markov model. First, we
construct rapidly exploring random trees (RRTs) to introduce the mixed
observable RRT for finding plausible mission plans giving way-points for each
agent. Leveraging this construction, we present a path-selection strategy based
on a dynamic programming approach, which accounts for the uncertainty from
partial observations and minimizes the expected cost. Finally, we combine the
high-level plan with model predictive control algorithms to evaluate the
approach on an experimental setup consisting of a quadruped robot and a drone.
It is shown that agents are able to make intelligent decisions to explore the
area efficiently and to locate the target through collaborative actions.
