  Mixed observable Markov decision processes (MOMDPs) are a modeling framework
for autonomous systems described by both fully and partially observable states.
In this work, we study the problem of synthesizing a control policy for MOMDPs
that minimizes the expected time to complete the control task while satisfying
syntactically co-safe Linear Temporal Logic (scLTL) specifications. First, we
present an exact dynamic programming update to compute the value function.
Afterwards, we propose a point-based approximation, which allows us to compute
a lower bound of the closed-loop probability of satisfying the specifications.
The effectiveness of the proposed approach and comparisons with standard
strategies are shown on high-fidelity navigation tasks with partially
observable static obstacles.
