]> 2026-06-28T14:11:19+00:00 Time-Optimal Navigation in Uncertain Environments with High-Level Specifications 0 en 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. Ugo Rosolia Mohamadreza Ahmadi Richard M. Murray Aaron D. Ames 2021c To appear, 2021 Conference on Decision and Control (CDC) RAMA21-cdc Conference paper 2022-10-09T18:23:11Z 2459862.2660995 Time-Optimal Navigation in Uncertain Environments with High-Level Specifications Title 102 en Title