View source for Time-Optimal Navigation in Uncertain Environments with High-Level Specifications

{{Paper
|Title=Time-Optimal Navigation in Uncertain Environments with High-Level Specifications
|Authors=Ugo Rosolia, Mohamadreza Ahmadi, Richard M. Murray, Aaron D. Ames
|Source=To appear, 2021 Conference on Decision and Control (CDC)
|Abstract=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.
|URL=https://arxiv.org/pdf/2103.01476
|Type=Conference paper
|ID=2021c
|Tag=RAMA21-cdc
|Funding=NSF T&E
}}