Time-Optimal Navigation in Uncertain Environments with High-Level Specifications
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.
Cite
@article{arxiv.2103.01476,
title = {Time-Optimal Navigation in Uncertain Environments with High-Level Specifications},
author = {Ugo Rosolia and Mohamadreza Ahmadi and Richard M. Murray and Aaron D. Ames},
journal= {arXiv preprint arXiv:2103.01476},
year = {2021}
}