Time-Optimal Navigation in Uncertain Environments with High-Level Specifications

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Title Time-Optimal Navigation in Uncertain Environments with High-Level Specifications
Authors Ugo Rosolia, Mohamadreza Ahmadi, Richard M. Murray and 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.
Type Conference paper
URL https://arxiv.org/pdf/2103.01476
Tag RAMA21-cdc
ID 2021c
Funding NSF T&E
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