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

Rosolia, Ugo and Ahmadi, Mohamadreza and Murray, Richard M. and Ames, Aaron D. (2021) Time-Optimal Navigation in Uncertain Environments with High-Level Specifications. In: 2021 60th IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 4287-4294. ISBN 978-1-6654-3659-5.

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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. Leveraging this result, 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.

Item Type:Book Section
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URLURL TypeDescription Paper
Rosolia, Ugo0000-0002-1682-0551
Ahmadi, Mohamadreza0000-0003-1447-3012
Murray, Richard M.0000-0002-5785-7481
Ames, Aaron D.0000-0003-0848-3177
Additional Information:© 2021 IEEE. Work supported by the NSF award #1932091.
Funding AgencyGrant Number
Record Number:CaltechAUTHORS:20210511-085123358
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Official Citation:U. Rosolia, M. Ahmadi, R. M. Murray and A. D. Ames, "Time-Optimal Navigation in Uncertain Environments with High-Level Specifications," 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 4287-4294, doi: 10.1109/CDC45484.2021.9683486
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109069
Deposited By: Tony Diaz
Deposited On:11 May 2021 17:10
Last Modified:15 Feb 2022 23:43

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