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Barrier Functions for Multiagent-POMDPs with DTL Specifications

Ahmadi, Mohamadreza and Singletary, Andrew and Burdick, Joel W. and Ames, Aaron D. (2020) Barrier Functions for Multiagent-POMDPs with DTL Specifications. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200527-080649931

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Abstract

Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation. In this paper, given a nominal policy provided by a human operator or a conventional planning method, we propose a technique based on barrier functions to design a minimally interfering safety-shield ensuring satisfaction of high-level specifications in terms of linear distribution temporal logic (LDTL). To this end, we use sufficient and necessary conditions for the invariance of a given set based on discrete-time barrier functions (DTBFs) and formulate sufficient conditions for finite time DTBF to study finite time convergence to a set. We then show that different LDTL mission/safety specifications can be cast as a set of invariance or finite time reachability problems. We demonstrate that the proposed method for safety-shield synthesis can be implemented online by a sequence of one-step greedy algorithms. We demonstrate the efficacy of the proposed method using experiments involving a team of robots.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2003.09267arXivDiscussion Paper
ORCID:
AuthorORCID
Ahmadi, Mohamadreza0000-0003-1447-3012
Singletary, Andrew0000-0001-6635-4256
Ames, Aaron D.0000-0003-0848-3177
Additional Information:This work was supported by DARPA Subterranean Challenge.
Funders:
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Record Number:CaltechAUTHORS:20200527-080649931
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200527-080649931
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103485
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 May 2020 15:53
Last Modified:03 Aug 2020 21:26

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