Informative path planning under temporal logic constraints with performance guarantees

Kevin J. Leahy, Derya Aksaray, Calin Belta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this work we consider an agent trying to maximize a submodular reward function while moving in a graph environment. Such reward functions can be used to capture a variety of crucial sensing objectives in robotics including, but not limited to, mutual information and entropy. Furthermore, the agent must satisfy a mission specified by temporal logic constraints, which can encode many rich and complex missions such as 'visit regions A or B, then visit C, infinitely often. Never visit D before visiting C.' We present an algorithm to maximize a submodular reward function under these constraints and provide an approximation for the performance of the proposed algorithm. The results are validated via simulation.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1859-1865
Number of pages7
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Externally publishedYes
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States
CitySeattle
Period5/24/175/26/17

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