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 language | English (US) |
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Title of host publication | 2017 American Control Conference, ACC 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1859-1865 |
Number of pages | 7 |
ISBN (Electronic) | 9781509059928 |
DOIs | |
State | Published - Jun 29 2017 |
Externally published | Yes |
Event | 2017 American Control Conference, ACC 2017 - Seattle, United States Duration: May 24 2017 → May 26 2017 |
Publication series
Name | Proceedings of the American Control Conference |
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ISSN (Print) | 0743-1619 |
Other
Other | 2017 American Control Conference, ACC 2017 |
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Country/Territory | United States |
City | Seattle |
Period | 5/24/17 → 5/26/17 |
Bibliographical note
Publisher Copyright:© 2017 American Automatic Control Council (AACC).