Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics

Ugurcan Celik; Mevlut Uzun; Gokhan Inalhan; Mike Woods

doi:10.1109/DASC62030.2024.10749622

Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics

Ugurcan Celik, Mevlut Uzun, Gokhan Inalhan, Mike Woods

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this work we develop an AI-aided tactics generator for uncrewed surface vessels (USVs) for protection of critical national infrastructure and maritime assets in face of surface vehicle attacks. Our scientific machine learning (SciML) based methodology incorporates physical principles into the learning process, enhancing the model's ability to generalize and perform accurately in scenarios not encountered during training. This innovation addresses a critical gap in existing AI applications for maritime defense: the ability to operate effectively in novel or changing conditions without the need for retraining.

Original language	English
Title of host publication	DASC 2024 - Digital Avionics Systems Conference, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350349610
DOIs	https://doi.org/10.1109/DASC62030.2024.10749622
Publication status	Published - 2024
Externally published	Yes
Event	43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024 - San Diego, United States Duration: 29 Sept 2024 → 3 Oct 2024

Publication series

Name	AIAA/IEEE Digital Avionics Systems Conference - Proceedings
ISSN (Print)	2155-7195
ISSN (Electronic)	2155-7209

Conference

Conference	43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024
Country/Territory	United States
City	San Diego
Period	29/09/24 → 3/10/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

autonomous decision making
maritime defence
pursuit evasion
scientific machine learning

Access to Document

10.1109/DASC62030.2024.10749622

Cite this

Celik, U., Uzun, M., Inalhan, G., & Woods, M. (2024). Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics. In DASC 2024 - Digital Avionics Systems Conference, Proceedings (AIAA/IEEE Digital Avionics Systems Conference - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC62030.2024.10749622

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title = "Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics",

abstract = "In this work we develop an AI-aided tactics generator for uncrewed surface vessels (USVs) for protection of critical national infrastructure and maritime assets in face of surface vehicle attacks. Our scientific machine learning (SciML) based methodology incorporates physical principles into the learning process, enhancing the model's ability to generalize and perform accurately in scenarios not encountered during training. This innovation addresses a critical gap in existing AI applications for maritime defense: the ability to operate effectively in novel or changing conditions without the need for retraining.",

keywords = "autonomous decision making, maritime defence, pursuit evasion, scientific machine learning",

author = "Ugurcan Celik and Mevlut Uzun and Gokhan Inalhan and Mike Woods",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024 ; Conference date: 29-09-2024 Through 03-10-2024",

year = "2024",

doi = "10.1109/DASC62030.2024.10749622",

language = "English",

series = "AIAA/IEEE Digital Avionics Systems Conference - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "DASC 2024 - Digital Avionics Systems Conference, Proceedings",

address = "United States",

}

Celik, U, Uzun, M, Inalhan, G & Woods, M 2024, Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics. in DASC 2024 - Digital Avionics Systems Conference, Proceedings. AIAA/IEEE Digital Avionics Systems Conference - Proceedings, Institute of Electrical and Electronics Engineers Inc., 43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024, San Diego, United States, 29/09/24. https://doi.org/10.1109/DASC62030.2024.10749622

Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics. / Celik, Ugurcan; Uzun, Mevlut; Inalhan, Gokhan et al.
DASC 2024 - Digital Avionics Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics

AU - Celik, Ugurcan

AU - Uzun, Mevlut

AU - Inalhan, Gokhan

AU - Woods, Mike

PY - 2024

Y1 - 2024

N2 - In this work we develop an AI-aided tactics generator for uncrewed surface vessels (USVs) for protection of critical national infrastructure and maritime assets in face of surface vehicle attacks. Our scientific machine learning (SciML) based methodology incorporates physical principles into the learning process, enhancing the model's ability to generalize and perform accurately in scenarios not encountered during training. This innovation addresses a critical gap in existing AI applications for maritime defense: the ability to operate effectively in novel or changing conditions without the need for retraining.

AB - In this work we develop an AI-aided tactics generator for uncrewed surface vessels (USVs) for protection of critical national infrastructure and maritime assets in face of surface vehicle attacks. Our scientific machine learning (SciML) based methodology incorporates physical principles into the learning process, enhancing the model's ability to generalize and perform accurately in scenarios not encountered during training. This innovation addresses a critical gap in existing AI applications for maritime defense: the ability to operate effectively in novel or changing conditions without the need for retraining.

KW - autonomous decision making

KW - maritime defence

KW - pursuit evasion

KW - scientific machine learning

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U2 - 10.1109/DASC62030.2024.10749622

DO - 10.1109/DASC62030.2024.10749622

M3 - Conference contribution

AN - SCOPUS:85211193580

T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings

BT - DASC 2024 - Digital Avionics Systems Conference, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

Y2 - 29 September 2024 through 3 October 2024

ER -

Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics

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Keywords

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