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Developing Digital Twins at the Subsystem Level Using Heterogenous Modeling and Simulation (M&S) for Development and Testing of Artifical Intelligence/Machine Learning (AI/ML) and Autonomous Ground Systems ATC

SAE Technical Papers (1906-current) Available online

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Format:
Book
Conference/Event
Author/Creator:
Van Emden, Kristin, author.
Contributor:
Bergin, Dennis
Bounker, Paul
Flint, Benjamin
Gates, Burhman
Huynh, Kevin
Ma, Lein
Madak, Joseph T.
McDonnell, Joseph
Nolta, Lukas
Song, Jae
Strickland, Jared
Weber, Kody
Whitt, John
Conference Name:
2025 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2025-08-12 : Novi, Michigan, United States)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2025
Summary:
To achieve Army modernization plans, advanced approaches for testing and evaluation of autonomous ground systems and their integration with human operators should be utilized. This paper presents a framework for developing digital twins at the subsystem level using heterogeneous modeling and simulation (M&S) to address the challenges of manned-unmanned teaming (MUM-T) in operational environments. Focusing on the interplay between robotic combat vehicles (RCVs) and human operations, the framework enables evaluation of soldiers' cognitive loads while managing tasks such as maneuvering robotic systems, interacting with aided target detection, and engaging simulated adversaries. By employing subsystem-level digital twins, we aim to isolate and control key variables, enabling a detailed assessment of both systems' performance and operator effectiveness. Through realistic operational scenarios and human-machine interface testing, our approach may help identify optimal solutions for soldier-robot collaborations, ensuring readiness in MUM-T operations. This methodology provides a pathway for refining AI/ML capabilities, enhancing autonomy, and informing the Army's broader testing and evaluation objectives
Notes:
Vendor supplied data
Publisher Number:
2025-01-0474
Access Restriction:
Restricted for use by site license

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