Abstract

The integration of new technologies into U.S. mining operations is necessary for achieving continuous improvement in worker health and safety, as well as improving operational efficiency, but many have questioned why uptake of autonomy in the U.S. mining industry has been slow compared to other nations. Despite extensive research on mining automation, there remains a critical gap in understanding and systematically identifying the barriers to its adoption within the U.S. mining industry and globally. To quantify these barriers, a workshop model of data collection was employed. A total of 708 invitations were extended to professionals to participate in nine different workshops between 2022 and 2024, representing 480 companies, with a total of 146 participants. In addition, online virtual meetings were held with leadership of the U.S. Mine Safety and Health Administration (MSHA), National Institute of Occupational Safety and Health (NIOSH), Society of Mining, Metallurgy and Exploration (SME) and academia, with a total of about 139 participants. Participants categorize major barriers to mining automation into regulation, economics, technology readiness, corporate willingness, and social license. The weighted average of the ranks of these barriers indicates that economics, technology readiness, and regulation are the three most significant barriers to mining automation, contributing 37.9%, 17.4%, and 16.6%, respectively. While the first two significant barriers may be handled by the private sector, removing the regulatory barrier requires government collaboration. The review of the regulation reveals that there are approximately 98 regulations identified as presenting potential barriers to automation or innovation in the U.S. mining industry.

Department(s)

Materials Science and Engineering

Comments

Centers for Disease Control and Prevention, Grant 75D301-22-C-14149

Keywords and Phrases

Artificial Intelligence; Communication; Equipment Autonomy; Mining Automation; Sensors

International Standard Serial Number (ISSN)

2524-3470; 2524-3462

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Publication Date

01 Jun 2026

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Metallurgy Commons

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