Human-machine collaboration in mining: A critical review of emerging frontiers of intelligence systems in the mining industry

IF 4.5 2区 社会学 Q2 ENVIRONMENTAL STUDIES
Rosebella Osei , Samuel Frimpong , Allada Venkat
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Abstract

Artificial Intelligence (AI) is driving significant transformations in numerous industries revolutionizing business processes, relationships, and engagements among individuals within organizations as well as with external service providers. These emerging smart technologies will also revolutionize the mining industry in significant ways. The future of AI in the mining industry will focus on autonomous equipment, drones and robots replacing humans in tasks performance. Mine automation is creating a new paradigm where technically savvy personnel are performing remote operations with improved workplace safety, health, and efficiencies. This study reviews the progress of mine automation, robotics, and other intelligent systems in the mining industry. We applied the technology, organization, and environment (TOE) framework to synthesize the various barriers associated with the implementation of these smart technologies in the various mining lifecycles. Using a preliminary literature review approach, we discuss enabling technologies facilitating human-machine collaboration along the mining life cycle, their impacts and synthesize the future of an industry where human and machine collaborate successfully to the benefit of humans. This review contributes to the best practices for managing change as an enabling factor to facilitate the smooth implementation of smart technologies in the mining industry.

Abstract Image

采矿中的人机协作:对采矿行业智能系统新兴前沿的批判性回顾
人工智能(AI)正在推动许多行业的重大变革,彻底改变企业内部个人以及外部服务提供商之间的业务流程、关系和约定。这些新兴的智能技术也将在很大程度上彻底改变采矿业。人工智能在采矿业的未来将集中在自动化设备、无人机和机器人上,取代人类完成任务。矿山自动化正在创造一种新的模式,在这种模式下,精通技术的人员可以执行远程操作,同时提高工作场所的安全、健康和效率。本研究综述了矿山自动化、机器人和其他智能系统在采矿业中的进展。我们应用了技术、组织和环境(TOE)框架来综合与在各种采矿生命周期中实施这些智能技术相关的各种障碍。通过初步的文献综述方法,我们讨论了在采矿生命周期中促进人机协作的使能技术,以及它们的影响,并综合了人类和机器成功协作以造福人类的行业的未来。该审查有助于将管理变革作为促进智能技术在采矿业顺利实施的有利因素的最佳实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.60
自引率
19.40%
发文量
135
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