Increased safety in deep mining with IoT and autonomous robots

Günther, Frauke; Mischo, Helmut; Lösch, R.; Grehl, Steve; Guth, Felipe

doi:10.1201/9780429320774-70

Cited by 12 publications

(9 citation statements)

References 1 publication

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“…Mobile autonomous robots and IoT technology play an important role in the occupational safety and health of mineworkers as they provide real-time information about the status of the mine and mine workers and allow the mine workers to avoid hazardous areas [ 147 ]. Sinha et al reported the use of IoT based on ZigBee techniques to actively monitor underground mineworkers and provide immediate assistance during an emergency [ 148 ].…”

Section: Resultsmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

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Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.

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Section: Resultsmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

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“…This has led to the adoption of hydraulics, mechanical, electronics and electrical systems in mining (Porter and Cunningham, 2005). Furthermore, given the concerns about safety in mining activities, the adoption of technologies has increased (Günther et al , 2019). Large-sized mining companies have adopted new emerging technologies such as the Internet of Things (IoT), blockchain and automation across various mining activities and processes (Barnewold and Lottermoser, 2020).…”

Section: Introductionmentioning

confidence: 99%

Emerging technologies in Indian mining industry: an exploratory empirical investigation regarding the adoption challenges

Bhattacharyya

Shah

2021

JSTPM

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Purpose Emerging technologies have been transforming most industries. A wide range of emerging technologies such as blockchain, internet of things (IoT), artificial intelligence (AI), machine learning (ML), robotics and many others have changed the way in which firm value chain activities or processes were executed traditionally. The mining industry has also witnessed the introduction of these emerging technologies in various processes from the exploration stage to the final processing of ores. The purpose of this paper is to understand the pace of adoption of emerging technologies in the Indian mining industry and identify the challenges that managers confront while adopting emerging technologies. Design/methodology/approach The authors undertook qualitative research. Data collection was done in two stages. Secondary research was conducted to arrive at a repository of use cases of the adoption of emerging technologies in the global mining industry. Primary data collection was also done. The insights on emerging technology adoption and challenges faced in the Indian mining industry were captured by in-depth interviewing of subject matter experts. The authors interviewed 21 mining subject matter experts with a semi-structured open-ended questionnaire. The responses were content analyzed by thematic content analysis. Technological-organizational-environmental (TOE) and diffusion of innovation (DOI) frameworks were applied to segregate different factors affecting the adoption of emerging technologies in the Indian mining industry. Findings Emerging technologies such as blockchain, IoT, AI, ML, robotics has been applied across various mining engineering value chain activities such as in drilling, blasting, excavation and ore hauling. However, emerging technologies adoption was hindered because of a lack of managerial awareness, cultural inertia, substantive upfront investments and the nature of intangible benefits in the short run. Research limitations/implications The research applied technology adoption frameworks in the mining industry. The authors used TOE and DOI frameworks to understand the challenges faced by Indian mining firms. The research findings, thus added to the conversation of TOE and DOI frameworks in the context of the Indian mining industry. Practical implications The research finding would help mining firm managers to anticipate the challenges with respect to technology adoption. This would allow mining executives to create a proper technology adoption plan and intervene proactively. The research would also provide information about the steps taken by competing firms with respect to emerging technologies adoption. The research would help managers to decide technology implementation steps in drilling, blasting, excavation and ore hauling to be undertaken for successful adoption of emerging technologies. Technology firms could gain insights into the issues faced by mining firms in adopting emerging technologies. This research would help managers to influence organizational technology policy and endorse the addition of pro-technology policies in mining activities. Policymakers involved in the mining sector could also incorporate industry-level policy decisions so as to facilitate the adoption of emerging technologies among mining firms and remove the barriers to the adoption of emerging technologies. This would create an opportunity for technology providers to redesign product offerings, which could be a good fit for Indian mining firms. Originality/value Indian mining industry contributed significantly to the Indian economy. Despite this, limited focus has been put regarding the adoption of emerging technologies in the mining industry. Mining managers did not have any framework to understand the challenges faced in the adoption of technologies across the mining value chain that is in drilling, blasting, excavation and ore hauling. This study focused on identifying those challenges through the use of technology adoption frameworks. This research was one of the first studies to gain insights on emerging technologies adoption in the context of the mining industry through the theoretical lens of TOE and DOI frameworks.

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“…In the mining industry, several studies of autonomous driving technology have been conducted in underground mining environments using autonomous robots [16][17][18][19][20][21][22][23][24][25][26]. Baker et al [27] developed "groundhog", an autonomous driving robot that can be used even in underground mines having poor road conditions.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

Kim

Choi

2021

Applied Sciences

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In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.

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Increased safety in deep mining with IoT and autonomous robots

Cited by 12 publications

References 1 publication

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Emerging technologies in Indian mining industry: an exploratory empirical investigation regarding the adoption challenges

Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

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