Autonomous Underground Loading and Transport Systems – Robust Safety Architecture and Practical Implementation

Automation in the mining industry has experienced a significant surge in development thanks to advances in the three areas of sensor technology, artificial intelligence (AI) and software architecture. Autonomous underground loading and transport systems boost productivity, improve safety and enable more efficient use of resources. Robust, cost-effective LIDAR and RADAR sensors from the automotive industry, together with modular software frameworks, form the basis for practical, bespoke automation solutions in mining. The Autonomous Operator System (AOS) developed by Nerospec SK GmbH, Velbert/Germany, enables fully autonomous driving cycles without human supervision. Control is carried out without cameras or broadband networks, enabling the system to operate reliably even in isolated underground environments. An integrated safety architecture, certified to ISO 13849 and IEC 61508, continuously monitors the vehicle’s functions and actively intervenes when necessary to bring the vehicle into a safe state. This intelligent interaction between safety-critical control and AI logic creates a dynamic balance between productivity and safety. The results show that autonomous systems developed in accordance with safety-critical principles represent a realistic and scalable solution for the mining industry. The concept presented makes a key contribution to the safe introduction of autonomy into industrial practice. The article is based on the authors presentation at the “MiningForum 2026”, held in Berlin/­Germany from 18th to 19th June 2026.

Authors

Publication Details

Journal
Mining Report
Published
2026-11-11
DOI
https://doi.org/10.66356/mrg.2026.05.02.enaults
Primary Topic
Safety Systems Engineering in Autonomy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Autonomous Underground Loading and Transport Systems – Robust Safety Architecture and Practical Implementation

Eric Pohlmann
Mining Report
Safety Systems Engineering in Autonomy
article

Autonomous Underground Loading and Transport Systems – Robust Safety Architecture and Practical Implementation

Eric Pohlmann
article en

Abstract

Automation in the mining industry has experienced a significant surge in development thanks to advances in the three areas of sensor technology, artificial intelligence (AI) and software architecture. Autonomous underground loading and transport systems boost productivity, improve safety and enable more efficient use of resources. Robust, cost-effective LIDAR and RADAR sensors from the automotive industry, together with modular software frameworks, form the basis for practical, bespoke automation solutions in mining. The Autonomous Operator System (AOS) developed by Nerospec SK GmbH, Velbert/Germany, enables fully autonomous driving cycles without human supervision. Control is carried out without cameras or broadband networks, enabling the system to operate reliably even in isolated underground environments. An integrated safety architecture, certified to ISO 13849 and IEC 61508, continuously monitors the vehicle’s functions and actively intervenes when necessary to bring the vehicle into a safe state. This intelligent interaction between safety-critical control and AI logic creates a dynamic balance between productivity and safety. The results show that autonomous systems developed in accordance with safety-critical principles represent a realistic and scalable solution for the mining industry. The concept presented makes a key contribution to the safe introduction of autonomy into industrial practice. The article is based on the authors presentation at the “MiningForum 2026”, held in Berlin/­Germany from 18th to 19th June 2026.

Mining ReportVol. 162(5)
Safety Systems Engineering in Autonomy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon