A digital twin simulation platform for 4D millimeter-wave radar-based navigation in underground mining environment
In underground mining, safe navigation and quick decision-making are critical and essential, especially in emergencies. Environmental conditions, such as, visibility, high humidity, and high amount of dust suspension, make traditional camera or LIDAR-based navigation risky and unreliable. Radars, particularly 4D millimetre-wave (MMW) radar, are relatively robust against these environmental factors, which make them suited to underground mine environment navigation applications. However, iterative testing and refinement of radar systems in real mines remain costly, time-consuming, and potentially hazardous.
To address these challenges, a digital twin simulation platform for developing and evaluating a 4D MMW radar-based navigation system for underground vehicles is presented. In the platform, a complex underground mining environment is reconstructed using Gazebo and ROS2, including two specific structures: flat-roof and arched-roof mine tunnels inspired by Queensland Mine Rescue Service (QMRS) conditions. Various obstacles, including rocks, overhead signs, lights, ladders, and a worker model, are integrated into the arched-roof tunnel to evaluate the radar’s detection capabilities. A simplified driftrunner-based vehicle model was designed with radars mounted at two heights: 1.25 m and 0.3 m. Radar parameters were set according to ARS548 hardware specifications using Gazebo’s plugin, while post-processing introduced Gaussian noise and random point cloud reduction to simulate real-world data sparsity and uncertainty.
Simulation tests were conducted to analyse radar performance at three distances (5 m, 10 m, and 15 m) along the tunnel centre and two radar heights. Results indicate that radar mounting height significantly influences detection capabilities. At a height of 1.25 m, elevated features, such as, overhead signs and lights can be captured effectively, but ground-level obstacles remained undetected at close range. In contrast, at a lower radar height of 0.3 m, ground-level obstacles can be detected with greater clarity, but elevated features were less distinguishable. This highlight is a tradeoff between the vertical coverage and the ground-level detection, showing the requirement for optimized radar positioning for improved navigation performance.
The developed platform allows safe, iterative testing of radar-based systems under simulated underground conditions, providing key insights into sensor configuration, obstacle detection, and point cloud processing. Although currently focused on simulation, the platform supports future integration with physical radar systems to further refine radar plugins and algorithms. This work ultimately aims to enhance safety and decision-making in underground operations.