Autonomous Mining Transportation Systems: Integrating 4D mmWave Radar for Enhanced Detection of Obstructed Static Objects

Abstract

‘‘Mining 5.0,” in response to “Industry 5.0,” requires autonomous haulage systems to operate fully autonomously in open-pit mines. Current autonomous mining transportation systems rely on wireless transmission for edge dumping operations, which is inefficient and poses risks of potential communication loss and cybersecurity issues. Sensors such as LiDAR, cameras, and 3D mmWave radar do not support autonomous haulage to complete automation of edge dumping, as they struggle to detect obscured static obstacles and operate in harsh mining environments. We propose an innovative approach to address this challenge: integrating 4D mmWave radar into autonomous haulage systems. We have collected the world's first 4D mmWave radar dataset in the open pit mine to evaluate this approach, including four haulage operating scenarios under various lighting conditions. To quantify the precision of the 4D mmWave radar, we induce three points cloud comparison methods, a static object tracking algorithm, and point cloud image comparison to assess the system's ability to detect obscured static obstacles. Based on our findings, we conclude that using 4D mmWave radar enhances the autonomous haulage system's ability to detect obscured static obstacles in open pit mines, particularly at dumping sites.