arXiv subjects

Ehsan Mihankhah

Publications and source records attributed to Ehsan Mihankhah.

3 recordsLinked to original sources

arXiv2025

Blast Hole Seeking and Dipping -- The Navigation and Perception Framework in a Mine Site Inspection Robot

In open-pit mining, holes are drilled into the surface of the excavation site and detonated with explosives to facilitate digging. These blast holes need to be inspected internally to assess subsurface material types and drill quality, in order to significantly reduce downstream material handling costs. Manual hole inspection is slow and expensive, limited in its ability to capture the geometric and geological characteristics of holes. This has been the motivation for the development of our autonomous mine-site inspection robot - "DIPPeR". In this paper, the automation aspect of the project is explained. We present a robust perception and navigation framework that provides streamlined blasthole seeking, tracking and accurate down-hole sensor positioning. To address challenges in the surface mining environment, where GPS and odometry data are noisy without RTK correction, we adopt a proximity-based adaptive navigation approach, enabling the vehicle to dynamically adjust its operations based on detected target availability and localisation accuracy. For perception, we process LiDAR data to extract the cone-shaped volume of drill-waste above ground, then project the 3D cone points into a virtual depth image to form accurate 2D segmentation of hole regions. To ensure continuous target-tracking as the robot approaches the goal, our system automatically adjusts projection parameters to preserve consistent hole image appearance. At the vicinity of the hole, we apply least squares circle fitting with non-maximum candidate suppression to achieve accurate hole detection and collision-free down-hole sensor placement. We demonstrate the effectiveness of our navigation and perception system in both high-fidelity simulation environments and on-site field trials. A demonstration video is available at https://www.youtube.com/watch?v=fRNbcBcaSqE.

cs.RO↗

arXiv2024

Femtosecond laser processing for blast-hole analysis: laser removal of slurry and effect on rocks

This study investigates the possibility of using a femtosecond pulse laser to remove iron ore slurry used to stabilise blast-hole structures by mining industries, intending to preserve the wall's stability and the chemical and compositional properties of the underlying rock. In situ minerals are often coated in other material deposits, such as dust or slurry in blast holes. To analyse the rock materials beneath, its surface must be exposed by removal of the surface layer. The ablation depth per pulse and ablation efficiency of the slurry were determined using femtosecond laser pulses. Then, the ablation of rocks of economic interest in Australia, including banded iron, limonite, goethite, shale, and hematite, was studied to establish their ablation thresholds and rates. Any damage induced by the laser was investigated by optical microscopy, optical profilometry, colourimetry, VIS/NIR spectroscopy and Fourier Transform Infrared spectroscopy (FTIR).

physics.app-ph↗

arXiv2023

SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline

Accurate and efficient object detection is crucial for safe and efficient operation of earth-moving equipment in mining. Traditional 2D image-based methods face limitations in dynamic and complex mine environments. To overcome these challenges, 3D object detection using point cloud data has emerged as a comprehensive approach. However, training models for mining scenarios is challenging due to sensor height variations, viewpoint changes, and the need for diverse annotated datasets. This paper presents novel contributions to address these challenges. We introduce a synthetic dataset SimMining 3D [1] specifically designed for 3D object detection in mining environments. The dataset captures objects and sensors positioned at various heights within mine benches, accurately reflecting authentic mining scenarios. An automatic annotation pipeline through ROS interface reduces manual labor and accelerates dataset creation. We propose evaluation metrics accounting for sensor-to-object height variations and point cloud density, enabling accurate model assessment in mining scenarios. Real data tests validate our models effectiveness in object prediction. Our ablation study emphasizes the importance of altitude and height variation augmentations in improving accuracy and reliability. The publicly accessible synthetic dataset [1] serves as a benchmark for supervised learning and advances object detection techniques in mining with complimentary pointwise annotations for each scene. In conclusion, our work bridges the gap between synthetic and real data, addressing the domain shift challenge in 3D object detection for mining. We envision robust object detection systems enhancing safety and efficiency in mining and related domains.

cs.CV↗