Deliverable 2.1 Drill-LIBS

Deliverable D2.1 of the DISCOVER project (HORIZON-CL4-2023-TWIN-TRANSITION 01-11-101129909) details the development of the Pokey drilling robotic platform for material characterization in the built environment. The system integrates a robotic drilling platform with three sensing modalities: X-ray Fluorescence (XRF), Near-Infrared (NIR), and RGB imaging. It is designed to autonomously collect and analyze drill dust samples from 17 distinct classes of construction materials. XRF was chosen over the initially proposed LIBS for its superior stability, reproducibility, and easier robotic integration. The mechanical design features a modular frame, automated sample transport, and Hilti drilling components for robust operation. A LabVIEW-based control system manages the workflow, communicating with motor drivers and sensors. The collected multi-modal data is used to train and evaluate AI models for real-time, high-accuracy material classification.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-31
DOI
https://doi.org/10.5281/zenodo.18338806
Primary Topic
Laser-induced spectroscopy and plasma
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deliverable 2.1 Drill-LIBS

Delft University of Technology
Zenodo (CERN European Organization for Nuclear Research)
Laser-induced spectroscopy and plasma
article

Deliverable 2.1 Drill-LIBS

Delft University of Technology
article en

Abstract

Deliverable D2.1 of the DISCOVER project (HORIZON-CL4-2023-TWIN-TRANSITION 01-11-101129909) details the development of the Pokey drilling robotic platform for material characterization in the built environment. The system integrates a robotic drilling platform with three sensing modalities: X-ray Fluorescence (XRF), Near-Infrared (NIR), and RGB imaging. It is designed to autonomously collect and analyze drill dust samples from 17 distinct classes of construction materials. XRF was chosen over the initially proposed LIBS for its superior stability, reproducibility, and easier robotic integration. The mechanical design features a modular frame, automated sample transport, and Hilti drilling components for robust operation. A LabVIEW-based control system manages the workflow, communicating with motor drivers and sensors. The collected multi-modal data is used to train and evaluate AI models for real-time, high-accuracy material classification.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 39%
Laser-induced spectroscopy and plasma
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.