Ageing reinforced concrete infrastructure demands reliable, cost-effective condition assessment to support timely interventions and extend asset service life, directly contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities). Ground Penetrating Radar (GPR) is a key non-destructive testing (NDT) method for this purpose, yet surveys are often executed manually, leading to inconsistencies in coverage, positioning and scan repeatability that limit robust, data-driven evaluation.

This work presents an autonomous robot-mounted GPR system capable of conducting controlled, repeatable surveys of reinforced concrete elements with minimal human intervention. The autonomous robotic platform follows prescribed trajectories, regulates scanning speed and stand-off, and records positional information to generate spatially consistent radargrams that can be directly linked to experimental layouts. It is conceived as an experimental and methodological backbone for corrosion and degradation studies, and as a stepping stone towards more advanced robotic inspection solutions in real structures.

Tests were performed on reinforced concrete specimens to demonstrate that the system reduces operator-dependent variability and produces structured GPR datasets suitable for repeated measurements over time on the same elements, as well as for integration with complementary techniques and machine learning-based analysis. The proposed framework standardises acquisition conditions and embeds automation at the data collection stage, advancing digitalised NDT workflows and supporting more reliable, evidence-based decision-making for the maintenance and life-cycle management of concrete infrastructure.