Max Robotics — Global Service Robot Directory + US Certification

About

Paul Gardner is a pioneering researcher at the intersection of structural health monitoring (SHM), non-destructive evaluation (NDE), and autonomous robotics. His work bridges machine learning and physical inspection, with a focus on developing intelligent, closed-loop control systems for soft robotics and automated ultrasonic inspection. Gardner’s most-cited paper (2020, 58 citations) provides a seminal framework for integrating machine learning into SHM and NDE, clarifying the distinctions and synergies between these fields. He has also made notable contributions to exoplanet science as part of the MINERVA project (2015, 37 citations), designing and commissioning a robotic telescope array for exoplanet discovery. His recent work on embedding recurrent neural networks into soft sensors (2022, 26 citations) enables real-time, time-variant control of soft robots—a breakthrough for adaptive automation. Gardner’s research on Bayesian optimisation for autonomous NDT inspection (2020, 24 citations) addresses the challenge of processing large datasets from robotic inspections, significantly advancing efficiency in non-destructive testing. With a career spanning astronomy, robotics, and materials science, Gardner’s interdisciplinary approach has earned him over 150 citations, positioning him as a key figure in intelligent sensing and autonomous systems.