James Hensman

University of Sheffield, University of Strathclyde

Papers

Total Citations

H-Index

About

James Hensman is a researcher whose work sits at the intersection of non-destructive evaluation (NDE) and robotics, with a particular focus on probabilistic methods for real-world inspection challenges. His key research areas include Bayesian filtering for NDE applications, robotic inspection in hazardous environments, and reconfigurable robotic strategies for structural health monitoring. Hensman’s major contributions center on developing practical, probabilistic frameworks that enable real-time data analysis and decision-making during robotic inspections, addressing critical constraints such as positioning accuracy and sensor integration. His most-cited paper, "Practical constraints on real time Bayesian filtering for NDE applications" (2013, 12 citations), demonstrates his ability to bridge theoretical probabilistic models with the harsh realities of industrial inspection. Additionally, his work on wireless, semi-autonomous robotic inspection vehicles—detailed in papers from 2010—has been foundational in advancing the use of reconfigurable platforms for NDE, particularly in inaccessible or dangerous settings. Hensman’s research, conducted as part of the UK Research Centre for Non Destructive Evaluation (RCNDE) at the University of Strathclyde, has laid important groundwork for integrating robotics and probabilistic reasoning into structural health monitoring, making inspection processes more reliable and autonomous.