Being confident in measurement data when acquired using remote robotics

In hazardous locations, robotic inspection and surveying is often considered to minimise human exposure to risk. However, transitioning to new levels of automation requires confidence in the measurement tools being deployed. This project therefore investigated the capability of a photogrammetric measurement process deployed on a robot for part inspection after manufacturing to determine feasible use cases.

In this work, the measurement system capability was judged from the perspective of uncertainty and error across various part geometries over several days. This was done by setting up a test environment where a series of systematic tests were carried out to determine the quality of measurements acquired in comparison to an established reference. The influence of operator technique on error and point acquisition ability for challenging geometry is also assessed to judge the benefits of automation.

The work found that the proposed measurement process is to be resistant to short-term error drift when operating in a controlled environment but systematic and random errors are demonstrated to be highly dependent on geometry. Operator influence on capability is found to be minimal when scanning freeform geometry, although this is unlikely for more complex parts. From these experiments, technological development opportunities are highlighted in the context of the robotic inspection application considered.

Overall, a thorough assessment of the measurement process capability was made and findings provide a quantification of current state, setting a base case for comparing research progress against. This allowed informed decisions to be made regarding deploying the measurement process robotically to aid manufacturing operations in the nuclear industry.

This work was published in Measurement: Journal of the International Measurement Confederation in 2016. For more information please contact us or see the full article:

Photogrammetric measurement process capability for metrology assisted robotic machining