From Robot Data to Actionable Insights

In the previous post of this series we showed how we collect diagnostic and state data from Universal Robots using a Raspberry Pi and the official RTDE protocol. This post is about the next step: visualization and analysis.

Using InfluxDB for time-series storage and Grafana for dashboards, the collected joint, state, and diagnostic data becomes something a maintenance team can actually use: trends per joint and per program, comparisons across robots and time periods, and alert thresholds on the signals that matter.

This is the pattern behind our robot condition monitoring solution: collect the data the robot already produces, structure it, visualize it – and turn it into early warnings and better maintenance decisions.