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Networking and connectivity are major trends in industrial production, and accordingly, advances in this area are implemented as comprehensively and efficiently as possible in robotics as well. With the SCOPE platform, Stäubli demonstrates how practical implementation works and the advantages of connected robot monitoring.

The acronym SCOPE stands for “Smart, Connect, Optimize, Prevent and Enable,” which neatly summarizes the nature of this digital solution for robot fleet monitoring. The on-site platform aggregates, processes, analyzes and visualizes not only robot data but also application and cell equipment data, allowing users to view an array of relevant operating data for each robotic system on a central dashboard. This enables the detection of irregularities or gradual deviations from specifications and allows for timely preventive measures to be taken before quality is compromised, components become damaged, or the production line comes to a halt.

The real-time data collected and the resulting SCOPE analysis can also be communicated to higher-level architecture layers as well as to databases and analysis tools. SCOPE is able to forward data via MQTT, HTTP response, and WebSocket, enabling connection to the user’s ERP, SCM, or MES system.

Transparency across the entire fleet

The benefits of SCOPE are immediately apparent: Manufacturing professionals always have a secure overview of the status of all robots. With the 2025 update, the solution is now even more powerful, enabling the monitoring of VAL3 program variables and I/Os to visualize trends and production-related data. This allows SCOPE to not only play a role in robot-specific data but also monitor cell equipment, sensors, and application-related data. Users can, for example, set thresholds for custom warning alerts. If these thresholds are exceeded, an instant notification is sent regarding deviations from target values. This empowers plant operators to react to trends early and avoid scrap. Further, the ability to evaluate log files remotely means errors can be analyzed and resolved from anywhere. Naturally, this solution represents the first step toward AI-driven optimization processes.