Robots integrate with other systems in industrial automation through standardized communication protocols, middleware, and application programming interfaces (APIs). These connections enable robots to exchange data, receive commands, and coordinate with equipment like programmable logic controllers (PLCs), sensors, and manufacturing execution systems (MES). For example, a robot might use OPC UA (a common industrial protocol) to send sensor data to a PLC, which then triggers a conveyor belt to align parts for pickup. This interoperability ensures robots operate as part of a synchronized workflow rather than isolated units.
Integration often relies on middleware or software layers that translate data between systems. Platforms like ROS-Industrial or proprietary solutions from robot manufacturers (e.g., ABB’s RobotStudio or Fanuc’s FIELD system) act as bridges. These tools handle protocol conversion, manage real-time data streams, and provide interfaces for developers to program interactions. For instance, a ROS-Industrial node might convert robot joint angles into MQTT messages for a cloud-based analytics tool. Middleware also simplifies tasks like error handling—if a vision system detects a defective part, the middleware can halt the robot and notify the MES without manual intervention.
Developers typically use APIs or SDKs to build custom integrations. Robots expose REST APIs for non-real-time tasks (e.g., logging production data to a database) or real-time interfaces like EtherCAT for motion control. For example, a Python script could use a robot’s SDK to adjust welding parameters based on input from a thermal camera. Cloud integration is increasingly common: robots stream performance data to platforms like AWS IoT Greengrass, enabling predictive maintenance. These APIs allow developers to extend robot functionality, such as integrating machine learning models for quality inspection or connecting to enterprise resource planning (ERP) systems for material tracking.