The synergy between is revolutionizing industrial automation, offering a robust path from prototype to production. By using shared memory bridges, developers can bridge the gap between AI-driven intelligence and hardened, deterministic control.
Step-by-Step Implementation Guide: Building an OPC UA Bridge
: This node will take your PLC data and use ros2 topic pub to push it into the ROS network. codesys ros2
Using the , you can link a PLC project to a robot simulated in Gazebo or NVIDIA Isaac Sim . This allows for "Software-in-the-Loop" (SiL) testing before the physical hardware is even built. Challenges to Consider
: PLCs excel at real-time, deterministic control. They manage microsecond-level I/O loops, execute safety functions, and communicate via robust industrial protocols like EtherCAT, PROFINET, and EtherNet/IP. However, they lack the computational flexibility needed for complex tasks like autonomous navigation, dynamic path planning, or machine learning. Using the , you can link a PLC
The primary challenge is bridging the gap between the IEC 61131-3 languages used in CODESYS and the publisher/subscriber messaging model of ROS 2. Shared Memory Bridge
Integrating CODESYS with ROS 2 allows companies to build robust, industrial-grade mobile robots and flexible manufacturing cells. By delegating high-level perception to ROS 2 and low-level deterministic control to CODESYS, engineers no longer have to compromise between cutting-edge software capabilities and rock-solid industrial reliability. They manage microsecond-level I/O loops
Instead of writing individual, unstable Linux drivers for every industrial servo and sensor, engineers can configure fieldbuses in CODESYS with just a few clicks.
It's helpful to visualize how these pieces typically fit together. The diagram below illustrates a high-performance integration pattern where CODESYS handles low-level hardware control on an edge computer, while ROS 2 runs on a separate computer for high-level processing.