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CODESYS publishes data to an MQTT broker; a simple ROS2 Python node subscribes to that broker and republishes the data as a ROS2 Topic.
If you are running on the same industrial PC as your ROS2 Humble or Iron distribution, shared memory is the fastest route.
High-performance applications like low-latency robotic arm control. Use Cases: Where CODESYS Meets ROS2 Autonomous Mobile Robots (AMRs)
Use the DDS (Data Distribution Service) backbone of ROS2 to create a unified communication layer across a factory floor.
Managing two distinct build environments (CODESYS IDE and the Linux terminal/Colcon) increases the learning curve for traditional PLC engineers. Conclusion
The synergy between represents the future of Industry 4.0. By offloading complex "thinking" to ROS2 and keeping the "acting" within CODESYS, engineers can build robots that are both incredibly smart and industrially robust.
Getting CODESYS (Structured Text/Ladder Logic) to talk to ROS2 (C++/Python) requires a middleware bridge. There are three primary ways to do this: 1. The Micro-ROS Approach
Bring AI-driven vision or machine learning (via ROS2 nodes) to standard industrial hardware.
Integrating these two ecosystems allows developers to combine the "hard" real-time reliability of a PLC with the cutting-edge libraries of the robotics world. Here is an in-depth look at why this integration matters and how to achieve it. Why Integrate CODESYS with ROS2?
CODESYS publishes data to an MQTT broker; a simple ROS2 Python node subscribes to that broker and republishes the data as a ROS2 Topic.
If you are running on the same industrial PC as your ROS2 Humble or Iron distribution, shared memory is the fastest route.
High-performance applications like low-latency robotic arm control. Use Cases: Where CODESYS Meets ROS2 Autonomous Mobile Robots (AMRs)
Use the DDS (Data Distribution Service) backbone of ROS2 to create a unified communication layer across a factory floor.
Managing two distinct build environments (CODESYS IDE and the Linux terminal/Colcon) increases the learning curve for traditional PLC engineers. Conclusion
The synergy between represents the future of Industry 4.0. By offloading complex "thinking" to ROS2 and keeping the "acting" within CODESYS, engineers can build robots that are both incredibly smart and industrially robust.
Getting CODESYS (Structured Text/Ladder Logic) to talk to ROS2 (C++/Python) requires a middleware bridge. There are three primary ways to do this: 1. The Micro-ROS Approach
Bring AI-driven vision or machine learning (via ROS2 nodes) to standard industrial hardware.
Integrating these two ecosystems allows developers to combine the "hard" real-time reliability of a PLC with the cutting-edge libraries of the robotics world. Here is an in-depth look at why this integration matters and how to achieve it. Why Integrate CODESYS with ROS2?
