Fast DDS Integration in Project Iris

**Fast DDS** (Fast Data Distribution Service) is the communication backbone of Project Iris. DDS is the industry-standard publish/subscribe middleware that ROS 2 relies on for data exchange, and eProsima’s Fast DDS is the default DDS vendor in ROS 2:contentReference[oaicite:9]{index=9}. By building directly on Fast DDS, Project Iris achieves native ROS 2 compatibility and outstanding performance.

Fast DDS is known for its low latency and high throughput. In fact, it has been benchmarked as the fastest open-source DDS implementation, outperforming even Eclipse Cyclone DDS in both throughput and latency:contentReference[oaicite:10]{index=10}. This means Project Iris can handle high-frequency sensor data and control messages with minimal delay, a critical factor for real-time robotics simulation.

Why Fast DDS over other approaches?

• **Direct ROS 2 Compatibility:** Using Fast DDS means Project Iris speaks the same "language" as ROS 2 out-of-the-box. There’s no translation layer or separate bridge process – Unreal Engine clients become DDS participants on the ROS 2 network. This contrasts with older solutions like *ROSIntegration* (which used a ROS1 rosbridge server for communication:contentReference[oaicite:11]{index=11}) and thereby required translating messages to JSON over WebSockets, adding latency and complexity.
• **Performance and Efficiency:** Fast DDS’s optimized C++ implementation and features like zero-copy transport give it an edge for real-time simulation data:contentReference[oaicite:12]{index=12}. Project Iris avoids unnecessary serialization overhead; for example, large image or point cloud messages can be transmitted efficiently without the significant slowdowns seen in rosbridge-based pipelines (which often struggle with high-bandwidth data).
• **Minimal Dependencies:** Because Project Iris integrates at the DDS layer, you don’t need a full ROS 2 installation to use it:contentReference[oaicite:13]{index=13}. Alternative plugins like *rclUE* embed the ROS 2 client libraries inside Unreal:contentReference[oaicite:14]{index=14}, which can increase complexity and package size. In contrast, Project Iris includes just the DDS middleware and generated message code it needs – keeping the plugin lightweight and deployment simple.
• **Quality of Service Control:** Leveraging DDS means we expose ROS 2’s rich QoS settings to Unreal developers. You can configure reliability (reliable vs. best-effort), durability, message history depth, and more to suit your simulation’s needs:contentReference[oaicite:15]{index=15}. This level of control isn't available in simplistic socket-based bridges. With Fast DDS, Project Iris can ensure, for instance, that critical control commands are delivered reliably, or that high-bandwidth topics like video can be sent best-effort to maximize frame rate.
• **Future-proof and Extensible:** Fast DDS is maintained as part of the ROS 2 ecosystem, benefiting from continuous improvements and full ROS 2 compatibility. By using DDS, Project Iris naturally stays compatible with new ROS 2 distributions (which continue using DDS) and can interoperate with other DDS-based tools and frameworks. The architecture is also extensible – if needed, support for other DDS vendors or transports could be added without changing the user-facing API, thanks to the abstraction DDS provides.