ROS 2 Integration: Scalable Modular Robotics

Updated 15 February 2026

ROS 2 integration is an open, modular robotics middleware that decouples application logic from hardware using a layered architecture and standardized APIs.
It employs flexible QoS settings, node composition, and real-time scheduling to achieve lower latency and optimized resource usage in complex deployments.
Real-world applications span land, sea, air, and space, integrating legacy systems, custom hardware, cloud, and web platforms for scalable robotic solutions.

The Robot Operating System 2 (ROS 2) is an open, modular, and production-oriented robotics middleware designed to address the challenges of building complex, scalable, and robust robotic systems. ROS 2 integration denotes the process, methods, and best practices of embedding, customizing, and extending ROS 2 in diverse robotics applications. Its layered architecture, flexible communication paradigms, multi-language toolchain, and extensive Quality-of-Service (QoS) features enable cross-platform, real-time, and large-scale deployments across land, air, sea, and space domains (Macenski et al., 2022).

1. Layered Architecture and Integration Abstractions

ROS 2 uses a strictly layered architecture that decouples application-level logic from communication middleware and hardware implementations. At the top, client libraries (rclcpp for C++, rclpy for Python, rclc for embedded C, etc.) expose the core primitives—Nodes, Topics, Services, and Actions—through standardized APIs. These interact with the common C API (rcl), which sits above the ROS Middleware (rmw) abstraction. The rmw interface allows integrators to select or replace the underlying DDS vendor (e.g., CycloneDDS, Fast-DDS, RTI Connext) or, less commonly, non-DDS transports (Macenski et al., 2022). This separation provides portability and enables integration with custom network stacks or real‐time communication substrates.

Nodes are the fundamental units of computation and can exist as individual OS processes or as composable components within a shared process. Topics provide asynchronous many-to-many communication; Services offer synchronous request/response; Actions extend Services to allow preemptible, long-running, feedback-driven tasks.

Executors determine the scheduling of callback processing for subscriptions, timers, services, and actions, and can be customized for single-threaded, multi-threaded, or user-specific execution models.

2. Integration Patterns, Code Exemplars, and Composition

ROS 2 nodes publish and subscribe to topics using explicit QoS settings:

auto qos = rclcpp::QoS(rclcpp::KeepLast(10));
auto pub = node->create_publisher<std_msgs::msg::String>("chatter", qos);
...
pub->publish(msg);

Dynamic parameter management is achieved using YAML configuration and C++/Python API access.

Component Composition

Node composition enables multiple components to be aggregated into a single process, exploiting intra-process zero-copy communication (IPC ring buffers) and reducing overhead (Macenski et al., 2023). The rclcpp_components framework allows dynamic or manual registration and loading of composable nodes. Composed nodes share a DDS participant and executor, enabling substantial reductions in memory footprint, message latency, and CPU load, particularly for high-bandwidth dataflows such as vision and LiDAR. Benchmarks demonstrate up to 2–5× lower latency, 3× lower memory, and 10× lower CPU utilization compared to multi-process arrangements (Macenski et al., 2023).

Nodes	Multi-Proc PSS [MB]	Dynamic PSS [MB]
1	9.5	10.2
20	72.1	13.2

Manual composition is favored in static, resource-constrained deployments; dynamic composition is optimal for prototyping and modular development.

3. QoS, Real-Time, and Validation Workflows

All core transport semantics are governed by finely tunable DDS QoS settings:

Reliability: $R \in \{\text{RELIABLE}, \text{BEST\_EFFORT}\}$ .
Durability: $\delta \in \{\text{VOLATILE}, \text{TRANSIENT\_LOCAL}\}$ .
History: $H \in \mathbb{N} \cup \{\text{ALL}\}$ , e.g., KEEP_LAST( $H$ ).
Deadline: $D \in \mathbb{R}^+$ , requiring $\forall i,\ t_{i+1} - t_i \le D$ .

Critical applications, especially those combining wired Ethernet with Wi-Fi or high-rate sensors, require explicit QoS selection to avoid default settings that degrade under heavy load or adverse network conditions (Macenski et al., 2022).

For hard real-time systems, recent advances allow the ROS 2 “events executor” to be retrofitted for classic non-preemptive scheduling policies (Rate-Monotonic, EDF), analytically bounding worst-case response times and end-to-end latency with minimal backend changes (Teper et al., 2024). Worst-case latency and schedulability are determined via standard formulas:

$\text{For fixed-priority (RM):}\quad t \geq C_k + \max_{i>k} C_i + \sum_{i<k} \lceil t/T_i \rceil C_i$

$\text{End-to-end chain latency:}\quad \text{Lat} \leq \sum_{j=1}^N (T_{i_j} + R_{i_j})$

Empirical studies show that such scheduling can achieve a median end-to-end latency reduction of ~35% (60% system utilization), with worst-case latency for tight Autoware pipelines dropping from 120 ms to ~45 ms (Teper et al., 2024).

4. Cross-Platform, Multi-Language, and Web Integration

ROS 2 supports a wide range of deployment targets:

Desktop: Ubuntu (20.04+), Debian, Fedora, Windows 10/11, macOS (limited).
Embedded: micro-ROS on Cortex-M MCUs with RTOS.
Cloud: DDS-WAN, cloud bridges (e.g., AWS RoboRunner), secure global overlays (Chen et al., 2023).
Web: The ROS2WASM project cross-compiles ROS 2 and its ecosystem (via RoboStack, emscripten, and rmw-wasm) into portable WebAssembly modules, enabling the execution of ROS 2 nodes and scientific stacks (PyTorch, Robotics Toolbox) directly in the browser with negligible setup, strict sandboxing, and reproducible results (Fischer et al., 2024). WASM integration yields latency overheads of only ~20% and sustains event rates >14 kHz, with memory and CPU usage increases under 30%.

Multi-language integration is achieved via direct client support (C++, Python, C) and robust meta-build (colcon, rosdep, ament).

5. Interfacing Hardware, Legacy Libraries, and Industry Examples

Hardware/Software Integration and Heterogeneous Computing

Integrating custom hardware and legacy libraries is supported via custom message types, bridges (e.g., ros1_bridge, MQTT), and the extension of DDS for zero-copy shared memory. For high-performance robotics (e.g., autonomous vehicles), frameworks such as ReconROS map compute-intensive nodes onto FPGA accelerators, with shared memory communication (e.g., CycloneDDS + Iceoryx) and hardware/software gateway architectures. These approaches yield up to 12.5× CPU reduction on perception/dataflow tasks and enable real-time system partitioning at the ROS 2 graph level (Lienen et al., 2023, Lienen et al., 2023).

Representative Real-World Deployments

Land: Ghost Robotics: Jetson Xavier with ROS 2, <1 ms latency stacking perception and control, lifecycle nodes to configure mission subsystems.
Sea: Mission Robotics: direct CycloneDDS with modular plug-in of sensor/driver nodes, containerized deployment.
Air: Auterion: full flight data logging via rosbag2, in-simulation CI with Gazebo.
Space: NASA VIPER: modular ROS 2 stack for earth-based ops, reusing hundreds of thousands of lines of upstream code.

Large deployments, e.g., warehouses by OTTO Motors, demonstrate ROS 2’s ability to scale from 25 to 100+ AGVs, saving millions in operational costs by leveraging DDS peer-to-peer models (Macenski et al., 2022).

6. Best Practices, Pitfalls, Security, and Toolchain

Key recommendations for integration include:

Definitively declare message types and QoS profiles appropriate to network topology; avoid default QoS especially over heterogeneous networks.
Exploit node composition for efficient locality and low-latency intra-process pipelines, particularly for vision and point cloud streams.
Leverage lifecycle nodes for coordinated startup/teardown, especially in multi-robot and safety-critical contexts.
Recognize and mitigate pitfalls: KEEP_ALL histories can exhaust memory, DDS default fragmentation settings can fail for >1 MB payloads, and inappropriate DDS tuning will degrade real-time properties.
Security: full DDS-Security via SROS2 is available for enabling authentication, encryption, and access control (Macenski et al., 2022).
Real-time: consolidate critical callbacks on isolated cores, use certified DDS implementations, and correctly configure deadline/lifespan/liveliness QoS for deterministic execution.

The preferred build/test workflow includes dependency installation (rosdep), multi-package build (colcon, ament), and simulation/validation via Gazebo, rosbag2, and continuous integration systems.

End-to-end examples, including package.xml and CMakeLists.txt construction, parameter YAMLs, and launch files, are standardized and critical for reproducibility.

For advanced, performance-sensitive, or heterogeneous workflows, the references and quantitative benchmarks in (Macenski et al., 2022) and (Macenski et al., 2023) provide validated integration strategies for building scalable, predictable, and robust ROS 2-based robotic systems.