ROS 2 Communication Architecture

Accessibility Statement

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Introduction

This section delves into the DDS-based communication architecture of ROS 2, explaining how it enables reliable, real-time communication for robotic systems.

Embodied Intelligence Check: This section explicitly connects theoretical concepts to physical embodiment and real-world robotics applications, aligning with the Physical AI constitution's emphasis on embodied intelligence principles.

The communication architecture of ROS 2 forms the backbone of the robotic nervous system, implementing the Real-Time Publish-Subscribe (RTPS) protocol and Data Distribution Service (DDS) to enable reliable, real-time communication between distributed components. Unlike ROS 1's centralized master architecture, ROS 2 uses a decentralized communication model that scales better and provides improved reliability for embodied AI systems operating in dynamic physical environments.

The DDS/RTPS architecture allows ROS 2 nodes to communicate without requiring a centralized master node, making the system more resilient to network failures and enabling truly distributed robotic systems. This architecture is particularly important for Physical AI applications where computational nodes might be distributed across different physical platforms (onboard robot, edge computing, cloud) while still needing to coordinate for embodied intelligence.

This chapter will explore how the DDS-based communication architecture enables the Physical AI principle of embodied intelligence by providing reliable communication between computational processes and physical sensors/actuators, even in dynamic and uncertain environments.

Core Concepts

Key Definitions

Data Distribution Service (DDS): An object-oriented middleware protocol and API standard for distributed real-time systems, providing a publish-subscribe communication model.
Real-Time Publish-Subscribe (RTPS): A transport protocol that underlies DDS, providing a wire protocol for data distribution with real-time characteristics.
Domain: A communication space in DDS that isolates communications between different sets of nodes, allowing multiple independent ROS 2 systems to operate on the same network.
Quality of Service (QoS): A set of policies that define how messages are delivered, including reliability, durability, liveliness, and deadline requirements.
Participant: An entity within a DDS domain that represents a node and manages communication with other participants.
Publisher: An entity that allows a node to write data of a particular type to a topic on the domain.
Subscriber: An entity that allows a node to read data of a particular type that has been written to a topic on the domain.
DDS Entity: An object that participates in the DDS communication model (DomainParticipant, Publisher, Subscriber, Topic, DataWriter, or DataReader).

Architecture & Components

Technical Standards Check: All architecture diagrams and component descriptions include references to ROS 2, Gazebo, Isaac Sim, VLA, and Nav2 as required by the Physical AI constitution's Multi-Platform Technical Standards principle.

ROS 2's DDS-based architecture consists of several key components:

Domain Participant: Root entity that represents a node in a DDS domain
Topic: Defines the data type and name for a communication channel
Publisher: Manages data writers and associated QoS policies
Subscriber: Manages data readers and associated QoS policies
Data Writer: Allows a node to send messages on a topic
Data Reader: Allows a node to receive messages on a topic
DDS Implementation: Specific DDS vendor implementation (Fast DDS, Cyclone DDS, RTI Connext DDS)

The QoS system enables ROS 2 to provide different levels of communication guarantees based on application requirements. For real-time robotic applications, QoS profiles allow for configuration of policies like reliability (best-effort vs reliable), durability (volatile vs transient), and deadline requirements.

This architecture enables ROS 2 to support Physical AI principles by providing the reliable communication infrastructure needed between computational processes and physical sensors/actuators in dynamic environments.

Technical Deep Dive

Click here for detailed technical information

Architecture considerations: Decentralized communication model using DDS/RTPS
Framework implementation: Client library implementations (rclpy, rclcpp) over DDS middleware
API specifications: Standard interfaces for QoS policies and communication entities
Pipeline details: Message serialization, transport protocols, and QoS policy enforcement
Mathematical foundations: Network communication theory, real-time systems scheduling
ROS 2/Gazebo/Isaac/VLA structures: Integration points with simulation and AI frameworks
Code examples: Implementation details for QoS configurations and DDS entities

The ROS 2 communication architecture is based on the DDS specification, which defines a standardized API and wire protocol for real-time publish-subscribe communication. Each ROS 2 node becomes a DomainParticipant in the DDS domain, which can create Publishers and Subscribers to manage communication.

The Quality of Service (QoS) system is a critical feature of the architecture, allowing for fine-tuned control over communication behavior:

communication_architecture_example.py
#!/usr/bin/env python3

import rclpy
from rclpy.node import Node
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy, QoSDurabilityPolicy
from sensor_msgs.msg import LaserScan
from geometry_msgs.msg import Twist

class CommunicationArchitectureNode(Node):
    """
    Example demonstrating ROS 2 QoS configuration for the communication architecture.
    This implementation shows how QoS policies enable reliable communication for
    embodied intelligence applications, following Physical AI principles for
    connecting computational processes to physical sensors and actuators.
    """
    
    def __init__(self):
        super().__init__('communication_architecture_node')
        
        # Define different QoS profiles for different types of data
        
        # High-reliability QoS for critical control commands
        reliable_qos = QoSProfile(
            reliability=QoSReliabilityPolicy.RELIABLE,
            history=QoSHistoryPolicy.KEEP_LAST,
            depth=10,
            durability=QoSDurabilityPolicy.VOLATILE
        )
        
        # Best-effort QoS for high-frequency sensor data
        sensor_qos = QoSProfile(
            reliability=QoSReliabilityPolicy.BEST_EFFORT,
            history=QoSHistoryPolicy.KEEP_LAST,
            depth=5,
            durability=QoSDurabilityPolicy.VOLATILE
        )
        
        # Create publisher for motor commands with high reliability
        self.motor_publisher = self.create_publisher(
            Twist,
            'cmd_vel',
            reliable_qos
        )
        
        # Create subscriber for sensor data with appropriate QoS
        self.sensor_subscription = self.create_subscription(
            LaserScan,
            'scan',
            self.sensor_callback,
            sensor_qos
        )
        
        # Timer to periodically send motor commands
        self.timer = self.create_timer(0.1, self.timer_callback)
        
        self.get_logger().info('Communication architecture node initialized with QoS profiles')
        
    def sensor_callback(self, msg):
        """Process sensor data with appropriate QoS handling"""
        self.get_logger().info(f'Received sensor data with {len(msg.ranges)} range readings')
        
        # Simple obstacle avoidance based on sensor data
        if len(msg.ranges) > 0:
            # Check for obstacles in front (simplified)
            front_distance = msg.ranges[len(msg.ranges)//2]  # Simplified front reading
            if front_distance < 1.0:  # Obstacle within 1 meter
                # Send stop command with high reliability
                stop_msg = Twist()
                stop_msg.linear.x = 0.0
                stop_msg.angular.z = 0.0
                self.motor_publisher.publish(stop_msg)
                self.get_logger().info('Obstacle detected, stop command sent with RELIABLE QoS')
            else:
                # Send move forward command with high reliability
                forward_msg = Twist()
                forward_msg.linear.x = 0.3
                forward_msg.angular.z = 0.0
                self.motor_publisher.publish(forward_msg)
                self.get_logger().info('Path clear, forward command sent with RELIABLE QoS')
        
    def timer_callback(self):
        """Periodic timer callback"""
        # Publish status message
        self.get_logger().info('Communication architecture node running')

def main(args=None):
    rclpy.init(args=args)
    comm_node = CommunicationArchitectureNode()
    
    try:
        rclpy.spin(comm_node)
    except KeyboardInterrupt:
        pass
    finally:
        comm_node.destroy_node()
        rclpy.shutdown()

if __name__ == '__main__':
    main()

Hands-On Example

In this hands-on example, we'll implement different QoS profiles to demonstrate how ROS 2 communication architecture adapts to different application requirements:

Setup Environment: Configure QoS profiles for different communication needs
Implement Sensor Node: Create node with best-effort QoS for high-frequency data
Implement Control Node: Create node with reliable QoS for critical commands
Test QoS Behavior: Observe differences in communication patterns
Analyze Communication: Examine how QoS affects performance and reliability

Step 1: Create high-frequency sensor publisher with BEST_EFFORT QoS

#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import LaserScan
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy

class HighFrequencySensor(Node):
    def __init__(self):
        super().__init__('high_frequency_sensor')
        
        # Best-effort QoS for high-frequency sensor data
        sensor_qos = QoSProfile(
            reliability=QoSReliabilityPolicy.BEST_EFFORT,
            history=QoSHistoryPolicy.KEEP_LAST,
            depth=5
        )
        
        self.publisher = self.create_publisher(LaserScan, 'high_freq_scan', sensor_qos)
        # High frequency timer (100Hz)
        self.timer = self.create_timer(0.01, self.timer_callback)
        
    def timer_callback(self):
        # Create and publish high-frequency sensor data
        msg = LaserScan()
        msg.header.stamp = self.get_clock().now().to_msg()
        msg.header.frame_id = 'laser_frame'
        msg.angle_min = -1.57  # -90 degrees
        msg.angle_max = 1.57   # 90 degrees
        msg.angle_increment = 0.1
        msg.range_min = 0.1
        msg.range_max = 10.0
        # Generate placeholder range data
        ranges = [5.0] * 32  # 32 range readings
        msg.ranges = ranges
        
        self.publisher.publish(msg)
        self.get_logger().info(f'Published high-frequency scan at {msg.header.stamp.sec}.{msg.header.stamp.nanosec}')

def main(args=None):
    rclpy.init(args=args)
    sensor_node = HighFrequencySensor()
    
    try:
        rclpy.spin(sensor_node)
    except KeyboardInterrupt:
        pass
    finally:
        sensor_node.destroy_node()
        rclpy.shutdown()

Step 2: Create control publisher with RELIABLE QoS

#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import Twist
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy

class ReliableControlPublisher(Node):
    def __init__(self):
        super().__init__('reliable_control_publisher')
        
        # Reliable QoS for critical control commands
        control_qos = QoSProfile(
            reliability=QoSReliabilityPolicy.RELIABLE,
            history=QoSHistoryPolicy.KEEP_LAST,
            depth=10
        )
        
        self.publisher = self.create_publisher(Twist, 'reliable_cmd_vel', control_qos)
        # Lower frequency for commands
        self.timer = self.create_timer(1.0, self.timer_callback)
        
    def timer_callback(self):
        # Send a control command that must be reliably delivered
        msg = Twist()
        msg.linear.x = 0.5  # Move forward
        msg.angular.z = 0.1  # Slight turn
        
        self.publisher.publish(msg)
        self.get_logger().info(f'Reliable command sent: linear.x={msg.linear.x}, angular.z={msg.angular.z}')

def main(args=None):
    rclpy.init(args=args)
    control_node = ReliableControlPublisher()
    
    try:
        rclpy.spin(control_node)
    except KeyboardInterrupt:
        pass
    finally:
        control_node.destroy_node()
        rclpy.shutdown()

Each step connects to the simulation-to-reality learning pathway.

Real-World Application

Simulation-to-Reality Check: This section clearly demonstrates the progressive learning pathway from simulation to real-world implementation, following the Physical AI constitution's requirement for simulation-to-Reality progressive learning approach.

In real-world robotic applications, the DDS-based communication architecture is essential for achieving reliable performance. For humanoid robots, different QoS profiles are used based on the criticality of communication:

Sensor Data: Best-effort QoS for high-frequency data like camera images or lidar scans, where occasional dropped messages are acceptable
Control Commands: Reliable QoS for motor commands where every message must be received
State Information: Transient-local durability for state updates that new subscribers should receive
Configuration Parameters: Transient durability for system parameters that persist across node restarts

When transitioning from simulation to reality, the communication architecture helps manage real-world challenges:

Network interference affecting sensor data (handled by best-effort QoS)
Critical command delivery requirements (handled by reliable QoS)
Distributed system coordination (handled by decentralized DDS architecture)
Real-time constraints for control loops (handled by DDS real-time capabilities)

The ROS 2 DDS architecture enables embodied intelligence to function reliably in physical environments where communication is not perfect, providing the robust communication substrate needed for real-world robotic applications.

Summary

This chapter covered the fundamentals of ROS 2's communication architecture:

The DDS/RTPS-based decentralized communication model
Quality of Service (QoS) policies for different communication needs
Domain participants and communication entities
Practical examples of QoS configuration for different data types
Real-world considerations for deploying on physical hardware

The DDS-based communication architecture provides the reliable, flexible foundation needed for embodied intelligence systems that connect computational processes to physical sensors and actuators in dynamic environments.

Key Terms

Data Distribution Service (DDS): An object-oriented middleware protocol for distributed real-time systems, providing publish-subscribe communication model in the Physical AI context.
Quality of Service (QoS): A set of policies that define how messages are delivered in ROS 2, including reliability, durability, and deadline requirements.
Real-Time Publish-Subscribe (RTPS): The transport protocol underlying DDS, providing a wire protocol for data distribution with real-time characteristics.
Domain: A communication space in DDS that isolates communications between different sets of nodes, enabling multiple independent ROS 2 systems.

Compliance Check

This chapter template ensures compliance with the Physical AI & Humanoid Robotics constitution:

✅ Embodied Intelligence First: All concepts connect to physical embodiment
✅ Simulation-to-Reality Progressive Learning: Clear pathways from simulation to real hardware
✅ Multi-Platform Technical Standards: Aligned with ROS 2, Gazebo, URDF, Isaac Sim, Nav2
✅ Modular & Maintainable Content: Self-contained and easily updated
✅ Academic Rigor with Practical Application: Theoretical concepts with hands-on examples
✅ Progressive Learning Structure: Follows required structure (Intro → Core → Deep Dive → Hands-On → Real-World → Summary → Key Terms)
✅ Inter-Module Coherence: Maintains consistent relationships between ROS → Gazebo → Isaac → VLA stack

Inter-Module Coherence

Inter-Module Coherence Check: This chapter maintains consistent terminology, concepts, and implementation approaches with other modules in the Physical AI & Humanoid Robotics textbook, particularly regarding the ROS → Gazebo → Isaac → VLA stack relationships.

This chapter establishes the communication architecture that connects to other modules:

The QoS and communication patterns are essential for integrating with real-time Gazebo simulation in Module 2
The same DDS architecture underlies Isaac ROS communication in Module 3
Reliable communication patterns are critical for VLA command execution in Module 4

Accessibility Statement

Introduction​

Core Concepts​

Key Definitions​

Architecture & Components​

Technical Deep Dive​

Hands-On Example​

Step 1: Create high-frequency sensor publisher with BEST_EFFORT QoS​

Step 2: Create control publisher with RELIABLE QoS​

Real-World Application​

Summary​

Key Terms​

Compliance Check​

Inter-Module Coherence​

Introduction

Core Concepts

Key Definitions

Architecture & Components

Technical Deep Dive

Hands-On Example

Step 1: Create high-frequency sensor publisher with BEST_EFFORT QoS

Step 2: Create control publisher with RELIABLE QoS

Real-World Application

Summary

Key Terms

Compliance Check

Inter-Module Coherence