Module 1: The Robotic Nervous System - ROS 2
Decoding the Future of Humanoid Robotics
Welcome, future architects of physical intelligence! In this module we learn the concepts and architecture of ROS 2 and how it serves as the central framework for humanoid robotics.
Learning Outcomes
Upon completing this module, you will be able to:
- Explain the architectural differences and advantages of ROS 2 over ROS 1.
- Describe nodes, topics, services, and actions and when to use each.
- Map AI agent components (perception, planning, control) onto ROS 2 architecture.
- Outline URDF/Xacro modeling workflow and visualization with RViz2.
- Apply debugging and best-practice approaches conceptually in a development process.
Why ROS 2 Matters
Key points:
- Distributed, reliable communication via DDS.
- Improved real-time support and QoS control.
- Language-agnostic ecosystem allowing Python/C++ interop.
- Strong community and industry adoption.
Core Concepts (High Level)
Nodes, Topics, Publishers/Subscribers
Nodes are independent processes. Topics are named channels for asynchronous streaming data. Publishers send, subscribers receive.
Services vs Actions
Services: synchronous request/response for short operations.
Actions: long-running goals with feedback and cancellation.
DDS and QoS
DDS provides discovery and QoS (reliability, durability, deadline) that determine communication behavior.
Real-time & Safety
ROS 2 supports real-time patterns but real hard-real-time requires OS/kernel configuration and careful system design.
Hands-on (Non-executable, Conceptual Steps)
- Workspace planning: create a workspace layout with one package per functional area (perception, planning, control).
- Node design: list required nodes, their responsibilities, and the topics/services/actions they use.
- Message design: define the high-level messages and expected fields (names and semantics).
- Simulation test plan: design test cases to validate message flows and failure scenarios.
- Debug checklist: common checks (QoS mismatch, message size, stalled executors).
Note: This page intentionally contains conceptual steps only. Implementation code lives in example repos (link in Appendix → Resources) if you decide to add runnable examples later.