Project 11: Multi-Robot Swarm (Global Data Space)

A 3-robot swarm in simulation with a controller that keeps formation.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 2-3 weeks
Main Programming Language Python
Alternative Programming Languages C++
Coolness Level Level 5: Pure Magic
Business Potential 4. The Open Core Infrastructure
Prerequisites ROS 2 namespaces, tf2 basics, Gazebo or simulation
Key Topics Namespaces and Remapping, Graph Introspection, TF Frame Isolation

1. Learning Objectives

By completing this project, you will:

  1. Explain how Namespaces and Remapping affects ROS 2 behavior in this project.
  2. Implement the core pipeline for Project 11 and validate it with a deterministic demo.
  3. Measure and document performance or correctness under at least one stress condition.
  4. Produce artifacts (configs, logs, scripts) that make the system reproducible.

2. All Theory Needed (Per-Concept Breakdown)

Namespaces and Remapping

Fundamentals

Namespaces and Remapping is how ROS 2 isolates multiple robots by prefixing names and remapping topics. In ROS 2, this concept defines how nodes coordinate, exchange data, and enforce guarantees. At a minimum you should be able to name the primary entities involved, identify where configuration lives, and explain how namespace and remap influence behavior. When you debug a system, you will almost always inspect ros-args or node name first because those details surface mismatches early. The practical goal is to build a mental map that connects the API knobs you change to the wire-level or runtime effects you observe. If you can explain this concept without naming a single ROS 2 command, you know it as a systems principle rather than a tooling trick, which is exactly what you need for production robotics.

Deep Dive into the concept

A deeper look at Namespaces and Remapping starts by tracing data from the API surface to the middleware. Every time you configure namespace or remap, ROS 2 expresses that intent in the rmw layer, which then maps the intent into DDS-RTPS structures. The mapping is not always one-to-one: a single policy or field can affect multiple runtime behaviors, including buffering, matching, and timing. This is why a simple change in ros-args can cause a subscriber to stop receiving data, or why two vendors can discover each other but never exchange payloads. The useful diagnostic strategy is to observe the graph (who matched), then the transport (what packets appear), and finally the runtime state (queues, deadlines, timers).

Failure modes cluster around mismatched assumptions. If node name is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If topic name is too restrictive, you will observe a graph that looks healthy but never transitions into active data flow. In embedded settings, this can appear as missed deadlines or watchdog resets rather than explicit errors. A robust design therefore includes explicit validation: log the effective policy, emit version identifiers, and test a known-good baseline before you change parameters. This project forces that discipline because you will create repeatable experiments and capture deterministic outputs, so you can explain not only what happened but why it happened.

How this fits on projects

This concept directly shapes how you implement and validate Project 11. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • namespace: namespace in the context of Namespaces and Remapping and ROS 2 systems.
  • remap: remap in the context of Namespaces and Remapping and ROS 2 systems.
  • ros-args: ros-args in the context of Namespaces and Remapping and ROS 2 systems.
  • node name: node name in the context of Namespaces and Remapping and ROS 2 systems.
  • topic name: topic name in the context of Namespaces and Remapping and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Namespaces and Remapping] -> [rmw/DDS] -> [Wire/Runtime Effects]
       |             |               |                 |
   Config/API     Policies        Entities         Observability

How it works (step-by-step, with invariants and failure modes)

  1. A node configures the concept through API calls or config files.
  2. The rmw layer translates the settings into DDS/RTPS fields (namespace, remap).
  3. Peers evaluate compatibility, matching, or timing using ros-args and node name.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm topic name behavior.

Minimal concrete example

ros2 run pkg node --ros-args -r __ns:=/robot1 -r /cmd_vel:=/robot1/cmd_vel

Common misconceptions

  • Assuming defaults are identical across vendors.
  • Believing that discovery implies data flow without validating compatibility.

Check-your-understanding questions

  1. Explain how Namespaces and Remapping changes runtime behavior in ROS 2.
  2. Predict what happens if namespace conflicts with remap.
  3. Why might two nodes discover each other but still exchange no data?

Check-your-understanding answers

  1. It alters matching, buffering, or timing constraints expressed via DDS/RTPS.
  2. The endpoints fail to match or drop messages due to incompatible policy/encoding.
  3. QoS or policy mismatch prevents writer-reader matching or delivery.

Real-world applications

  • multi-robot simulation
  • fleet deployments

Where you’ll apply it

  • You will apply it in Section 5.4 (Concepts You Must Understand First), Section 5.10 (Implementation Phases), and Section 6.2 (Critical Test Cases).
  • Also used in: P12-the-performance-tuner-dds-xml-profiles.md and other projects in this series.

References

  • ROS 2 remapping docs

Key insights

  • Namespaces and Remapping is the lever that connects configuration to observable system behavior.

Summary

This concept is the bridge between theory and runtime evidence. Mastery means you can predict outcomes, not just observe them.

Homework/Exercises to practice the concept

  1. Capture or log a minimal trace where this concept is visible.
  2. Change one policy/setting and predict the system impact before running it.
  3. Explain the failure mode you expect if the configuration is wrong.

Solutions to the homework/exercises

  1. The trace should show the concept-specific fields or events you expect.
  2. Your prediction should name which endpoints match and how latency/loss changes.
  3. A wrong configuration should lead to mismatch, dropped data, or timeouts.

Graph Introspection

Fundamentals

Graph Introspection is observing the ROS graph via CLI or APIs to verify nodes, topics, and services. In ROS 2, this concept defines how nodes coordinate, exchange data, and enforce guarantees. At a minimum you should be able to name the primary entities involved, identify where configuration lives, and explain how ros2 node list and ros2 topic list influence behavior. When you debug a system, you will almost always inspect graph cache or rqt_graph first because those details surface mismatches early. The practical goal is to build a mental map that connects the API knobs you change to the wire-level or runtime effects you observe. If you can explain this concept without naming a single ROS 2 command, you know it as a systems principle rather than a tooling trick, which is exactly what you need for production robotics.

Deep Dive into the concept

A deeper look at Graph Introspection starts by tracing data from the API surface to the middleware. Every time you configure ros2 node list or ros2 topic list, ROS 2 expresses that intent in the rmw layer, which then maps the intent into DDS-RTPS structures. The mapping is not always one-to-one: a single policy or field can affect multiple runtime behaviors, including buffering, matching, and timing. This is why a simple change in graph cache can cause a subscriber to stop receiving data, or why two vendors can discover each other but never exchange payloads. The useful diagnostic strategy is to observe the graph (who matched), then the transport (what packets appear), and finally the runtime state (queues, deadlines, timers).

Failure modes cluster around mismatched assumptions. If rqt_graph is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If configuration is too restrictive, you will observe a graph that looks healthy but never transitions into active data flow. In embedded settings, this can appear as missed deadlines or watchdog resets rather than explicit errors. A robust design therefore includes explicit validation: log the effective policy, emit version identifiers, and test a known-good baseline before you change parameters. This project forces that discipline because you will create repeatable experiments and capture deterministic outputs, so you can explain not only what happened but why it happened.

How this fits on projects

This concept directly shapes how you implement and validate Project 11. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • ros2 node list: ros2 node list in the context of Graph Introspection and ROS 2 systems.
  • ros2 topic list: ros2 topic list in the context of Graph Introspection and ROS 2 systems.
  • graph cache: graph cache in the context of Graph Introspection and ROS 2 systems.
  • rqt_graph: rqt_graph in the context of Graph Introspection and ROS 2 systems.
  • configuration: configuration in the context of Graph Introspection and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Graph Introspection] -> [rmw/DDS] -> [Wire/Runtime Effects]
       |             |               |                 |
   Config/API     Policies        Entities         Observability

How it works (step-by-step, with invariants and failure modes)

  1. A node configures the concept through API calls or config files.
  2. The rmw layer translates the settings into DDS/RTPS fields (ros2 node list, ros2 topic list).
  3. Peers evaluate compatibility, matching, or timing using graph cache and rqt_graph.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm configuration behavior.

Minimal concrete example

ros2 node list; ros2 topic info /scan

Common misconceptions

  • Assuming defaults are identical across vendors.
  • Believing that discovery implies data flow without validating compatibility.

Check-your-understanding questions

  1. Explain how Graph Introspection changes runtime behavior in ROS 2.
  2. Predict what happens if ros2 node list conflicts with ros2 topic list.
  3. Why might two nodes discover each other but still exchange no data?

Check-your-understanding answers

  1. It alters matching, buffering, or timing constraints expressed via DDS/RTPS.
  2. The endpoints fail to match or drop messages due to incompatible policy/encoding.
  3. QoS or policy mismatch prevents writer-reader matching or delivery.

Real-world applications

  • debugging discovery
  • deployment validation

Where you’ll apply it

  • You will apply it in Section 5.4 (Concepts You Must Understand First), Section 5.10 (Implementation Phases), and Section 6.2 (Critical Test Cases).
  • Also used in: P12-the-performance-tuner-dds-xml-profiles.md and other projects in this series.

References

  • ROS 2 CLI docs
  • rcl graph APIs

Key insights

  • Graph Introspection is the lever that connects configuration to observable system behavior.

Summary

This concept is the bridge between theory and runtime evidence. Mastery means you can predict outcomes, not just observe them.

Homework/Exercises to practice the concept

  1. Capture or log a minimal trace where this concept is visible.
  2. Change one policy/setting and predict the system impact before running it.
  3. Explain the failure mode you expect if the configuration is wrong.

Solutions to the homework/exercises

  1. The trace should show the concept-specific fields or events you expect.
  2. Your prediction should name which endpoints match and how latency/loss changes.
  3. A wrong configuration should lead to mismatch, dropped data, or timeouts.

TF Frame Isolation

Fundamentals

TF Frame Isolation is keeping TF frames unique per robot to avoid transform collisions. In ROS 2, this concept defines how nodes coordinate, exchange data, and enforce guarantees. At a minimum you should be able to name the primary entities involved, identify where configuration lives, and explain how tf_prefix and frame_id influence behavior. When you debug a system, you will almost always inspect tree or transform first because those details surface mismatches early. The practical goal is to build a mental map that connects the API knobs you change to the wire-level or runtime effects you observe. If you can explain this concept without naming a single ROS 2 command, you know it as a systems principle rather than a tooling trick, which is exactly what you need for production robotics.

Deep Dive into the concept

A deeper look at TF Frame Isolation starts by tracing data from the API surface to the middleware. Every time you configure tf_prefix or frame_id, ROS 2 expresses that intent in the rmw layer, which then maps the intent into DDS-RTPS structures. The mapping is not always one-to-one: a single policy or field can affect multiple runtime behaviors, including buffering, matching, and timing. This is why a simple change in tree can cause a subscriber to stop receiving data, or why two vendors can discover each other but never exchange payloads. The useful diagnostic strategy is to observe the graph (who matched), then the transport (what packets appear), and finally the runtime state (queues, deadlines, timers).

Failure modes cluster around mismatched assumptions. If transform is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If namespace is too restrictive, you will observe a graph that looks healthy but never transitions into active data flow. In embedded settings, this can appear as missed deadlines or watchdog resets rather than explicit errors. A robust design therefore includes explicit validation: log the effective policy, emit version identifiers, and test a known-good baseline before you change parameters. This project forces that discipline because you will create repeatable experiments and capture deterministic outputs, so you can explain not only what happened but why it happened.

How this fits on projects

This concept directly shapes how you implement and validate Project 11. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • tf_prefix: tf_prefix in the context of TF Frame Isolation and ROS 2 systems.
  • frame_id: frame_id in the context of TF Frame Isolation and ROS 2 systems.
  • tree: tree in the context of TF Frame Isolation and ROS 2 systems.
  • transform: transform in the context of TF Frame Isolation and ROS 2 systems.
  • namespace: namespace in the context of TF Frame Isolation and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [TF Frame Isolation] -> [rmw/DDS] -> [Wire/Runtime Effects]
       |             |               |                 |
   Config/API     Policies        Entities         Observability

How it works (step-by-step, with invariants and failure modes)

  1. A node configures the concept through API calls or config files.
  2. The rmw layer translates the settings into DDS/RTPS fields (tf_prefix, frame_id).
  3. Peers evaluate compatibility, matching, or timing using tree and transform.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm namespace behavior.

Minimal concrete example

frame_id: robot1/base_link

Common misconceptions

  • Assuming defaults are identical across vendors.
  • Believing that discovery implies data flow without validating compatibility.

Check-your-understanding questions

  1. Explain how TF Frame Isolation changes runtime behavior in ROS 2.
  2. Predict what happens if tf_prefix conflicts with frame_id.
  3. Why might two nodes discover each other but still exchange no data?

Check-your-understanding answers

  1. It alters matching, buffering, or timing constraints expressed via DDS/RTPS.
  2. The endpoints fail to match or drop messages due to incompatible policy/encoding.
  3. QoS or policy mismatch prevents writer-reader matching or delivery.

Real-world applications

  • multi-robot navigation
  • simulation testbeds

Where you’ll apply it

  • You will apply it in Section 5.4 (Concepts You Must Understand First), Section 5.10 (Implementation Phases), and Section 6.2 (Critical Test Cases).
  • Also used in: P12-the-performance-tuner-dds-xml-profiles.md and other projects in this series.

References

  • tf2 docs
  • ROS 2 TF tutorials

Key insights

  • TF Frame Isolation is the lever that connects configuration to observable system behavior.

Summary

This concept is the bridge between theory and runtime evidence. Mastery means you can predict outcomes, not just observe them.

Homework/Exercises to practice the concept

  1. Capture or log a minimal trace where this concept is visible.
  2. Change one policy/setting and predict the system impact before running it.
  3. Explain the failure mode you expect if the configuration is wrong.

Solutions to the homework/exercises

  1. The trace should show the concept-specific fields or events you expect.
  2. Your prediction should name which endpoints match and how latency/loss changes.
  3. A wrong configuration should lead to mismatch, dropped data, or timeouts.

3. Project Specification

3.1 What You Will Build

A 3-robot swarm in simulation with a controller that keeps formation.

Included features:

  • Deterministic startup with explicit configuration.
  • Observability (logs/CLI output) that exposes discovery/data flow.
  • A reproducible demo and a failure case.

Excluded on purpose:

  • Full robot control stacks or SLAM pipelines.
  • Custom GUIs beyond CLI output.

3.2 Functional Requirements

  1. **Namespace isolation: **Namespace isolation -> /robot1, /robot2, /robot3.
  2. **TF frame management: **TF frame management -> Unique frame IDs.
  3. **Distributed control: **Distributed control -> Aggregating state across robots.
  4. Deterministic startup: The project must start with a reproducible, logged configuration.
  5. Observability: Provide CLI or log output that confirms each major component is working.

3.3 Non-Functional Requirements

  • Performance: Must meet the throughput/latency targets documented in the benchmark.\n- Reliability: Must handle common network or runtime failures gracefully.\n- Usability: CLI flags and logs must make configuration and diagnosis obvious.

3.4 Example Usage / Output

$ ros2 launch swarm_sim swarm.launch.py robot_count:=3
[INFO] formation maintained

3.5 Data Formats / Schemas / Protocols

launch args: robot_count=3, base_ns=/robot

3.6 Edge Cases

  • Namespace collision
  • TF frame conflict
  • Partial robot availability

3.7 Real World Outcome

By the end of this project you will have a reproducible system that produces the same observable signals every time you run it. You will be able to point to console output, captured packets, or bag files and explain exactly why the result is correct. You will also be able to force a failure and demonstrate a clean error path.

3.7.1 How to Run (Copy/Paste)

# Build
colcon build --packages-select project_11
# Run
source install/setup.bash
# Start the main node/tool
./run_project_11.sh

3.7.2 Golden Path Demo (Deterministic)

$ ros2 launch swarm_sim swarm.launch.py robot_count:=3
[INFO] formation maintained

3.7.3 Failure Demo (Deterministic)

$ ros2 launch swarm_sim swarm.launch.py robot_count:=0
[ERROR] robot_count must be >=1

4. Solution Architecture

4.1 High-Level Design

[Input/Config] -> [Core Engine] -> [ROS 2/DDS] -> [Observability Output]

4.2 Key Components

Component Responsibility Key Decisions
Robot Instances Launch N robots with unique namespaces Consistent remapping
Formation Controller Aggregate state and command velocity Shared time base
TF Manager Ensure per-robot frame isolation Prefix enforcement

4.3 Data Structures (No Full Code)

struct RobotState {
  std::string ns;
  Pose2D pose;
};

4.4 Algorithm Overview

Key Algorithm: Core Pipeline

  1. Launch robots
  2. Subscribe to each state
  3. Compute formation
  4. Publish commands

Complexity Analysis:

  • Time: O(n) over messages/events processed
  • Space: O(1) to O(n) depending on buffering

5. Implementation Guide

5.1 Development Environment Setup

# Install ROS 2 and dependencies
sudo apt-get update
sudo apt-get install -y ros-$ROS_DISTRO-ros-base python3-colcon-common-extensions

5.2 Project Structure

project-root/
|-- src/
|   |-- main.cpp
|   |-- config.yaml
|   `-- utils.cpp
|-- scripts/
|   `-- run_project.sh
|-- tests/
|   `-- test_core.py
`-- README.md

5.3 The Core Question You’re Answering

“How can multiple robots share one DDS data space without collisions?”

5.4 Concepts You Must Understand First

Stop and research these before coding:

  1. Namespaces and Remapping
    • What breaks if this is misconfigured?
    • How will you observe it?
  2. Graph Introspection
    • What breaks if this is misconfigured?
    • How will you observe it?
  3. TF Frame Isolation
    • What breaks if this is misconfigured?
    • How will you observe it?

5.5 Questions to Guide Your Design

  1. How will you avoid topic name collisions?
  2. How will you coordinate transforms (TF)?

5.6 Thinking Exercise

Design the namespace scheme for 10 robots.

5.7 The Interview Questions They’ll Ask

  1. “Why are namespaces critical in multi-robot systems?”
  2. “How do you avoid TF frame conflicts?”

5.8 Hints in Layers

Hint 1: Use --ros-args -r remapping Hint 2: Prefix TF frames with robot namespace Hint 3: Verify with ros2 node list Confirm that each robot has a unique namespace. Hint 4: Use launch files for repetition Launch the same node multiple times with different namespaces.

5.9 Books That Will Help

Topic Book Chapter
Topic Book Chapter
Distributed Systems “Computer Networks” Ch. 7

5.10 Implementation Phases

Phase 1: Foundation (2-3 days)

Goals:

  • Reproduce the baseline example from the original project outline.
  • Validate toolchain, dependencies, and environment variables.

Tasks:

  1. Create the repository and baseline project structure.
  2. Run a minimal example to confirm discovery/data flow.

Checkpoint: You can reproduce the minimal example and collect logs.

Phase 2: Core Functionality (2-3 weeks)

Goals:

  • Implement the full feature set from the requirements.
  • Instrument key metrics and logs.

Tasks:

  1. Implement each component and integrate them.
  2. Add CLI/config flags for core parameters.

Checkpoint: Golden path demo succeeds with deterministic output.

Phase 3: Polish & Edge Cases (3-5 days)

Goals:

  • Handle failure scenarios and document them.
  • Create a short report/README describing results.

Tasks:

  1. Add error handling, timeouts, and validation.
  2. Capture failure demo output and metrics.

Checkpoint: Failure demo yields the expected errors and exit codes.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Transport UDP, shared memory, serial UDP for baseline Simplest to observe and debug
QoS Default, tuned Default then tune Establish baseline before optimization

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Validate parsers and helpers Packet decoder, config parser
Integration Tests End-to-end ROS 2 flow Publisher -> Subscriber -> Metrics
Edge Case Tests Failures & mismatches Wrong domain ID, missing config

6.2 Critical Test Cases

  1. Test 1: Baseline message flow works end-to-end.
  2. Test 2: Configuration mismatch produces a clear, actionable error.
  3. Test 3: Performance/latency stays within documented bounds.

6.3 Test Data

Use a fixed dataset or fixed random seed to make metrics reproducible.

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
QoS mismatch Discovery works but no data Align policies explicitly
Misconfigured env vars No nodes discovered Print and validate env on startup
Network filtering Intermittent data Check firewall and multicast settings

7.2 Debugging Strategies

  • Start from the graph: confirm discovery before tuning QoS.
  • Capture packets: validate that RTPS traffic appears on expected ports.

7.3 Performance Traps

If throughput is low, check for unnecessary serialization, small history depth, or lack of shared memory.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add verbose logging and a dry-run mode.
  • Add a simple configuration file parser.

8.2 Intermediate Extensions

  • Add metrics export to CSV or JSON.
  • Add automated regression tests.

8.3 Advanced Extensions

  • Implement cross-vendor compatibility validation.
  • Add chaos testing with randomized loss/latency patterns.

9. Real-World Connections

9.1 Industry Applications

  • Fleet robotics where reliability must be guaranteed under lossy Wi-Fi.
  • Industrial systems that require deterministic startup and clear failure modes.
  • ROS 2 core repositories (rcl, rmw, rosidl)
  • DDS vendors: Fast DDS, Cyclone DDS

9.3 Interview Relevance

  • Explain QoS compatibility and discovery failures.
  • Describe how to debug why nodes discover but do not communicate.

10. Resources

10.1 Essential Reading

  • “A Concise Introduction to Robot Programming with ROS 2” (focus on the sections related to Namespaces and Remapping)
  • ROS 2 official docs for the specific APIs used in this project

10.2 Video Resources

  • ROS 2 community talks on middleware and DDS
  • Vendor tutorials on discovery and QoS

10.3 Tools & Documentation

  • ROS 2 CLI and rclcpp/rclpy docs
  • Wireshark or tcpdump for network visibility
  • Project 10: Builds prerequisite concepts
  • Project 12: Extends the middleware layer

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain Namespaces and Remapping without notes
  • I can explain how QoS and discovery interact
  • I understand why the system fails when policies mismatch

11.2 Implementation

  • All functional requirements are met
  • Golden path demo succeeds
  • Failure demo produces expected errors

11.3 Growth

  • I can explain this project in a technical interview
  • I documented lessons learned and configs
  • I can reproduce the results on another machine

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Golden path demo output matches documentation
  • At least one failure scenario is documented
  • Metrics or logs demonstrate correct behavior

Full Completion:

  • All minimum criteria plus:
  • Compatibility verified across at least two QoS settings
  • Results written to a short report

Excellence (Going Above & Beyond):

  • Automated regression tests for discovery/QoS behavior
  • Clear compatibility matrix or benchmark chart