Project 7: The “Path Follower” (Actions vs. Services)

A path-following robot implemented once as a service and once as an action, then compare behavior.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 2-3 weeks
Main Programming Language C++
Alternative Programming Languages Python
Coolness Level Level 2: Practical but Forgettable
Business Potential 2. The Micro-SaaS / Pro Tool
Prerequisites ROS 2 actions, C++/Python async, basic kinematics
Key Topics Action Lifecycle, Service Blocking Behavior, Asynchronous Design Patterns

1. Learning Objectives

By completing this project, you will:

  1. Explain how Action Lifecycle affects ROS 2 behavior in this project.
  2. Implement the core pipeline for Project 7 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)

Action Lifecycle

Fundamentals

Action Lifecycle is the goal-feedback-result model used by ROS 2 actions for long-running tasks. 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 goal and feedback influence behavior. When you debug a system, you will almost always inspect result or cancel 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 Action Lifecycle starts by tracing data from the API surface to the middleware. Every time you configure goal or feedback, 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 result 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 cancel is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If status 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 7. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • goal: goal in the context of Action Lifecycle and ROS 2 systems.
  • feedback: feedback in the context of Action Lifecycle and ROS 2 systems.
  • result: result in the context of Action Lifecycle and ROS 2 systems.
  • cancel: cancel in the context of Action Lifecycle and ROS 2 systems.
  • status: status in the context of Action Lifecycle and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Action Lifecycle] -> [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 (goal, feedback).
  3. Peers evaluate compatibility, matching, or timing using result and cancel.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm status behavior.

Minimal concrete example

SendGoal -> Feedback -> Result (or Cancel)

Common misconceptions

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

Check-your-understanding questions

  1. Explain how Action Lifecycle changes runtime behavior in ROS 2.
  2. Predict what happens if goal conflicts with feedback.
  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

  • navigation goals
  • manipulation tasks

Where you’ll apply it

References

  • ROS 2 actions docs
  • rclcpp_action API

Key insights

  • Action Lifecycle 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.

Service Blocking Behavior

Fundamentals

Service Blocking Behavior is how synchronous services can block executors and why actions are different. 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 request/response and blocking influence behavior. When you debug a system, you will almost always inspect executor starvation or timeouts 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 Service Blocking Behavior starts by tracing data from the API surface to the middleware. Every time you configure request/response or blocking, 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 executor starvation 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 timeouts 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 7. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • request/response: request/response in the context of Service Blocking Behavior and ROS 2 systems.
  • blocking: blocking in the context of Service Blocking Behavior and ROS 2 systems.
  • executor starvation: executor starvation in the context of Service Blocking Behavior and ROS 2 systems.
  • timeouts: timeouts in the context of Service Blocking Behavior and ROS 2 systems.
  • configuration: configuration in the context of Service Blocking Behavior and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Service Blocking Behavior] -> [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 (request/response, blocking).
  3. Peers evaluate compatibility, matching, or timing using executor starvation and timeouts.
  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

client->async_send_request(req); spin_until_future_complete(...)

Common misconceptions

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

Check-your-understanding questions

  1. Explain how Service Blocking Behavior changes runtime behavior in ROS 2.
  2. Predict what happens if request/response conflicts with blocking.
  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

  • control services
  • configuration APIs

Where you’ll apply it

References

  • ROS 2 services docs
  • executor scheduling discussions

Key insights

  • Service Blocking Behavior 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.

Asynchronous Design Patterns

Fundamentals

Asynchronous Design Patterns is patterns for keeping ROS 2 nodes responsive using futures, callbacks, and state machines. 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 future and promise influence behavior. When you debug a system, you will almost always inspect callback or state machine 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 Asynchronous Design Patterns starts by tracing data from the API surface to the middleware. Every time you configure future or promise, 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 callback 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 state machine is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If event loop 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 7. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • future: future in the context of Asynchronous Design Patterns and ROS 2 systems.
  • promise: promise in the context of Asynchronous Design Patterns and ROS 2 systems.
  • callback: callback in the context of Asynchronous Design Patterns and ROS 2 systems.
  • state machine: state machine in the context of Asynchronous Design Patterns and ROS 2 systems.
  • event loop: event loop in the context of Asynchronous Design Patterns and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Asynchronous Design Patterns] -> [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 (future, promise).
  3. Peers evaluate compatibility, matching, or timing using callback and state machine.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm event loop behavior.

Minimal concrete example

auto future = client->async_send_request(req, cb);

Common misconceptions

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

Check-your-understanding questions

  1. Explain how Asynchronous Design Patterns changes runtime behavior in ROS 2.
  2. Predict what happens if future conflicts with promise.
  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

  • action servers
  • non-blocking GUI integration

Where you’ll apply it

References

  • C++ Concurrency in Action
  • ROS 2 async examples

Key insights

  • Asynchronous Design Patterns 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 path-following robot implemented once as a service and once as an action, then compare behavior.

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. **Goal preemption: **Goal preemption -> Canceling active paths.
  2. **Feedback loop: **Feedback loop -> Reporting progress.
  3. **Async execution: **Async execution -> Avoiding blocked threads.
  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 action send_goal /follow_path my_msgs/action/FollowPath "{path: [...]}"
[FEEDBACK] progress: 0.5
[RESULT] success: true

3.5 Data Formats / Schemas / Protocols

Action goal: list of poses; feedback: progress 0-1; result: success flag

3.6 Edge Cases

  • Cancel mid-path
  • Goal timeout
  • Invalid path length

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_7
# Run
source install/setup.bash
# Start the main node/tool
./run_project_7.sh

3.7.2 Golden Path Demo (Deterministic)

$ ros2 action send_goal /follow_path my_msgs/action/FollowPath "{path: [...]}"
[FEEDBACK] progress: 0.5
[RESULT] success: true

3.7.3 Failure Demo (Deterministic)

$ ros2 action send_goal /follow_path my_msgs/action/FollowPath "{path: []}"
[RESULT] success: false (empty path)

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
Action Server Executes path following goal Publishes feedback and result
Action Client Sends goals and handles cancel Timeouts and retries
Planner Stub Generates target waypoints Deterministic path set

4.3 Data Structures (No Full Code)

# FollowPath.action
geometry_msgs/Pose[] path
---
bool success
---
float32 progress

4.4 Algorithm Overview

Key Algorithm: Core Pipeline

  1. Accept goal
  2. Iterate through waypoints
  3. Publish feedback
  4. Return result

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

“Why are actions essential for robotics control instead of services?”

5.4 Concepts You Must Understand First

Stop and research these before coding:

  1. Action Lifecycle
    • What breaks if this is misconfigured?
    • How will you observe it?
  2. Service Blocking Behavior
    • What breaks if this is misconfigured?
    • How will you observe it?
  3. Asynchronous Design Patterns
    • What breaks if this is misconfigured?
    • How will you observe it?

5.5 Questions to Guide Your Design

  1. What feedback should be sent to the client?
  2. How will you handle goal cancellation?

5.6 Thinking Exercise

Imagine sending a new path while the robot is mid-path. What should happen?

5.7 The Interview Questions They’ll Ask

  1. “When should you use an action instead of a service?”
  2. “How does action preemption work?”

5.8 Hints in Layers

Hint 1: Use rclcpp_action::create_server Hint 2: Send feedback periodically Hint 3: Implement cancel callbacks Handle cancel requests to stop the robot safely. Hint 4: Compare with a blocking service Deliberately block a service to see why actions are better.

5.9 Books That Will Help

Topic Book Chapter
Topic Book Chapter
Concurrency “Operating Systems: Three Easy Pieces” Concurrency

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 Action Lifecycle)
  • 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 6: Builds prerequisite concepts
  • Project 8: Extends the middleware layer

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain Action Lifecycle 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