Running ROS2 Action Callbacks In A Separate Thread: A Guide

Aug 26, 2025 by Marco 60 views

How to Run ROS2 Action Callbacks in a Specific Thread?

Hey guys, let's dive into a common challenge when working with ROS2 and actions: how to handle action callbacks in a separate thread. If you're building a ROS2 node that needs to juggle action invocations with some heavy-duty background processing, you've probably run into this. It's all about keeping your main node loop responsive while those long-running computations chug along. Let's break down the problem, why it matters, and how to get it done.

The Problem: Blocking Computations and the ROS2 Node Loop

So, imagine this: you've got a ROS2 node, and its job includes sending action requests. Maybe it's controlling a robot arm, navigating a drone, or processing some sensor data. When an action completes, you need to do something with the result, right? Now, what if that "something" takes a while? Perhaps you need to perform some intense calculations, save a bunch of data to disk, or maybe interact with a slow external device. If you do this work directly within the action callback function, you're essentially blocking the ROS2 node loop.

This is a big no-no. Blocking the node loop means your node can't quickly process incoming messages, respond to service requests, or even send out its own publications. Your robot might become unresponsive, your drone could become a lawn dart, or your sensor data processing could grind to a halt. No good, right? The core of the problem is that ROS2, by default, runs callbacks in the same thread as the node's executor. The executor is the heart of the node, responsible for dispatching work. If a callback hogs the thread, everything else suffers. So, how do we prevent this?

This is where threading comes in. By offloading those long-running computations to a separate thread, you free up the main node loop to keep things running smoothly. Your action callbacks can still receive results, but the blocking work happens in the background, without interfering with the rest of your node's operations. This keeps everything responsive, and your robot/drone/data processing pipeline happy.

Think of it like this: your main node loop is the conductor of an orchestra. If the conductor gets stuck playing a single, super-long note, the whole orchestra falls apart. Threading allows you to assign that long note to a different instrument (a different thread), while the conductor keeps the rest of the orchestra (your node) playing beautifully. The key is to understand how to safely move these callbacks to a separate thread and handle the data exchange between the threads.

Why Threading Matters for ROS2 Action Callbacks

Alright, so we've touched on why blocking the node loop is bad. But let's really drive home why using threads for action callbacks is so important for ROS2 applications. Think about the real-world impact. Firstly, responsiveness is key. A robot that's slow to react to commands is a safety hazard. A drone that's slow to respond to obstacle avoidance is going to crash. By keeping your node loop responsive, you ensure that your system can react quickly to changes in the environment and to user commands. This responsiveness directly translates to better safety and reliability.

Secondly, performance improvements. If you have computationally intensive tasks, running them in parallel with the rest of your node's operations can significantly speed up overall processing time. Imagine processing a stream of high-resolution images. You could use an action to trigger the processing, and then offload the image processing to another thread. While the processing happens, your node can continue to receive new images. This creates a processing pipeline, where new data is constantly flowing, rather than waiting for each image to be fully processed before starting the next. This parallelization can lead to dramatic performance gains.

Thirdly, resource management. Threading allows you to use system resources more efficiently. For example, if your long-running task involves waiting for data from an external device (like a sensor or a network connection), the thread can be put to sleep (or blocked) while it waits. This frees up the main node loop to handle other tasks. Without threading, your node might be stuck waiting, effectively wasting CPU time. Threading allows you to use your resources more intelligently.

Finally, and importantly, it improves modularity and maintainability. By isolating the long-running tasks in separate threads, you make your code cleaner and easier to understand. Your main node loop remains focused on its primary responsibilities (communication, control, etc.), and the background tasks are neatly organized in their own threads. This makes it easier to debug, update, and extend your code in the future.

So, in a nutshell, threading is not just a good idea; it's often a necessity for building robust, responsive, and high-performing ROS2 applications that involve actions and any form of blocking computations. From improved safety to enhanced performance and better code organization, the benefits are clear.

Implementing Threaded Action Callbacks in ROS2: A Step-by-Step Guide

Okay, let's get down to the nitty-gritty. How do you actually implement threaded action callbacks in ROS2? Here's a step-by-step guide, along with some code snippets to get you started. We'll focus on the fundamental concepts and provide enough information for you to adapt this to your specific needs.

Step 1: Include Necessary Headers

First things first, you'll need to include the right headers in your ROS2 node code. You'll need the standard ROS2 headers for actions, plus headers for threading. This sets the foundation for creating and managing your threads. Specifically, include the following:

#include "rclcpp/rclcpp.hpp"
#include "rclcpp_action/rclcpp_action.hpp"
#include <thread>
#include <functional>
#include <mutex>
#include <future>

rclcpp/rclcpp.hpp: This is the main ROS2 header, providing access to core ROS2 functionality, like nodes, topics, and services.
rclcpp_action/rclcpp_action.hpp: This header provides the necessary classes and functions for working with ROS2 actions, including action clients and servers.
<thread>: This is the C++ standard library header for creating and managing threads.
<functional>: This header is important for using std::bind or lambdas to pass arguments to your thread functions.
<mutex>: Used for thread safety to protect shared resources.
<future>: Allows you to obtain results from a thread.

These headers give you the tools you need to set up and manage threads, ensuring your actions are handled safely and efficiently.

Step 2: Define Your Action Client and Action Goal

Next, you'll need to set up your action client and define your action goal. This is pretty standard ROS2 action client setup.

Create an action client: Instantiate an rclcpp_action::Client for your specific action type. This client is used to send action goals to the action server.
Define an action goal: Create a goal message that corresponds to your action's specifications. This message will contain the data you want to send to the action server. Populate the goal with the necessary data for your action. For example, the desired position for a robot arm, or the parameters for an image processing algorithm.

#include "my_action/action/my_action.hpp"

class MyNode : public rclcpp::Node
{
  public:
    MyNode() : Node("my_node")
    {
      action_client_ = rclcpp_action::create_client<my_action::action::MyAction>(this, "my_action");
    }

  private:
    rclcpp_action::Client<my_action::action::MyAction>::SharedPtr action_client_;
};

Step 3: The Core: Threading the Callback

This is where the magic happens! Instead of directly processing the result in the action callback, you'll offload the processing to a separate thread.

Define your action callback: Create the action callback function. This function will be triggered when the action server returns a result, feedback, or a cancel request. This callback function should be designed to handle the information received from the server.
Launch a new thread: Inside your action callback, create a new thread using the std::thread class. Pass the long-running computation function and any necessary data as arguments to the thread. Use std::bind or a lambda expression to capture the necessary arguments from your callback function. Your callback now immediately returns to allow the node to continue.
Implement the long-running computation: Create a separate function that will be executed in the new thread. This function performs the long-running, blocking operations (calculations, file I/O, etc.). This function must be thread-safe, particularly if it accesses any shared resources.

Here's a basic example to illustrate this:

#include <thread>
#include <functional>

void longRunningComputation(const my_action::action::MyAction::Result::SharedPtr result)
{
  // Perform your long-running, blocking computations here
  // ... for example, save the result to a file:
  // std::ofstream outputFile("result.txt");
  // outputFile << result->result << std::endl;
  // outputFile.close();
  RCLCPP_INFO(rclcpp::get_logger("my_node"), "Long running computation complete");
}

void resultCallback(rclcpp_action::ClientGoalHandle<my_action::action::MyAction>::WrappedResult result)
{
  switch (result.code) {
    case rclcpp_action::ClientStatus::SUCCEEDED:
      RCLCPP_INFO(rclcpp::get_logger("my_node"), "Action succeeded!");
      // Launch a new thread to perform the long-running computation
      std::thread([result](const my_action::action::MyAction::Result::SharedPtr res) {
          longRunningComputation(res);
      }, result.result);
      break;
    case rclcpp_action::ClientStatus::ABORTED:
      RCLCPP_INFO(rclcpp::get_logger("my_node"), "Action was aborted");
      return;
    case rclcpp_action::ClientStatus::CANCELED:
      RCLCPP_INFO(rclcpp::get_logger("my_node"), "Action was canceled");
      return;
    default:
      RCLCPP_ERROR(rclcpp::get_logger("my_node"), "Unknown action result");
      return;
  }
}


class MyNode : public rclcpp::Node
{
  public:
    MyNode() : Node("my_node")
    {
      action_client_ = rclcpp_action::create_client<my_action::action::MyAction>(this, "my_action");
    }

    void sendGoal()
    {
      auto goal_options = rclcpp_action::Client<my_action::action::MyAction>::SendGoalOptions();
      goal_options.result_callback = std::bind(&MyNode::resultCallback, this, std::placeholders::_1);
      auto goal = my_action::action::MyAction::Goal();
      // Populate the goal
      auto future_goal_handle = action_client_->async_send_goal(goal, goal_options);
    }

  private:
    rclcpp_action::Client<my_action::action::MyAction>::SharedPtr action_client_;
};

Step 4: Thread Safety (Important!) and Data Sharing

When working with threads, data races and other concurrency issues are a real concern. Make sure your shared resources (data that is accessed by both the main thread and the worker thread) are protected.

Use Mutexes: Use std::mutex to protect access to any shared variables. Before accessing a shared variable, the thread must acquire the mutex using lock(). After it's done accessing the variable, the thread must release the mutex using unlock(). This helps guarantee that only one thread modifies the data at a time. Think of the mutex as a lock on the shared resource: only one thread can hold the key to unlock it at a time.
Consider Alternatives: Think about whether it's possible to avoid shared mutable state altogether. For instance, copy the data to the thread's private variables. This avoids the need for mutexes. Immutable data is inherently thread-safe.
Minimize Critical Sections: Keep the code sections where you're holding the mutex (the critical sections) as short as possible. This minimizes the time threads are blocked waiting for the mutex.

Step 5: Handling Feedback (Optional)

If your action server provides feedback, you might want to process the feedback in your main node. You don't usually need a separate thread for feedback, since the feedback callback should be lightweight, but it's good practice to keep it that way. If the feedback processing is complex, you can also consider using a separate thread for it, using the same principles as above. Feedback gives you real-time information on the action's progress.

Step 6: Action Cancellation

Be sure to implement action cancellation to provide an option for graceful termination of running actions.

Practical Tips and Best Practices

Alright, you've got the basics down. Now, let's look at some practical tips to make your threaded action callbacks robust and efficient. These are things that'll make your code cleaner, easier to maintain, and more reliable.

Error Handling: Always include robust error handling. Make sure you check for errors when sending action goals, receiving results, and within your long-running computation functions. Handle exceptions and unexpected situations gracefully. Use try-catch blocks within your thread functions. Log errors with clear, informative messages so you know what went wrong and where. Remember to handle errors gracefully, for example by cancelling the action and/or trying again.
Logging: Use a good logging framework (like ROS2's built-in logging) to log events, errors, and debug information. Log messages in both your main thread and your worker threads. This will be invaluable when debugging your code. Log the thread ID as well to make it clear which thread is logging the message.
Thread Pool: For more complex applications with many actions or long-running tasks, consider using a thread pool instead of creating a new thread for each action. Thread pools manage a fixed number of worker threads. This avoids the overhead of creating and destroying threads repeatedly, which can improve performance, and provides a more scalable solution.
Asynchronous Operations: Whenever possible, use asynchronous operations. For example, if you need to read data from a file, use asynchronous file I/O. This means your thread doesn't block waiting for the I/O operation to complete; instead, it can continue with other tasks and be notified when the I/O is complete. Similarly, asynchronous networking can prevent your threads from blocking while waiting for network responses.
Resource Management: Make sure you manage resources carefully within your threads. Release any allocated memory, close files, and release any other resources when the thread is done. Consider using smart pointers to automatically manage memory.
Testing: Thoroughly test your code with different scenarios, including edge cases and error conditions. Use a debugger to step through the code and verify that your threads are behaving as expected.
Monitor Performance: Measure the performance of your node. Use tools to monitor CPU usage, memory usage, and thread activity. If your threads are causing performance problems, analyze the code in those threads to identify bottlenecks. Optimize where necessary, for instance, by reducing the amount of data exchanged between threads, or by using more efficient algorithms.

Conclusion: Unleashing the Power of Threads in ROS2

So there you have it! Running ROS2 action callbacks in a specific thread can be a powerful technique for building more responsive and efficient ROS2 applications. You've seen how to offload those heavy-duty tasks to separate threads, keeping your main node loop from getting bogged down. By following the steps outlined in this guide, you can ensure that your ROS2 nodes remain responsive, even when performing complex calculations, interacting with external devices, or processing large amounts of data. Remember the core concepts: threading, thread safety (using mutexes), and resource management. Put these principles into practice, and you'll be well on your way to building robust and high-performing ROS2 systems. Keep in mind the best practices, such as error handling, logging, and careful resource management, as you develop your applications. Happy coding!