Real-Time Executor

Table of contents

Introduction and Goal

Predictable execution under given real-time constraints is one of the hallmarks of embedded systems, but is also a complex subject that can cause many errors. The real-time executor aims to provide a convenient API for the developers of micro-ROS based components and systems to simplify scheduling and to reduce errors, taking advantage of typical processing patterns in robotics applications.

The approach is based on the concept of executors, which have been introduced in ROS 2. The real-time executor shall provide fine-grained control of the mapping of callbacks to the scheduling primitives and mechanisms of the underlying RTOS, even across multiple nodes.

Background: ROS 2 Executor Concept

ROS 2 allows to bundle multiple nodes in one operating system process. To coordinate the execution of the callbacks of the nodes of a process, the Executor concept was introduced in rclcpp (and also in rclpy).


The ROS 2 design defines one Executor (instance of rclcpp::executor::Executor) per process, which is typically created either in a custom main function or by the launch system. The Executor coordinates the execution of all callbacks issued by these nodes by checking for available work (timers, services, messages, subscriptions, etc.) from the DDS queue and dispatching it to one or more threads, implemented in SingleThreadedExecutor and MultiThreadedExecutor, respectively.

The dispatching mechanism resembles the ROS 1 spin thread behavior: the Executor looks up the wait queues, which notifies it of any pending callback in the DDS queue. If there are pending callbacks, the ROS 2 Executor simply executes them in a FIFO manner.


The following diagram depicts the relevant classes of the ROS 2 Executor concept:

ROS 2 Executor class diagram

Note that an Executor instance maintains weak pointers to the NodeBaseInterfaces of the nodes only. Therefore, nodes can be destroyed safely, without notifying the Executor.

Also, the Executor does not maintain an explicit callback queue, but relies on the queue mechanism of the underlying DDS implementation as illustrated in the following sequence diagram:

Call sequence from executor to DDS


In the present Executor concept there is no notion of prioritization or categorization of the incoming callback calls. Moreover, it does not leverage the real-time characteristics of the underlying operating-system scheduler to have finer control on the order of executions. The overall implication of this behavior is that time-critical callbacks could suffer possible deadline misses and degraded performance since they are serviced later than non-critical callbacks. Additionally, due to the FIFO mechanism, it is difficult to determine usable bounds on the worst-case latency that each callback execution may incur.

Callback-group-level Executor

In order to address the challenges mentioned above, some changes are imminent: Firstly, an API to express real-time requirements on a callback level is needed and secondly, the Executor must be redesigned to respect these real-time requirements in its scheduling decisions. As the current ROS 2 Executor works at a node-level granularity – which is a limitation given that a node may issue different callbacks needing different real-time guarantees - we decided to refine the ROS 2 Executor API for more fine-grained control over the scheduling of callbacks on the granularity of callback groups using. We leverage the callback-group concept existing in rclcpp by introducing real-time profiles such as RT-CRITICAL and BEST-EFFORT in the callback-group API (i.e. rclcpp/callback_group.hpp). Each callback needing specific real-time guarantees, when created, may therefore be associated with a dedicated callback group. With this in place, we enhanced the Executor and depending classes (e.g., for memory allocation) to operate at a finer callback-group granularity. This allows a single node to have callbacks with different real-time profiles assigned to different Executor instances - within one process.

Thus, an Executor instance can be dedicated to specific callback group(s) and the Executor’s thread(s) can be prioritized according to the real-time requirements of these groups. For example, all time-critical callbacks are handled by an “RT-CRITICAL” Executor instance running at the highest scheduler priority.

The following figure illustrates this approach with two nodes served by three Callback-group-level Executors in one process:

Sample system with two nodes and three Callback-group-level Executors in one process

In this section, we describe the necessary changes to the Executor API and report on first experiments with it.

API Changes

  • include/rclcpp/callback_group.hpp:

    Introduced enum to distinguish up to three real-time classes (requirements) per node.

    enum class RealTimeClass

    Also, changed association with Executor instance from nodes to callback groups.

    class CallbackGroup
      std::atomic_bool &
  • include/rclcpp/executor.hpp

    Added functions to add and remove individual callback groups in addition to whole nodes.

    class Executor
      virtual void
        rclcpp::callback_group::CallbackGroup::SharedPtr group_ptr,
        rclcpp::node_interfaces::NodeBaseInterface::SharedPtr node_ptr, bool notify = true);
      virtual void
        rclcpp::callback_group::CallbackGroup::SharedPtr group_ptr,
        bool notify = true);

    Replaced private vector of nodes with a map from callback groups to nodes.

    typedef std::map<rclcpp::callback_group::CallbackGroup::WeakPtr,
      std::owner_less<rclcpp::callback_group::CallbackGroup::WeakPtr>> WeakCallbackGroupsToNodesMap;
    WeakCallbackGroupsToNodesMap weak_groups_to_nodes_;
  • include/rclcpp/memory_strategy.hpp

    Changed all functions that expect a vector of nodes to the just mentioned map.

  • include/rclcpp/node.hpp and include/rclcpp/node_interfaces/node_base.hpp

    Extended arguments of create_callback_group function for the real-time class.

      rclcpp::callback_group::CallbackGroupType group_type,
      rclcpp::callback_group::RealTimeClass real_time_class =

    Removed the get_associated_with_executor_atomic function.

All the changes can be found in the branches cbg-executor-0.5.1 and cbg-executor-0.6.1 for the corresponding version 0.5.1 and 0.6.1 of the rclcpp in the fork at

Meta-Executor Concept

The idea of the Meta Executor is to abstract away the callback-group assignment, thread allocation and other inner workings of the Executors from the user, thereby presenting a simple API that resembles the original Executor interface. Internally, the Meta Executor maintains multiple instances of our Callback-group-level Executor (Cbg-Executor).

The Meta Executor internally binds these Executors to the underlying kernel threads, assigns them a priority, chooses the scheduling mechanism (e.g., SCHED-FIFO policy) and then dispatches them. When adding a node with its list of callback group and real-time profiles to the Meta Executor, it parses the real-time profiles and assigns the node’s callback groups to the relevant internal Executors.

Illustration of the Meta-Executor concept

Test Bench

As a proof of concept, we implemented a small test bench in the present package cbg-executor_ping-pong_cpp. The test bench comprises a Ping node and a Pong node which exchange real-time and best-effort messages simultaneously with each other. Each class of messages is handled with a dedicated Executor, as illustrated in the following figure.

Architecture for the Callback-group-level Executor test bench

With the test bench, we validated the functioning of the approach - here on ROS 2 v0.5.1 with the Fast-RTPS DDS implementation - on a typical laptop.

Results from Callback-group-level Executor test bench

The test bench is provided in the bg-executor_ping-pong_cpp package of the micro-ROS_experiments repository.



  • In-depth analysis of the ROS 2 Executor concept.
  • Designed Callback-group-level Executor and implemented it - at first, prototypically, for ROS 2.
  • Designed the Meta Executor concept.
  • Validated correct functioning with underlying layers and middleware.


  • Implementation of the Callback-group-level Executor and Meta Executor for urcl.
  • Research of concepts for model-based optimization of end-to-end latencies.


  • Integration with a selected advanced scheduling and resource monitoring mechanisms such as reservation-based scheduling.
  • Integration with selected sleep modes and low-power modes.

In this section, we provide an overview to related approaches and link to the corresponding APIs.

Sense-Plan-Act Cycle in Fawkes

Fawkes is a robotic software framework, which supports synchronization points for sense-plan-act like execution. It has been developed by RWTH Aachen since 2006. Source code is available at

Fawkes provides developers different synchronization points, which are very useful for defining an execution order of a typical sense-plan-act application. These ten synchronization points (wake-up hooks) are the following (cf. libs/aspect/blocked_timing.h):


At compile time, a desired synchronization point is defined as a constructor parameter for a module. For example, assuming that mapLaserGenThread shall be executed in SENSOR_ACQUIRE, the constructor is implemented as:

  :: Thread("MapLaserGenThread", Thread::OPMODE_WAITFORWAKEUP),
     TransformAspect(TransformAspect::BOTH_DEFER_PUBLISHER, "Map Laser Odometry")

Similarly, if NaoQiButtonThread shall be executed in the SENSOR_PROCESS hook, the constructor is:

  :: Thread("NaoQiButtonThread", Thread::OPMODE_WAITFORWAKEUP),

At runtime, the executor iterates through the list of synchronization points and executes all registered threads until completion. Then, the threads of the next synchronization point are called.

A module (thread) can be configured independent of these sense-plan-act synchronization points. This has the effect, that this thread is executed in parallel to this chain.

The high level overview of the Fawkes framework is shown in the next figure. At compile-time the configuration of the sense-plan act wakeup hook is done (upper part), while at run-time the scheduler iterates through this list of wakeup-hooks (lower part):

Sequence diagram for executor concept in Fawkes

Hence, at run-time, the hooks are executed as a fixed static schedule without preemption. Multiple threads registered in the same hook are executed in parallel.

Orthogonal to the sequential execution of sense-plan-act like applications, it is possible to define further constraints on the execution order by means of a Barrier. A barrier defines a number of threads, which need to have finished before the thread can start, which owns the barrier.

These concepts are implemented by the following main classes:

  • Wakeup hook by SyncPoint and SyncPointManager, which manages a list of synchronization points.
  • Executor by the class FawkesMainThread, which is the scheduler, responsible for calling the user threads.
  • ThreadManager, which is derived from BlockedTimingExecutor, provides the necessary API to add and remove threads to wakeup hooks as well as for sequential execution of the wakeup-hooks.
  • Barrier is an object similar to condition_variable in C++.

Discussion: All threads are executed with the same priority. If multiple sense-plan-act chains shall be executed with different priorities, e.g. to prefer execution of emergency-stop over normal operation, then this framework reaches its limits.

Also, different execution frequencies cannot be modeled by a single instance of this sense-plan-act chain. However, in robotics the fastest sensor will drive the chain and all other hooks are executed with the same frequency.

The option to execute threads independent of the predefined wakeup-hooks is very useful, e.g. for diagnostics. The concept of the Barrier is useful for satisfying functional dependencies which need to be considered in the execution order.

  • Ralph Lange: Callback-group-level Executor for ROS 2. Lightning talk at ROSCon 2018. Madrid, Spain. Sep 2018. [Slides] [Video]


This activity has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n° 780785).