Generate C++ or Python code for ROS 2 parameter declaration, getting, and validation using declarative YAML. The generated library contains a C++ struct with specified parameters. Additionally, dynamic parameters and custom validation are made easy.

• Declarative YAML syntax for ROS 2 Parameters converted into C++ or Python struct
• Declaring, Getting, Validating, and Updating handled by generated code
• Dynamic ROS 2 Parameters made easy
• Custom user specified validator functions

1. Create YAML parameter codegen file
2. Add parameter library generation to project
3. Use generated struct into project source code

Write a yaml file to declare your parameters and their attributes.

src/turtlesim_parameters.yaml

turtlesim:
  background:
    r: {
      type: int,
      default_value: 0,
      description: "Red color value for the background, 8-bit",
      validation: {
        bounds<>: [0, 255]
      }
    }
    g: {
      type: int,
      default_value: 0,
      description: "Green color value for the background, 8-bit",
      validation: {
        bounds<>: [0, 255]
      }
    }
    b: {
      type: int,
      default_value: 0,
      description: "Blue color value for the background, 8-bit",
      validation: {
        bounds<>: [0, 255]
      }
    }

package.xml

<depend>generate_parameter_library</depend>

CMakeLists.txt

find_package(generate_parameter_library REQUIRED)

generate_parameter_library(
  turtlesim_parameters # cmake target name for the parameter library
  src/turtlesim_parameters.yaml # path to input yaml file
)

add_executable(minimal_node src/turtlesim.cpp)
target_link_libraries(minimal_node PRIVATE
  rclcpp::rclcpp
  turtlesim_parameters
)

setup.py

from generate_parameter_library_py.setup_helper import generate_parameter_module

generate_parameter_module(
  "turtlesim_parameters", # python module name for parameter library
  "turtlesim/turtlesim_parameters.yaml", # path to input yaml file
)

src/turtlesim.cpp

#include <rclcpp/rclcpp.hpp>
#include "turtlesim_parameters.hpp"

int main(int argc, char * argv[])
{
  rclcpp::init(argc, argv);
  auto node = std::make_shared<rclcpp::Node>("turtlesim");
  auto param_listener = std::make_shared<turtlesim::ParamListener>(node);
  auto params = param_listener->get_params();

  auto color = params.background;
  RCLCPP_INFO(node->get_logger(),
    "Background color (r,g,b): %d, %d, %d",
    color.r, color.g, color.b);

  return 0;
}

turtlesim/turtlesim.py

import rclpy
from rclpy.node import Node
from turtlesim_pkg.turtlesim_parameters import turtlesim_parameters

def main(args=None):
  rclpy.init(args=args)
  node = Node("turtlesim")
  param_listener = turtlesim_parameters.ParamListener(node)
  params = param_listener.get_params()

  color = params.background
  node.get_logger().info(
    "Background color (r,g,b): %d, %d, %d" %
    color.r, color.g, color.b)

When using parameter library generation it can happen that there are issues when executing tests since parameters are not defined and the library defines them as mandatory. To overcome this it is recommended to define example yaml files for tests and use them as follows:

find_package(ament_cmake_gtest REQUIRED)
add_rostest_with_parameters_gtest(test_turtlesim_parameters test/test_turtlesim_parameters.cpp
  ${CMAKE_CURRENT_SOURCE_DIR}/test/example_turtlesim_parameters.yaml)
target_include_directories(test_turtlesim_parameters PRIVATE include)
target_link_libraries(test_turtlesim_parameters turtlesim_parameters)
ament_target_dependencies(test_turtlesim_parameters rclcpp)

when using gtest, or:

find_package(ament_cmake_gmock REQUIRED)
add_rostest_with_parameters_gmock(test_turtlesim_parameters test/test_turtlesim_parameters.cpp
  ${CMAKE_CURRENT_SOURCE_DIR}/test/example_turtlesim_parameters.yaml)
target_include_directories(test_turtlesim_parameters PRIVATE include)
target_link_libraries(test_turtlesim_parameters turtlesim_parameters)
ament_target_dependencies(test_turtlesim_parameters rclcpp)

when using gmock test library.

🤖 P.S. having this example yaml files will make your users very grateful because they will always have a working example of a configuration for your node.

• Cpp namespace
• Parameter definition
• Built-In Validators
• Custom validator functions
• Nested structures
• Use generated struct in Cpp
• Dynamic Parameters
• Example Project
• Generated code output

The root element of the YAML file determines the namespace used in the generated C++ code. We use this to put the Params struct in the same namespace as your C++ code.

cpp_namespace:
# additionally fields  ...

The YAML syntax can be thought of as a tree since it allows for arbitrary nesting of key-value pairs. For clarity, the last non-nested value is referred to as a leaf. A leaf represents a single parameter and has the following format.

cpp_namespace:
  param_name: {
    type: int,
    default_value: 3,
    read_only: true,
    description: "A read only  integer parameter with a default value of 3",
    validation:
      # validation functions ...
  }

A parameter is a YAML dictionary with the only required key being type.

The types of parameters in ros2 map to C++ types.

Fixed size types are denoted with a suffix _fixed_XX, where XX is the desired size. The corresponding C++ type is a data wrapper class for conveniently accessing the data. Note that any fixed size type will automatically use a size_lt validator. Validators are explained in the next section.

The purpose of none type is purely documentation, and won't generate any C++ code. See Parameter documentation for details.

Validators are C++ functions that take arguments represented by a key-value pair in yaml. The key is the name of the function. The value is an array of values that are passed in as parameters to the function. If the function does not take any values you write null or [] to for the value.

joint_trajectory_controller:
  command_interfaces: {
    type: string_array,
    description: "Names of command interfaces to claim",
    validation: {
      size_gt<>: [0],
      unique<>: null,
      subset_of<>: [["position", "velocity", "acceleration", "effort",]],
    }
  }

Above are validations for command_interfaces from ros2_controllers. This will require this string_array to have these properties:

• There is at least one value in the array
• All values are unique
• Values are only in the set ["position", "velocity", "acceleration", "effort",]

You will note that some validators have a suffix of <>, this tells the code generator to pass the C++ type of the parameter as a function template. Some of these validators work only on value types, some on string types, and others on array types. The built-in validator functions provided by this package are:

Value validators

String validators

Array validators

Validators are functions that return a tl::expected<void, std::string> type and accept a rclcpp::Parameter const& as their first argument and any number of arguments after that can be specified in YAML. Validators are C++ functions defined in a header file similar to the example shown below.

Here is an example custom allocator.

#include <rclcpp/rclcpp.hpp>

#include <fmt/core.h>
#include <tl_expected/expected.hpp>

namespace my_project {

tl::expected<void, std::string> integer_equal_value(
    rclcpp::Parameter const& parameter, int expected_value) {
  int param_value = parameter.as_int();
    if (param_value != expected_value) {
        return tl::make_unexpected(fmt::format(
            "Invalid value {} for parameter {}. Expected {}",
            param_value, parameter.get_name(), expected_value);
    }

  return {};
}

}  // namespace my_project

To configure a parameter to be validated with the custom validator function integer_equal_value with an expected_value of 3 you could would this to the YAML.

validation: {
  "my_project::integer_equal_value": [3]
}

After the top level key, every subsequent non-leaf key will generate a nested c++ struct. The struct instance will have the same name as the key.

cpp_name_space:
  nest1:
    nest2:
      param_name: { # this is a leaf
        type: string_array
      }

The generated parameter value can then be access with params.nest1.nest2.param_name

The generated header file is named based on the target library name you passed as the first argument to the cmake function. If you specified it to be turtlesim_parameters you can then include the generated code with #include "turtlesim_parameters.hpp".

#include "turtlesim_parameters.hpp"

In your initialization code, create a ParamListener which will declare and get the parameters. An exception will be thrown if any validation fails or any required parameters were not set. Then call get_params on the listener to get a copy of the Params struct.

auto param_listener = std::make_shared<turtlesim::ParamListener>(node);
auto params = param_listener->get_params();

If you are using dynamic parameters, you can use the following code to check if any of your parameters have changed and then get a new copy of the Params struct.

if (param_listener->is_old(params_)) {
  params_ = param_listener->get_params();
}

In some case, parameters might be unknown only at compile-time, and cannot be part of the generated C++ code. However, for documentation purpose of such parameters, the type none was introduced.

Parameters with none type won't generate any C++ code, but can exist to describe the expected name or namespace, that might be declared by an external piece of code and used in an override.

A typical use case is a controller, loading pluginlib-based filters, that themselves require (and declare) parameters in a known structure.

Example of declarative YAML

force_torque_broadcaster_controller:
  sensor_name: {
    type: string,
    default_value: "",
    description: "Name of the sensor used as prefix for interfaces if there are no individual interface names defined.",
  }
  frame_id: {
    type: string,
    default_value: "",
    description: "Sensor's frame_id in which values are published.",
  }
  sensor_filter_chain: {
    type: none,
    description: "Map of parameters that defines a filter chain, containing filterN as key and underlying map of parameters needed for a specific filter. See <some docs> for more details.",
  }

Example of parameters for that controller

force_torque_broadcaster_controller:
  ros__parameters:
    sensor_name: "fts_sensor"
    frame_id: "fts_sensor_frame"
    sensor_filter_chain:
      filter1:
        type: "control_filters/LowPassFilterWrench"
        name: "low_pass_filter"
        params:
          sampling_frequency: 200.0
          damping_frequency: 50.0
          damping_intensity: 1.0

See example project for a complete example of how to use the generate_parameter_library.

The generated code is primarily consists of two major components:

1. struct Params that contains values of all parameters and
2. class ParamListener that handles parameter declaration, updating, and validation. The general structure is shown below.

namespace cpp_namespace {

struct Params {
  int param_name = 3;
  struct {
    struct{
      std::string param_name;
      // arbitrary nesting depth...
    } nest2;
  } nest1;
  // for detecting if the parameter struct has been updated
  rclcpp::Time __stamp;
};

class ParamListener {
 public:
  ParamListener(rclcpp::ParameterInterface);
  ParamListener(rclcpp::Node::SharedPtr node)
    : ParameterListener(node->get_parameters_interface()) {}
  ParamListener(rclcpp_lifecycle::LifecycleNode::SharedPtr node)
    : ParameterListener(node->get_parameters_interface()) {}

  // create a copy of current parameter values
  Params get_params() const;

  // returns true if parameters have been updated since last time get_params was called
  bool is_old(Params const& other) const;

  // loop over all parameters: perform validation then update
  rcl_interfaces::msg::SetParametersResult update(const std::vector<rclcpp::Parameter> &parameters);

  // declare all parameters and throw exception if non-optional value is missing or validation fails
  void declare_params(const std::shared_ptr<rclcpp::node_interfaces::NodeParametersInterface>& parameters_interface);

 private:
  Params params_;
};

} // namespace cpp_namespace

The structure of the Params struct and the logic for declaring and updating parameters is generated from a YAML file specification.

Q. What happens if I declare a parameter twice? Will I get an error at runtime? A. The declare routine that is generated checks to see if each parameter has been declared first before declaring it. Because of this you can declare a parameter twice but it will only have the properties of the first time you declared it. Here is some example generated code.

if (!parameters_interface_->has_parameter(prefix_ + "scientific_notation_num")) {
    rcl_interfaces::msg::ParameterDescriptor descriptor;
    descriptor.description = "Test scientific notation";
    descriptor.read_only = false;
    auto parameter = to_parameter_value(updated_params.scientific_notation_num);
    parameters_interface_->declare_parameter(prefix_ + "scientific_notation_num", parameter, descriptor);
}

Q: How do I log when parameters change? A. The generated library outputs debug logs whenever a parameter is read from ROS.