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
- Automatically create documentation of parameters
- Create YAML parameter codegen file
- Add parameter library generation to project
- 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
- Generate markdown documentation
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
additional_constraints: "{ type: 'number', multipleOf: 3 }"
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.
| Field | Description |
|---|---|
| type | The type (string, double, etc) of the parameter. |
| default_value | Value for the parameter if the user does not specify a value. |
| read_only | Can only be set at launch and are not dynamic. |
| description | Displayed by ros2 param describe. |
| validation | Dictionary of validation functions and their parameters. |
| additional_constraints | Additional constraints that end up on the ParameterDescriptor but are not used for validation by this package. |
The types of parameters in ros2 map to C++ types.
| Parameter Type | C++ Type |
|---|---|
| string | std::string |
| double | double |
| int | int |
| bool | bool |
| string_array | std::vector<std::string> |
| double_array | std::vector<double> |
| int_array | std::vector<int> |
| bool_array | std::vector<bool> |
| string_fixed_XX | FixedSizeString<XX> |
| none | NO CODE GENERATED |
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 the 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
| Function | Arguments | Description |
|---|---|---|
| bounds<> | [lower, upper] | Bounds checking (inclusive) |
| lt<> | [value] | parameter < value |
| gt<> | [value] | parameter > value |
| lt_eq<> | [value] | parameter <= value |
| gt_eq<> | [value] | parameter >= value |
| one_of<> | [[val1, val2, ...]] | Value is one of the specified values |
String validators
| Function | Arguments | Description |
|---|---|---|
| fixed_size<> | [length] | Length string is specified length |
| size_gt<> | [length] | Length string is greater than specified length |
| size_lt<> | [length] | Length string is less less specified length |
| not_empty<> | [] | String parameter is not empty |
| one_of<> | [[val1, val2, ...]] | String is one of the specified values |
Array validators
| Function | Arguments | Description |
|---|---|---|
| unique<> | [] | Contains no duplicates |
| subset_of<> | [[val1, val2, ...]] | Every element is one of the list |
| fixed_size<> | [length] | Number of elements is specified length |
| size_gt<> | [length] | Number of elements is greater than specified length |
| size_lt<> | [length] | Number of elements is less less specified length |
| not_empty<> | [] | Has at-least one element |
| element_bounds<> | [lower, upper] | Bounds checking each element (inclusive) |
| lower_element_bounds<> | [lower] | Lower bound for each element (inclusive) |
| upper_element_bounds<> | [upper] | Upper bound for each element (inclusive) |
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 validator.
#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_projectTo 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_arrayThe generated parameter value can then be accessed with params.nest1.nest2.param_name
You can use parameter maps, where a map with keys from another string_array parameter is created. Add the __map_ prefix followed by the key parameter name as follows:
cpp_name_space:
joints:
type: string_array
default_value: ["joint1", "joint2", "joint3"]
description: "specifies which joints will be used by the controller"
interfaces:
type: string_array
default_value: ["position", "velocity", "acceleration"]
description: "interfaces to be used by the controller"
# nested mapped example
gain:
__map_joints: # create a map with joints as keys
__map_interfaces: # create a map with interfaces as keys
value:
type: double
# simple mapped example
pid:
__map_joints: # create a map with joints as keys
values:
type: double_arrayThe generated parameter value for the nested map example can then be accessed with params.gain.joints_map.at("joint1").interfaces_map.at("position").value.
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 cases, 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: 1.0See cpp example or python example for complete examples of how to use the generate_parameter_library.
The generated code primarily consists of two major components:
struct Paramsthat contains values of all parameters andclass ParamListenerthat 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> ¶meters);
// declare all parameters and throw an exception if a 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_namespaceThe structure of the Params struct and the logic for declaring and updating parameters is generated from a YAML file specification.
Using generate_parameter_library you can generate a Markdown-file for your parameters.yaml file.
generate_parameter_library_markdown --input_yaml example/src/parameters.yaml --output_markdown_file parameters.md
This will generate a file parameters.md in the current folder that contains a markdown
representation of the parameters.yaml file that you can directly include into your documentation.
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.