README.md

ft_tools_ros2

Wrench estimation and calibration of F/T sensor for ROS2 applications.

The current devs are based on the jazzy ROS 2 distribution (Ubuntu 22.04 LTS)

Author: Thibault Poignonec: [email protected]

Warning

This package is currently under development!

Stack content

F/T sensor calibration utils

FT parameters object

The common interface to perform sensor calibration and wrench estimation is the ft_tools::FtParameters object that is used by both ft_tools::FtCalibration and ft_tools::FtEstimation. The calibration sensor-specific parameters are:

mass: 0.0
sensor_frame_to_com: [0.0, 0.0, 0.0]
force_offset: [0.0, 0.0, 0.0]
torque_offset: [0.0, 0.0, 0.0]

The parameters can be loaded:

• from a yaml file:

ft_tools::FtParameters ft_parameters
// Load parameters from YAML file (absolute path)
const std::string filename("...")
bool ok = ft_parameters.from_yaml(filename);
// or from a config folder
bool ok = ft_parameters.from_yaml(config_filename, config_package);

• from a msg:

// Get calibration msg (typically from a service call)
// i.e., ft_msgs::srv::FtCalibration ft_parameters_msg;

ft_tools::FtParameters ft_parameters
bool ok = ft_parameters.from_msg(ft_parameters_msg);

The parameters can likewise be written to dump to a yaml file or sent as a msg.

FT calibration

The ft_tools::FtCalibration implements a simple sensor calibration method [1] using a least square regression (i.e., eigen SVD solver).

We are given N sets of measurements $\left( g, f, \tau \right)$ expressed in the sensor frame of reference where $g \in \mathbb{R}^3$ is the (signed) gravity and $f, \tau \in \mathbb{R}^3$ are the raw force and torque, respectively.

We want to retrieve the F/T sensor calibration that consists in

• the mass $m$ in Kg
• the center of mass $c \in \mathbb{R}^3$ in m
• the force offset $f_0 \in \mathbb{R}^3$ in N
• the torque offset $\tau_0 \in \mathbb{R}^3$ in N.m

If enough measurements were provided (i.e., about 10 well-chosen robot poses), the different parameters are identified using a least square regression (i.e., eigen SVD solver) such that

$$ f_\text{meas} = -mg + f_0 \text{ and } \tau_\text{meas} = -mc \times g + \tau_0$$

This process returns a ft_tools::FtParameters object.

Credits: the code is inspired by the ROS1 package force_torque_tools.

FT estimation

The ft_tools::FtEstimation implements a generic wrench estimator with the following features:

• Gravity and offsets compensation such that

  • ${}^{s}f_\text{est} = {}^{s}f_\text{meas} + m{}^{s}g - {}^{s}f_0$
  • ${}^{s}\tau_\text{est} = {}^{s}\tau_\text{meas} + {}^sp_{com} \times m{}^{s}g - {}^{s}\tau_0$

• Display force at interaction point (typically, the end-effector) such that

  • ${}^{ee}f_\text{est} = {}^{ee}R_s {}^{s}f_\text{est}$
  • ${}^{ee}\tau_\text{est} = {}^{ee}R_s \left( {}^{s}\tau_\text{est} - {}^s p_{ee} \times {}^{s}f_\text{est} \right) $

• Apply wrench deadband on ${}^{ee}f_\text{est}$ and ${}^{ee}\tau_\text{est}$ (optional)

• Apply low-pass filtering (200Hz cutoff by default)

Convenience nodes

F/T calibration node

Topics

• ~/joint_states (input topic) [sensor_msgs::msg::JointState]

  Joint states of the robot used to monitor the Cartesian pose of the robot.

• ~/raw_wrench (input topic) [geometry_msgs::msg::WrenchStamped]

  Raw (can be filtered) wrench measurement by the f/t sensor, expressed in the sensor frame.

Services

• ~/ft_calibration_node/add_calibration_sample [std_srvs::srv::Trigger]

  Add a calibration sample using the latest robot pose and measured wrench.

  The service call will return success = false in the following cases:

  • failure to update robot pose;
  • stale wrench measurement (older than 1s);
  • wrench measurement not expressed in supported frame;
  • invalid parameters.

• ~/ft_calibration_node/get_calibration [ft_msgs::srv::GetCalibration]

  If enough samples were collected, returns the computed calibration parameters as a ft_msgs::srv::FtCalibration msg.

  The service call will return success = false in the following cases:

  • not enough measurements;
  • failure to solve the identification problem;
  • invalid parameters.

• ~/ft_calibration_node/save_calibration [std_srvs::srv::Trigger]

  Save the estimated parameters to a YAML file.

  The destination file is <calibration_package_share_dir>/config/<calibration_filename>. It can be tuned with the parameters:

    ft_calibration_node:
    ros__parameters:
        ...
        calibration:
        ...
        calibration_filename: calibration.yaml
        calibration_package: ft_tools
    ...

• ~/ft_calibration_node/reset [std_srvs::srv::Trigger]

  Reset calibration procedure.

Wrench estimation node

Topics

• ~/joint_states (input topic) [sensor_msgs::msg::JointState]

  Joint states of the robot used to monitor the Cartesian pose of the robot.

• ~/raw_wrench (input topic) [geometry_msgs::msg::WrenchStamped]

  Raw (can be filtered) wrench measurement by the f/t sensor, expressed in the sensor frame.

• ~/estimated_wrench (output topic) [geometry_msgs::msg::WrenchStamped]

  Estimated wrench expressed in the sensor frame with:

  • gravity compensation;
  • sensor offsets compensation;
  • deadband.

• ~/interaction_wrench (output topic) [geometry_msgs::msg::WrenchStamped]

  Estimated interaction wrench at interaction_frame expressed in the reference_frame. The interaction is computed from the estimated_wrench above.

Services

• ~/ft_estimation_node/set_calibration [ft_msgs::srv::SetCalibration]

  Set f/t sensor calibration parameters from a ft_msgs::srv::FtCalibration msg.

• ~/ft_estimation_node/get_calibration [ft_msgs::srv::GetCalibration]

  See ~/ft_calibration_node/get_calibration for details.

• ~/ft_estimation_node/save_calibration [std_srvs::srv::Trigger]

  See ~/ft_calibration_node/save_calibration for details.

• ~/ft_estimation_node/reload_calibration [std_srvs::srv::Trigger]

  Reload calibration from config YAML file.

Basic GUI

Although everything can be done from RQT (i.e., using the service caller), a basic GUI is provided for convenience. Note that the GUI only has limited features and is overall not very robust to mishandling...

Perform calibration

1. Edit namespace service if needed;
2. Click on ROS2 Connection to initialize ROS2 communication (i.e., rclpy init, register services, etc.);
3. Click on Add calibration sample to call the service ~/ft_calibration_node/add_calibration_sample each time the robot is in position.

Retrieve calibration parameters

Once enough samples have been collected, go to the second tab of the GUI (see below) and click on Get calibration or Save calibration to call, respectively, the services:

• ~/ft_calibration_node/get_calibration
• ~/ft_calibration_node/save_calibration

Also, the Get calibration button will retrieve the estimated calibration parameters and display them in the GUI.

Set wrench estimator calibration parameters

TODO

Examples

Moveit2-assisted F/T calibration

1. Install third party utils for ft_tools_examples

source /opt/ros/jazzy/setup.bash
cd <ws>/src
vcs import . < ft_tools_ros2/ft_tools_examples/ft_tools_examples.repos
rosdep install --ignore-src --from-paths . -y -r
cd ..
colcon build --cmake-args -DCMAKE_BUILD_TYPE=Release --symlink-install

2. Launch

Launch robot controllers in a first terminal:

source install/local_setup.bash
ros2 launch ft_tools_example launch_robot_controllers.launch.py

Launch moveit2 planning + calibration/estimation nodes:

source install/local_setup.bash
ros2 launch ft_tools_example launch_moveit_ft_calibration.launch.py

N.B., if the calibration files is updated, the service ft_estimation_node/reload_calibration must be called to refresh.

(optional) Run calibration GUI

source install/local_setup.bash
ros2 run ft_gui ft_calibration_gui

3. Move robot with moveit rviz2 plugin and use GUI to perform calibration

F/T calibration during comanipulation

TODO

Wrench estimation for Cartesian admittance control

TODO

References

• [1] D. Kubus, T. Kroger and F. M. Wahl, "On-line rigid object recognition and pose estimation based on inertial parameters," 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, pp. 1402-1408, doi: 10.1109/IROS.2007.4399184.

Contacts

ICube Laboratory, University of Strasbourg, France

Thibault Poignonec: [email protected], @github: tpoignonec