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The goal of the project ROSdyn is to realize a ROS-based package that implement a fully automated procedure able to calibrate the robot dynamics model. The use of ROS ecosystem enable the standardizat...

ROSdyn implements a fully automated procedure able to calibrate the robot dynamics model.

It is integrated with MoveIt! to automatically compute, simulate, and execute identification trajectory. The result is stored in a URDF file.

Build/Installation

The software can be installed with the following rosinstall file.

Travis CI Kinetic Build: Build Status

IMPORTANT rosdyn_identification has been moved here.

List of packages

rosdyn_core see README:

Dynamics header library based on Eigen. With respect to KDL, it has two advantages: it is faster and it allow computing model regressor.

An example of usage can be found here

The following list shows the computation times for a 6DOF robot on a laptop Asus PU551J with Ubuntu 16.04 (Release build, average on 10000 trials).

** computation time in microseconds: **

pose = 0.75970 [us]

pose + jacobian = 1.06562 [us]

pose + jacobian + velocity twists for all links = 1.25589 [us]

pose + jacobian + velocity twists for all links + linear aceleration twists for all links = 1.25351 [us]

pose + jacobian + velocity twists for all links + non linear acceleration twists for all links = 1.51663 [us]

pose + jacobian + velocity twists for all links + acceleration twists for all links = 1.83826 [us]

pose + jacobian + velocity twists for all links + acceleration twists for all links + jerk twists for all links = 2.68916 [us]

pose + jacobian + velocity twists for all links + acceleration twists for all links + joint torque = 3.76733 [us]

pose + jacobian + joint inertia matrix = 10.06761 [us]

Acknowledgements

RosDyn is developed by CNR-STIIMA (www.stiima.cnr.it)


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Supported by ROSIN - ROS-Industrial Quality-Assured Robot Software Components.
More information: rosin-project.eu

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This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement no. 732287.