pyDynaMapp : Python Dynamics Mapping Package For Robotics

The primary goal of this project is to offer a flexible framework for modeling and identifying serial manipulators, including the incorporation of nonlinear effects such as friction, stiffness, and backlash in robot joints. Additionally, a predictive feedback control mechanism is being developed to compensate these effects, making the framework suitable for collaborative robotics applications.

Getting started

Prerequisites

The code was tested successfully on the following platform:

• Windows 11:
  • python 3.12.4
  • miniconda 4.5.0
  • cuda 12.50 (optional)

⚠️ Warning: The package has not been tested or built for Unix-like systems (macOS, Linux). Please refer to the Unix installation section for more details.

Installation

Windows

Note: If Anaconda is installed on your system, skip the first step. Conda is the only Python package manager supported at the moment.

1. Download and install Miniconda. For Windows installation, refer to the Miniconda Windows Installation Guide.

2. Install the package using the following command:

   pip install -i https://pypi.anaconda.org/chiha/simple pydynamapp

Using Git:

1. Clone the repository:

   git clone https://github.com/wissem01chiha/dynamic-identification

2. Install the following enviroment dependencies manually:

   • numpy==1.26.4
   • pinocchio==2.7.1
   • scipy==1.14.0
   • nlopt==2.7.1
   • pyyaml==6.0.2
   • pandas==2.2.2
   • seaborn==0.13.2
   • matplotlib==3.8.4

   -- using conda:

   conda config --add channels conda-forge
   conda install pinocchio=2.7.1
   conda install nlopt=2.7.1
   ...

   -- or for Windows platforms, run the command to install all requirements:

    conda env create -f environment.yml 

   by defualt it create a virtual enviroment named dynamapp and place the python packages in it.

⚠️ Warning: The Pinocchio release 2.7.1 is the officially supported version. Newer releases (3.x and above) may cause errors. If you encounter any issues, please open an issue.

3. Run all tests to check the installation:

   python3 run_tests.py

for any bugs encountered during installation, please make sure to open an issue at: Issues

Unix

We strongly recommend using Conda to create a virtual environment with:

conda env create -f environment.yml
conda activate dynamapp

an then follow the same instruction in using git to insatll code dependancies in the virtual enviroment. run test to check installation: .. on Linux platforms:

  chmod u+x run_tests.sh
  ./run_tests.sh

for any bugs encountered during installation, please make sure to open an issue at: Issues

Package Structure

• The script folder contains executable identification scripts. Settings for each algorithm are passed through command-line arguments.

• The dynamics folder contains all direct and inverse dynamics model computations, state-space representation, and regressor formulation.

• The viscoelastic folder contains all base classes for contact models.

• The package relies on the URDF file format in the robot folder to represent the rigid body tree of the robot and contains basic inertial and geometric parameters. For more information, refer to:

  • Pinocchio Documentation
  • Pinocchio's GitHub Page
  • Pinocchio Cheat Sheet

• create a robot model: Use a URDF file along with a configuration file. The configuration file should follow the same layout as the Kinova config file.

  from pyDynaMapp.dynamics import Robot
  
  kinova = Robot(urdf_file_path, configuration_file_path)
  
  # Example: Compute mass and Coriolis matrices for home position
  C = kinova.computeCoriolisMatrix()
  M = kinova.computeMassMatrix()
  
  # Example: Compute the joint torques vector for home position
  T = kinova.computeGeneralizedTorques()

• create a state-space representation :

  from pyDynaMapp.dynamics import StateSpace
  import numpy as np
  
  kinova_ss = StateSpace(urdf_file_path, configuration_file_path)
  
  # Example: Compute the state-space matrices at a given configuration using a state vector x
  x = np.random.rand(14)
  A, B, C, D = kinova_ss.model.computeStateMatrices(x)
  
  # Example: Compute the state-space matrices at a given configuration using position and velocity vectors
  q = np.random.rand(7)
  qp = np.random.rand(7)
  A, B, C, D = kinova_ss.model.computeStateMatrices(q, qp)

Documentation

official documentation is not yet released and is a work in progress.
for more information, please visit the pyDynaMapp-Anaconda page.

References

• A three-loop physical parameter identification method of robot manipulators considering physical feasibility and nonlinear friction mode, Tangzhong Song, Lijin Fang,Guanghui Liu, Hanyu Pang, 2024
• Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators, Emil Madsen, Oluf Skov Rosenlund, David Brandt, Xuping Zhang, 2020
• Robotics Modelling Planning and Control, Bruno Siciliano, Lorenzo Sciavicco Luigi Villani,Giuseppe Oriolo,
• Global Identification of Joint Drive Gains and Dynamic Parameters of Robots , Maxime Gautier, Sebastien Brio, 2014
• Direct Calculation of Minimum Set of Inertial Parameters of Serial Robots, Maxime Gautier, Wisama khalil, 1990
• Efficient Dynamic Computer Simulation of Robotic Mechanisms, M.W.Walker, D. E. Orin, 1982
• Inertial Parameter Identification in Robotics: A Survey, Quentin Leboutet, Julien Roux, Alexandre Janot, Julio Rogelio, Gordon Cheng, 2021
• Practical Modeling and Comprehensive System Identification of a BLDC Motor, Changle Xiang, Xiaoliang Wang, Yue Ma, and Bin Xu, 2015
• Identiable Parameters and Optimum Congurations for Robots Calibration, W. Khalil, M. Gautier and Ch. Enguehard, 2009, Robotica
• Recursive identification of certain structured time varying state-space models, M.H. Moazzam T. Hesketh, D.J.Clements, 1997
• Comparison Between the CLOE Method and the DIDIM Method for Robots Identification, Alexandre Janot, Maxime Gautier, Anthony Jubien, and Pierre Olivier Vandanjon, 2014
• Robot Joint Modeling and Parameter Identification Using the Clamping Method, Christian Lehmann ∗ Bjorn Olofsson, Klas Nilsson, Marcel Halbauer, Mathias Haage, Anders Robertsson, Olof Sornmo, Ulrich Berger, 2013
• Fundamentals of friction modeling, Farid Al-Bender, 2015
• Constrained State Estimation - A Review, Nesrine Amor, Ghualm Rasool, and Nidhal C. Bouaynaya, arXiv, 2022
• The Pinocchio C++ library,J.Carpentier, G.Saurel, G.Buondonno, J.Mirabel, F.Lamiraux, O.Stasse, N.Mansard, 2019

Development

See the CONTRIBUTING guide.

License

See LICENSE file.