sedkilmm / dynamic-identification Goto Github PK

Python code and datasets for the identification of state-dependent dynamic model parameters in collaborative robotic manipulator systems.

• trajectory data preprocessing
• dynamic model including effects of frictions, actuator inertia, joint stiffness, and torque offset.
• generation of optimal exciting trajectories.
• calculation of physically consistent standard inertial parameters.

see documentation file.

Basic examples for kinova Gen3-R07 robot can be found in the examples directory.

• CUDA Compilation Toolkit > 11
• Python interpreter > 3.12
• Conda > 4.5.0

The computation of basic rigid body algorithms for the manipulator is done with Pinocchio. For more information, refer to Pinocchio's GitHub page or pinocchio cheat sheet.

create a new conda environment and install all the dependencies:

conda env create -f environment.yml
conda activate dynamapp

• 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

see the CONTRIBUTING guide.

See LICENSE file.