Dynamic Identification of KUKA LWR-IV and Inverse Dynamics

Comparrison Between Standard DH and Modified DH Parameters in Recursive Newton-Euler Algorithm

Robotics II Couse Project

Francesco Roscia, Matteo Sodano, Andrea Wrona (2020)

Supervisors: Prof. Alessandro DE LUCA, Dr. Claudio GAZ

The project purpose is to analyze the CPU-time required by the two algorithms for compute the joint torques having the desired joint positions and velocities. We defined a set of exciting trajectories using the CAD software V-REP (Coppelia-Robotics), which have been executed by the robot. The collected data from the laboratory activities are the inputs for estimation of dynamics coefficient of the robot (the producer did not publish them).

The code is in the zip folders, and it is written in Matlab

References

[1] Siciliano B., Sciavicco L., Villani L., Oriolo G., (2009), "Robotics - Modelling, Planning and Control", Springer.

[2] Craig S. J., (1986), "Introduction to Robotics", Upper Saddle River (New Jersey), Prentice Hall.

[3] Ayusawa K., Rioux A., Yoshida E., Venture G., Gautier M., (2017), "Generating persistently exciting trajectory based on condition number optimization", IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 6518-6524.

[4] Gautier M., Khalil W., (1991), "Exciting trajectories for the identification of base inertial parameters of robots", Procedings of the 30th Conference on Decision and Control, England, pp. 494-499.

[5] Jubien A., Gautier M., Janot A., (2014), "Dynamic Identification of the Kuka LightWeight Robot: comparison between actual and confidential Kuka's parameters", IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), France, pp. 483-488.

[6] Gaz. C., Flacco F., De Luca A., (2014), "Identifying the Dynamic Model Used by the KUKA LWR: A Reverse Engineering Approach", IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, pp. 1386,1392.

[7] Gaz. C., Flacco F., De Luca A., (2016), "Extracting Feasible Robot Parameters from Dynamic Coefficients using Nonlinear Optimization Methods", IEEE International Conference on Robotics and Automation (ICRA), Sweden, pp. 2075-2081.

[8] Gaz C., Cognetti M., Oliva A., Robuffo Giordano P., De Luca A., (2019), "Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization", IEEE Robotics and Automation Letters Vol. 4 No.4.

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