DocumentCode
249929
Title
Identifying the dynamic model used by the KUKA LWR: A reverse engineering approach
Author
Gaz, Claudio ; Flacco, Fabrizio ; De Luca, A.
Author_Institution
Dipt. di Ing. Inf., Auto-matica e Gestionale, Sapienza Univ. di Roma, Rome, Italy
fYear
2014
fDate
May 31 2014-June 7 2014
Firstpage
1386
Lastpage
1392
Abstract
An approach is presented for the model identification of the so-called link dynamics used by the KUKA LWR-IV, a lightweight manipulator with elastic joints that is very popular in robotics research but for which a complete and reliable dynamic model is not yet publicly available. The control software interface of this robot provides numerical values of the link inertia matrix and the gravity vector at each configuration, together with link position and joint torque sensor data. Taking advantage of this information, a general procedure is set up for determining the structure and identifying the value of the relevant dynamic coefficients used by the manufacturer in the evaluation of these robot model terms. We call this a reverse engineering approach, because our main goal is to match the numerical data provided by the software interface, using a suitable symbolic model of the robot dynamics and the inertial and gravity coefficients that are being estimated. Only configuration-dependent terms are involved in this process, and thus static experiments are sufficient for this task. The main issues of dynamic model identification for robots with elastic joints are discussed in general, highlighting the pros and cons of the approach taken for this class of KUKA lightweight manipulators. The main identification results, including training and validation tests, are reported together with additional dynamic validation experiments that use the complete identified model and joint torque sensor data.
Keywords
identification; manipulator dynamics; matrix algebra; reverse engineering; KUKA LWR lightweight manipulators; configuration-dependent terms; dynamic coefficients; elastic joints; gravity coefficients; gravity vector; inertial coefficients; joint torque sensor data; lightweight robot; link dynamics model identification; link inertia matrix; link position; numerical data; reverse engineering approach; robot control software interface; robot dynamics symbolic model; robot model term evaluation; Gravity; Joints; Mathematical model; Robot sensing systems; Torque; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location
Hong Kong
Type
conf
DOI
10.1109/ICRA.2014.6907033
Filename
6907033
Link To Document