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Identifying the dynamic model used by the KUKA LWR: A reverse engineering approach
81
Citations
7
References
2014
Year
Unknown Venue
Robot KinematicsLink PositionEngineeringGravity CoefficientsKuka LwrMechanical EngineeringReverse Engineering ApproachModeling MethodSimulation ModellingSoft RoboticsDynamic ModelIndustrial RoboticsSystems EngineeringModeling And SimulationKinematicsLink Inertia MatrixMechatronicsMotion ControlRobot ControlModel-based System EngineeringFeedforward ControlAerospace EngineeringMechanical SystemsStructural MechanicsRoboticsVibration ControlModel AnalysisData Modeling
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.
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