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Chapter 6 — Model Identification

John Hollerbach, Wisama Khalil and Maxime Gautier

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts.

For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (DH) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated.

Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

Calibration of ABB's IRB 120 industrial robot

Author  Ilian Bonev

Video ID : 422

The video depicts the process for the geometric calibration of the 6 DOF IRB 120. The calibration is based on the measurement of the position and the orientation of a tool using the laser tracking system from FARO. The video shows in sequence the steps in the acquisition of various configurations which can then be be employed using an algorithm similar to that of Sect. 6.2.

Robot calibration using a touch probe

Author  Ilian Bonev

Video ID : 425

The video shows a kinematic calibration experiment using a touch probe. The system realizes the point-plan contact with different plans. The calibration is thus based on position contact without orientation.

Calibration and accuracy validation of a FANUC LR Mate 200iC industrial robot

Author  Ilian Bonev

Video ID : 430

This video shows excerpts from the process of calibrating a FANUC LR Mate 200iC industrial robot using two different methods. In the first method, the position of one of three points on the robot end-effector is measured using a FARO laser tracker in 50 specially selected robot configurations (not shown in the video). Then, the robot parameters are identified. Next, the position of one of the three points on the robot's end-effector is measured using the laser tracker in 10,000 completely arbitrary robot configurations. The mean positioning error after calibration was found to be 0.156 mm, the standard deviation (std) 0.067 mm, the mean+3*std 0.356 mm, and the maximum 0.490 mm. In the second method, the complete pose (position and orientation) of the robot end-effector is measured in about 60 robot configurations using an innovative method based on Renishaw's telescoping ballbar. Then, the robot parameters are identified. Next, the position of one of the three points on the robot's end-effector is measured using the laser tracker in 10,000 completely arbitrary robot configurations. The mean position error after calibration was found to be 0.479 mm, the standard deviation (std) 0.214 mm, and the maximum 1.039 mm. However, if we limit the zone for validations, the accuracy of the robot is much better. The second calibration method is less efficient but relies on a piece of equipment that costs only $12,000 (only one tenth the cost of a laser tracker).

Dynamic identification of Staubli TX40 : Trajectory without load

Author  Maxime Gautier

Video ID : 480

This video shows a trajectory without load used to identify the dynamic parameters of the links, the load and the joint drive chain of an industrial Staubli TX 40 manipulator. Details and results are provided in the paper: M. Gautier, S. Briot: Global identification of joint drive gains and dynamic parameters of robots, ASME J. Dyn. Syst. Meas. Control 136(5), 051025-051025-9 (2014); doi:10.1115/1.4027506

Dynamic identification of Staubli TX40 : Trajectory with load

Author  Maxime Gautier

Video ID : 481

This video shows a trajectory with a known payload mass of 4.5 kg attached to the end effector of an industrial Staubli TX 40 manipulator. Joint position and current reference data are collected on this short-time (8s) trajectory and used with data collected on a trajectory without load to identify all the dynamic parameters of the links, load and joint drive chain in a single global LS procedure. Details and results are given in the paper : M. Gautier, S. Briot: Global identification of joint drive gains and dynamic parameters of robots, ASME J. Dyn. Syst. Meas. Control 136(5), 051025̶ 051025-9 (2014); doi:10.1115/1.4027506

Dynamic identification of Kuka LWR : Trajectory without load

Author  Maxime Gautier

Video ID : 482

This video shows a trajectory without load used to identify the dynamic parameters of the links, load and torque sensor gain of the Kuka LWR manipulator. Details and results are given in the papers: A. Jubien, M. Gautier, A. Janot: Dynamic identification of the Kuka LWR robot using motor torques and joint torque sensors data, preprint 19th IFAC World Congress, Cape Town (2014) pp. 8391-8396, M. Gautier, A. Jubien: Force calibration of the Kuka LWR-like robots including embedded joint torque sensors and robot structure, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Chicago (2014) pp. 416-421

Dynamic identification of Kuka LWR : Trajectory with load

Author  Maxime Gautier

Video ID : 483

This video shows a trajectory with a known payload mass of 4.6 (kg) used to identify the dynamic parameters and torque-sensor gains of the KUKA LWR manipulator. Details and results are given in the papers: A. Jubien, M. Gautier, A. Janot: Dynamic identification of the Kuka LWR robot using motor torques and joint torque sensors data, preprints 19th IFAC World Congress, Cape Town (2014) pp. 8391-8396 M. Gautier, A. Jubien: Force calibration of the Kuka LWR-like robots including embedded joint torque sensors and robot structure, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Chicago (2014) pp. 416-421

Dynamic identification of a parallel robot: Trajectory with load

Author  Maxime Gautier

Video ID : 485

This video shows a trajectory with a known mass payload attached to the platform, used to identify the dynamic parameters and joint drive gains of a parallel prototype robot Orthoglyde. Details and results are given in the paper: S. Briot, M. Gautier: Global identification of joint drive gains and dynamic parameters of parallel robots, Multibody Syst. Dyn. 33(1), 3-26 (2015); doi 10.1007/s11044-013-9403-6

Dynamic identification of Kuka KR270 : Trajectory without load

Author  Maxime Gautier

Video ID : 486

This video shows a trajectory without load used to identify the dynamic parameters of the links, load, joint drive gains and gravity compensator of a heavy industrial Kuka KR 270 manipulator. Details and results are given in the paper: A. Jubien, M. Gautier: Global identification of spring balancer, dynamic parameters and drive gains of heavy industrial robots, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013) pp. 1355-1360

Dynamic identification of Kuka KR270 : Trajectory with load

Author  Maxime Gautier

Video ID : 487

This video shows a trajectory with a known payload mass used to identify the dynamic parameters of the links, load, joint drive gains and gravity compensator of a heavy industrial Kuka KR 270 manipulator Details and results are given in the paper: A. Jubien, M. Gautier, Global identification of spring balancer, dynamic parameters and drive gains of heavy industrial robots, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013), pp. 1355-1360