Walter Verdonck

Dynamic Model Identification for Industrial Robots

The use of periodic excitation is the key feature of the presented robot identification method. Periodic excitation allows us to integrate the experiment design, signal processing, and parameter estimation. This integration simplifies the identification procedure and yields accurate models. Experimental results on an industrial robot manipulator show that the estimated dynamic robot model can accurately predict the actuator torques for a given robot motion. Accurate actuator torque prediction is a fundamental requirement for robot models that are used for offline programming, task optimization, and advanced model-based control. A payload identification approach is derived from the integrated robot identification method, and possesses the same favorable properties.

Maximum likelihood identification of a dynamic robot model: implementation issues

This paper considers the practical implementation of a new maximum likelihood robot identification method, developed by Olsen and Petersen. In particular, the practical issue concerning the estimation of the joint velocities and accelerations from joint angle measurements, and its consequence on the parameter estimation and accuracy, is considered. Simulation and experimental results on a KUKA IR 361 industrial robot are discussed, and compared with models obtained using a much simpler weighted least squares method.

An Experimental Robot Load Identification Method for Industrial Application

In this paper, we discuss a new experimental robot load identification method that is used in industry. The method is based on periodic robot excitation and the maximum likelihood estimation of the parameters, techniques adopted from Swevers et al. (1997 IEEE Transactions on Robotics and Automation 13(5):730—740). This method provides: (1) accurate estimates of the robot load inertial parameters; and (2) accurate actuator torque predictions. These are both essential for the acceptance of the results in an industrial environment. The key element to the success of this method is the comprehensiveness of the applied model, which includes, besides the dynamics resulting from the robot load and motor inertia, the coupling between the actuator torques, the mechanical losses in the motors and the efficiency of the transmissions. Accurate estimates of the robot link and motor inertial parameters, which can be considered identical for all robots of the same type, are obtained from separate experiments (see Swevers et al.), and used as a priori knowledge for the robot load identification. We present experimental results on a KUKA industrial robot equipped with a calibrated test load.

A demonstration tool with Kalman filter data processing for robot programming by human demonstration

This paper presents a modular demonstration tool for robot programming by human demonstration and an approach for the calibration of the tools sensors. The tool is equipped with a wrench sensor, twelve LED markers for fast and accurate six dimensional position tracking with the Krypton K600 camera system, a compact camera and a laser distance sensor. A gripper mechanism is mounted on the tool for grasping and manipulating objects. The design of the tool specifically focused on the demonstration of compliant motion task, with applications in manipulation and assembly tasks. The calibration approach first uses an extended Kalman Filter to convert the measured positions of three to twelve visible LEDs into the pose of the tool frame relative to the Krypton camera frame. Then, using a non minimal state Kalman filter, the force sensor calibration parameters are calculated, and the orientation of the Krypton camera frame relative to the world frame is defined. This calibration approach is verified in a real world experiment.

Improving the dynamic accuracy of industrial robots by trajectory pre-compensation

This paper presents a method to improve the path tracking accuracy of an industrial robot without replacing the standard industrial controller. By calculating off-line an appropriate trajectory pre-compensation, the effects of the nonlinear dynamics are compensated. This is realized by filtering the desired trajectory with the inverse dynamic model of the robot and its velocity controller. This compensation is applied as a velocity feedforward in the standard industrial controller avoiding the need for a torque control interface. The method presented is validated experimentally on a KUKA IR 361 industrial robot. The results show clearly an improved path tracking accuracy on circular trajectories.

Combining internal and external robot models to improve model parameter estimation

Experimental robot identification techniques can principally be divided into two categories, based on the type of models they use: internal or external. Internal models relate the joint torques or forces and the motion of the robot; external models relate the reaction forces and torques on the bedplate and the motion data. This paper describes how internal and external robot models can be combined into one identifiable minimal model. This model allows to combine joint torque/force and reaction torque/force measurements in one parameter estimation scheme. This combined model estimation will yield more accurate parameter estimates, and consequently better actuator torque predictions, which is shown experimentally on an industrial robot (KUKA IR 361).