Fredrik Bagge Carlson

Modeling and identification of position and temperature dependent friction phenomena without temperature sensing

This paper investigates both positional dependence in systems with friction and the influence an increase in temperature has on the friction behavior. The positional dependence is modeled with a Radial Basis Function network and the temperature dependence is modeled as a first order system with the power loss due to friction as input, eliminating the need for temperature sensing. The proposed methods are evaluated in both simulations and experiments on two industrial robots with strong positional and temperature friction dependence.

Sensorless friction-compensated passive lead-through programming for industrial robots

Industrial robots are important when the degree of automation in industry is increased. To enable the use of robots also when the products change rapidly, the programming must be quick and easy to perform. One way to accomplish this is to use lead-through programming, i.e., the user manually guides the robot. This paper presents a sensorless approach, and thus avoids the need for a typically expensive sensor. The method is based on disabling low-level joint controllers combined with gravity compensation. It is reported how the performance can be improved by compensating for friction. Further, a method for detecting small external torques is described, based on the use of the low-level joint controllers with increased integral gain. The lead-through programming is experimentally evaluated using two different industrial robots.

Six DOF eye-to-hand calibration from 2D measurements using planar constraints

This article presents a linear, iterative method to solve the eye-to-hand calibration problem between a wrist-mounted laser scanner and the tool flange of a robot. Measurement data are acquired from a set of non parallel planes where after the plane equations and desired rigid transformation matrix are found in a two-step, iterative fashion. The method is shown to handle large error in the initial estimate of the transform and results are verified in both simulations and experiments using a seam tracking laser sensor for welding applications.

Polynomial Reconstruction of 3D sampled Curves Using Auxiliary Surface Data

This paper proposes a method for structural enhancement of a 3D sampled curve. The curve is assumed to be organized, but corrupted with low frequency noise. The proposed method approaches the notion of curve reconstruction in a novel way, where information about the structure in a scanned surface is used to reconstruct the curve. Principal Component Analysis is carried out on successive neighborhoods along the curve to estimate reduced dimensionality spaces, which allows polynomial reconstruction. The effectiveness of the proposed method is verified by both simulations and experiments.

Linear parameter-varying spectral decomposition

We develop a linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical motor.

Particle filter framework for 6D seam tracking under large external forces using 2D laser sensors

We provide a framework for 6 DOF pose estimation in seam-tracking applications using particle filtering. The particle filter algorithm developed incorporates measurements from both a 2 DOF laser seam tracker and the robot forward kinematics under an assumed external force. Special attention is paid to modeling of disturbances in the respective measurements, and methods are developed to assist the selection of sensor configurations for optimal estimation performance. The developed estimation algorithm and simulation environment are provided as an open-source, extendable package, written with an intended balance between readability and performance.