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Sensor Fusion for Position Estimation of an Industrial Robot
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2004 (English)In: Proceedings of Reglermöte 2004, 2004Conference paper, Published paper (Refereed)
Abstract [en]

A modern industrial robot control system is often based only upon measurements from the motors of the manipulator. Hence to follow a trajectory with the tool an accurate description of the system must be used. In the paper a sensor fusion technique is presented to achieve good estimates of the position of the robot using a simple model. By using information from an accelerometer the effect of unmodelled dynamics can be measured. Hence, the estimate of the tool position can be improved to enhance the positioning. We formulate the computation of the position as a Bayesian estimation problem and propose two solutions. First using the extended Kalman filter EKF as a fast but linearized estimator. Second the particle filter which can solve the Bayesian estimation problem without linearizations or any Gaussian noise assumptions. Since the aim is to use the estimates to improve position accuracy using an iterative learning control method, no computational constraints arises. The methods are applied to experimental data from an ABBIRB1400 commercial industrial robot. We also discuss some preliminary results from using a detailed simulation model.

Place, publisher, year, edition, pages
2004.
Keyword [en]
Extended kalman filter, Particle filter, Robotics, Experiment
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-22515Local ID: 1774OAI: oai:DiVA.org:liu-22515DiVA: diva2:242828
Conference
Reglermöte 2004, Göteborg, Sweden, May, 2004
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-03-28

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Karlsson, RickardNorrlöf, Mikael

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