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On Sensor Fusion Applied to Industrial Manipulators
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2011 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

One of the main tasks for an industrial robot is to move the end-effector in a predefined path with a specified velocity and acceleration. Different applications have different requirements of the performance. For some applications it is essential that the tracking error is extremely low, whereas other applications require a time optimal tracking. Independent of the application, the controller is a crucial part of the robot system. The most common controller configuration uses only measurements of the motor angular positions and velocities, instead of the position and velocity of the end-effector.

The development of new cost optimised robots have introduced unwanted flexibilities in the joints and the links. It is no longer possible to get the desired performance and robustness by only measuring the motor angular positions. This thesis investigates if it is possible to estimate the end-effector position when an accelerometer is mounted at the end-effector. The main focus is to investigate Bayesian estimation methods for state estimation, here represented by the extended Kalman filter (EKF) and the particle filter (PF).

A simulation study is performed on a two degrees of freedom industrial robot model using an EKF. The study emphasises three important problems to take care of in order to get a good performance. The first one is related to model errors which in general requires better identification methods. The second problem is about tuning of the EKF, i.e., the choice of covariance matrices for the measurement and process noise. It is desirable to have an automatic tuning procedure which minimises the estimation error and is robust to initial conditions of the tuned parameters. A variant of the expectation maximisation (EM) algorithm is proposed for estimation of the process noise covariance matrix Q. The EM algorithm iteratively estimates the unobserved state sequence and the matrix Q based on the observations of the process, where the extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence.

The third problem considers the orientation and position of the accelerometer mounted to the end-effector. A novel method to find the orientation and position of the triaxial accelerometer is proposed and evaluated on experimental data. The method consists of two consecutive steps, where the first is to estimate the orientation of the sensor from static experiments. In the second step the sensor position relative to the robot base is identified using sensor readings when the sensor moves in a circular path and where the sensor orientation is kept constant in a path fixed coordinate system.

Finally, experimental evaluations are performed on an ABB IRB4600 robot. Different observers using the EKF, EKS and PF with different estimation models are proposed. The estimated paths are compared to the true path measured by a laser tracking system. There is no significant difference in performance between the six observers. Instead, execution time, model complexities and implementation issues have to be considered when choosing the method. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2011. , 114 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1511
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-72235Local ID: LiU-TEK-LIC-2011:50ISBN: 978-91-7393-031-4OAI: diva2:458630
2011-12-16, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 14:24 (Swedish)
Available from: 2011-11-28 Created: 2011-11-23 Last updated: 2011-11-28Bibliographically approved

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