Evaluation of Six Different Sensor Fusion Methods for an Industrial Robot using Experimental Data

IFAC Proceedings Volumes

2012

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A novel method to find the orientation and position of a triaxial accelerometer mounted on a six degrees-of-freedom industrial robot is proposed and evaluated on experimental data. The method consists of two consecutive steps, where the first is to estimate the orientation of the accelerometer from static experiments. In the second step the accelerometer position relative to the robot base is identified using accelerometer readings when the accelerometer moves in a circular path and where the accelerometer orientation is kept constant in a path fixed coordinate system. Once the accelerometer position and orientation are identified it is possible to use the accelerometer in robot model parameter identification and in advanced control solutions. Compared to previous methods, the accelerometer position estimation is completely new, whereas the orientation is found using an analytical solution to the optimisation problem. Previous methods use a parameterisation where the optimisation uses an iterative solver.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method to Estimate the Position and Orientation of a Triaxial Accelerometer Mounted to an Industrial Manipulator

IFAC Proceedings Volumes

2012

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“…This will improve the stiffness of the system, although it will not eliminate the stationary error for the end-effector position. The ultimate solution is to measure the end-effector position, but for practical reasons this is in general not possible, instead the end-effector position can be estimated, as described in Axelsson [2012], , Chen and Tomizuka [2013], and used in the feedback loop.…”

Section: Discussionmentioning

confidence: 99%

ℋ∞-Controller Design Methods Applied to One Joint of a Flexible Industrial Manipulator

Helmersson

IFAC Proceedings Volumes

2014

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Control of a flexible joint of an industrial manipulator using H ∞ design methods is presented. The considered design methods are i) mixed-H ∞ design, and ii) H ∞ loop shaping design. Two different controller configurations are examined: one uses only the actuator position, while the other uses the actuator position and the acceleration of the end-effector. The four resulting controllers are compared to a standard pid controller where only the actuator position is measured. The choices of the weighting functions are discussed in details. For the loop shaping design method, the acceleration measurement is required to improve the performance compared to the pid controller. For the mixed-H ∞ method it is enough to have only the actuator position to get an improved performance. Model order reduction of the controllers is briefly discussed, which is important for implementation of the controllers in the robot control system.

“…The PF, [9], [10], provides an approximate solution to the Bayesian estimation problem formulated in (5). The PF approximates the density p(x t |Y t ) by a large set of N samples (particles), {x…”

Section: The Particle Filter (Pf)mentioning

confidence: 99%

“…[18], [22]. The recent publications [5], [6] present results based on real data rickard.karlsson@niradynamics.se from an ABB industrial robot where actuator positions and acceleration of the tool are fused using an EKF and PF.…”

Section: Introductionmentioning

confidence: 99%

Estimation-based ILC using particle filter with application to industrial manipulators

Karlsson

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

2013

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Abstract-An estimation-based iterative learning control (ILC) algorithm is applied to a realistic industrial manipulator model. By measuring the acceleration of the end-effector, the arm angular position accuracy is improved when the measurements are fused with motor angle observations. The estimation problem is formulated in a Bayesian estimation framework where three solutions are proposed: one using the extended Kalman filter (EKF), one using the unscented Kalman filter (UKF), and one using the particle filter (PF). The estimates are used in an ILC method to improve the accuracy for following a given reference trajectory. Since the ILC algorithm is repetitive no computational restrictions on the methods apply explicitly. In an extensive Monte Carlo simulation study it is shown that the PF method outperforms the other methods and that the ILC control law is substantially improved using the PF estimate.