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Velocity estimation for ultra lightweight tendon driven series elastic robots

Kirchhoff, Jérôme and Stryk, Oskar von (2018):
Velocity estimation for ultra lightweight tendon driven series elastic robots.
In: IEEE Robotics and Automation Letters, pp. 664-671, 3, (2), DOI: 10.1109/LRA.2017.2729663,
[Article]

Abstract

Accurate velocity estimation is an important basis for robot control, but especially challenging for highly elastically driven robots. These robots show large swing or oscillation effects if they are not damped appropriately during the performed motion. In this paper, we consider an ultra lightweight tendon driven series elastic robot arm equipped with low-resolution joint position encoders. We propose an adaptive Kalman filter for velocity estimation that is suitable for these kinds of robots with a large range of possible velocities and oscillation frequencies. Based on an analysis of the parameter characteristics of the measurement noise variance, an update rule based on the filter position error is developed that is easy to adjust for use with different sensors. Evaluation of the filter both in simulation and in robot experiments shows a smooth and accurate performance, well suited for control purposes.

Item Type: Article
Erschienen: 2018
Creators: Kirchhoff, Jérôme and Stryk, Oskar von
Title: Velocity estimation for ultra lightweight tendon driven series elastic robots
Language: English
Abstract:

Accurate velocity estimation is an important basis for robot control, but especially challenging for highly elastically driven robots. These robots show large swing or oscillation effects if they are not damped appropriately during the performed motion. In this paper, we consider an ultra lightweight tendon driven series elastic robot arm equipped with low-resolution joint position encoders. We propose an adaptive Kalman filter for velocity estimation that is suitable for these kinds of robots with a large range of possible velocities and oscillation frequencies. Based on an analysis of the parameter characteristics of the measurement noise variance, an update rule based on the filter position error is developed that is easy to adjust for use with different sensors. Evaluation of the filter both in simulation and in robot experiments shows a smooth and accurate performance, well suited for control purposes.

Journal or Publication Title: IEEE Robotics and Automation Letters
Volume: 3
Number: 2
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Simulation, Systems Optimization and Robotics Group
Date Deposited: 19 Nov 2018 10:48
DOI: 10.1109/LRA.2017.2729663
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