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Research Papers

Kinematic Kalman Filter (KKF) for Robot End-Effector Sensing

[+] Author and Article Information
Soo Jeon

Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720soojeon@me.berkeley.edu

Masayoshi Tomizuka

Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720tomizuka@me.berkeley.edu

Tetsuaki Katou

Robot Laboratory, FANUC Ltd., Oshino-Mura, Yamanashi Prefecture 401-0597, Japankatou.tetsuaki@fanuc.co.jp

J. Dyn. Sys., Meas., Control 131(2), 021010 (Feb 05, 2009) (8 pages) doi:10.1115/1.3023124 History: Received December 12, 2007; Revised September 20, 2008; Published February 05, 2009

In control of industrial manipulators, the position from the motor encoder has been the only sensor measurement for axis control. In this case, it is not easy to estimate the end-effector motion accurately because of the kinematic errors of links, joint flexibility of gear mechanisms, and so on. Direct measurement of the end-effector using the vision sensor is considered as a solution but its performance is often limited by the slow sampling rate and the latency. To overcome these limitations, this paper extends the basic idea of the kinematic Kalman filter (KKF) to general rigid body motion leading to the formulation of the multidimensional kinematic kalman filter (MD-KKF). By combining the measurements from the vision sensor, the accelerometers and the gyroscopes, the MD-KKF can recover the intersample values and compensate for the measurement delay of the vision sensor providing the state information of the end-effector fast and accurately. The performance of the MD-KKF is verified experimentally using a planar two-link robot. The MD-KKF will be useful for widespread applications such as the high speed visual servo and the high-performance trajectory learning for robot manipulators, as well as the control strategies which require accurate velocity information.

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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Schematic for the derivation of the MD-KKF

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Figure 2

Implementation of the delay effect

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Figure 3

Schematic of the two-link robot

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Figure 4

A two-link direct drive robot for MD-KKF: (a) overview and (b) sensor unit

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Figure 5

Image processing to find the center of the camera

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Figure 6

Comparison of KKF with measurements from vision and encoder for Nv=10: (a) position and (b) velocity

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Figure 7

Comparison of KKF with measurements from vision and encoder for Nv=20: (a) position and (b) velocity

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