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Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques

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Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques. / Mihaylova, L.; Bruyninckx, H.; De Schutter, J.; Staffetti, E.

Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on. 2001. p. 329-334.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Mihaylova, L, Bruyninckx, H, De Schutter, J & Staffetti, E 2001, Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques. in Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on. pp. 329-334, International Conf. on Multisensor Fusion and Integration for Intelligent Systems, MFI' 2001, Baden-Baden, Germany, 20/08/01. https://doi.org/10.1109/MFI.2001.1013556

APA

Mihaylova, L., Bruyninckx, H., De Schutter, J., & Staffetti, E. (2001). Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques. In Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on (pp. 329-334) https://doi.org/10.1109/MFI.2001.1013556

Vancouver

Mihaylova L, Bruyninckx H, De Schutter J, Staffetti E. Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques. In Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on. 2001. p. 329-334 https://doi.org/10.1109/MFI.2001.1013556

Author

Mihaylova, L. ; Bruyninckx, H. ; De Schutter, J. ; Staffetti, E. / Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques. Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on. 2001. pp. 329-334

Bibtex

@inproceedings{d4df5964510f4af18e5d7ef8bb9a1978,
title = "Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques",
abstract = "The paper presents a solution to the problem of planar contour tracking with a force-controlled robot. The contour shape is unknown and is characterized at each time step by the curvature together with the orientation angle and arc length. The unknown contour curvature, continuously changing, is supposed to be within a preliminary given interval. An Interacting Multiple Model (IMM) filter is implemented to cope with the uncertainties. The interval of possible curvature values is discretized, i.e., a grid is formed and several Extended Kalman filters (EKFs) are run in parallel. The curvature estimate represents a fusion of the values from the grid with the IMM probabilities. The orientation angle estimate is also a fusion of the estimates, obtained from the separate Kalman filters with the mode probabilities. A single-model EKF is implemented to localize the unknown initial robot end-effector position over the contour. The performance of both algorithms is investigated and results, based on real data, are presented.",
keywords = "estimation, robotics, IMM filter, model and noise uncertainties, Kalman filter, force control DCS-publications-id, inproc-441, DCS-publications-credits, dsp-fa, DCS-publications-personnel-id, 121",
author = "L. Mihaylova and H. Bruyninckx and {De Schutter}, J. and E. Staffetti",
note = "pp. 329-334 doi:10.1109/MFI.2001.1013556; International Conf. on Multisensor Fusion and Integration for Intelligent Systems, MFI' 2001, ; Conference date: 20-08-2001 Through 21-08-2001",
year = "2001",
month = oct
day = "21",
doi = "10.1109/MFI.2001.1013556",
language = "English",
pages = "329--334",
booktitle = "Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on",

}

RIS

TY - GEN

T1 - Planar contour tracking in the presence of pose and model errors by Kalman filtering techniques

AU - Mihaylova, L.

AU - Bruyninckx, H.

AU - De Schutter, J.

AU - Staffetti, E.

N1 - pp. 329-334 doi:10.1109/MFI.2001.1013556

PY - 2001/10/21

Y1 - 2001/10/21

N2 - The paper presents a solution to the problem of planar contour tracking with a force-controlled robot. The contour shape is unknown and is characterized at each time step by the curvature together with the orientation angle and arc length. The unknown contour curvature, continuously changing, is supposed to be within a preliminary given interval. An Interacting Multiple Model (IMM) filter is implemented to cope with the uncertainties. The interval of possible curvature values is discretized, i.e., a grid is formed and several Extended Kalman filters (EKFs) are run in parallel. The curvature estimate represents a fusion of the values from the grid with the IMM probabilities. The orientation angle estimate is also a fusion of the estimates, obtained from the separate Kalman filters with the mode probabilities. A single-model EKF is implemented to localize the unknown initial robot end-effector position over the contour. The performance of both algorithms is investigated and results, based on real data, are presented.

AB - The paper presents a solution to the problem of planar contour tracking with a force-controlled robot. The contour shape is unknown and is characterized at each time step by the curvature together with the orientation angle and arc length. The unknown contour curvature, continuously changing, is supposed to be within a preliminary given interval. An Interacting Multiple Model (IMM) filter is implemented to cope with the uncertainties. The interval of possible curvature values is discretized, i.e., a grid is formed and several Extended Kalman filters (EKFs) are run in parallel. The curvature estimate represents a fusion of the values from the grid with the IMM probabilities. The orientation angle estimate is also a fusion of the estimates, obtained from the separate Kalman filters with the mode probabilities. A single-model EKF is implemented to localize the unknown initial robot end-effector position over the contour. The performance of both algorithms is investigated and results, based on real data, are presented.

KW - estimation

KW - robotics

KW - IMM filter

KW - model and noise uncertainties

KW - Kalman filter

KW - force control DCS-publications-id

KW - inproc-441

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1109/MFI.2001.1013556

DO - 10.1109/MFI.2001.1013556

M3 - Conference contribution/Paper

SP - 329

EP - 334

BT - Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on

T2 - International Conf. on Multisensor Fusion and Integration for Intelligent Systems, MFI' 2001,

Y2 - 20 August 2001 through 21 August 2001

ER -