Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -