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Abstract| This paper addresses the control problem of hydraulic robot manipulators. The backstepping design methodology is adopted to develop a novel nonlinear position tracking controller. The tracking errors are shown to be exponentially stable under the proposed control law. The controller is further augmented with adaptation laws to compensate for parametric uncertainties in the system dynamics. Acceleration feedback i s a voided by using two new adaptive and robust sliding type observers. The adaptive controllers are proven to be asymptotically stable via Lyapunov analysis. Simulation and experimental results performed with a hydraulic Stewart platform demonstrate the e ectiveness of the approach.

This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented.

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This paper presents the development of a novel, fully-automatic tracking and segmentation system to extract the boundary of the carotid artery from ultrasound images in real-time. The center of the carotid artery is tracked by using the Star algorithm [5]. The stability of the Star algorithm has been improved by using a temporal Kalman filter. A spatial Kalman filter is used to estimate the carotid artery boundary. Since the method does not employ any numerical optimization, convergence is very fast. The stability and accuracy of the method is demonstrated by tracking the carotid artery over a 30 second sequence of ultrasound images taken during a carotid artery examination. An application of the tracking method to ultrasound image servoing is also presented.