International audienceThis paper addresses the problem of synchronizing networks of nonidentical, nonlinear dynamical systems described by Euler-Lagrange equations, which are assumed fully-actuated, with their states available for measurement, but with unknown parameters. The only assumption made on the communication graph is that it is connected. Moreover, the communication is subject to constant time delays, which are also unknown. The main result of the paper is the construction of an adaptive controller that achieves global full-state synchronization, i.e., the difference between the agents positions and velocities asymptotically converges to zero. If a desired trajectory for all systems is given, a slight modification to the proposed scheme achieves also full-state synchronization. Simulations using a ten robot manipulator network are used to illustrate the performance of the proposed schemes

Fig. 11. Two successive outdoor range scans, smoothed and segmented by the line mask. The line intersection points ( ) in the first scan, and inverted triangles ( ) in the second, can be used as occlusion-invariant, point features. VII. CONCLUSIONUnlike RANSAC, the presented algorithm smoothes range data, to produce a local description of features, which, in some circumstances, can be more beneficial than global descriptions for robot navigation. Also, its computational complexity is independent of the number of model outliers, and is less affected by the use of higher order geometric models.The smoothing and segmentation of range data is fundamentally different from that of image data. Structure preserving, and noise reduction algorithms in vision, use the local intensity gradient as a measure of noise. Range values are completely environment dependent, and not constant between features. Therefore, in this algorithm, the Mahalanobis distance between observed range values and their geometric-model-based predictions is used as the "measure of noise." A mask weighting function of the Mahalanobis distance was derived, which behaves as the diffusion coefficient in the anisotropic diffusion equation, often applied in vision, which guarantees that no new features are introduced with increase of scale. This mask can be applied iteratively, providing smoothing at different scales. The results demonstrated that the number of extracted features (lines or circles) converged to the true number with increase of scale, and the error between the extracted and true feature coordinates converged to a minimum. It has been shown that with increase of scale, the algorithm automatically reduces noise, only within the model-compliant regions of the range scans, yielding superior, postsmoothing, segmentation possibilities. REFERENCES[1] P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans.

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In this paper the problem of position tracking in the presence of variable time delay is studied. It is proved that simple P-like and PDlike controllers can stabilize the teleoperator under variable time delays and, moreover, they provide position tracking. Then, a controller based on the scattering transformation that also provides position tracking is proposed. In this paper we present the conditions under which the velocities and position error of the non-linear teleoperator, for the three controllers, are bounded, and if the human does not move the local manipulator and the remote manipulator does not interact with the environment, then it is proved that velocities and position error converge to zero. Simulations and real experiments, using the Internet from Urbana-Champaign (USA) to Barcelona (Spain), validate the proposed schemes.

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