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Wardi, Yorai Y.

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Author: Wardi, Yorai Y. × End date: 2009 × search.filters.applied.f.isOrgUnitOfPublicationTitle: School of Electrical and Computer Engineering × search.filters.applied.f.resourcesubtype: Proceedings ×

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Now showing 1 - 4 of 4
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    Optimal Switching Surfaces in Behavior-Based Robotics
    (Georgia Institute of Technology, 2006-12) Axelsson, Henrik ; Egerstedt, Magnus B. ; Wardi, Yorai Y.
    In this paper an optimal solution is presented for the problem of avoiding obstacles while progressing towards a goal for a single robot. In particular, the solution is obtained by allowing the robot to switch between a fixed number of behaviors and optimizing over what behaviors to use and when to switch between them. It is moreover shown that the structure of the switching law only depends on the distance between the obstacle and the goal. Hence, once initial simulations are done, a guard can be generated with a fixed structure, and, given that the robot knows the distance between the obstacle and the goal, it knows when to switch in order to execute the pre-computed (optimal) solution. Therefore the solution lends itself nicely to real-time implementations. Experiments moreover verify that the proposed methods transitions well onto a real robotic platform.
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    Reactive Robot Navigation Using Optimal Timing Control
    (Georgia Institute of Technology, 2005-06) Axelsson, Henrik ; Egerstedt, Magnus B. ; Wardi, Yorai Y.
    In this paper a solution is presented for the problem of avoiding obstacles while progressing towards a goal for a single robot. In particular, an optimal solution is obtained by allowing the robot to switch between a fixed number of behaviors and optimizing over what behaviors to use and when to switch between them. We moreover show that the structure of the switch law only depends on the distance between the obstacle and the goal. Hence, once initial simulations are done, the structure of the guard is known to the robot and, given that the robot knows the distance between the obstacle and the goal, it knows when to switch to obtain the optimal solution. Therefore the solution lends itself to real-time implementations. The feasibility of the approach is verified in real robotics experiments.
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    IPA for Loss Volume and Buffer Workload in Tandem SFM
    (Georgia Institute of Technology, 2002) Wardi, Yorai Y. ; Riley, George F.
    This paper considers congestion-related performance metrics in tandem networks of Stochastic Fluid Models (SFMs), and derives their IPA gradient estimators with respect to buffer sizes. Specifically, the performance metrics in question are the total loss volume and the cumulative buffer workload (buffer contents), and the control parameter consists of buffer limits at both the node where the performance is measured and at an upstream node. The IPA estimators are unbiased and nonparametric, and hence can be computed on-line from field measurements as well as off-line from simulation experiments. The IPA derivatives are applied to packet-based networks, where simulation results support the theoretical developments. Possible applications to congestion management in telecommunications networks are discussed.
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    A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications
    (Georgia Institute of Technology, 2002) Ding, Yuhua ; Vachtsevanos, George J. ; Yezzi, Anthony ; Zhang, Yingchuan ; Wardi, Yorai Y.
    Segmentation accuracy is shown to be a critical factor in detection rate improvement. With accurate segmentation, results are easier to interpret, and classification performance is better. Therefore, it is required to have a performance measure for segmentation evaluation. However, a number of restrictions limit using existing segmentation performance measures. In this paper a recursive segmentation and classification scheme is proposed to improve segmentation accuracy and classification performance in real-time machine vision applications. In this scheme, the confidence level of classification results is used as a new performance measure to evaluate the accuracy of segmentation algorithm. Segmentation is repeated until a classification with desired confidence level is achieved. This scheme can be implemented automatically. Experimental results show that it is efficient to improve segmentation accuracy and the overall detection performance, especially for real-time machine vision applications, where the scene is complicated and a single segmentation algorithm cannot produce satisfactory results.