The Experts below are selected from a list of 11709 Experts worldwide ranked by ideXlab platform
Yann Chevaleyre - One of the best experts on this subject based on the ideXlab platform.
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The robot swarm re-Localization Problem
2008 IEEE International Conference on Robotics and Biomimetics, 2009Co-Authors: Nicolas Bredeche, Yann ChevaleyreAbstract:This paper tackles the Problem of re-Localization in a swarm of moving virtual robots where some robots might be lost and where the distance between each neighboring robot is known within a limited communication radius (e.g. using the strength of a radio/wifi signal), whereas the precise Localization is not known. The basic process in reLocalization relies in a robot swarm that re-aggregates based on local information only by recruiting lost robots so as to build a swarm configuration where Localization is possible and non-ambiguous. The main issues in reLocalization is to provide highly decentralised behaviors for ensuring efficient and fast re-aggregation of the swarm that is scale independent. In this paper, the Problem of reLocalization is defined as well as criteria for evaluating swarm reLocalization efficiency. Moreover, a set of decentralized behaviors based on local reactive behaviors is presented and experimentally studied.
Nicolas Bredeche - One of the best experts on this subject based on the ideXlab platform.
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The robot swarm re-Localization Problem
2008 IEEE International Conference on Robotics and Biomimetics, 2009Co-Authors: Nicolas Bredeche, Yann ChevaleyreAbstract:This paper tackles the Problem of re-Localization in a swarm of moving virtual robots where some robots might be lost and where the distance between each neighboring robot is known within a limited communication radius (e.g. using the strength of a radio/wifi signal), whereas the precise Localization is not known. The basic process in reLocalization relies in a robot swarm that re-aggregates based on local information only by recruiting lost robots so as to build a swarm configuration where Localization is possible and non-ambiguous. The main issues in reLocalization is to provide highly decentralised behaviors for ensuring efficient and fast re-aggregation of the swarm that is scale independent. In this paper, the Problem of reLocalization is defined as well as criteria for evaluating swarm reLocalization efficiency. Moreover, a set of decentralized behaviors based on local reactive behaviors is presented and experimentally studied.
Wu-sheng Lu - One of the best experts on this subject based on the ideXlab platform.
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Penalty convex-concave procedure for source Localization Problem
2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016Co-Authors: Darya Ismailova, Wu-sheng LuAbstract:In this paper, we focus on the least-squares (LS) formulation for the Localization Problem, where the 12-norm of the residual errors is minimized in a setting known as difference-of-convex-functions programming. The Problem at hand is then solved by applying a penalty convex-concave procedure (PCCP) in a successive manner. Algorithmic details that are tailored to the Localization Problem, such as imposing additional constraints to enforce iteration path towards the LS solution and strategies to secure a good initial point, are also provided. Simulation results demonstrate promising Localization performance when compared with some best known results from the literature.
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CCECE - Penalty convex-concave procedure for source Localization Problem
2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016Co-Authors: Darya Ismailova, Wu-sheng LuAbstract:In this paper, we focus on the least-squares (LS) formulation for the Localization Problem, where the 1 2 -norm of the residual errors is minimized in a setting known as difference-of-convex-functions programming. The Problem at hand is then solved by applying a penalty convex-concave procedure (PCCP) in a successive manner. Algorithmic details that are tailored to the Localization Problem, such as imposing additional constraints to enforce iteration path towards the LS solution and strategies to secure a good initial point, are also provided. Simulation results demonstrate promising Localization performance when compared with some best known results from the literature.
M.h. Shor - One of the best experts on this subject based on the ideXlab platform.
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Model-based solution techniques for the source Localization Problem
IEEE Transactions on Control Systems Technology, 2000Co-Authors: M.e. Alpay, M.h. ShorAbstract:The Problem of locating sources in dynamical systems described by partial differential equations (PDEs) is a particular case of the more general class of inverse Problems. Source Localization Problems are often approached using non-model-based techniques. By using a priori knowledge of system dynamics, model-based approaches to this Problem can be developed, reducing the number of sensors required to solve the Problem. The paper presents three such approaches: off-line numerical computation of the time response data at the sensor(s) from all possible source locations and functions of source strength, spatial and time discretization of the PDE model, and off-line solution of a dual ("forward") PDE Problem based on the adjoint system model. In each case, a particular algorithm is presented, and analysis of appropriate sensor placement and the minimal number of sensors required is given. In all three approaches, a minimal amount of online processing is required. The relative strengths and shortcomings of the three approaches are discussed and are demonstrated through application to the two-dimensional isotropic heat equation.
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Model-based solution techniques for the source Localization Problem
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998Co-Authors: M.e. Alpay, M.h. ShorAbstract:This paper studies the Problem of using point sensor measurements to locate a point source in a class of distributed systems described by partial differential equations (PDEs). Assuming that a priori knowledge of system dynamics is available, three model-based approaches are developed for this Problem: off-line numerical computation of the time-response data at the sensor(s) resulting from each possible source location and function of source strength; spatial and time discretization of the PDE model; and off-line solution of a dual (forward) PDE Problem based on the dual system model. In each case, a particular algorithm is presented, and an analysis of appropriate sensor placements and the minimal number of sensors required is given. In all three approaches, a minimal amount of online processing is required. The relative advantages and shortcomings of the three approaches are discussed and are demonstrated by applying them to the two-dimensional heat conduction Problem.
Pulkit Grover - One of the best experts on this subject based on the ideXlab platform.
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Lower bounds on the minimax risk for the source Localization Problem
2017 IEEE International Symposium on Information Theory (ISIT), 2017Co-Authors: Praveen Venkatesh, Pulkit GroverAbstract:The “source Localization” Problem is one in which we estimate the location of a point source observed through a diffusive medium using an array of sensors. We obtain lower bounds on the minimax risk (mean squared-error in location) in estimating the location of the source, which apply to all estimators, for certain classes of diffusive media, when using a uniformly distributed sensor array. We show that for sensors of a fixed size, the lower bound decays to zero with increasing numbers of sensors. We also analyze a more physical sensor model to understand the effect of shrinking the size of sensors as their number increases to infinity, wherein the bound saturates for large sensor numbers. In this scenario, it is seen that there is greater benefit to increasing the number of sensors as the signal-to-noise ratio increases. Our bounds are the first to give a scaling for the minimax risk in terms of the number of sensors used.
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ISIT - Lower bounds on the minimax risk for the source Localization Problem
2017 IEEE International Symposium on Information Theory (ISIT), 2017Co-Authors: Praveen Venkatesh, Pulkit GroverAbstract:The “source Localization” Problem is one in which we estimate the location of a point source observed through a diffusive medium using an array of sensors. We obtain lower bounds on the minimax risk (mean squared-error in location) in estimating the location of the source, which apply to all estimators, for certain classes of diffusive media, when using a uniformly distributed sensor array. We show that for sensors of a fixed size, the lower bound decays to zero with increasing numbers of sensors. We also analyze a more physical sensor model to understand the effect of shrinking the size of sensors as their number increases to infinity, wherein the bound saturates for large sensor numbers. In this scenario, it is seen that there is greater benefit to increasing the number of sensors as the signal-to-noise ratio increases. Our bounds are the first to give a scaling for the minimax risk in terms of the number of sensors used.
