The Experts below are selected from a list of 14346 Experts worldwide ranked by ideXlab platform
V. Isler - One of the best experts on this subject based on the ideXlab platform.
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sensor placement and selection for bearing sensors with Bounded Uncertainty
International Conference on Robotics and Automation, 2013Co-Authors: Pratap Tokekar, V. IslerAbstract:We study the problem of placing bearing sensors so as to estimate the location of a target in a square environment. We consider sensors with unknown but Bounded noise: the true location of the target is guaranteed to be in a 2α-wedge around the measurement, where α is the maximum noise. The quality of the placement is given by the area or diameter of the intersection of measurements from all sensors in the worst-case (i.e. regardless of the target's location). We study the bi-criteria optimization problem of placing a small number of sensors while guaranteeing a worst-case bound on the Uncertainty. Our main result is a constant-factor approximation: We show that in general when α ≤ Π/4, at most 9n* sensors placed on a triangular grid has diameter and area Uncertainty of at most 5.88UD* and 7.76UA* respectively, where n*,UD* and UA* are the number of sensors, diameter and area Uncertainty of an optimal algorithm. In obtaining these results, we present some structural properties which may be of independent interest. We also show that in the triangular grid placement, only a constant number of sensors need to be activated to achieve the desired Uncertainty, a property that can be used for designing energy/bandwidth efficient sensor selection schemes.
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The Sensor Selection Problem for Bounded Uncertainty Sensing Models
IEEE Transactions on Automation Science and Engineering, 2006Co-Authors: V. Isler, R. BajcsyAbstract:We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be interpreted as polygonal, convex subsets of the plane. In our model, the measurements are merged by intersecting corresponding subsets, and the measurement Uncertainty corresponds to the area of the intersection. This model applies to a large class of sensors, including cameras. We present an approximation algorithm which guarantees that the resulting error in estimation is within factor 2 of the least possible error. In establishing this result, we formally prove that a constant number of sensors suffice for a good estimate-an observation made by many researchers. We demonstrate the utility of this result in an experiment where 19 cameras are used to estimate the position of a target on a known plane. In the second part of this paper, we study relaxations of the problem formulation. We consider 1) a scenario where we are given a set of possible locations of the target (instead of a single estimate) and 2) relaxations of the sensing model. Note to Practitioners-This paper addresses a problem which arises in applications where many sensors are used to estimate the position of a target. For most sensing models, the estimates get better as the number of sensors increases. On the other hand, energy and communication constraints may render it impossible to use the measurements from all sensors. In this case, we face the sensor selection problem: how to select a "good" subset of sensors so as to obtain "good" estimates. We show that under a fairly restricted sensing model, a constant number of sensors are always competitive with respect to all sensors and present an algorithm for selecting such sensors. In obtaining this result, we assume that the sensor locations are known. In future research, we will investigate methods that are robust with respect to errors in sensor localization/calibration
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the sensor selection problem for Bounded Uncertainty sensing models
Information Processing in Sensor Networks, 2005Co-Authors: V. Isler, R. BajcsyAbstract:We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be interpreted as polygonal, convex subsets of the plane. This model applies to a large class of sensors including cameras. We present an approximation algorithm which guarantees that the resulting error in estimation is within a factor 2 of the least possible error. In establishing this result, we formally prove that a constant number of sensors suffice for a good estimate - an observation made by many researchers. In the second part of the paper, we study the scenario where the target's position is given by an Uncertainty region and present algorithms for both probabilistic and online versions of this problem.
Ian R. Petersen - One of the best experts on this subject based on the ideXlab platform.
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A Gramian-Based Approach to Model Reduction for Uncertain Systems
IEEE Transactions on Automatic Control, 2010Co-Authors: Ian R. PetersenAbstract:The technical note considers a problem of model reduction for a class of uncertain systems with structured norm Bounded Uncertainty. The technical note introduces controllability and observability Gramians in terms of certain parameterized algebraic Riccati inequalities. Based on these Gramians, three model reduction approaches are investigated for the underlying uncertain systems.
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CDC - A Gramian-based approach to model reduction for uncertain systems
2007 46th IEEE Conference on Decision and Control, 2007Co-Authors: Ian R. PetersenAbstract:The paper considers the problem of model reduction for a class of uncertain systems with structured norm Bounded Uncertainty. The paper introduces controllability and observability Gramians in terms of certain parameterized algebraic Riccati inequalities. This enables a balanced truncation model reduction procedure for uncertain systems to be presented. Error bounds for this model reduction procedure are derived. The paper also investigates Hinfin model reduction for uncertain systems. The solution to this problem is shown to involve constructing the underlying Gramians satisfying a certain rank constraint.
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optimal guaranteed cost control and filtering for uncertain linear systems
IEEE Transactions on Automatic Control, 1994Co-Authors: Ian R. Petersen, Duncan McfarlaneAbstract:The paper presents results on the design of robust state feedback controllers and steady-state robust state estimators for a class of uncertain linear systems with norm Bounded Uncertainty. The state feedback results extend the linear quadratic regulator to the case in which the underlying system is dependent on uncertain parameters. The state estimation results extend the steady-state Kalman filter to the case in which the underlying system is also uncertain. >
R. Bajcsy - One of the best experts on this subject based on the ideXlab platform.
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The Sensor Selection Problem for Bounded Uncertainty Sensing Models
IEEE Transactions on Automation Science and Engineering, 2006Co-Authors: V. Isler, R. BajcsyAbstract:We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be interpreted as polygonal, convex subsets of the plane. In our model, the measurements are merged by intersecting corresponding subsets, and the measurement Uncertainty corresponds to the area of the intersection. This model applies to a large class of sensors, including cameras. We present an approximation algorithm which guarantees that the resulting error in estimation is within factor 2 of the least possible error. In establishing this result, we formally prove that a constant number of sensors suffice for a good estimate-an observation made by many researchers. We demonstrate the utility of this result in an experiment where 19 cameras are used to estimate the position of a target on a known plane. In the second part of this paper, we study relaxations of the problem formulation. We consider 1) a scenario where we are given a set of possible locations of the target (instead of a single estimate) and 2) relaxations of the sensing model. Note to Practitioners-This paper addresses a problem which arises in applications where many sensors are used to estimate the position of a target. For most sensing models, the estimates get better as the number of sensors increases. On the other hand, energy and communication constraints may render it impossible to use the measurements from all sensors. In this case, we face the sensor selection problem: how to select a "good" subset of sensors so as to obtain "good" estimates. We show that under a fairly restricted sensing model, a constant number of sensors are always competitive with respect to all sensors and present an algorithm for selecting such sensors. In obtaining this result, we assume that the sensor locations are known. In future research, we will investigate methods that are robust with respect to errors in sensor localization/calibration
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the sensor selection problem for Bounded Uncertainty sensing models
Information Processing in Sensor Networks, 2005Co-Authors: V. Isler, R. BajcsyAbstract:We address the problem of selecting sensors so as to minimize the error in estimating the position of a target. We consider a generic sensor model where the measurements can be interpreted as polygonal, convex subsets of the plane. This model applies to a large class of sensors including cameras. We present an approximation algorithm which guarantees that the resulting error in estimation is within a factor 2 of the least possible error. In establishing this result, we formally prove that a constant number of sensors suffice for a good estimate - an observation made by many researchers. In the second part of the paper, we study the scenario where the target's position is given by an Uncertainty region and present algorithms for both probabilistic and online versions of this problem.
John R Spletzer - One of the best experts on this subject based on the ideXlab platform.
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a Bounded Uncertainty approach to cooperative localization using relative bearing constraints
Intelligent Robots and Systems, 2007Co-Authors: Camillo J Taylor, John R SpletzerAbstract:This paper describes an approach to cooperative localization which finds its roots in robust estimation, employing an unknown-but-Bounded error model for sensor measurements. In this framework, range and bearing measurements obtained by the robots are viewed as constraints which implicitly define a set of feasible solutions in the joint configuration space of the robot team. The scheme produces Bounded Uncertainty estimates for the relative configuration of the team by using convex optimization techniques to approximate the projection of this feasible set onto various subspaces of the configuration space. The scheme can also be used to localize distributed sensor nodes. An important advantage of the proposed approach is that it is able to produce Bounded Uncertainty estimates for the relative configuration of the robots even in the case where the relative orientations of the robots are completely unknown. This is an important practical advance since errors in relative orientation are often a major contributor to positioning Uncertainty in multi-robot localization schemes.
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IROS - A Bounded Uncertainty approach to cooperative localization using relative bearing constraints
2007 IEEE RSJ International Conference on Intelligent Robots and Systems, 2007Co-Authors: Camillo J Taylor, John R SpletzerAbstract:This paper describes an approach to cooperative localization which finds its roots in robust estimation, employing an unknown-but-Bounded error model for sensor measurements. In this framework, range and bearing measurements obtained by the robots are viewed as constraints which implicitly define a set of feasible solutions in the joint configuration space of the robot team. The scheme produces Bounded Uncertainty estimates for the relative configuration of the team by using convex optimization techniques to approximate the projection of this feasible set onto various subspaces of the configuration space. The scheme can also be used to localize distributed sensor nodes. An important advantage of the proposed approach is that it is able to produce Bounded Uncertainty estimates for the relative configuration of the robots even in the case where the relative orientations of the robots are completely unknown. This is an important practical advance since errors in relative orientation are often a major contributor to positioning Uncertainty in multi-robot localization schemes.
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a Bounded Uncertainty approach to multi robot localization
Intelligent Robots and Systems, 2003Co-Authors: John R Spletzer, Camillo J TaylorAbstract:We offer a new approach to the multi-robot localization problem. Using an unknown-but-Bounded model for sensor error, we are able to define convex polytopes in the configuration space of the robot team that represent the set of configurations consistent with all sensor measurements. Estimates for the Uncertainty in various parameters of the team's configuration such as the absolute position of a single robot, or the relative positions of two or more nodes can be obtained by projecting this polytope onto appropriately chosen subspaces of the configuration space. We propose a novel approach to approximating these projections using linear programming techniques. The approach can handle both bearing and range measurements with a computational complexity scaling polynomially in the number of roots. Finally, the workload is readily distributed - requiring only the communication of sensor measurements between robots. We provide simulation results for this approach implemented on a multi-robot team.
Jalel Zrida - One of the best experts on this subject based on the ideXlab platform.
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a sliding mode control approach for systems subjected to a norm Bounded Uncertainty
International Journal of Robust and Nonlinear Control, 2007Co-Authors: Anis Sellami, Denis Arzelier, Radhi Mhiri, Jalel ZridaAbstract:This paper proposes a design approach of continuous sliding mode control of uncertain systems, the Uncertainty being norm Bounded. The two steps of the design methodology are investigated. The existence step, in which we choose the sliding surface that gives good behaviour during the sliding mode, is formulated as a pole assignment of linear uncertain system in a sector through convex optimization. The solution to this problem is therefore numerically tractable via linear matrix inequalities (LMI) optimization. In the reaching step, we propose a continuous nonlinear control strategy ensuring a Bounded motion about the ideal sliding mode, thus approximating the ideal dynamic behaviour in the presence of Uncertainty. Finally, the validity and the applicability of this approach are illustrated by a flight stabilization benchmark example. Copyright © 2006 John Wiley & Sons, Ltd.