The Experts below are selected from a list of 24753 Experts worldwide ranked by ideXlab platform
Sonia Martinez - One of the best experts on this subject based on the ideXlab platform.
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brief paper gradient algorithms for polygonal approximation of convex contours
Automatica, 2009Co-Authors: S Susca, Francesco Bullo, Sonia MartinezAbstract:The subjects of this paper are descent algorithms to optimally approximate a strictly convex contour with a polygon. This classic geometric problem is relevant in interpolation theory and data compression, and has potential applications in Robotic Sensor networks. We design gradient descent laws for intuitive performance metrics such as the area of the inner, outer, and ''outer minus inner'' approximating polygons. The algorithms position the polygon vertices based on simple feedback ideas and on limited nearest-neighbor interaction.
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monitoring environmental boundaries with a Robotic Sensor network
IEEE Transactions on Control Systems and Technology, 2008Co-Authors: S Susca, Francesco Ullo, Sonia MartinezAbstract:In this brief, we propose and analyze an algorithm to monitor an environmental boundary with mobile agents. The objective is to optimally approximate the boundary with a polygon. The mobile Sensors rely only on sensed local information to position some interpolation points and define an approximating polygon. We design an algorithm that distributes the vertices of the approximating polygon uniformly along the boundary. The notion of uniform placement relies on a metric inspired by approximation theory for convex bodies. The algorithm is provably convergent for static boundaries and efficient for slowly-moving boundaries because of certain input-to-state stability properties.
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distributed algorithms for polygonal approximation of convex contours
Conference on Decision and Control, 2006Co-Authors: S Susca, Sonia Martinez, Francesco BulloAbstract:We propose algorithms that compute polygon approximations for convex contours. This geometric problem is relevant in interpolation theory, data compression, and has potential applications in Robotic Sensor networks. The algorithms are based on simple feedback ideas, on limited nearest-neighbor information, and amount to gradient descent laws for appropriate cost functions. The approximations are based on intuitive performance metrics, such as the area of the inner, outer, and "outer minus inner" approximating polygons.
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monitoring environmental boundaries with a Robotic Sensor network
American Control Conference, 2006Co-Authors: Sara Susca, Sonia Martinez, Francesco UlloAbstract:In this paper we propose and analyze an algorithm to monitor an environmental boundary with mobile Sensors. The objective is to optimally approximate the boundary with a polygon. The mobile Sensors rely only on sensed local information to position some interpolation points and define an approximating polygon. We design an algorithm that distributes the vertices of the approximating polygon uniformly along the boundary. The notion of uniform placement relies on a metric inspired by known results on approximation of convex bodies. The algorithm is provably convergent for static boundaries and also for slowly-moving boundaries because of certain input-to-state stability properties.
S Susca - One of the best experts on this subject based on the ideXlab platform.
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brief paper gradient algorithms for polygonal approximation of convex contours
Automatica, 2009Co-Authors: S Susca, Francesco Bullo, Sonia MartinezAbstract:The subjects of this paper are descent algorithms to optimally approximate a strictly convex contour with a polygon. This classic geometric problem is relevant in interpolation theory and data compression, and has potential applications in Robotic Sensor networks. We design gradient descent laws for intuitive performance metrics such as the area of the inner, outer, and ''outer minus inner'' approximating polygons. The algorithms position the polygon vertices based on simple feedback ideas and on limited nearest-neighbor interaction.
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monitoring environmental boundaries with a Robotic Sensor network
IEEE Transactions on Control Systems and Technology, 2008Co-Authors: S Susca, Francesco Ullo, Sonia MartinezAbstract:In this brief, we propose and analyze an algorithm to monitor an environmental boundary with mobile agents. The objective is to optimally approximate the boundary with a polygon. The mobile Sensors rely only on sensed local information to position some interpolation points and define an approximating polygon. We design an algorithm that distributes the vertices of the approximating polygon uniformly along the boundary. The notion of uniform placement relies on a metric inspired by approximation theory for convex bodies. The algorithm is provably convergent for static boundaries and efficient for slowly-moving boundaries because of certain input-to-state stability properties.
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distributed algorithms for polygonal approximation of convex contours
Conference on Decision and Control, 2006Co-Authors: S Susca, Sonia Martinez, Francesco BulloAbstract:We propose algorithms that compute polygon approximations for convex contours. This geometric problem is relevant in interpolation theory, data compression, and has potential applications in Robotic Sensor networks. The algorithms are based on simple feedback ideas, on limited nearest-neighbor information, and amount to gradient descent laws for appropriate cost functions. The approximations are based on intuitive performance metrics, such as the area of the inner, outer, and "outer minus inner" approximating polygons.
Jorge Cortes - One of the best experts on this subject based on the ideXlab platform.
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deployment of an unreliable Robotic Sensor network for spatial estimation
Systems & Control Letters, 2012Co-Authors: Jorge CortesAbstract:Abstract This paper studies an optimal deployment problem for a network of Robotic Sensors moving in the real line. Given a spatial process of interest, each individual Sensor sends a packet that contains a measurement of the process to a data fusion center. We assume that, due to communication limitations or hardware unreliability, only a fraction of the packets arrive at the center. Using convex analysis, nonsmooth analysis, and combinatorics, we show that, for various fractional rates of packet arrival, the optimal deployment configuration has the following features: agents group into clusters, clusters deploy optimally as if at least one packet from each cluster was guaranteed to reach the center, and there is an optimal cluster size for each fractional arrival rate.
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deployment of an unreliable Robotic Sensor network for spatial estimation
Conference on Decision and Control, 2010Co-Authors: Jorge CortesAbstract:This paper studies an optimal deployment problem for a network of Robotic Sensors moving in the real line. Consider the scenario where each Sensor is to take a measurement of a spatial process of interest and send it back to a data fusion center. Assume only a specific fraction of the messages containing the measurements will arrive at the center. We show that, for several fraction values, the optimal deployment configurations have the following features: agents are grouped into clusters, the clusters are deployed optimally as if at least a message from each cluster was guaranteed to reach the center, and for each fraction value, there is a specific optimal cluster size. The technical approach combines convex analysis, nonsmooth analysis, and combinatorics.
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coverage optimization and spatial load balancing by Robotic Sensor networks
IEEE Transactions on Automatic Control, 2010Co-Authors: Jorge CortesAbstract:This technical note studies Robotic Sensor networks performing static coverage optimization with area constraints. Given a density function describing the probability of events happening and a performance function measuring the cost to service a location, the objective is to position Sensors in the environment so as to minimize the expected servicing cost. Moreover, because of load balancing considerations, the area of the region assigned to each robot is constrained to be a pre-specified amount. We characterize the optimal configurations as center generalized Voronoi configurations. The generalized Voronoi partition depends on a set of weights, one per robot, assigned to the network. We design a Jacobi iterative algorithm to find the weight assignment whose corresponding generalized Voronoi partition satisfies the area constraints. This algorithm is distributed over the generalized Delaunay graph. We also design the ?move-to-center-and-compute-weight? strategy to steer the Robotic network towards the set of center generalized Voronoi configurations while monotonically optimizing coverage.
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distributed kriged kalman filter for spatial estimation
IEEE Transactions on Automatic Control, 2009Co-Authors: Jorge CortesAbstract:This paper considers Robotic Sensor networks performing spatially-distributed estimation tasks. A Robotic Sensor network is deployed in an environment of interest, and takes successive point measurements of a dynamic physical process modeled as a spatio-temporal random field. Taking a Bayesian perspective on the Kriging interpolation technique from geostatistics, we design the distributed Kriged Kalman filter for predictive inference of the random field and of its gradient. The proposed algorithm makes use of a novel distributed strategy to compute weighted least squares estimates when measurements are spatially correlated. This strategy results from the combination of the Jacobi overrelaxation method with dynamic average consensus algorithms. As an application of the proposed algorithm, we design a gradient ascent cooperative strategy and analyze its convergence properties in the absence of measurement errors via stochastic Lyapunov functions. We illustrate our results in simulation.
Franz S Hover - One of the best experts on this subject based on the ideXlab platform.
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underwater data collection using Robotic Sensor networks
IEEE Journal on Selected Areas in Communications, 2012Co-Authors: Geoffrey A Hollinger, Urbashi Mitra, Gaurav S Sukhatme, Sunav Choudhary, Parastoo Qarabaqi, C Murphy, Milica Stojanovic, Hanumant Singh, Franz S HoverAbstract:We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater Sensor network. The Sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.
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communication protocols for underwater data collection using a Robotic Sensor network
Global Communications Conference, 2011Co-Authors: Geoffrey A Hollinger, Urbashi Mitra, Gaurav S Sukhatme, Sunav Choudhary, Parastoo Qarabaqi, C Murphy, Milica Stojanovic, Hanumant Singh, Franz S HoverAbstract:We examine the problem of collecting data from an underwater Sensor network using an autonomous underwater vehicle (AUV). The Sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication to the AUV. One challenge in this scenario is to plan paths that maximize the information collected and minimize travel time. While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To solve this problem, we develop and test a multiple access control protocol for the underwater data collection scenario. We perform simulated experiments that utilize a realistic model of acoustic communication taken from experimental test data. These simulations demonstrate that properly designed scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.
Xiaobo Tan - One of the best experts on this subject based on the ideXlab platform.
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profiling aquatic diffusion process usingRobotic Sensor networks
IEEE Transactions on Mobile Computing, 2014Co-Authors: Yu Wang, Rui Tan, Guoliang Xing, Jianxun Wang, Xiaobo TanAbstract:Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile Sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile Sensors such as Robotic fish. In our approach, the Robotic Sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the Robotic Sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to the limited Sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of GPS localization errors, Robotic fish movement, and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our profiling algorithms on mote-class Robotic Sensor platforms.
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accuracy aware aquatic diffusion process profiling using Robotic Sensor networks
Information Processing in Sensor Networks, 2012Co-Authors: Yu Wang, Rui Tan, Guoliang Xing, Jianxun Wang, Xiaobo TanAbstract:ABSTRACT Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile Sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic en-vironments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile Sensors such as Robotic fish. In our approach, the Robotic Sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the Robotic Sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited Sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of Robotic fish movement and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. More-over, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our movement scheduling algorithms on mote-class Robotic Sensor platforms.
