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Distributed Approximation of Joint Measurement Distributions Using Mixtures of Gaussians
"... Abstract—This paper presents algorithms to distributively approximate the continuous probability distribution that describes the fusion of sensor measurements from many networked robots. Each robot forms a weighted mixture of scaled Gaussians to represent the continuous measurement distribution (i.e ..."
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Abstract—This paper presents algorithms to distributively approximate the continuous probability distribution that describes the fusion of sensor measurements from many networked robots. Each robot forms a weighted mixture of scaled Gaussians to represent the continuous measurement distribution (i.e., likelihood) of its local observation. From this mixture set, each robot then draws samples of Gaussian elements to enable the use of a consensusbased algorithm that evolves the corresponding canonical parameters. We show that these evolved parameters form a distribution that converges weakly to the joint of all the robots’ unweighted mixture distributions, which itself converges weakly to the joint measurement distribution as more system resources are allocated. The innovation of this work is the combination of samplebased sensor fusion with the notion of preconvergence termination without the risk of ‘doublecounting ’ any single observation. We also derive bounds and convergence rates for the approximated joint measurement distribution, specifically the elements of its information vectors and the eigenvalues of its information matrices. Most importantly, these performance guarantees do not come at a significant cost of complexity, since computational and communication complexity of the canonical parameters scales quadratically with respect to the Gaussian dimension, linearly with respect to the number of samples, and constant with respect to the number of robots. Results from numerical simulations for object localization are discussed using both Gaussians and mixtures of Gaussians. estimation calculations in a centralized manner, and then globally broadcast the results to enable the robots to better position their sensors. For large systems, the central processor quickly becomes a computational and communication bottleneck, and thus is not considered to be scalable [4]. ˆj dimension
A COMPARATIVE STUDY OF UNDERWATER ROBOT PATH PLANNING ALGORITHMS FOR ADAPTIVE SAMPLING IN A NETWORK OF SENSORS
, 2014
"... Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex largescale ecosystems. We introduce a method of underwater monitoring using semimobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dim ..."
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Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex largescale ecosystems. We introduce a method of underwater monitoring using semimobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dimension while the sensor nodes are anchored to the bottom of the water column and can move only up and down along the depth of the water column. We develop three different algorithms to optimize the path of the
1 An Optimal Control Approach to the MultiAgent Persistent Monitoring Problem in TwoDimensional Spaces
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Article Adaptive Decentralized Control of Mobile Underwater Sensor Networks and Robots for Modeling Underwater Phenomena
, 2014
"... Abstract: Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradientbased decentralized controller that dynamically adjusts the depth of a network of underwater sensors t ..."
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Abstract: Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradientbased decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum and show how the controller can be extended to work with hybrid robot and sensor network systems. We implement the controller on an underwater sensor network with depth adjustment capabilities. Through simulations and insitu experiments, we verify the functionality and performance of the system and algorithm.