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Multiagent coordination by decentralized estimation and control
 IEEE Transactions on Automatic Control
, 2008
"... Abstract — We describe a framework for the design of collective behaviors for groups of identical mobile agents. The approach is based on decentralized simultaneous estimation and control, where each agent communicates with neighbors and estimates the global performance properties of the swarm neede ..."
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Abstract — We describe a framework for the design of collective behaviors for groups of identical mobile agents. The approach is based on decentralized simultaneous estimation and control, where each agent communicates with neighbors and estimates the global performance properties of the swarm needed to make a local control decision. Challenges of the approach include designing a control law with desired convergence properties, assuming each agent has perfect global knowledge; designing an estimator that allows each agent to make correct estimates of the global properties needed to implement the controller; and possibly modifying the controller to recover desired convergence properties when using the estimates of global performance. We apply this framework to two different problems: (1) controlling the moment statistics describing the location and shape of a swarm, and (2) cooperative target localization. For the swarm formation control problem, we derive smallgain conditions which, if satisfied, guarantee that the formation statistics are driven to desired values, even in the presence of a changing network topology and the addition and deletion of robots. Index Terms — Multiagent systems, decentralized control, distributed control, dynamic average consensus estimation, formation control. I.
Sukhatme, Reconfiguration methods for mobile sensor networks
 ACM Trans. Sensor Networks
"... Motion may be used in sensor networks to achieve a desirable network configurations for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resou ..."
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Cited by 20 (4 self)
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Motion may be used in sensor networks to achieve a desirable network configurations for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resource overhead, hence we exploit a constrained form of mobility which has very low overheads but provides significant reconfiguration potential. We present an architecture which allows each node in the network to learn the medium and phenomenon characteristics. We describe a quantitative metric for sensing performance which is concretely tied to real sensor and medium characteristics, rather than assuming an abstract range based model. The problem of determining the desirable network configuration is expressed as an optimization of this metric. We present a distributed optimization algorithm which computes a desirable network configuration, and adapts it to environmental changes. The relationship of the proposed algorithm to simulated annealing and incremental subgradient descent based methods is discussed. A key property of our algorithm is that convergence to a desirable configuration can be proved even though no global coordination is involved. A network protocol to implement this algorithm is discussed, followed by simulations and experiments on a laboratory testbed.
Virtual highresolution for sensor networks
 Proceedings of the 4th international
, 2006
"... The resolution at which a sensor network collects data is a crucial parameter of performance since it governs the range of applications that are feasible to be developed using that network. A higher resolution, in most situations, enables more applications and improves the reliability of existing on ..."
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Cited by 11 (4 self)
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The resolution at which a sensor network collects data is a crucial parameter of performance since it governs the range of applications that are feasible to be developed using that network. A higher resolution, in most situations, enables more applications and improves the reliability of existing ones. In this paper we discuss a system architecture that uses controlled motion to provide virtual highresolution in a network of cameras. Several orders of magnitude advantage in resolution may be achieved, depending on tolerable tradeoffs. We discuss several system design choices in the context of our prototype camera network implementation that realizes the proposed architecture. We also mention how some of our techniques may apply to sensors other than cameras. Real world data is collected using our prototype system and used for the evaluation of our proposed methods.
A OneParameter Family of Distributed Consensus Algorithms with Boundary: From Shortest Paths to Mean Hitting Times
"... Abstract — We present a oneparameter family of consensus algorithms over a timevarying network of agents. The proposed family of algorithms contains the average and minimum consensus algorithms as two special cases. Furthermore, we investigate a closely related family of distributed algorithms whi ..."
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Cited by 7 (0 self)
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Abstract — We present a oneparameter family of consensus algorithms over a timevarying network of agents. The proposed family of algorithms contains the average and minimum consensus algorithms as two special cases. Furthermore, we investigate a closely related family of distributed algorithms which can be considered as a consensus scheme with fixed boundary conditions and constant inputs. The proposed algorithms recover both the BellmanFord iteration for finding shortest paths as well as the algorithm for calculating the mean hitting time of a random walk on a graph. Finally, we demonstrate the potential utility of these algorithms for routing in adhoc networks. I.
On recurrence of graph connectivity in Vicsek’s model of motion coordination for mobile autonomous agents
 Proc. 2007 American Control Conference
, 2007
"... Abstract — In this paper we complete the analysis of Vicsek’s model of distributed coordination among kinematic planar agents. The model is a simple discrete time heading update rule for a set of kinematic agents (or selfpropelled particles as referred to by Vicsek) moving in a finite plane with pe ..."
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Cited by 5 (0 self)
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Abstract — In this paper we complete the analysis of Vicsek’s model of distributed coordination among kinematic planar agents. The model is a simple discrete time heading update rule for a set of kinematic agents (or selfpropelled particles as referred to by Vicsek) moving in a finite plane with periodic boundary conditions. Contrary to existing results in the literature, we do not make any assumptions on connectivity but instead prove that under the update scheme, the network of agents stays jointly connected infinitely often for almost all initial conditions, resulting in global heading alignment. Our main result is derived using a famous theorem of Hermann Weyl on equidistribution of fractional parts of sequences. We also show that the Vicsek update scheme is closely related to the Kuramoto model of coupled nonlinear oscillators. I.
Variance analysis of randomized consensus in switching directed networks
 in American Control Conference (ACC), 2010
, 2010
"... ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other wo ..."
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Cited by 4 (2 self)
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©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
On asymptotic consensus value in directed random networks
 in 49th IEEE Conference on Decision and Control
, 2010
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REFERENCES
, 2003
"... (1) First we obtain the reduced form of system 6 by Algorithm 3.2. (2) Obtain characteristic polynomial q(z) = (A22) of the unreachable part. (3) Check if f (z) is a multiple of q(z). (4) If answer to (3) is YES then (4) obtain polynomials (Ar:r), (Ar01:r;...; (A1:r) and (5) solve modular identitie ..."
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(1) First we obtain the reduced form of system 6 by Algorithm 3.2. (2) Obtain characteristic polynomial q(z) = (A22) of the unreachable part. (3) Check if f (z) is a multiple of q(z). (4) If answer to (3) is YES then (4) obtain polynomials (Ar:r), (Ar01:r;...; (A1:r) and (5) solve modular identities arising from Theorem 2.3. Collecting the costs of all procedures give us a total of O(rn 3) arithmetic operations in R plus the calculation of the characteristic polynomial (A22) of degree n 0 r. This characteristic polynomial can be computed deterministically (see [15, Th. 5.1]) up to a cost of O(((n 0 r) 3+1=3) 1+o(1) ) arithmetic operations in R. If R is an infinite domain then generic case for a single input system is rank r = n. Hence neither line (3) in the above procedure nor the calculation of (A22) are needed in most cases. The generic case on an infinite Euclidean domain involves a cost of O(n 4) arithmetic operations in R.
Consensus Over Martingale Graph Processes
"... © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to s ..."
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© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper is posted at ScholarlyCommons.
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"... © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to s ..."
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© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper is posted at ScholarlyCommons.