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437
Robust computation of aggregates in wireless sensor networks: distributed randomized algorithms and analysis.
 IEEE Trans. Parallel Distr. Syst.,
, 2006
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Multivehicle consensus with a timevarying reference state
, 2007
"... In this paper, we study the consensus problem in multivehicle systems, where the information states of all vehicles approach a timevarying reference state under the condition that only a portion of the vehicles (e.g., the unique team leader) have access to the reference state and the portion of th ..."
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Cited by 53 (13 self)
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In this paper, we study the consensus problem in multivehicle systems, where the information states of all vehicles approach a timevarying reference state under the condition that only a portion of the vehicles (e.g., the unique team leader) have access to the reference state and the portion of the vehicles might not have a directed path to all of the other vehicles in the team. We first analyze a consensus algorithm with a constant reference state using graph theoretical tools. We then propose consensus algorithms with a timevarying reference state and show necessary and sufficient conditions under which consensus is reached on the timevarying reference state. The timevarying reference state can be an exogenous signal or evolve according to a nonlinear model. These consensus algorithms are also extended to achieve relative state deviations among the vehicles. An application example to multivehicle formation control is given as a proof of concept.
Asynchronous consensus in continuoustime multiagent systems with switching topology and timevarying delays
 IEEE Transactions on Automatic Control
, 2008
"... In this paper, we study asynchronous consensus problems of continuoustime multiagent systems with discontinuous information transmission. The proposed consensus control strategy is implemented only based on the state information at some discrete times of each agent’s neighbors. The asynchronization ..."
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Cited by 46 (1 self)
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In this paper, we study asynchronous consensus problems of continuoustime multiagent systems with discontinuous information transmission. The proposed consensus control strategy is implemented only based on the state information at some discrete times of each agent’s neighbors. The asynchronization means that each agent’s update times, at which the agent adjusts its dynamics, are independent of others’. Furthermore, it is assumed that the communication topology among agents is timedependent and the information transmission is with bounded timevarying delays. If the union of the communication topology across any time interval with some given length contains a spanning tree, the consensus problem is shown to be solvable. The analysis tool developed in this paper is based on the nonnegative matrix theory and graph theory. The main contribution of this paper is to provide a valid distributed consensus algorithm that overcomes the difficulties caused by unreliable communication channels, such as intermittent information transmission, switching communication topology, and timevarying communication delays, and therefore has its obvious practical applications. Simulation examples are provided to demonstrate the effectiveness of our theoretical results. Key words: Multiagent systems, asynchronous consensus, switching topology, timevarying delays, coordination. PACS:
Distributed Tracking for Mobile Sensor Networks with InformationDriven Mobility
"... In this paper, we address distributed target tracking for mobile sensor networks using the extension of a distributed Kalman filtering (DKF) algorithm introduced by the author in [11]. It is shown that improvement of the quality of tracking by mobile sensors (or agents) leads to the emergence of f ..."
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Cited by 44 (0 self)
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In this paper, we address distributed target tracking for mobile sensor networks using the extension of a distributed Kalman filtering (DKF) algorithm introduced by the author in [11]. It is shown that improvement of the quality of tracking by mobile sensors (or agents) leads to the emergence of flocking behavior. We discuss the benefits of a flockingbased mobility model for distributed Kalman filtering over mobile networks. This mobility model uses author’s flocking algorithm with a natural choice of a moving rendezvous point that is the target itself. As the agents “flock ” towards the target, the information value of their sensor measurements improves in time. During this process, smaller flocks merge and form larger flocks and eventually a single flock with a connected topology emerges. This allows the agents to perform cooperative filtering using the DKF algorithm which considerably improves their tracking performance. We show that this flocking algorithm is in fact an informationdriven mobility that acts as a cooperative control strategy that enhances the aggregate information value of all sensor measurements. A metric for information value is given that has close connections to Fisher information. Simulation results are provided for a group of UAVs with embedded sensors tracking a mobile target using cooperative filtering.
Distributed control of robotic networks: a mathematical approach to motion coordination algorithms
, 2009
"... (i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 ..."
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Cited by 41 (1 self)
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(i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 “Distributed Control of Robotic Networks ” by F. Bullo, J. Cortés and S. Martínez
Some necessary and sufficient conditions for secondorder consensus in multiagent dynamical systems,”
 Automatica,
, 2010
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Distributed geodesic control laws for flocking of nonholonomic agents
 IEEE Transaction on Automatic Control
, 2005
"... Abstract—We study the problem of flocking and velocity alignment in a group of kinematic nonholonomic agents in 2 and 3 dimensions. By analyzing the velocity vectors of agents on a circle (for planar motion) or sphere (for 3D motion), we develop a geodesic control law that minimizes a misalignment ..."
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Cited by 35 (6 self)
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Abstract—We study the problem of flocking and velocity alignment in a group of kinematic nonholonomic agents in 2 and 3 dimensions. By analyzing the velocity vectors of agents on a circle (for planar motion) or sphere (for 3D motion), we develop a geodesic control law that minimizes a misalignment potential and results in velocity alignment and flocking. The proposed control laws are distributed and will provably result in flocking when the underlying proximity graph which represents the neighborhood relation among agents is connected. We further show that flocking is possible even when the topology of the proximity graph changes over time, so long as a weaker notion of joint connectivity is preserved. Index Terms—Cooperative control, distributed coordination, flocking, multiagent systems. I.
An overview of recent progress in the study of distributed multiagent coordination
, 2012
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Selfimproving algorithms
 in SODA ’06: Proceedings of the seventeenth annual ACMSIAM symposium on Discrete algorithm
"... We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such selfimproving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i) an al ..."
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Cited by 33 (6 self)
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We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such selfimproving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i) an algorithm to sort a list of numbers with optimal expected limiting complexity; and (ii) an algorithm to compute the Delaunay triangulation of a set of points with optimal expected limiting complexity. In both cases, the algorithm begins with a training phase during which it adjusts itself to the input distribution, followed by a stationary regime in which the algorithm settles to its optimized incarnation. 1
Flocking of multiagents with a virtual leader
 IEEE Transactions on Automatic Control
, 2009
"... Abstract—All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multiagent systems. Under these assumptions, OlfatiSaber in a recent IEEE TRANSACTIONS ON AUTOMATIC CONTROL paper proposed a flo ..."
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Cited by 33 (1 self)
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Abstract—All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multiagent systems. Under these assumptions, OlfatiSaber in a recent IEEE TRANSACTIONS ON AUTOMATIC CONTROL paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multiagent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the OlfatiSaber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the OlfatiSaber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given. Index Terms—Distributed control, flocking, informed agents, nonlinear systems, virtual leader. I.