Results 1  10
of
56
Consensus and cooperation in networked multiagent systems
 Proceedings of the IEEE
, 2007
"... Summary. This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An ove ..."
Abstract

Cited by 807 (4 self)
 Add to MetaCart
(Show Context)
Summary. This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in smallworld networks, Markov processes and gossipbased algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with latticetype nearest neighbor interactions. Simulation results are presented that demonstrate the role of smallworld effects on the speed of consensus algorithms and cooperative control of multivehicle formations.
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 ..."
Abstract

Cited by 41 (1 self)
 Add to MetaCart
(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
Discretetime dynamic average consensus
, 2009
"... We propose a class of discretetime dynamic average consensus algorithms that allow a group of agents to track the average of their reference inputs. The convergence results rely on the inputtooutput stability properties of static average consensus algorithms and require that the union of communic ..."
Abstract

Cited by 23 (7 self)
 Add to MetaCart
We propose a class of discretetime dynamic average consensus algorithms that allow a group of agents to track the average of their reference inputs. The convergence results rely on the inputtooutput stability properties of static average consensus algorithms and require that the union of communication graphs over a bounded period of time be strongly connected. The only requirement on the set of reference inputs is that the maximum relative deviation between the n thorder differences of any two reference inputs be bounded for some integer n ≥ 1.
Distributed InNetwork Channel Decoding
"... Abstract—Average loglikelihood ratios (LLRs) constitute sufficient statistics for centralized maximumlikelihood block decoding as well as for a posteriori probability evaluation which enables bitwise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to p ..."
Abstract

Cited by 23 (9 self)
 Add to MetaCart
(Show Context)
Abstract—Average loglikelihood ratios (LLRs) constitute sufficient statistics for centralized maximumlikelihood block decoding as well as for a posteriori probability evaluation which enables bitwise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a lowcomplexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among singlehop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to nonideal intersensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts. Index Terms—Channel coding, decoding, distributed detection, wireless sensor networks (WSNs). I.
Distributed consensusbased demodulation: algorithms and error analysis
 IEEE Trans. Wireless Commun
, 2010
"... Abstract—This paper deals with distributed demodulation of spacetime transmissions of a common message from a multiantenna access point (AP) to a wireless sensor network. Based on local message exchanges with singlehop neighboring sensors, two algorithms are developed for distributed demodulation. ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
(Show Context)
Abstract—This paper deals with distributed demodulation of spacetime transmissions of a common message from a multiantenna access point (AP) to a wireless sensor network. Based on local message exchanges with singlehop neighboring sensors, two algorithms are developed for distributed demodulation. In the first algorithm, sensors consent on the estimated symbols. By relaxing the finitealphabet constraints on the symbols, the demodulation task is formulated as a distributed convex optimization problem that is solved iteratively using the method of multipliers. Distributed versions of the centralized zeroforcing (ZF) and minimum meansquare error (MMSE) demodulators follow as special cases. In the second algorithm, sensors iteratively reach consensus on the average (cross) covariances of locally available persensor data vectors with the corresponding APtosensor channel matrices, which constitute sufficient statistics for maximum likelihood demodulation. Distributed versions of the sphere decoding algorithm and the ZF/MMSE demodulators are also developed. These algorithms offer distinct merits in terms of error performance and resilience to nonideal intersensor links. In both cases, the periteration error performance is analyzed, and the approximate number of iterations needed to attain a prescribed error rate are quantified. Simulated tests verify the analytical claims. Interestingly, only a few consensus iterations (roughly as many as the number of sensors), suffice for the distributed demodulators to approach the performance of their centralized counterparts. Index Terms—Detection and estimation, sensor networks, cooperative diversity. I.
A scalable information theoretic approach to distributed robot coordination
, 2011
"... This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of ..."
Abstract

Cited by 13 (8 self)
 Add to MetaCart
(Show Context)
This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots’ joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quadrotor flying robots and simulations with 100 robots.
Consensus in Networked MultiAgent Systems with Adversaries
"... In the past decade, numerous consensus protocols for networked multiagent systems have been proposed. Although some forms of robustness of these algorithms have been studied, reaching consensus securely in networked multiagent systems, in spite of intrusions caused by malicious agents, or adversar ..."
Abstract

Cited by 11 (6 self)
 Add to MetaCart
(Show Context)
In the past decade, numerous consensus protocols for networked multiagent systems have been proposed. Although some forms of robustness of these algorithms have been studied, reaching consensus securely in networked multiagent systems, in spite of intrusions caused by malicious agents, or adversaries, has been largely underexplored. In this work, we consider a general model for adversaries in Euclidean space and introduce a consensus problem for networked multiagent systems similar to the Byzantine consensus problem in distributed computing. We present the Adversarially Robust Consensus Protocol (ARCP), which combines ideas from consensus algorithms that are resilient to Byzantine faults and from linear consensus protocols used for control and coordination of dynamic agents. We show that ARCP solves the consensus problem in complete networks whenever there are more cooperative agents than adversaries. Finally, we illustrate the resilience of ARCP to adversaries through simulations and compare ARCP with a linear consensus protocol for networked multiagent systems.
Distributed decision through selfsynchronizing sensor networks in the presence of propagation delays and asymmetric channels
 IEEE Transactions on Signal Processing
, 2008
"... In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a selfsynchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a direct ..."
Abstract

Cited by 11 (5 self)
 Add to MetaCart
(Show Context)
In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a selfsynchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights (possibly) dependent on the radio channels and we pose special attention to the effect of the propagation delay occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is reached with exponential convergence speed for any bounded delay condition if and only if the directed graph is quasistrongly connected. We provide a closed form expression for the global consensus, showing that the effect of delays is, in general, the introduction of a bias in the final decision. Finally, we exploit our closed form expression to devise a doublestep consensus mechanism able to provide an unbiased estimate with minimum extra complexity, without the need to know or estimate the channel parameters. 1