Coordination of groups of mobile autonomous agents using nearest neighbor rules

By Ali Jadbabaie, Jie Lin, A. Stephen Morse. 2002

8,562 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Public Health, Environmental and Occupational Health

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Citations in the corpus

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, methodology): “In order to show consensus in multi-agent systems with time-varying network structures, stochastic matrix theory [5]–[7], [10] and convexity analysis [ 11] are often applied.”
Deep Reinforcement Learning for Swarm Systems (2018, methodology): “A common strategy to obtain control strategies for swarm systems is to apply optimization–based approaches using the model of the agents or a graph abstraction of the swarm [Lin et al., 2004, Jadbabaie et al., 2003].”
Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization (2008, neutral): “1 The work in this paper is also related at a much broader level to the distributed consensus algorithms [2, 11, 13, 17, 19, 20, 28–30 , 32].”
Communication-efficient algorithms for decentralized and stochastic optimization (2017, neutral): “uction Decentralized optimization problems deﬁned over complex multiagent networks are ubiquitous in signal processing, machine learning, control, and other areas in science and engineering (see e.g. [47,21,50,15]). In this paper, we consider the following decentralized optimization problem which is”
Distributed optimization over time-varying directed graphs (2013, positive): “all tand i. Therefore, for all i, [A0(t+ 1) A0(0)] ii 1 n [A0(t) A0(0)] ii for t0: Thus, we certainly have [10A0(t) A0(1)] i 1=nnB for all iand all tin the range 1 tnnB. However, it was shown in [8], [33] that for t>(n 1)B, every entry of A0(t) A0(1) is positive and has value at least 1=nnB. Sinc”
On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers (2013, background): “Such gradient (3)This work is clearly also related to the extensive literatur e on consensus and cooperative control, where the goal is to d esign local deterministic or random update rules to achieve global coor dination (for deterministic update rules, see [4], [7], [15 ], [23], [27], [38], [39], ”
Distributed subgradient methods and quantization effects (2008, neutral): “, [26], [9], [4], [16], [5], and [17], [18]).”
Convergence speed in distributed consensus over dynamically switching random networks (2009, methodology): “Convergence conditions have been derived for both stationary network topologies and switching network topologies in Jadbabaie, Lin, and Morse (2003), Olfati-Saber andMurray (2004), Moreau (2005), Ren and Beard (2005), Olfati-Saber and Murray (2003), Fax and Murray (2004) and Olshevsky and…”
On Krause's Multi-Agent Consensus Model With State-Dependent Connectivity (2008, background): “[4], [17], [25]), with a few notable exceptions [7], [8], [18 ].”
Consensus problem of high‐order multi‐agent systems with external disturbances: An H∞ analysis approach (2010, methodology): “observed consensus behavior and generalized Vicsek’s model by using algebraic graph theory, matrix theory, and control theory [3].”
On distributed averaging algorithms and quantization effects (2007, background): “Then, for all , we have This fact is a minor modiﬁcation of known results in [3], [10], [18], [19], where the convergence of each to the same value is established under weaker versions of Assumptions 1 and 2.”
Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior (2013, methodology): “In earlier works [63], [70], uniform combination rules (or averaging strategies) were employed in consensus-based implementations such as / a n 1 k k = , for Nk , ! and / ( ) b n 1 1 k k = , for \ { } .”
A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems (2014, background): “…considerable attention due to its wide applications in cooperative control of mobile robots, formation control of unmanned vehicles, see, e.g., Jadbabaie, Lin, and Morse (2003), Ma and Zhang (2010), Olfati-Saber andMurray (2004), Ren (2008), Ren and Beard (2005) and Yu, Chen, and Cao (2010).”