Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms

By Francesco Bullo, Jorge Cortés, Sonia Martínez. 2009

1,210 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/83acb451de66aab548a7f1464e151ae7ac6a56ec

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In Computer Networks and Communications

In Mechanical Engineering

Citations in the corpus

Finite-time consensus and collision avoidance control algorithms for multiple AUVs (2013, methodology): “In this paper, inspired by the homogeneous control method (Wang & Hong, 2008) and potential field techniques (Bullo et al., 2009; Hokayem et al., 2010; Hou & Cheah, 2011; Meng et al., 2012; Olfati-Saber, 2006), both position consensus and collision avoidance problems for multi-AUV systems are…”
Convergence Rates for Distributed Stochastic Optimization Over Random Networks (2018, background): “ and learning algorithms attract a great interest in recent years, thanks to their widespread applications including distributed estimation in networked systems, e.g., [1], distributed control, e.g., [2], and big data analytics, e.g., [3]. In this paper, we study distributed stochastic optimization ”
Distributed Consensus of Second-Order Multi-Agent Systems With Heterogeneous Unknown Inertias and Control Gains Under a Directed Graph (2016, background): “INTRODUCTION Coordination of multi-agent systems has attracted a considerable amount of attention in the last decade due to its board applications in a variety of scenarios, including sensor networks, robotic networks, and unmanned aerial vehicles [1]–[4].”
Human Interaction With Robot Swarms: A Survey (2016, background): “The physical model of individual robots is defined in [32] as a continuous-time continuous-space dynamical system with a state space, input space, allowable initial states, and a control vector field that determines robot motion given a state and input.”
Differential inclusions in Wasserstein spaces: The Cauchy-Lipschitz framework (2020, background): “Multi-agent systems are ubiquitous in a wide number of applications, ranging from the understanding of patterns formation in several branches of the animal kingdom [14, 19, 33] to the analysis of pedestrian dynamics [2, 32], ensembles of autonomous vehicles [15, 26], and opinion formation on network”
DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers (2015, background): “The nodes’ goal is to ﬁnd the optimal argument of the global cost function Problems of this form arise in, e.g., decentralized control [2]– [4], wireless communication [5], [6], sensor networks [7]–[9], and large scale machine learning [10]–[12].”
A new class of finite-time nonlinear consensus protocols for multi-agent systems (2014, background): “Network consensus problem, due to a broad application of multi-agent systems including formation control (Abdessameud & Tayebi, 2011; Bullo, Cort´es, & Mart´ınez, 2009), ﬂocking (Toner & Tu, 1998) and attitude alignment (Ren, 2010), has emerged as a challenging topic in con-trol area and has been…”
Towards a swarm of agile micro quadrotors (2012, background): “While models of leaderless aggregation and swarming with aerial robots are discussed in the robotics community [11, 26, 19], here the challenge of enumerating labelled interactions between robots is circumvented by controlling such aggregate descriptors of formation as statistical distributions.”
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication (2014, neutral): “We brieﬂy review basic concepts from algebraic graph theory following [Bullo et al., 2009].”
Linear Model Regression on Time-series Data: Non-asymptotic Error Bounds and Applications (2018, background): “Symmetric system matrices appear in a wide range of applications where interactions leading to the dynamics is bidirectional; such systems are of interest in biological networks [16], social interactions [17], robotic swarms [18], [19], and networks security [20].”
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], ”
Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design (2015, methodology): “We present some useful notions on algebraic graph theory following [Bullo et al., 2009].”
Communication-Censored ADMM for Decentralized Consensus Optimization (2019, background): “The consensus optimization problem in the form of (1) appears in various applications, such as wireless sensor networks [1], [2], communication networks [4], [5], multi-robot networks [6], [7], smart grids [8], [9], machine learning systems [10], [11], to name a few.”
Proximity Without Consensus in Online Multiagent Optimization (2016, background): “Distributed optimization has a rich history in networked control systems, [5]–[8] wireless systems [9], [10], sensor networks [11], [12], and machine learning [13], [14].”
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate (2017, background): “Problems of this form arise in machine learning [3, 2, 30, 8], control [5, 7, 15], and wireless communications [27, 23, 24].”