Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Usevitch, James; Panagou, Dimitra

doi:10.1109/tac.2019.2934954

Cited by 71 publications

(35 citation statements)

References 22 publications

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“…Following this line, resilient formations have been investigated in time-varying communication graphs [18], triangular robust graphs [37], and triangular and square lattices [38,39]. Then Usevitch and Panagou studied a resilient leader-follower formation problem in [41] where [57] they proposed a resilient controller that allows wellbehaving robots to track a reference state even though a bounded subset of leaders and followers are adversarial. In these studies, the robots update their motion synchronously and periodically.…”

Section: Resilient Formation Control and Estimationmentioning

confidence: 99%

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Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Zhou

Tokekar

2021

Curr Robot Rep

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Purpose of Review Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks.Recent Findings We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmental uncertainty; (3) Graph neural networks based coordination to learn decentralized multi-robot coordination policies. These algorithms have been applied to tasks such as formation control, task assignment and scheduling, search and planning, and informative data collection. SummaryIn order for multi-robot systems to become practical, we need coordination algorithms that can scale to large teams of robots dealing with dynamically changing, failure-prone, contested, and uncertain environments. There has been significant recent research on multi-robot coordination that has contributed resilient and risk-aware algorithms to deal with these issues and reduce the gap between theory and practice. Learning-based approaches have been seen to be promising, especially since they can learn who, when, and how to communicate for effective coordination. However, these algorithms have also been shown to be vulnerable to adversarial attacks, and as such developing learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.

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Section: Resilient Formation Control and Estimationmentioning

confidence: 99%

“…Resilient multi-robot coordination: tasksFormation Control[14,18,36,37,38,39,40,41,42,43,44] …”

mentioning

confidence: 99%

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Zhou

Tokekar

2021

Curr Robot Rep

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“…A main drawback in the existing line of research in distributed resilient consensus including those mentioned above is that the global information regarding the number of malicious agents is shared among all cooperative agents. For example, in a typical Weighted-Mean-Subsequence-Reduced (W-MSR) algorithm [4], [7], [8], [18], cooperative agents will need to scrap the r largest and r smallest neighbor states during each iteration, where r is the estimated upper bound of malicious neighbors. In heuristic algorithms mentioned above, on the other hand, optimization problems are often have to be solved by employing advanced techniques such as neural networks [16], [19].…”

Section: Introductionmentioning

confidence: 99%

Median-Based Resilient Consensus Over Time-Varying Random Networks

Shang

2022

IEEE Trans. Circuits Syst. II

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This brief investigates the resilient consensus control for multiagent systems over a time-varying directed random network. We propose a median-based consensus strategy, which is purely distributed and, as opposed to the Weighted-Mean-Subsequence-Reduced approaches in the existing literature, shared estimate regarding the number of malicious agents in the neighborhood of each cooperative agent is not required. This offers more applicability and flexibility as seeking a shared estimate of surrounding threats is often difficult in practice. In addition to malicious agents, random availability of communication edges is accommodated in the random network framework. Sufficient conditions are derived for reaching almost sure consensus by using a martingale convergence theorem. Finally, the theoretical findings are illustrated by numerical simulations.

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“…Similarly, in [17], the author allows each node in a network to ignore a possibly distinct number of extreme neighbors and show graph conditions that ensure network consensus. In [18], the authors introduced a resilient leader-follower consensus to arbitrary reference values. Under the same idea, where an agent ignores a number of the largest and the smallest received values, their work assures a consensus value belonging to the convex hull of initial nodes states.…”

Section: Introductionmentioning

confidence: 99%

A general discrete-time method to achieve resilience in consensus algorithms

Ramos

Silvestre

2020

2020 59th IEEE Conference on Decision and Control (CDC)

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In this paper, we approach the problem of a set of network agents reaching resilient consensus in the presence of a subset of attacked nodes. We devise a generalized method, with polynomial time complexity, which receives as input a discrete-time, synchronous-communication consensus algorithm, a dynamic network of agents, and the maximum number of attacked nodes. The distributed algorithm enables each normal node to detect and discard the values of the attacked agents while reaching the consensus of normal agents for the input consensus algorithm. Hence, the proposed method adds an extra layer of resilience to a given discrete-time and synchronous-communication consensus algorithm. Finally, we demonstrate the effectiveness of the method with experimental results, showing some attack circumstances which we can counter, where the state-of-the-art methods fail.

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Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs

Cited by 71 publications

References 22 publications

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

Median-Based Resilient Consensus Over Time-Varying Random Networks

A general discrete-time method to achieve resilience in consensus algorithms

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