Consensus-Based Control Barrier Function for Swarm

Machida, Manao; Ichien, Masumi

doi:10.1109/icra48506.2021.9561971

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…For a path graph, note that N 1 = {2}, N N = {N − 1}, and N i = {i − 1, i + 1} for all i ̸ = {1, N }. Therefore, using (19), Lemma 2, and the trig identity…”

Section: ∂λ2(xp)mentioning

confidence: 99%

“…Control barrier functions (CBFs) offer a simple, compact way to incorporate multiple control objectives and constraints while preserving certain performance guarantees. Because of this, CBFs have gained popularity in many robot applications such as environmental monitoring [22], constrained navigation [36,37], biped robots [13], robot swarms [19], as well as autonomous vehicles [14,16]. However, the benefits of CBFs for problems of resilience in multi-robot systems have only recently been investigated [12,35] and this constitutes the objective of the current paper.…”

Section: Introductionmentioning

confidence: 99%

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Multirobot Adversarial Resilience Using Control Barrier Functions

Cavorsi,

Sabattini,

Gil

2024

IEEE Trans. Robot.

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In this paper we present a control barrier functionbased (CBF) resilience controller that provides resilience in a multi-robot network to adversaries. Previous approaches provide resilience by virtue of specific linear combinations of multiple control constraints. These combinations can be difficult to find and are sensitive to the addition of new constraints. Unlike previous approaches, the proposed CBF provides network resilience and is easily amenable to multiple other control constraints, such as collision and obstacle avoidance. The inclusion of such constraints is essential in order to implement a resilience controller on realistic robot platforms. We demonstrate the viability of the CBF-based resilience controller on real robotic systems through case studies on a multi-robot flocking problem in cluttered environments with the presence of adversarial robots.

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“…For a path graph, note that N 1 = {2}, N N = {N − 1}, and N i = {i − 1, i + 1} for all i ̸ = {1, N }. Therefore, using (19), Lemma 2, and the trig identity…”

Section: ∂λ2(xp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multirobot Adversarial Resilience Using Control Barrier Functions

Cavorsi,

Sabattini,

Gil

2024

IEEE Trans. Robot.

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show abstract

“…Moreover, [5], [10], [11] assume that each agent has access to (part of) the states of the other agents that share a same coupling constraint. Alternatives [14] that do not assume any specific communication structure are limited and can only obtain an approximate solution. In contrast, in [15] a distributed implementation scheme to obtain the optimal solution to the CBF-induced quadratic program for multiagent systems with a general connected communication graph is proposed.…”

Section: Introductionmentioning

confidence: 99%

Distributed barrier function-enabled human-in-the-loop control for multi-robot systems

Fernandez-Ayala,

Tan,

Dimarogonas

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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In this work, we propose a distributed control scheme for multi-robot systems in the presence of multiple constraints using control barrier functions. The proposed scheme expands previous work where only one single constraint can be handled. Here we show how to transform multiple constraints to a collective one using a smoothly approximated minimum function. Additionally, human-in-the-loop control is also incorporated seamlessly to our control design, both through the nominal control in the optimization objective as well as a safety condition in the constraints. Possible failure regions are identified and a suitable fix is proposed. Two types of human-inthe-loop scenarios are tested on real multi-robot systems with multiple constraints, including collision avoidance, connectivity maintenance, and arena range limits.

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“…Moreover, [5], [9], [10] assume that each agent has access to (part of) the states of the other agents that share a same coupling constraint. [13], instead, does not assume any specific communication structure; however, it can only deal with a certain form of CBF candidates and only an approximate solution is obtained.…”

Section: Introductionmentioning

confidence: 99%

Distributed Implementation of Control Barrier Functions for Multi-agent Systems

Tan

Dimarogonas

2022

IEEE Control Syst. Lett.

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In this work, we propose a distributed implementation framework for control barrier functions induced quadratic programs for multi-agent systems. The quadratic program aims at minimally modifying nominal local controllers, which relate to the underlying system tasks, while always respecting a single coupling constraint which relates to system safety. Unlike previous implementations, no approximation or pre-allocation of the coupling constraint over the agents is needed. Instead, to solve the quadratic problem exactly, an auxiliary variable is assigned to each agent and then locally updated and transmitted among agents. The proposed distributed implementation ensures that the control barrier function constraint is enforced at every time instant, and the optimal to the quadratic program control signal is achieved in finite time. The efficacy of our method is demonstrated through two numerical examples.

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Consensus-Based Control Barrier Function for Swarm

Cited by 6 publications

References 24 publications

Multirobot Adversarial Resilience Using Control Barrier Functions

Multirobot Adversarial Resilience Using Control Barrier Functions

Distributed barrier function-enabled human-in-the-loop control for multi-robot systems

Distributed Implementation of Control Barrier Functions for Multi-agent Systems

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