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Rigid graph control architectures for autonomous formations

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TLDR
In this paper, a theory for analyzing and creating architectures appropriate to the control of formations of autonomous vehicles is presented. The theory is based on ideas of rigid graph theory, some but not all of which are old.
Abstract
This article sets out the rudiments of a theory for analyzing and creating architectures appropriate to the control of formations of autonomous vehicles. The theory rests on ideas of rigid graph theory, some but not all of which are old. The theory, however, has some gaps in it, and their elimination would help in applications. Some of the gaps in the relevant graph theory are as follows. First, there is as yet no analogue for three-dimensional graphs of Laman's theorem, which provides a combinatorial criterion for rigidity in two-dimensional graphs. Second, for three-dimensional graphs there is no analogue of the two-dimensional Henneberg construction for growing or deconstructing minimally rigid graphs although there are conjectures. Third, global rigidity can easily be characterized for two-dimensional graphs, but not for three-dimensional graphs.

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Citations
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Journal ArticleDOI

A survey of multi-agent formation control

TL;DR: A survey of formation control of multi-agent systems focuses on the sensing capability and the interaction topology of agents, and categorizes the existing results into position-, displacement-, and distance-based control.
Journal ArticleDOI

Distributed Formation Control of Multi-Agent Systems Using Complex Laplacian

TL;DR: A new technique based on complex Laplacian is introduced to address the problems of which formation shapes specified by inter-agent relative positions can be formed and how they can be achieved with distributed control ensuring global stability.
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Bearing Rigidity and Almost Global Bearing-Only Formation Stabilization

TL;DR: In this article, it is shown that a framework in an arbitrary dimension can be uniquely determined up to a translation and a scaling factor by the bearings if and only if the framework is infinitesimally bearing rigid.
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Robust consensus tracking of a class of second-order multi-agent dynamic systems

TL;DR: A robust consensus tracking problem for a class of second-order multi-agent systems has been addressed in the presence of disturbances and unmodeled dynamics using algebraic graph theory, Lyapunov-based analysis, and an invariance-like theorem.
Journal ArticleDOI

Clustering in diffusively coupled networks

TL;DR: This paper shows how different mechanisms may lead to clustering behavior in connected networks consisting of diffusively coupled agents, and presents two other mechanisms under which cluster synchronization might be achieved.
References
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Journal ArticleDOI

Necessary and sufficient graphical conditions for formation control of unicycles

TL;DR: It is proved that formation stabilization to a point is feasible if and only if the sensor digraph has a globally reachable node.
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Stability analysis of swarms

TL;DR: It is shown that the individuals (autonomous agents or biological creatures) will form a cohesive swarm in a finite time and an explicit bound on the swarm size is obtained, which depends only on the parameters of the swarm model.
Journal Article

Leader-to-formation stability.

TL;DR: Leader-to-formation stability (LFS) gains quantify error amplification, relate interconnection topology to stability and performance, and offer safety bounds for different formation topologies.
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Leader-to-formation stability

TL;DR: In this article, the stability properties of mobile agent formations are investigated based on leader following and nonlinear gain estimates that capture how leader behavior affects the interconnection errors observed in the formation are derived.
Journal ArticleDOI

A Theory of Network Localization

TL;DR: This paper constructs grounded graphs to model network localization and applies graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks, and further study the computational complexity of network localization.
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