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Journal ArticleDOI

Stabilisation of infinitesimally rigid formations of multi-robot networks

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TLDR
It is shown that infinitesimal rigidity is a sufficient condition for local asymptotical stability of the equilibrium manifold of the multivehicle system.
Abstract
This article considers the design of a formation control for multivehicle systems that uses only local information. The control is derived from a potential function based on an undirected infinitesimally rigid graph that specifies the target formation. A potential function is obtained from the graph, from which a gradient control is derived. Under this controller the target formation becomes a manifold of equilibria for the multivehicle system. It is shown that infinitesimal rigidity is a sufficient condition for local asymptotical stability of the equilibrium manifold. A complete study of the stability of the regular polygon formation is presented and results for directed graphs are presented as well. Finally, the controller is validated experimentally.

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Citations
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Proceedings ArticleDOI

Formation control of mobile agent groups based on localization

TL;DR: It is shown that the formation of a single-integrator modeled agent group globally exponentially converges to the desired formation with the estimated formation globally exponentially convergence to the actual formation up to translation if and only if the interaction graph of the group contains a spanning tree.
Journal ArticleDOI

Topological Reconfiguration Planning for a Variable Topology Truss

TL;DR: This paper gives a procedure to compute all possible distinct reconfiguration actions for a given robot topology, and exhaustively enumerates all possible reconfigurable topologies for VTTs up to 29 members.
Journal ArticleDOI

Cooperative Event-Based Rigid Formation Control

TL;DR: In this article, a centralized event-based formation control system is proposed, in which a central event controller determines the next triggering time and broadcasts the event signal to all the agents for control input update.
Journal ArticleDOI

Distance-Based Formation Control With Goal Assignment for Global Asymptotic Stability of Multi-Robot Systems

TL;DR: In this article, a distance-based formation control with the goal assignment for multiple nonholonomic mobile robots is presented, which helps to avoid the convergence to undesirable formations caused by the gradient-based control law.
Proceedings ArticleDOI

Directed Formation Control of n Planar Agents with Distance and Area Constraints

TL;DR: In this article, a distance and area-based scheme for the formation control of n-agent systems in two dimensions using directed graphs and the single-integrator model is proposed.
References
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Book

Introduction to Applied Nonlinear Dynamical Systems and Chaos

TL;DR: The Poincare-Bendixson Theorem as mentioned in this paper describes the existence, uniqueness, differentiability, and flow properties of vector fields, and is used to prove that a dynamical system is Chaotic.
Journal ArticleDOI

Flocking for multi-agent dynamic systems: algorithms and theory

TL;DR: A theoretical framework for design and analysis of distributed flocking algorithms, and shows that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders."
MonographDOI

Algebraic graph theory

TL;DR: In this article, the authors introduce algebraic graph theory and show that the spectrum of a graph can be modelled as a graph graph, and the spectrum can be represented as a set of connected spanning trees.
Journal ArticleDOI

Coverage control for mobile sensing networks

TL;DR: In this paper, the authors describe decentralized control laws for the coordination of multiple vehicles performing spatially distributed tasks, which are based on a gradient descent scheme applied to a class of decentralized utility functions that encode optimal coverage and sensing policies.
Posted Content

Coverage control for mobile sensing networks

TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
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