Distributed Rigidity Recovery in Distance-Based Formations Using Configuration Lattice

Amani, Ali Moradi; Chen, Guanrong; Jalili, Mahdi; Yu, Xinghuo; Stone, Lewi

doi:10.1109/tcns.2020.2984683

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…We present the relationship between the rigidity of a framework and the rigidity of its subframeworks. The work done in [9] defines subframeworks that are constrained to have only one-hop of extent, which allowed the authors to state only a sufficient condition for rigidity. By allowing multi-hops, in Theorem 4.2 we are able to present the necessary and sufficient condition for subframework-based rigidity.…”

Section: A Rigiditymentioning

confidence: 99%

“…Since rigidity is a desired property of networked systems, important efforts have been made towards control strategies for maintaining or recovering rigidity in networks with dynamic topology. The strategies adopted in earlier work can be classified as continuum [5]- [7] or combinatorial [8], [9]. Nevertheless, to develop fully decentralized algorithms is a common difficulty.…”

Section: Introductionmentioning

confidence: 99%

“…However, the rigidity maintenance strategy developed is currently only valid in a two dimensional space, limiting its applicability. A more recent work [9], presents a distributed technique to recover from the loss of rigidity caused by a link failure. A lattice of configurations is applied to locally recover the rigidity of faulty subframeworks by adding new links.…”

Section: Introductionmentioning

confidence: 99%

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Subframework-Based Rigidity Control in Multirobot Networks

Presenza,

Alvarez-Hamelin,

Mas

et al. 2021

Preprint

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This paper presents an alternative approach for analyzing distance-based rigidity in networks of mobile agents, based on a subframework scheme. The advantage of this point of view lies in expressing framework rigidity, which is inherently global, as a localized property. Also, we show that a framework's normalized rigidity eigenvalue degrades as its graph diameter increases. Thus, the rigidity eigenvalues associated to the subframeworks arise naturally as localized rigidity metrics. A decentralized subframework-based controller for maintaining rigidity using only range measurements is developed, which is also aimed to minimize the network's communication load. Finally, we show that the information exchange required by the controller is completed in a finite number of iterations, showing the convenience of the proposed scheme.

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Section: A Rigiditymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Subframework-Based Rigidity Control in Multirobot Networks

Presenza,

Alvarez-Hamelin,

Mas

et al. 2021

Preprint

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“…Proportional control analogous to equation ( 17) can be used to achieve this. In each step the agents calculate their setpoint s res as per (23), and then set their rate of change v in the direction from their current state x towards the setpoint s res of the magnitude…”

Section: A 1d Restrainingmentioning

confidence: 99%

On training datasets for machine learning-based visual relative localization of micro-scale UAVs

Walter

Vrba

Saska

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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A technique that allows a formation-enforcing control (FEC) derived from graph rigidity theory to interface with a realistic relative localization system is proposed in this paper. Recent research in sensor-based multi-robot control has given rise to multiple modalities of mutual relative localization systems. In particular, vision-based relative localization has reached the stage where it can be carried onboard lightweight UAVs in order to retrieve the relative positions and relative orientations of cooperating units. A separate stream of development spawned distributed formation-enforcing control which can lead individual robots into a desired formation using relative localization of their neighbors. These two fields naturally complement each other by achieving real-world flights of UAVs in formation without the need for absolute localization in the world. However, real relative localization systems are, without exception, burdened by nonnegligible sensory noise, which is typically not fully taken into account in formation-enforcing control algorithms. Such noise can lead to rapid changes in velocity, which further interferes with visual localization. Our approach provides a solution to these challenges, enabling practical deployment of FEC under realistic conditions, as we demonstrated in real-world experiments.

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“…To sum up, the studies concerning formation control focus primarily on the topology schematization and the stability analysis [12]. Such as mission planning and obstacle avoiding based formation design [13][14][15], the analysis of formation tracking errors [16][17][18], and changeable formation control [19,20].…”

Section: Introductionmentioning

confidence: 99%

Distributed cooperative formation of stochastic multi‐agent systems in finite horizon

Jun

Chen

Shen

et al. 2022

IET Control Theory & Appl

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The paper addresses the distributed cooperative formation control problem for a class of general stochastic multi-agent systems subject to time-varying dynamics. A type of novel modified distributed state observer is constructed, whose estimation information is used to design the H ∞ control scheme for guaranteeing the mean-square formation maintenance in finite-horizon. Furthermore, the observer and controller parameters are obtained iteratively through solving a set of constrained recursive matrix equations. Finally, the proposed scheme has been validated by a simulation study.

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Distributed Rigidity Recovery in Distance-Based Formations Using Configuration Lattice

Cited by 9 publications

References 31 publications

Subframework-Based Rigidity Control in Multirobot Networks

Subframework-Based Rigidity Control in Multirobot Networks

On training datasets for machine learning-based visual relative localization of micro-scale UAVs

Distributed cooperative formation of stochastic multi‐agent systems in finite horizon

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