Formation reconfiguration for mobile robots with network connectivity constraints

Navaravong, Leenhapat; Kan, Zhen; Shea, John M.; Dixon, Warren E.

doi:10.1109/mnet.2012.6246748

Cited by 21 publications

(19 citation statements)

References 9 publications

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“…The control algorithm for repositioning the nodes uses an information flow between pairs of nodes that determines a path of movement along the nodes in the graph for at least one of the nodes. In this work, these paths of movement are determined by using an approach first proposed in our previous work [17]. In [17], a prefix labeling and routing algorithm is developed to "route" each autonomous vehicle through an initial tree topology to achieve the desired tree topology while preserving network connectivity.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this work, these paths of movement are determined by using an approach first proposed in our previous work [17]. In [17], a prefix labeling and routing algorithm is developed to "route" each autonomous vehicle through an initial tree topology to achieve the desired tree topology while preserving network connectivity. However, finding a good node mapping to reduce the amount of node movement in topology reconfiguration is not considered in [17].…”

Section: Problem Formulationmentioning

confidence: 99%

“…In [17], a prefix labeling and routing algorithm is developed to "route" each autonomous vehicle through an initial tree topology to achieve the desired tree topology while preserving network connectivity. However, finding a good node mapping to reduce the amount of node movement in topology reconfiguration is not considered in [17]. In this work, since G f can be assumed to be a spanning tree, it is desirable to find a tree G t int in the specified G int that minimizes the node movement required in topology reconfiguration, and the algorithm in [17] can be applied to determine how G int can be transformed into G f .…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this paper, based on the weighted graph matching algorithm in [10], a node mapping algorithm is developed to determine the node correspondence between the arbitrary initial topology and the specified desired topology, and build a tree on the initial graph such that the node movement required in topology reconfiguration is minimized and the routing algorithm developed from our previous work [15]- [17] can be applied to specify how the initial graph can be transformed to the desired graph. After node correspondence is determined, nodes are required to physically move to perform network reconfiguration with limited communication.…”

Section: Introductionmentioning

confidence: 99%

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Graph Matching-Based Formation Reconfiguration of Networked Agents With Connectivity Maintenance

Kan

Navaravong

Shea

et al. 2015

IEEE Trans. Control Netw. Syst.

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Various applications require networked agents to cooperatively achieve specified formations. In this paper, formation reconfiguration for a group of identical agents with limited communication capabilities is considered. Since the agents considered are identical, their roles are interchangeable, and each position in the desired formation can be taken by any agent. To reduce the total amount of node movement required for formation reconfiguration, a weighted graph matching based node mapping strategy is developed to specify the node correspondence between an arbitrary initial graph and the desired graph. After the node mapping is determined, agents are required to move physically to form the desired formation. Since agents are only able to communicate within a certain range, formation reconfiguration must be accomplished with network connectivity constraints (i.e., specified nodes remain within specified sensing and communication ranges). A decentralized control scheme is developed to guarantee network connectivity by maintaining a desired neighborhood determined by the node mapping algorithm, and to ensure convergence of all agents to the desired configuration with collision avoidance among agents. The developed strategy is demonstrated through simulation results.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Graph Matching-Based Formation Reconfiguration of Networked Agents With Connectivity Maintenance

Kan

Navaravong

Shea

et al. 2015

IEEE Trans. Control Netw. Syst.

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“…Otherwise, a disconnected network may prevent robots from cooperating, exchanging data, accepting orders, or initiating new tasks. Some solutions have been proposed for rendezvous, formation control, or collision avoidance tasks [6], [8], [11], [17], [20]. When the motion task is coverage, the problem of keeping the network connectivity is of special interest.…”

Section: Introductionmentioning

confidence: 99%

Triggered Minimum Spanning Tree for distributed coverage with connectivity maintenance

Aragüés¹,

Sagüés

Mezouar³

2014

2014 European Control Conference (ECC)

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We study the problem of distributed coverage with connectivity maintenance for robot networks with range-limited communication and sensing. We let the robots perform coverage while keeping a set of communication links. The structure that ensures that the network does not get disconnected, and that provides the highest freedom of robot motions is the Minimumdistance Spanning Tree (MST). As robots move to perform the coverage task, their distances change as well and this MST becomes obsolete. Computing the MST at each coverage step may be too communication demanding. The contribution of this paper is the proposal of a triggering strategy, where agents decide when to compute a new MST of the graph. In our simulations, we show that our method achieves a similar coverage goal as the one computing the MST at each step, whereas it has much lower communication costs.

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Balanced containment control and cooperative timing of a multiagent system over random communication graphs

Kan

Mehta

Shea

et al. 2018

Intl J Robust & Nonlinear

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Summary A multiagent system is considered, which is tasked with the objective of approaching a predetermined target from a desired region to minimize detection and then simultaneously converge at the target. The considered cooperative timing problem consists of 2 stages: navigation and simultaneous arrival. During the navigation stage, the agents are driven from a distant starting point toward the target while restricting their motion within a desired area. Only a few agents (ie, leaders) are equipped with the desired bearing information to the target, whereas the remaining agents (ie, followers) may only have local feedback with neighboring agents to coordinate their headings. No range information to the target and no absolute or other relative position information among agents are available. The arrival stage begins when agents enter a neighborhood of the target (ie, range information becomes available during the arrival stage), and agents coordinate their motion to perform simultaneous arrival. The agents could experience random loss of communication with immediate neighbors, which results in a stochastic communication network. On the basis of the random communication network, balanced containment control is developed, which almost surely restricts the motion of the group within a desired region while equally spacing the agents. An almost sure consensus algorithm is designed for agents to coordinate the simultaneous arrival time by achieving a consensus on informed agents during the arrival stage. Simulation results demonstrate the performance of the developed approach.

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Formation reconfiguration for mobile robots with network connectivity constraints

Cited by 21 publications

References 9 publications

Graph Matching-Based Formation Reconfiguration of Networked Agents With Connectivity Maintenance

Graph Matching-Based Formation Reconfiguration of Networked Agents With Connectivity Maintenance

Triggered Minimum Spanning Tree for distributed coverage with connectivity maintenance

Balanced containment control and cooperative timing of a multiagent system over random communication graphs

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