A Framework and Architecture for Multi-Robot Coordination

Fierro, Rafael; Das, Arindam; Spletzer, John; Esposito, Joel M.; Kumar, Vijay; Ostrowski, James; Pappas, George J.; Taylor, Camillo J.; Hur, Yerang; Alur, Rajeev; Lee, Insup; Grudić, Gregory Z.; Southall, Ben

doi:10.1177/027836402128964080

Cited by 19 publications

(24 citation statements)

References 40 publications

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“…Figure 11 shows reference and measured signals for each of the 16 variables as defined in Eqs. (9)(10)(11)(12)(13). The robots successfully track the desired trajectories.…”

Section: Simulations In the Matlab Environmentmentioning

confidence: 94%

“…The concept of defining position, orientation and shape characteristics for a formation of mobile robots has been previously proposed by many authors. Initial works relied on leader-follower schemes, using graphs to define a rigid formation shape, and defining trajectories for the leader [10,11]. Other variations allowed for role changes between leaders and followers [12].…”

Section: Introductionmentioning

confidence: 99%

“…Because it conserves the dimensionality of the system, the CS approach can be computationally challenging for large numbers of robots and highly coupled cluster state definitions. This can be addressed by using alternate state representations that reduce the number of robots used to define cluster position and/or various shape variables; taken to its limit, the CS approach devolves into a leader-follower chain [10][11][12], which is completely scalable.…”

Section: Introductionmentioning

confidence: 99%

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Quaternions and Dual Quaternions: Singularity-Free Multirobot Formation Control

Mas

Kitts

2016

J Intell Robot Syst

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Cluster space control is a method of multirobot formation keeping that considers a group of robots to be a single entity, defining state variables to represent characteristics of the group, such as position, orientation, and shape. This technique, however, suffers from singularities when a minimal state representation is used. This paper presents three alternative implementations of this control approach that eliminate singularities through changes in the control architecture or through redundant formation definitions. These proposed solutions rely on quaternions, dual quaternions, and control implementations that produce singularity-free trajectories while maintaining a cluster level abstraction that allows for simple This work has been sponsored through USAIT Grant W911NF-14-1-0008, ITBACyT 2013-17, and Agencia Nacional de Promoción Científica y Tecnológica, FONCYT PICT 2014-2055.

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“…Figure 11 shows reference and measured signals for each of the 16 variables as defined in Eqs. (9)(10)(11)(12)(13). The robots successfully track the desired trajectories.…”

Section: Simulations In the Matlab Environmentmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quaternions and Dual Quaternions: Singularity-Free Multirobot Formation Control

Mas

Kitts

2016

J Intell Robot Syst

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“…Some authors characterize the performance of formations by analyzing the error between pairs of robots (Fierro and Alur 2002). This is a valid metric when the controllers are designed taking in consideration the leader's heading.…”

Section: Resultsmentioning

confidence: 99%

Attractor dynamics approach to formation control: theory and application

Monteiro

Bicho

2010

Auton Robot

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In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.

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“…In previous work [17], an object-oriented software architecture supporting hierarchical composition of robot agents and behaviors or modes was developed. Key features of the software architecture are summarized below.…”

Section: Cooperative Hybrid Controllermentioning

confidence: 99%