Cluster space collision avoidance for mobile two-robot systems

Kitts, Christopher; Stanhouse, Kyle; Chindaphorn, Piya

doi:10.1109/iros.2009.5354360

Cited by 18 publications

(10 citation statements)

References 43 publications

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“…This framework has been successfully demonstrated implementing kinematic controllers-where the dynamics of the system are considered negligible-for both holonomic and non-holonomic systems, with augmentations for potential field-based obstacle avoidance [22], and utilizing different robot platforms ranging from planar rovers [23] to marine autonomous surface vessels [24] and aerial blimps [25]. Centralized and distributed implementations of cluster space control were also proposed [26].…”

Section: A Cluster Space Approachmentioning

confidence: 99%

Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation

Mas

Kitts

2014

IEEE Access

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The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed. INDEX TERMSCluster space, multirobot systems, dynamic control, formation control, marine robotics.

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Section: A Cluster Space Approachmentioning

confidence: 99%

Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation

Mas

Kitts

2014

IEEE Access

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“…This work has included experiments with planar land rover clusters [21]- [23], with surface vessel systems [24], for holonomic and non-holonomic robots, for robots negotiating obstacle fields [25], and for applications such as escorting and patrolling [24], [26].…”

Section: Cluster Space Specification and Controlmentioning

confidence: 99%

Cluster space control of aerial robots

Agnew

Canto

Kitts

et al. 2010

2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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The cluster space state representation for multirobot systems provides a simple means of specifying and monitoring the geometry and motion characteristics of a cluster of mobile robots. In previous work, this approach has been experimentally verified and validated for controlling the motion of planar mobile multi-robot systems ranging from land rovers to autonomous boats. In this paper we present the application of cluster space control to non-planar groups of two and three aerostatically stable blimps, each with four degrees of freedom. This paper reviews the relevant kinematic transforms and presents both simulation and experimental results that demonstrate successful formation control of these clusters.

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“…In the literature, the problem of multi‐non‐holonomic cluster formation control has been tackled by adding a fast inner control loop to change the robot‐heading angle towards the desired motion profile, while the outer controller handles the formation and tracking tasks [1, 25]. However, having two control loops makes the system more complicated, gives a generally slower time response, and the outer controller always assumes that the robot is heading to the target, which is not always true‐causing the tracking performance to decrease.…”

Section: Introductionmentioning

confidence: 99%