Cohesion and segregation in swarm navigation

Santos, Vinicius Graciano; Chaimowicz, Luiz

doi:10.1017/s0263574714000563

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Observe that for the root cluster J the equation ( 91) simplifies and yields (64). Further, using (91), we obtain (63) for any I ∈ V τ (J)\{J} with parent P = Pr (I, τ ) as follows:…”

Section: Proof Of Propositionmentioning

confidence: 99%

Coordinated Robot Navigation via Hierarchical Clustering

2016

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Abstract-We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of n perfectly sensed and actuated Euclidean spheres in a d-dimensional ambient space (for arbitrary n and d). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller which is quadratic in n and algebraic in d and prove that its execution brings all but a measure zero set of initial configurations to the desired goal with the guarantee of no collisions along the way.

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“…Observe that for the root cluster J the equation ( 91) simplifies and yields (64). Further, using (91), we obtain (63) for any I ∈ V τ (J)\{J} with parent P = Pr (I, τ ) as follows:…”

Section: Proof Of Propositionmentioning

confidence: 99%

Coordinated Robot Navigation via Hierarchical Clustering

2016

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“…The AUVs group automatically performs the underwater search mission (sixth stage J MAS ∈А MUC ) by the AUVs group coordinated motion to specified trajectories [18]. Navigation support for this mission stage can be organized using a bottom navigation system, pre-installed in the search area, or using navigation AUVs, equipped with GPS devices to determine own geographical coordinates, and hydroacoustic systems navigation support of underwater operations [19].…”

Section: The Development Results Of Muc Control Automation Tasks 1 Dmentioning

confidence: 99%

Control Automation of Maritime Unmanned Complex With a Group of Autonomous Underwater Vehicles

Blintsov

Aloba

2019

EUREKA: Physics and Engineering

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It is expedient to perform underwater search operations on large water areas using a group of autonomous self-propelled underwater vehicles. However, with a large distance to the search areas, the sea transition (from one point to the other) of the underwater vehicles requires high energy costs. This leads to the necessity to use heavy-duty underwater vehicles, which determines the high cost of the search operation. The transport of underwater vehicles is proposed to be carried out with an unmanned surface vessel, equipped with actuators for the automatic release of a group of vehicles under water and receiving on board after the end of the underwater mission. The maritime unmanned complex consisting of an unmanned surface vessel and a group of autonomous underwater vehicles on its board forms a new type of marine robotics, the complete automation of which is an actual scientific and technical task. For its implementation, the underlying (basic) automation technology of the marine search underwater mission has been developed as the theoretical basis for the development of the generalized structure of the complex automatic control system. Ten implementation stages of the underlying technology are formulated and the analysis of their automation features with the use of modern methods in the field of marine robotics is performed. Automation of the underlying technology stages involves the transfer of the vessel to a given water area, the automatic release (launch) of the group of underwater vehicles and their coordinated motion to the search area, the search operations and the return to the unmanned surface vessel, as well as the recovery of the vessel to the base. The generalized requirements for automatic control systems constituting the maritime unmanned complex at each stage of its functioning are provided. The spiral trajectory of waiting for the motion of the underwater vehicles at the group formation stages, for the search operation execution and after its completion, is proposed. For the spatial motion of the autonomous underwater vehicle as an agent of the group, the automatic control system was improved by introducing the blocks of the "Navigation Situation Model" and the "Navigation Threat Identifier, which make it impossible for emergency collision with the neighboring underwater vehicles of the group and disintegrate the group due to the data communication loss between them.

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“…Group behaviors have been studied in computer graphics [19,20,21,22], robotics [23], pedestrian dynamics [2], and social psychology [24]. Prior techniques have been mainly used to simulate static or fixedsized groups and perform group-based collision avoidance [25,26,27]. However, none of these methods can efficiently simulate dynamic groups of varying sizes in arbitrary environments.…”

Section: Group Behavior Simulationmentioning

confidence: 99%

“…However, prior agent-agent collision avoidance schemes such as ORCA [4] or social forces [5] may not maintain the group assignment. Some recent methods [25] extend the traditional agentagent velocity obstacle by considering each group as a super-agent. However, they assume the group shape and size is should be fixed during the navigation, and thus requires all agents in the group must always choose the same velocity.…”

Section: Collision Avoidancementioning

confidence: 99%

Dynamic Group Behaviors for Interactive Crowd Simulation

He,

Pan,

Narang

et al. 2016

Preprint

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Cohesion and segregation in swarm navigation

Cited by 11 publications

References 38 publications

Coordinated Robot Navigation via Hierarchical Clustering

Coordinated Robot Navigation via Hierarchical Clustering

Control Automation of Maritime Unmanned Complex With a Group of Autonomous Underwater Vehicles

Dynamic Group Behaviors for Interactive Crowd Simulation

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