Swarm robots circle formation via a virtual viscoelastic control model

Khaldi, Belkacem; Cherif, Foudil

doi:10.1109/icmic.2016.7804207

Cited by 13 publications

(31 citation statements)

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“…The method is based on an interpolation technique that approximates physical quantities that are moving with particles [13]; here we approximate the density as a physical quantity moving with the robots. As in [12,14], A c c e p t e d M a n u s c r i p t we use a virtual viscoelastic-based proximity model (Section 4.1) as the intra-virtual physics connectivity among the neighbouring robots; the virtual viscoelastic connections are Voigt links, and each link can be represented by a purely elastic spring and a purely viscous damper connected in parallel. We also incorporate a repulsive primitive to avoid obstacles and collisions with the robots that are not virtually linked together (Subsection 4.4).…”

Section: Page 2 Of 30mentioning

confidence: 99%

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Self-organization in aggregating robot swarms: A DW-KNN topological approach

et al. 2018

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In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.

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Section: Page 2 Of 30mentioning

confidence: 99%

“…In this method, the robots sense and respond to virtual forces that are driven by regular physics laws. Here, the robot was based on our previously proposed model [47,12,14], where the robot is subjected to the virtual force vectorâ…”

Section: Robot Modelmentioning

confidence: 99%

Self-organization in aggregating robot swarms: A DW-KNN topological approach

et al. 2018

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“…The field has a strong influence when a robot is close and a decreasing effect as the robot moves further away. We compute the repulsive control vector as follows [7], [11]:…”

Section: Repulsive Controlmentioning

confidence: 99%

“…At each control step, each robot transforms the total vector, , into signals that update the forward speeds ( and ) of its left and right wheels respectively as follows [7], [11]:…”

Section: E Motion Controlmentioning

confidence: 99%

“…We use our virtual viscoelastic based proximity model (Section. III-C) [7], [11] as intra virtual physics connectivity among neighbors; the virtual viscoelastic connections are Voigt links, with each one consists of a Hookean elastic spring and a Newtonian damper connected in parallel. We also incorporate a repulsive primitive to avoid 978-1-5090-4508-2/17/$31.…”

Section: Introductionmentioning

confidence: 99%

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