A Triangular Formation Strategy for Collective Behaviors of Robot Swarm

Li, Xiang; Ercan, M. Fikret; Fung, Yu-Fai

doi:10.1007/978-3-642-02454-2_70

Cited by 13 publications

(15 citation statements)

References 11 publications

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“…The starting point of the proposed approach is the triangle formation algorithm (TFA), previously described in [71,72]. Such a technique represents an interactive control algorithm used to organize the swarm into an equilateral triangle-based formation.…”

Section: Introductionmentioning

confidence: 99%

“…The application of the Delaunay triangulation in the presence of obstacles represents a novel aspect requiring the definition of a visibility property. Furthermore, while the idea of triangular mesh-based formation has already been exploited [72], the new feature consists in the capability to generate sub-swarms without the definition of any physical or virtual leader.…”

Section: Introductionmentioning

confidence: 99%

“…This is tracked by a proportional navigation-based control algorithm that cannot take into account performance and environmental constraints. In [72], an artificial potential field was also used to take obstacles into account. Our approach is to use a potential field [76,77] to compute the aircraft's desired heading angle and speed to keep the desired mutual distances between aircraft, whereas an MPC-based control algorithm performs the trajectory tracking and the obstacle collision avoidance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

Bassolillo

D’Amato

Notaro

et al. 2020

Sensors

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This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

Bassolillo

D’Amato

Notaro

et al. 2020

Sensors

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“…This work is extended from our earlier study which was developed for swarm robots operating in two-dimensional space [11]. The algorithm described in [11] tries to form and maintain isosceles among the three neighboring robots which is now extended to form regular tetrahedrons among four neighboring robots in three-dimensional space. In the following, the RTF method will be presented in detail, together with results obtained from a series of simulation studies.…”

Section: Introductionmentioning

confidence: 99%

An artifical physics-based 3D swarm control strategy

Zeng

2011

2011 International Conference on Electric Information and Control Engineering

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A new decentralized control method, named Regular Tetrahedron Formation (RTF) strategy, is presented for a swarm of simple robots operating in three dimensional space. This strategy is based on virtual spring mechanism and basically, allows four neighboring robots to autonomously form a Regular Tetrahedron (RT) regardless of their initial positions. RTF strategy is made scalable and applied for various sizes of swarms through a dynamic neighbor selection procedure. Thus, each robot's behavior in each time step is only dependent on the local position information of three dynamically selected neighbors. In addition, an obstacle avoidance model suitable for swarm maneuvering is also introduced. Algorithm is studied with computational experiments which demonstrated that it is effective and practical.

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“…That is, robots flock towards a target point by avoiding obstacles in the environment as a whole. This work is extended from our earlier study, in which we have presented a decentralized control strategy, named Triangular Formation Algorithm (TFA) [19]. The TFA is based on triangular geometry and developed for swarm robots moving in two-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

A 3D Local Interaction Strategy for Swarm Robot

Li¹,

Liang²

2011

JCP

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A decentralized control strategy for a robot swarm is presented, where each robot try to form a regular tetrahedron with its three neighbors. The proposed method is based on virtual spring and demands minimum local information. Four neighboring robots can form a regular tetrahedron formation regardless of their initial positions. A neighbor selection procedure is used to extend the strategy In addition, an obstacle avoidance mechanism, based on artificial physics, is also introduced. As a result, the basic swarm behaviors such as aggregation, flocking and obstacle avoidance are demonstrated through simulation

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A Triangular Formation Strategy for Collective Behaviors of Robot Swarm

Cited by 13 publications

References 11 publications

Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

An artifical physics-based 3D swarm control strategy

A 3D Local Interaction Strategy for Swarm Robot

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