phi Clust: Pheromone-based Aggregation for Robotic Swarms

Date

2018-10-05

Author

Arvin, Farshad
Turgut, Ali Emre
Krajnik, Tomas
Rahimi, Salar
Okay, Ilkin Ege
Yue, Shigang
Watson, Simon
Lennox, Barry

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

166
views

0
downloads

In this paper, we proposed a pheromone-based aggregation method based on the state-of-the-art BEECLUST algorithm. We investigated the impact of pheromone-based communication on the efficiency of robotic swarms to locate and aggregate at areas with a given cue. In particular, we evaluated the impact of the pheromone evaporation and diffusion on the time required for the swarm to aggregate. In a series of simulated and real-world evaluation trials, we demonstrated that augmenting the BEECLUST method with artificial pheromone resulted in faster aggregation times.

Subject Keywords

Bio-inspired, Pheromone, Swarm Robotics, Aggregation

URI

https://hdl.handle.net/11511/54161

Collections

Department of Mechanical Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Inverse design of compressor cascades Kaplan, B.; Eyi, Sinan (2001-12-01) In this paper an inverse design method is presented which couples a Navier-Stokes flow solver and a numerical optimization algorithm. The design method generates a compressor cascade, producing a specified surface pressure distribution at a transonic speed. A least-square optimization technique is used to minimize pressure discrepancies between the target and designed cascades. In order to represent the nonlinear, rotational and viscous physics of transonic flows, Navier-Stokes equations are used to predict...
Implementation Studies of Robot Swarm Navigation Using Potential Functions and Panel Methods Merheb, Abdel-Razzak; GAZİ, VEYSEL; Sezer Uzol, Nilay (2016-10-01) This paper presents a practical swarm navigation algorithm based on potential functions and properties of inviscid incompressible flows. Panel methods are used to solve the flow equations around complex shaped obstacles and to generate the flowlines, which provide collision-free paths to the goal position. Safe swarm navigation is achieved by following the generated streamlines. Potential functions are used to achieve and maintain group cohesion or a geometric formation during navigation. The algorithm is i...
Cooperative terrain based navigation and coverage identification using consensus Kasebzadeh, Parinaz; Fritsche, Carsten; Özkan, Emre; Gunnarsson, Fredrik; Gustafsson, Fredrik ( Institute of Electrical and Electronics Engineers Inc.; 2015-07-06) This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approxim...
Domain-Structured Chaos in a Hopfield Neural Network Akhmet, Marat (World Scientific Pub Co Pte Lt, 2019-12-30) In this paper, we provide a new method for constructing chaotic Hopfield neural networks. Our approach is based on structuring the domain to form a special set through the discrete evolution of the network state variables. In the chaotic regime, the formed set is invariant under the system governing the dynamics of the neural network. The approach can be viewed as an extension of the unimodality technique for one-dimensional map, thereby generating chaos from higher-dimensional systems. We show that the dis...
Computational Electromagnetic Analysis of Deformed Nanowires Using the Multilevel Fast Multipole Algorithm Yilmaz, Akif; Karaosmanoglu, Bariscan; Ergül, Özgür Salih (2015-02-16) We consider computational analysis of deformed nanowires and their arrays using a full-wave simulation environment based on integral-equation formulations and the multilevel fast multipole algorithm (MLFMA). Without requiring any periodicity assumptions, MLFMA allows for fast and accurate simulations of complex nanowire structures with three-dimensional geometries and random deformations. We present the results of hundreds of simulations, where deformed nanowires are considered as isolated, as well as in ar...

Citation Formats

F. Arvin et al., “phi Clust: Pheromone-based Aggregation for Robotic Swarms,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54161.