Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Collective sorting and segregation in robots with minimal sensing
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Self-Organization in Biological Systems
Self-Organization in Biological Systems
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Cooperation through self-assembly in multi-robot systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Forming nested 3D structures based on the Brazil nut effect
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Vision-based segregation behaviours in a swarm of autonomous robots
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Segregation in swarms of e-puck robots based on the Brazil nut effect
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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We study a simple algorithm inspired by the Brazil nut effect for achieving segregation in a swarm of mobile robots. The algorithm lets each robot mimic a particle of a certain size and broadcast this information locally. The motion of each particle is controlled by three reactive behaviors: random walk, taxis, and repulsion by other particles. The segregation task requires the swarm to self-organize into a spatial arrangement in which the robots are ranked by particle size (e.g., annular structures or stripes). Using a physics-based computer simulation, we study the segregation performance of swarms of 50 mobile robots. The robots represent particles of three different sizes. We first analyze the problem of how to combine the basic behaviors so as to minimize the percentage of errors in rank. We then show that the system is very robust to noise on inter-robot perception and communication. For a noise level of 50%, the mean percentage of errors in rank is 1%. Moreover, we investigate a simplified version of the control algorithm, which does not rely on communication. Finally, we show that the mean percentage of errors in rank decreases exponentially as the particles' size ratio increases. As the error is bounded, one can achieve 100% error-free segregation. The reduction in error, however, comes at the expense of an increase in the required sensing/communication range.