Probabilistic analysis of long-term swarm performance under spatial interferences

Yara Khaluf, Mauro Birattari, Franz Rammig

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

8 Citations (Scopus)

Abstract

Swarm robotics is a branch of collective robotics that outperforms many other systems due to its large number of robots. It allows for performing several tasks that are beyond the capability of a single or multi robot systems. Its global behaviour emerges from the local rules implemented on the level of its individual robots. Thus, estimating the obtained performance in a self-organized manner represents one of the main challenges, especially under complex dynamics like spatial interferences. In this paper, we exploit the central limit theorem (CLT) to analyse and estimate the swarm performance over long-term deadlines and under potential spatial interferences. The developed model is tested on the well-known foraging task, however, it can be generalized to be applied on any constrictive robotic task.

Original languageEnglish
Title of host publicationTheory and Practice of Natural Computing - Second International Conference, TPNC 2013, Proceedings
Pages121-132
Number of pages12
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2nd International Conference on the Theory and Practice of Natural Computing, TPNC 2013 - Caceres, Spain
Duration: 3 Dec 20135 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8273 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference/symposium

Conference/symposium2nd International Conference on the Theory and Practice of Natural Computing, TPNC 2013
Country/TerritorySpain
CityCaceres
Period3/12/135/12/13

Keywords

  • Central limit theorem
  • Swarm robotics
  • Time-constrained tasks

Fingerprint

Dive into the research topics of 'Probabilistic analysis of long-term swarm performance under spatial interferences'. Together they form a unique fingerprint.

Cite this