Comparison of Different Cue-Based Swarm Aggregation Strategies

Farshad Arvin, Ali Emre Turgut, Nicola Bellotto, Shigang Yue

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


    In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naïve with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naïve method.
    Original languageEnglish
    Title of host publicationInternational Conference in Swarm Intelligence
    Subtitle of host publicationAdvances in Swarm Intelligence
    Publication statusPublished - 2014

    Publication series

    NameLecture Notes in Computer Science


    • swarm robotics
    • collective behavior
    • cue-based aggregation


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