LOST Highway: A Multiple-Lane Ant-Trail Algorithm to Reduce Congestion in Large-Population Multi-robot Systems

We propose a modification of a well-known ant-inspired trail-following algorithm to reduce congestion in multi-robot systems. Our method results in robots moving in multiple lanes towards their goal location. Our algorithm is inspired by the idea of building multiple-lane highways to mitigate traffi...

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Bibliographic Details
Published inCRV 2017 : proceedings 2017 14th Conference on Computer and Robot Vision : Edmonton, Alberta, Canada, 17-19 May 2017 pp. 161 - 167
Main Authors Abdelaal, Alaa Eldin, Sakr, Maram, Vaughan, Richard
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Interference
Multi-robot systems
Robot kinematics
Sociology
Statistics
Task analysis
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DOI10.1109/CRV.2017.24

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Summary:We propose a modification of a well-known ant-inspired trail-following algorithm to reduce congestion in multi-robot systems. Our method results in robots moving in multiple lanes towards their goal location. Our algorithm is inspired by the idea of building multiple-lane highways to mitigate traffic congestion in traffic engineering. We consider the resource transportation task where autonomous robots repeatedly transport goods between a food source and a nest in an initially unknown environment. To evaluate our algorithm, we perform simulation experiments in several environments with and without obstacles. Compared with the baseline SO-LOST algorithm, we find that our modified method increases the system throughput by up to 3.9 times by supporting a larger productive robot population.
DOI:10.1109/CRV.2017.24