Collective event detection using bio-inspired minimalistic communication in a swarm of underwater robots

Joshua Cherian Varughese, Hannes Hornischer, Ronald Thenius*, Franz Wotawa, Thomas Schmickl

*Corresponding author for this work

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


Mobile sensor networks and robotic swarms are being used for monitoring and exploring environments or environmental events due to the advantages offered by their distributed nature. However, coordination and self-organization of a large number of individuals is often costly in terms of energy and computation power, thus limiting the longevity of the distributed system. In this paper we present a bio-inspired algorithm enabling a robotic swarm to collectively detect anomalies in environmental parameters in a self-organized, reliable and energy efficient manner. Individuals in the swarm communicate via 1-bit signals to collectively confirm the detection of an anomaly while minimizing energy spent for communication and taking measurements. This algorithm is specifically designed for a swarm of underwater robots called “aMussels” to examine a phenomenon referred to as “anoxia” which results in oxygen depletion in the lagoon of Venice. We present the algorithm, conduct simulations and robotic experiments to examine the performance of the algorithm with respect to early detection of anoxia while minimizing energy consumption.

Original languageEnglish
Title of host publicationALIFE 2019
Subtitle of host publicationProceedings of the Artificial Life Conference 2019
PublisherMIT Press
Number of pages8
Publication statusPublished - 1 Jan 2020
Event2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges - Newcastle upon Tyne, United Kingdom
Duration: 29 Jul 20192 Aug 2019


Conference2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges
Abbreviated titleALIFE 2019
Country/TerritoryUnited Kingdom
CityNewcastle upon Tyne

ASJC Scopus subject areas

  • Modelling and Simulation

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