M
Mohamed S. Talamali
Researcher at University of Sheffield
Publications - 6
Citations - 137
Mohamed S. Talamali is an academic researcher from University of Sheffield. The author has contributed to research in topics: Swarm behaviour & Swarm robotics. The author has an hindex of 4, co-authored 5 publications receiving 76 citations. Previous affiliations of Mohamed S. Talamali include University College London.
Papers
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Journal ArticleDOI
Sophisticated collective foraging with minimalist agents: a swarm robotics test
Mohamed S. Talamali,Thomas Bose,Matthew Haire,Xu Xu,James A. R. Marshall,Andreagiovanni Reina +5 more
TL;DR: This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour and constructs an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy.
Book ChapterDOI
Quality-Sensitive Foraging by a Robot Swarm Through Virtual Pheromone Trails
Anna Font Llenas,Anna Font Llenas,Mohamed S. Talamali,Xu Xu,James A. R. Marshall,Andreagiovanni Reina +5 more
TL;DR: This work designs and implements a robot swarm composed of up to 100 Kilobots and investigates how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides.
Book ChapterDOI
Simulating Kilobots within ARGoS: models and experimental validation
TL;DR: A plugin for the ARGoS simulator designed to simplify and accelerate experimentation with Kilobots, which supports cross-compiling against the real robot platform, removing the need to translate algorithms across different languages.
Journal ArticleDOI
When less is more: Robot swarms adapt better to changes with constrained communication
Mohamed S. Talamali,Mohamed S. Talamali,Arindam Saha,James A. R. Marshall,Andreagiovanni Reina,Andreagiovanni Reina +5 more
TL;DR: In this article, a swarm composed of robots relying on local sensing is shown to adapt to changes by processing the latest information and discarding outdated beliefs if the robots have a shorter rather than longer communication range.
Proceedings ArticleDOI
Improving collective decision accuracy via time-varying cross-inhibition
TL;DR: A decentralised algorithm is proposed, inspired by house-hunting honeybees, to efficiently aggregate noisy estimations and limits the spreading of errors within the population and allows swarms of simple noisy units with minimal communication capabilities to make highly accurate collective decisions in predictable time.