Manuele Brambilla

TL;DR: This paper analyzes the literature from the point of view of swarm engineering and proposes two taxonomies: in the first taxonomy, works that deal with design and analysis methods are classified; in the second, works according to the collective behavior studied are classified.

TL;DR: It is shown how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy, and the efficiency and flexibility of the multi-robot simulator are assessed.

TL;DR: It is believed that swarm robotics designers must embrace heterogeneity if they ever want swarm robotics systems to approach the complexity required of real-world systems.

TL;DR: In this article, the authors introduce a novel event-based feature representation together with a new machine learning architecture, which uses local memory units to efficiently leverage past temporal information and build a robust eventbased representation.

TL;DR: Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time, and paves the way for a new approach to parallelism in robotics simulation.