Random walks represent fundamental search strategies for both animal and robots, especially when there are no environmental cues that can drive motion, or when the cognitive abilities of the searching agent do not support complex localisation and mapping behaviours. In swarm robotics, random walks are basic building blocks for the individual behaviour and support the emergent collective pattern. However, there has been limited account for the correct parameterisation to be used in different search scenarios, and the relationship between search efficiency and information transfer within the swarm has been often overlooked. In this study, we analyse the efficiency of random walk patterns for a swarm of Kilobots searching a static target in two different environmental conditions entailing a bounded or an open space. We study the search efficiency and the ability to spread information within the swarm through numerical simulations and real robot experiments, and we determine what kind of random walk best fits each experimental scenario.
