Titolo della tesi: Adaptive Random Walks in Minimalist Robot Swarms
Swarm robotics, a field inspired by the many collective behaviour observed in natural systems,
explores how large groups of relatively simple robots can achieve complex tasks through local in-
teractions and environmental cues. This PhD dissertation, starting from theoretical random walks,
investigates the use of adaptive walks as a mechanism to facilitate emergent collective behaviours
in swarm robotics systems. The research focuses on three main research questions.
The first research question examines the impact of varying parameterisations of random walks
in swarm robotics systems. Through a large-scale analysis, we explore how diffusion properties,
search efficiency, and information sharing change as random walk parameters and swarm size are
modified. The findings provide valuable insights into the behaviour of random walks in different
experimental conditions, enabling informed decisions on random walk design and implementation.
Building upon the understanding gained from the first research question, the second research
question explores how random walks can be exploited for the emergence of collective behaviours.
By employing adaptive strategies based on random walks, we optimise team formation and task
execution in minimalist robots. These strategies enable the biasing of spatial distributions of robots,
allowing for improved team performance and the achievement of complex tasks. The results demon-
strate the effectiveness of adaptive random walks in promoting emergent collective behaviours and
highlight the potential for their application in various swarm robotics scenarios.
The third research question investigates the exploitation of adaptive random walks to generate
heterogeneous swarm robotics systems with desired collective behaviours. Through the implemen-
tation of cue-based and neighbour-based controllers, we examine the aggregation of minimalist
robot swarms. By leveraging environmental cues and the presence of neighbouring robots, adaptive
random walks generate aggregation behaviours, leading to the formation of desired spatial distribu-
tions and dense clusters. The study demonstrates the ability to achieve aggregation solely through
adaptive random walks and explores the impact of heterogeneity on swarm aggregation.
Overall, this PhD thesis contributes to the understanding and employment of adaptive random
walks in swarm robotics. The findings shed light on the behaviour of random walks in different
experimental conditions and provide guidance for their implementation in swarm robotics systems.
By harnessing the power of adaptive random walks, this research opens new avenues for achieving
emergent collective behaviours, optimising team formation, and promoting self-organised aggrega-
tion in swarm robotics. The insights gained from this thesis pave the way for future advancements
in the field, offering exciting possibilities for the development of intelligent and adaptive swarm