Robotic swarms, inspired by the behavior of social insects like ants and bees, are groups of robots that work together to perform tasks more efficiently and robustly than a single robot. Swarms of robots can collaborate by autonomously coordinating their actions and sharing resources, can increase efficiency to reduce operational costs, or can monitor and manage traffic flow in real-time, reducing congestion and improving urban mobility. These applications demonstrate the versatility and potential of robotic swarms across various fields, leveraging their collective intelligence, adaptability, and efficiency to solve complex problems.
This talk will explore the interplay between theoretical models and experimental validations of interacting dynamical agents that, on the one hand, can contribute to the understanding of complex social
phenomena and, on the other hand, can be exploited for the control of robotic swarms.Theoretical frameworks, including coupled oscillator models and graph-based synchronization theory, provide insights into the conditions and mechanisms necessary for achieving synchronized states
and cooperative tasks among micro robots. These models predict critical parameters such as coupling strength, communication topology, and phase lag, which are essential for robust coordination. Experimental investigations, on the other hand, offer empirical data and real-world
validation of these theoretical predictions. By deploying micro robot teams in controlled environments, we assess the practical challenges and deviations from idealized models, such as noise, communication problem, and hardware constraints. Our findings highlight the strengths and limitations of current theories, emphasizing the need for iterative refinement through continuous feedback. This synergy between theory and experiments advances our understanding of synchronization and cooperative behavior in micro robot teams, paving the way for more efficient and resilient practical applications.