Grant: Discovery Grants Program - Individual

Advances in automation are fundamental in contributing to the improvement of the quality of life, by delegating to autonomous systems several high-risk tasks. Examples involve robotic assistants, maintenance and monitoring in toxic environments, and quality control of contaminated ecosystems. Additionally, robotic systems can be coupled with biological systems, to co-drive them in specific scenarios such as crowd dynamics and control, for example in evacuation situations caused by sudden hazards. In designing a class of autonomous systems dedicated to these operations, a defining fundamental characteristic is the capability of reliably negotiating an evolving environment, with high degrees of uncertainty with respect to nominal operating conditions. This poses a set of challenges in designing a locomotion system, since typical constraints in terms of mechanical efficiency are optimally fulfilled through high-specialization, whereas robustness and versatility are well addressed through low-specialization and trade-offs in terms of best averaged behavior across different environments. Robust locomotion features have been acquired through evolutionary processes by small scale biological structures through the development of hair-like structures such as cilia and flagella. Recent research revealed the fundamental nature and ubiquity of these structures, with the discovery of additional functions such as fluidic transport at the microscale, as well as more complex functions such as managing of symbiotic interactions in heterogeneous biosystems, and the regulation of processes in embryonic development. The high versatility and robustness of the coordinated dynamics of this class of systems make it attractive for applications that include locomotion robotics characterized by low-specialization and multi-function. It is therefore crucial to study the fundamental physics of the coupling that can induce the emergence of coordination and collective dynamics in the form of wave propagating phenomena that are suitable for locomotion, to inform the design of robust autonomous walkers with versatile characteristics.  The study of the nonlinear dynamics of emerging coordination phenomena in this class of systems of coupled oscillators extends to networks of autonomous robots, that is, from coordination for walking (individual) to group coordination in task-oriented missions that may be broadly defined, for example in terms of monitoring vulnerable environments and subsequent targeted interventions. Machine learning based on central pattern generator models has been applied to robotics to mimic the fundamental neural structures behind animal locomotion. The connection between deep learning and the underlying nonlinear dynamics of collective motions will be investigated to gain insights about the necessity of collective patterns emerging in nature, and about neural networks architectures suitable to reproduce them.