Emergent intelligence of buckling-driven elasto-active structures

Abstract

Active systems of self-propelled agents, e.g., birds, fish, and bacteria, can organize their collective motion into myriad autonomous behaviors. Ubiquitous in nature and across length scales, such phenomena are also amenable to artificial settings, e.g., where brainless self-propelled robots orchestrate their movements into spatio-temportal patterns via the application of external cues or when confined within flexible boundaries. Very much like their natural counterparts, these approaches typically require many units to initiate collective motion such that controlling the ensuing dynamics is challenging. Here, we demonstrate a novel yet simple mechanism that leverages nonlinear elasticity to tame near-diffusive motile particles in forming structures capable of directed motion and other emergent intelligent behaviors. Our elasto-active system comprises two centimeter-sized self-propelled microbots connected with elastic beams. These microbots exert forces that suffice to buckle the beam and set the structure in motion. We first rationalize the physics of the interaction between the beam and the microbots. Then we use reduced order models to predict the interactions of our elasto-active structure with boundaries, e.g., walls and constrictions, and demonstrate how they can exhibit intelligent behaviors such as maze navigation. The findings are relevant to designing intelligent materials or soft robots capable of autonomous space exploration, adaptation, and interaction with the surrounding environment.