Emergent intelligence of buckling-driven elasto-active structures

TL;DR

This paper introduces a simple mechanism using nonlinear elasticity in elasto-active microbot structures to achieve directed motion and emergent intelligent behaviors like maze navigation, with potential applications in soft robotics and autonomous exploration.

Contribution

It demonstrates how elastic buckling in microbot assemblies can induce complex behaviors, providing a new approach to control collective motion in active matter systems.

Findings

Elasto-active microbot structures can buckle and move autonomously.

Reduced models predict boundary interactions and behaviors.

Structures can navigate mazes demonstrating emergent intelligence.

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.