Multimodal motion and behavior switching of multistable ciliary walkers

TL;DR

This paper introduces millimeter-scale flexible cilia that enable soft robots to autonomously switch between different modes of motion through multistability, mimicking biological cilia and enabling complex behaviors without centralized control.

Contribution

The study presents a novel design of flexible cilia that couple multistability and actuation, allowing minimalist robots to exhibit multimodal motion and autonomous switching based on environmental feedback.

Findings

Cilia-based walkers can reverse direction upon obstacle encounter.

Swarm behavior transitions from spinning to translation with density increase.

Shape and placement of cilia control motion modes.

Abstract

The collective motion of arrays of cilia - tiny, hairlike protrusions - drives the locomotion of numerous microorganisms, enabling multimodal motion and autonomous switching between gaits to navigate complex environments. To endow minimalist centimeter-scale robots with similarly rich dynamics, we introduce millimeter-scale flexible cilia that buckle under the robots weight, coupling multistability and actuation within a single physical mechanism. When placed on a vibrating surface, these ciliary walkers select their propulsion direction through the buckled states of their cilia, allowing multimodal motion and switching between modes in response to perturbations. We first show that bimodal walkers with left-right symmetric cilia can autonomously reverse direction upon encountering obstacles. Next, we demonstrate that walkers with isotropic cilia exhibit both translational and rotational motion and switch between them in response to environmental interactions. At increasing densities, swarms of such walkers collectively transition from predominantly spinning to translational motion. Finally, we show that the shape, placement and number of cilia controls the modes of motion of the walkers. Our results establish a rational, physically grounded strategy for designing minimalist soft robots where complex behaviors emerge from feedback between internal mechanical states and environmental interactions, laying the foundation for autonomous robotic collectives without the need for centralized control.