Control of uniflagellar soft robots at low Reynolds number using buckling instability

TL;DR

This paper presents a control method for a bacteria-inspired uniflagellar robot at low Reynolds number, leveraging buckling instability of the flagellum to achieve complex 3D trajectories with a simple control input.

Contribution

It introduces a novel control strategy exploiting flagellum buckling for 3D path following in soft robots, supported by fluid-structure interaction modeling and neural network analysis.

Findings

Robot can follow arbitrary 3D paths by triggering flagellum buckling.

Deep neural network captures the input-output relationship of control and trajectory.

Buckling instability can be exploited for locomotion control in natural bacteria.

Abstract

In this paper, we analyze the inverse dynamics and control of a bacteria-inspired uniflagellar robot in a fluid medium at low Reynolds number. Inspired by the mechanism behind the locomotion of flagellated bacteria, we consider a robot comprised of a flagellum -- a flexible helical filament -- attached to a spherical head. The flagellum rotates about the head at a controlled angular velocity and generates a propulsive force that moves the robot forward. When the angular velocity exceeds a threshold value, the hydrodynamic force exerted by the fluid can cause the soft flagellum to buckle, characterized by a dramatic change in shape. In this computational study, a fluid-structure interaction model that combines Discrete Elastic Rods (DER) algorithm with Lighthill's Slender Body Theory (LSBT) is employed to simulate the locomotion and deformation of the robot. We demonstrate that the robot can follow a prescribed path in three dimensional space by exploiting buckling of the flagellum. The control scheme involves only a single (binary) scalar input -- the angular velocity of the flagellum. By triggering the buckling instability at the right moment, the robot can follow an arbitrary path in three dimensional space. We also show that the complexity of the dynamics of the helical filament can be captured using a deep neural network, from which we identify the input-output functional relationship between the control inputs and the trajectory of the robot. Furthermore, our study underscores the potential role of buckling in the locomotion of natural bacteria.