Programming tunable active dynamics in a self-propelled robot

TL;DR

This paper introduces a self-propelled robot capable of exhibiting various active particle dynamics by tuning its control parameters, enabling studies of active matter physics and bio-inspired robotics.

Contribution

The work presents a novel method to produce tunable active dynamics in a robot by encoding active particle models into its microcontroller, allowing dynamic switching of motion behaviors.

Findings

Robot accurately reproduces active Brownian, run and tumble, and Brownian dynamics.

Motion trajectories match theoretical predictions with high precision.

External light intensity can switch the robot's motion between different dynamic states.

Abstract

We present a scheme for producing tunable active dynamics in a self-propelled robotic device. The robot moves using the differential drive mechanism where two wheels can vary their instantaneous velocities independently. These velocities are calculated by equating robot's equations of motion in two dimensions with well-established active particle models and encoded into the robot's microcontroller. We demonstrate that the robot can depict active Brownian, run and tumble, and Brownian dynamics with a wide range of parameters. The resulting motion analyzed using particle tracking shows excellent agreement with the theoretically predicted trajectories. Finally, we demonstrate that its motion can be switched between different dynamics using light intensity as an external parameter. This work opens an avenue for designing tunable active systems with the potential of revealing the physics of active matter and its application for bio- and nature-inspired robotics.