Programming tunable active dynamics in a self-propelled robot

Area of Science:

• Robotics
• Active Matter Physics
• Biophysics

Background:

• Active matter systems exhibit complex dynamics inspired by biological organisms.
• Controlling the motion of artificial active matter is crucial for understanding fundamental physics and developing novel applications.
• Robotic devices offer a platform to experimentally investigate active matter models.

Purpose of the Study:

• To design and implement a self-propelled robotic device with tunable active dynamics.
• To demonstrate the robot's ability to replicate various active particle models.
• To explore light-controlled navigation and obstacle avoidance using stochastic reorientation.

Main Methods:

• Utilizing a differential drive mechanism for independent wheel velocity control.
• Calculating robot velocities by equating 2D equations of motion with active particle models.
• Encoding control algorithms into the robot's microcontroller.
• Analyzing robot trajectories using particle tracking and comparing with theoretical predictions.

Main Results:

• The robot successfully depicted active Brownian, run and tumble, and Brownian dynamics across a range of parameters.
• Experimental trajectories showed excellent agreement with theoretically predicted motion.
• Robot dynamics were switched between different models using light intensity as an external control parameter.
• The robot demonstrated efficient obstacle navigation via light-gradient-driven stochastic reorientation.

Conclusions:

• A tunable active robotic system was successfully developed, capable of mimicking diverse active matter behaviors.
• Light intensity serves as an effective external parameter for controlling robot dynamics and enabling navigation.
• This work provides a platform for studying active matter physics and developing bio- and nature-inspired robotic systems.