Simulation and time series analysis of responsive active Brownian particles (rABPs) with memory

Area of Science:

• Robotics
• Active Matter Physics
• Biomimicry

Background:

• Next-generation microrobots require autonomous environmental sensing and response capabilities.
• Memory effects in responsive systems are crucial for complex dynamics, mirroring natural strategies.
• Escherichia coli (E. coli) utilize integral feedback control for environmental sensing.

Purpose of the Study:

• To develop a numerical model for responsive active Brownian particles (rABPs) that mimic E. coli's sensing mechanisms.
• To analyze the dynamics and responsiveness of rABPs based on their interaction with the environment.
• To train recurrent neural networks (RNNs) for quantitative characterization of rABP behavior.

Main Methods:

• Developed a numerical model for responsive active Brownian particles (rABPs).
• Analyzed time series data (diffusion coefficients, velocity, position) from rABP dynamics.
• Identified conditional heteroscedasticity in the physics of rABPs.
• Trained recurrent neural networks (RNNs) on 2D trajectories to describe rABP responsiveness.

Main Results:

• Demonstrated that rABPs continuously react to environmental parameter changes.
• Characterized rABP response through analysis of dynamic diffusion coefficients, velocity, and position.
• Successfully trained RNNs to quantitatively describe the responsiveness of rABPs.
• Identified conditional heteroscedasticity in the physics of responsive active Brownian particles.

Conclusions:

• The proposed strategy enables quantitative description of rABP dynamics and responsiveness.
• This approach can guide the design of microrobots with built-in physical intelligence.
• Mimicking biological feedback mechanisms is a promising avenue for advanced microrobot design.