Self-Organizing Particle Systems

Project: Algorithmic Active Matter

We take a task-oriented approach to studying programmable active matter, wherein we translate some desired macroscopic behavior — such as clustering, flocking, exploration, or desegregation — into distributed, stochastic algorithms that can be run by simple robots. Our theoretical framework in which we model and develop algorithms for active matter is informed by the physical robotic systems we build. These simple robotic systems provide a testbed for realizing and testing our algorithms in more realistic settings. Ultimately, this project will provide a better end-to-end understanding of how microscopic rules can induce macroscopic behaviors, providing a more systematic approach to building and analyzing swarm robotic systems.

Publications

Refereed Conference Proceedings

  • [Appeared] Phototactic Supersmarticles. Sarah Cannon, Joshua J. Daymude, William Savoie, Ross Warkentin, Shengkai Li, Daniel I. Goldman, Dana Randall, and Andréa W. Richa. Appeared at the 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM ’17). 2017. [Video/Simulation]

People

Current Team

Andréa W. Richa

Andréa W. Richa

PI / Professor, Arizona State U.

[Website]
Joshua J. Daymude

Joshua J. Daymude

PhD Student, Arizona State U.

[Website]
Shengkai Li

Shengkai Li

PhD Student, Georgia Tech

[Website]
Dana Randall

Dana Randall

PI / Professor, Georgia Tech

[Website]
Sarah Cannon

Sarah Cannon

PhD Candidate, Georgia Tech

[Website]
Cem Gokmen

Cem Gokmen

Undergraduate Researcher, Georgia Tech

[Website]
Daniel I. Goldman

Daniel I. Goldman

PI / Professor, Georgia Tech

[Website]
William Savoie

William Savoie

PhD Student, Georgia Tech

[Website]

Past Members

Ross Warkentin

Ross Warkentin

Masters of Science, Georgia Tech

[Website]

Funding