programmable-matter

Bio-Inspired Energy Distribution for Programmable Matter
In this talk, we present our algorithm for energy distribution in programmable matter. We also detail our self-stabilizing extension of the amoebot model’s well-established spanning forest primitive. We conclude by showing how these algorithms generalize previous work to also consider energy constraints.
January 7, 2021 9:35 AM ICDCN 2021
Project Slides Video Paper
Bio-Inspired Energy Distribution for Programmable Matter
Bio-Inspired Energy Distribution for Programmable Matter
We extend the amoebot model to consider energy constraints and introduce an algorithm that distributes energy to all particles that need it to participate in the system’s collective behavior. This generalizes existing algorithms in the amoebot catalogue to also respect energy constraints.
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Bio-Inspired Energy Distribution for Programmable Matter
Convex Hull Formation for Programmable Matter
We give an algorithm under the amoebot model for forming the convex hull of an object, enclosing it with the minimum number of particles. Ours is the first distributed algorithm for convex hull formation restricted to local sensing, constant-size memory, and no shared orientation or coordinates.
Cite Project DOI arXiv
Convex Hull Formation for Programmable Matter
A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
In this talk, I present our stochastic algorithm for separation and integration in heterogeneous particle systems. I detail how our analysis techniques from compression break down in the heterogeneous setting and how we use new tools like the cluster expansion to address these obstacles.
September 21, 2019 9:00 AM RANDOM 2019
Project Slides Paper
A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
We extend the stochastic approach to heterogeneous self-organizing particle systems made up of particles of different color classes. We show that by biasing random particle movements based on the number of same-color neighbors, these systems can collectively separate or integrate.
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A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly
We bridge the gap between self-organizing particle systems and tile self-assembly systems by providing a simulation of the amoebot model within the tile automata model.
Cite Project DOI arXiv
Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly
Stochastic Algorithms for Programmable Matter
In this talk, I give an overview of the stochastic approach to self-organizing particle systems, including Markov chain design and analysis for the compression, shortcut bridging, and separation problems.
April 3, 2019 1:00 PM Discrete Math Seminar
Project Slides
Stochastic Algorithms for Programmable Matter
Computing by Programmable Particles
In this book chapter, we recap the first five years of work on the amoebot model and its variant, hybrid programmable matter. This includes distributed algorithms for shape formation and recognition, object coating, compression, shortcut bridging, separation, and some underlying primitives.
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Self-Organizing Particle Systems: an Algorithmic Approach to Programmable Matter
In this talk, I give a comprehensive overview of the amoebot model for programmable matter, dividing algorithms and applications into three main categories: deterministic/stateful algorithms, fully stochastic algorithms, and applications to swarm robotics.
November 4, 2018 4:00 PM WSSR 2018
Project Project Slides
Self-Organizing Particle Systems: an Algorithmic Approach to Programmable Matter
A Stochastic Approach to Shortcut Bridging in Programmable Matter
We extend the stochastic approach to self-organizing particle systems used in compression to shortcut bridging, in which particles self-assemble bridges over gaps that balance a tradeoff between bridge length and cost. This work is inspired by the bridging behavior of Eciton army ants, and demonstrates how local interactions can guide a system to globally optimal configurations.
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A Stochastic Approach to Shortcut Bridging in Programmable Matter