Computing by Programmable Particles

Daymude, Joshua J.; Hinnenthal, Kristian; Richa, Andréa W.; Scheideler, Christian

doi:10.1007/978-3-030-11072-7_22

Cited by 35 publications

(48 citation statements)

References 47 publications

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“…Here, researchers are less concerned with the kinematics of the computational units (referred to as particles), but are rather interested in the basic abilities of the particles in relation to their own state knowledge. The chief model that has been considered on this particular form of metamorphic systems, named Self-Organizing Particle Systems (SOPS), is the Amoebot model (Derakhshandeh et al, 2015b;Daymude et al, 2019), inspired by the biological behavior of Amoebae. In its most abstract version, the particles reside on a graph, that represents all the possible positions that a connected set of particle may assume.…”

Section: Theoretical Approachmentioning

confidence: 99%

A survey of autonomous self-reconfiguration methods for robot-based programmable matter

Thalamy

Piranda

Bourgeois

2019

Robotics and Autonomous Systems

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While researchers envision exciting applications for metamorphic systems like programmable matter, current solutions to the shape formation problem are still a long way from meeting their requirements. To dive deeper into this issue, we propose an extensive survey of the current state of the art of selfreconfiguration algorithms and underlying models in modular robotic and self-organizing particle systems. We identify three approaches for solving this problem and we compare the different solutions using a synoptic graphical representation. We then close this survey by confronting existing methods to our vision of programmable matter, and by discussing a number of future research directions that would bring us closer to making it a reality.

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Section: Theoretical Approachmentioning

confidence: 99%

A survey of autonomous self-reconfiguration methods for robot-based programmable matter

Thalamy

Piranda

Bourgeois

2019

Robotics and Autonomous Systems

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“…The amoebot model envisions programmable matter as a collection of individual, homogeneous computational elements called particles. In what follows, we extend the exposition of the model in [6] to the level of formality needed for our simulation.…”

Section: The Amoebot Modelmentioning

confidence: 99%

Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly

Alumbaugh

Daymude

Demaine³

et al. 2019

Lecture Notes in Computer Science

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Self-assembly refers to the process by which small, simple components mix and combine to form complex structures using only local interactions. Designed as a hybrid between tile assembly models and cellular automata, the Tile Automata (TA) model was recently introduced as a platform to help study connections between various models of self-assembly. However, in this paper we present a result in which we use TA to simulate arbitrary systems within the amoebot model, a theoretical model of programmable matter in which the individual components are relatively simple state machines that are able to sense the states of their neighbors and to move via series of expansions and contractions. We show that for every amoebot system, there is a TA system capable of simulating the local information transmission built into amoebot particles, and that the TA "macrotiles" used to simulate its particles are capable of simulating movement (via attachment and detachment operations) while maintaining the necessary properties of amoebot particle systems. The TA systems are able to utilize only the local interactions of state changes and binding and unbinding along tile edges, but are able to fully simulate the dynamics of these programmable matter systems.

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“…Among the multiple concrete realizations of programmable matter, the amoebot model, first proposed by Derakhshandeh et al [13] (also explained in detail by Daymude et al [11]), has gained much traction in recent years. In this model, each particle can move from one grid point of a triangular grid to a neighboring grid point in a way that resembles an amoeba.…”

Section: Introductionmentioning

confidence: 99%

“…Previous literature assumed that the algorithm ensures that the system is connected at all times, although (see [11]) the amoebot model does not require that the system remains connected. 1 Allowing the system another operation (i.e., to disconnect) obviously gives algorithm designers another potential tool.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deterministic Leader Election for Programmable Matter

Dufoulon

Kutten

Moses

2021

Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing

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It was suggested that a programmable matter system (composed of multiple computationally weak mobile particles) should remain connected at all times since otherwise, reconnection is difficult and may be impossible. At the same time, it was not clear that allowing the system to disconnect carried a significant advantage in terms of time complexity. We demonstrate for a fundamental task, that of leader election, an algorithm where the system disconnects and then reconnects automatically in a non-trivial way (particles can move far away from their former neighbors and later reconnect to others). Moreover, the runtime of the temporarily disconnecting deterministic leader election algorithm is linear in the diameter. Hence, the disconnecting -reconnecting algorithm is as fast as previous randomized algorithms. When comparing to previous deterministic algorithms, we note that some of the previous work assumed weaker schedulers. Still, the runtime of all the previous deterministic algorithms that did not assume special shapes of the particle system (shapes with no holes) was at least quadratic in , where is the number of particles in the system. (Moreover, the new algorithm is even faster in some parameters than the deterministic algorithms that did assume special initial shapes.)Since leader election is an important module in algorithms for various other tasks, the presented algorithm can be useful for speeding up other algorithms under the assumption of a strong scheduler. This leaves open the question: "can a deterministic algorithm be as fast as the randomized ones also under weaker schedulers?" CCS CONCEPTS• Theory of computation → Self-organization; Distributed algorithms; • Computing methodologies → Mobile agents.

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Computing by Programmable Particles

Cited by 35 publications

References 47 publications

A survey of autonomous self-reconfiguration methods for robot-based programmable matter

A survey of autonomous self-reconfiguration methods for robot-based programmable matter

Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly

Efficient Deterministic Leader Election for Programmable Matter

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