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

Thalamy, Pierre; Piranda, Benoît; Bourgeois, Julien

doi:10.1016/j.robot.2019.07.012

Cited by 39 publications

(20 citation statements)

References 52 publications

(127 reference statements)

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“…The most common solution known is to use a huge number of small robots comparable to the cells that compose our body. This comparison gave its name to this solution: Amoebots [13,21,32]. Modular robotics are robots designed with parts that can be reconfigured to accept different functions [6].…”

Section: Modular Robotics and Programmable Mattermentioning

confidence: 99%

Prolisean

Hourany

Habib

Fountaine

et al. 2021

ACM Trans. Internet Technol.

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The vision for programmable matter is to create a material that can be reprogrammed to have different shapes and to change its physical properties on demand. They are autonomous systems composed of a huge number of independent connected elements called particles. The connections to one another form the overall shape of the system. These particles are capable of interacting with each other and take decisions based on their environment. Beyond sensing, processing, and communication capabilities, programmable matter includes actuation and motion capabilities. It could be deployed in different domains and will constitute an intelligent component of the IoT. A lot of applications can derive from this technology, such as medical or industrial applications. However, just like any other technology, security is a huge concern. Given its distributed architecture and its processing limitations, programmable matter cannot handle the traditional security protocols and encryption algorithms. This article proposes a new security protocol optimized and dedicated for IoT programmable matter. This protocol is based on lightweight cryptography and uses the same encryption protocol as a hashing function while keeping the distributed architecture in mind. The analysis and simulation results show the efficiency of the proposed method and that a supercomputer will need about 5.93 × 10 25 years to decrypt the message.

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Section: Modular Robotics and Programmable Mattermentioning

confidence: 99%

Prolisean

Hourany

Habib

Fountaine

et al. 2021

ACM Trans. Internet Technol.

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“…Hence, identifying bases of comparison for evaluating this work in regard to other assembly or self-reconfiguration solutions is arduous and the resulting findings might be inconclusive. As a matter of fact, this a much deeper problem in this line of work, as traditional (i.e., shape to shape) self-reconfiguration works are already afflicted by this evaluation conundrum, due to the variance in robotic models, capabilities, and modes of motion (Thalamy et al, 2019b;Ahmadzadeh et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Deterministic scaffold assembly by self-reconfiguring micro-robotic swarms

Thalamy

Piranda

Lassabe

et al. 2020

Swarm and Evolutionary Computation

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The self-reconfiguration of large swarms of modular robotic units from one object into another is an intricate problem whose critical parameter that must be optimized is the time required to perform a transformation. Various optimizations methods have been proposed to accelerate transformations, as well as techniques to engineer the shape itself, such as scaffolding which creates an internal object structure filled with holes for easing the motion of modules. In this paper, we propose a novel deterministic and distributed method for rapidly constructing the scaffold of an object from an organized reserve of modules placed underneath the reconfiguration scene. This innovative scaffold design is parameterizable and has a face-centered-cubic lattice structure made from our rotating-only micro-modules. Our method operates at two levels of planning, scheduling the construction of components of the scaffold to avoid deadlocks at one level, and handling the navigation of modules and their coordination to avoid collisions in the other. We provide an analysis of the method and perform simulations on shapes with an increasing level of intricacy to show that our method has a reconfiguration time complexity of O(3 √ N) time steps for a subclass of convex shapes, with N the number of modules in the shape. We then proceed to explain how our solution can be further extended to any shape.

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“…While self-assembly is usually unconcerned with the actual dispatch of the modular robotic modules to their respective destination inside the shape, self-reconfiguration consider this extra step, and aims to transform an initial configuration of such interconnected modules into a goal configuration, through the motion of its components. This has been extensively studied in lattice-based modular robots [14] using both stochastic [21,5] and deterministic approaches [11,7], and was shown to be an NP-complete problem in [20].…”

Section: Related Workmentioning

confidence: 99%

3D Coating Self-Assembly for Modular Robotic Scaffolds

Thalamy¹,

Piranda²,

Bourgeois³

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper addresses the self-reconfiguration problem in large-scale modular robots for the purpose of shape formation for object representation. It aims to show that this process can be accelerated without compromising on the visual aspect of the final object, by creating an internal skeleton of the shape using the previously introduced sandboxing and scaffolding techniques, and then coating this skeleton with a layer of modules for higher visual fidelity.We discuss the challenges of the coating problem, introduce a basic method for constructing the coating of a scaffold layer by layer, and show that even with a straightforward algorithm, our scaffolding and coating combo uses much fewer modules than dense shapes and offers attractive reconfiguration times. Finally, we show that it could be a strong alternative to the construction of dense shapes using traditional selfreconfiguration algorithms.

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

Cited by 39 publications

References 52 publications

Prolisean

Prolisean

Deterministic scaffold assembly by self-reconfiguring micro-robotic swarms

3D Coating Self-Assembly for Modular Robotic Scaffolds

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