The statistical dynamics of programmed self-assembly

Napp, Nils; Burden, Samuel A.; Klavins, Eric

doi:10.1109/robot.2006.1641916

Cited by 40 publications

(46 citation statements)

References 11 publications

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“…There are three classes of modular robots: chain (1), lattice (2), and hybrid (3)(4)(5). The modules move relative to each other using relative module motion (3,5), module disconnection (6), or random motion (7,8). The theoretical investigations consider motion planning and design bounds (9)(10)(11), generic planners that can be instantiated to different robot bodies (12), and architecture-specific planners.…”

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confidence: 99%

Programmable matter by folding

Hawkes

Benbernou

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

583

474

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Programmable matter is a material whose properties can be programmed to achieve specific shapes or stiffnesses upon command. This concept requires constituent elements to interact and rearrange intelligently in order to meet the goal. This paper considers achieving programmable sheets that can form themselves in different shapes autonomously by folding. Past approaches to creating transforming machines have been limited by the small feature sizes, the large number of components, and the associated complexity of communication among the units. We seek to mitigate these difficulties through the unique concept of self-folding origami with universal crease patterns. This approach exploits a single sheet composed of interconnected triangular sections. The sheet is able to fold into a set of predetermined shapes using embedded actuation. To implement this self-folding origami concept, we have developed a scalable end-to-end planning and fabrication process. Given a set of desired objects, the system computes an optimized design for a single sheet and multiple controllers to achieve each of the desired objects. The material, called programmable matter by folding, is an example of a system capable of achieving multiple shapes for multiple functions.reconfigurable robotics | self-assembly | multifunctional materials | computational origami E very day, scientists and engineers design new devices to solve a current problem. Each device has a unique function and thus has a unique form. The geometry of a cup is designed to hold liquid and is therefore different from that of a knife which is meant to cut. Even if both are made of the same material (e.g., metal, ceramic, or plastic), neither can perform both tasks. Is this redundancy in material, yet limitation in tasks entirely necessary? Is it possible to create a programmable material that can reshape for multiple tasks?Programmable matter is a material whose properties can be programmed to achieve specific shapes or stiffnesses upon command. In this paper we consider the theory and design of programmable matter material that can assume multiple desired shapes on demand.We have developed a unique concept of self-folding origami with universal crease patterns that decreases the complexity in individual elements and is scalable in the number and size of elements. Instead of relying on many individual subunits, which may be complex and difficult to orient correctly, we utilize a single sheet with repeated triangular tiles connected by flexible creases. This sheet can fold with a certain crease pattern to create multiple three-dimensional shapes, depending on which creases fold, in which direction, and in which order. We build on a large body of prior work in self-reconfiguring robotics to realize machines with changing shapes. Self-reconfiguring robots are modular systems whose bodies consist of multiple modules that can communicate and move relative to each other to form different shapes. These shapes support the different locomotive, manipulative, or sensing needs of the robo...

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mentioning

confidence: 99%

Programmable matter by folding

Hawkes

Benbernou

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

583

474

View full text Add to dashboard Cite

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“…Often the categorization of a robot is ambiguous and there are other systems, like the Digital Clay project [8], that lack any innate actuation capability and rely on a user to rearrange the modules. There are many interesting systems which rely on stochastic selfassembly with rigid modules to create shapes [9]- [12]. More recent research [13] has investigated scaling the size of a selfassembled object based on the number of modules available.…”

Section: B Related Workmentioning

confidence: 99%

Making self-disassembling objects with multiple components in the Robot Pebbles system

Gilpin¹,

Koyanagi

Rus³

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-This paper describes several novel algorithms for shape formation by subtraction in programmable matter systems. These algorithms allow the simultaneous formation of multiple different shapes from a single block of host material. The resulting shapes are allowed to intertwine in arbitrarily complex ways. We also present a proof that the algorithms operate correctly to form the desired shapes. Finally, we show experimental results from close to 100 trials using both the Robot Pebbles hardware and a unique software simulator. Multiple trials of several different experiments demonstrate the algorithms operating correctly.

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“…This paper studies different aspects of developing Markovian probabilistic models for a class of self-assembling robotic systems where the constituting modules are randomly stirred in a confined arena and interact to assemble structures based on their embedded ruleset controllers. For a given desired target structure, the engineering goal in programmable stochastic self-assembling systems is to derive and program proper ruleset controllers on the modules such that the target structure emerges in a reliable and predictable fashion [5]. The environmental features may also be controlled to assist the assembly of the target structure [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Several works have addressed developing Markovian probabilistic models for stochastic self-assembling systems to date [1], [5], [6], [10], [11]. The choice of employing probabilistic modeling techniques for such systems is essentially motivated by the randomness lying at the core of these systems: random motion of the modules in the environment, explicit random decisions made by the modules' embedded controller, and random interactions among the modules [12].…”

Section: Introductionmentioning

confidence: 99%

“…We use a high-fidelity calibrated simulation of the system as the ground truth and address different aspects of developing probabilistic macrostate models for the system. The contributions of this work are along three axes (1) a new rate estimation method for computing Markov model parameters is introduced and compared with two existing ones [1], [5], (2) a new method for estimating diffusion coefficient and evaluating the well-mixed condition is studied and compared with an existing one [5], and (3) a systematic method in the literature for refining Markov models using Hidden Markov Model (HMM) formalism, presented in [6], is employed to develop an HMM through automatic refinement of an initial Markov model of the system.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic modeling of programmable stochastic self-assembly of robotic modules

Haghighat

Thandiackal

Mordig

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Creating accurate models of stochastically selfassembling systems is a key step in developing control strategies, centralized or distributed, for the self-assembly process. This paper comparatively studies several aspects of developing probabilistic models for programmable self-assembling systems of stochastically interacting modules. In particular, we systematically investigate Markov models as well as hidden Markov models to predict the self-assembly process dynamics. We consider a case study leveraging our fluidic self-assembly robotic system. The ground truth is obtained through a high-fidelity simulation, calibrated using real experimental data. We first consider Markov models and employ the formalism of chemical reaction networks. In order to compute the model parameters, i.e. the reaction rates in the network, three different methods are studied. We then investigate the validity of the underlying well-mixed assumption, and thus the Markov property, for our system through estimation of the diffusion coefficient, through two different approaches. The system is shown to be borderline well-mixed, motivating extension of the initial Markov models to more complex models in order to achieve improved model accuracy. We formulate an automatic method for creating a hidden Markov model starting from a Markov model, based on a previously existing systematic method. Sample trajectories of the models are realized using the Gillespie's method. The resulting hidden Markov model is shown to achieve an improved accuracy over the standard Markov model.

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The statistical dynamics of programmed self-assembly

Cited by 40 publications

References 11 publications

Programmable matter by folding

Programmable matter by folding

Making self-disassembling objects with multiple components in the Robot Pebbles system

Probabilistic modeling of programmable stochastic self-assembly of robotic modules

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