An Emergent System for Self-Aligning and Self-Organizing Shape Primitives

Bai, Linge; Eyiyurekli, Manolya; Breen, David E.

doi:10.1109/saso.2008.54

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…(ii) Different types of Turing patterns on fish skin. gradients (21)(22)(23)(24)(25)(26)(27)(28)(29) or have used more complicated interactions between signals, such as RD systems (30,31), gene regulatory networks (GRNs) (32)(33)(34)(35), and swarm chemistry (36) [for a more extensive review, see (37)].…”

Section: Introductionmentioning

confidence: 99%

Morphogenesis in robot swarms

et al. 2018

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Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.

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Section: Introductionmentioning

confidence: 99%

Morphogenesis in robot swarms

et al. 2018

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“…For instance, in computational systems biology, a gene regulatory network model for generating chemotaxis was proposed [21]. In the robotics and systems fields, various applications of chemotaxis controllers have been reported, e.g., source seeking [11]- [18], target search and trapping [22], coverage [23], [24], pattern formation [25], [26], aggregation [27], and sorting [28], [29]. Among these studies, [23]- [27] addressed tasks similar to those in this study.…”

Section: B Related Workmentioning

confidence: 87%

“…However, because they assumed that each robot could receive the concentration signal of the substance to move toward its desired position, their methods are not applicable to our coverage problem, in which such a concentration signal is not available. In [25], [26], methods for pattern formation were developed by designing a function describing a virtual chemical concentration field via genetic programming. However, the complexity of these methods increases with the number of robots because genetic programming requires many simulations of the entire system.…”

Section: B Related Workmentioning

confidence: 99%

Multi-Robot Control Inspired by Bacterial Chemotaxis: Coverage and Rendezvous via Networking of Chemotaxis Controllers

2020

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This paper presents networked controllers for the coordination of multi-robot systems, inspired by the chemotaxis of bacteria. Chemotaxis is a biological phenomenon wherein each organism senses the concentration of a chemical in its environment and moves to the highest (or lowest) concentration point. The problem studied herein is a coverage problem, specifically, the problem of finding networked controllers to deploy robots so that they are located uniformly on a given space. To solve this problem, we decompose a global performance index quantifying the achieved degree of coverage into local indices that can be calculated in a distributed manner over the network of robots. By combining this with a controller causing chemotaxis, we present a solution to the coverage problem wherein each robot performs either a forward movement or random rotation based on the local performance index at each time step. Moreover, we extend this solution to rendezvous at an unspecified point. Simulation and experimental results demonstrate that our solution achieves coverage and rendezvous only via the above two types of robot movements and can handle different tasks simply by changing the performance index, through the appropriate use of the chemotaxis controller. INDEX TERMS Chemotaxis, distributed control, Escherichia coli, multi-robot systems.

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“…To locate their target, macrophages use chemotaxis [34], i.e., they move along a gradient of chemicals in their environment. Our model can be extended to include chemotaxis, e.g., as in [5]. The macrophage problem models the behavior of macrophages in the following way.…”

Section: Research Challengesmentioning

confidence: 99%

Ameba-inspired Self-organizing Particle Systems

Dolev,

Gmyr,

Richa

et al. 2013

Preprint

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Particle systems are physical systems of simple computational particles that can bond to neighboring particles and use these bonds to move from one spot to another (non-occupied) spot. These particle systems are supposed to be able to self-organize in order to adapt to a desired shape without any central control. Self-organizing particle systems have many interesting applications like coating objects for monitoring and repair purposes and the formation of nano-scale devices for surgery and molecular-scale electronic structures. While there has been quite a lot of systems work in this area, especially in the context of modular self-reconfigurable robotic systems, only very little theoretical work has been done in this area so far. We attempt to bridge this gap by proposing a model inspired by the behavior of ameba that allows rigorous algorithmic research on self-organizing particle systems.

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An Emergent System for Self-Aligning and Self-Organizing Shape Primitives

Cited by 12 publications

References 18 publications

Morphogenesis in robot swarms

Morphogenesis in robot swarms

Multi-Robot Control Inspired by Bacterial Chemotaxis: Coverage and Rendezvous via Networking of Chemotaxis Controllers

Ameba-inspired Self-organizing Particle Systems

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