24 research outputs found

    Applications of Biological Cell Models in Robotics

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    In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

    A Hierarchical Gene Regulatory Network for Adaptive Multirobot Pattern Formation

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    Towards a Boolean network-based Computational Model for Cell Differentiation and its applications to Robotics

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    Living organisms are the ultimate product of a series of complex processes that take place within—and among—biological cells. Most of these processes, such as cell differentiation, are currently poorly understood. Cell differentiation is the process by which cells progressively specialise. Being a fundamental process within cells, its dysregulations have dramatic implications in biological organisms ranging from developmental issues to cancer formation. The thesis objective is to contribute to the progress in the understanding of cell differentiation and explore the applications of its properties for designing artificial systems. The proposed approach, which relies on Boolean networks based modelling and on the theory of dynamical systems, aims at investigating the general mechanisms underlying cell differentiation. The results obtained contribute to taking a further step towards the formulation of a general theoretical framework—so far missing—for cellular differentiation. We conducted an in-depth analysis of the impact of self-loops in random Boolean networks ensembles. We proposed a new model of differentiation driven by a simplified bio-inspired methylation mechanism in Boolean models of genetic regulatory networks. On the artificial side, by introducing the conceptual metaphor of the “attractor landscape” and related proofs of concept that support its potential, we paved the way for a new research direction in robotics called behavioural differentiation robotics: a branch of robotics dealing with the designing of robots capable of expressing different behaviours in a way similar to that of biological cells that undergo differentiation. The implications of the results achieved may have beneficial effects on medical research. Indeed, the proposed approach can foster new questions, experiments and in turn, models that hopefully in the next future will take us to cure differentiation-related diseases such as cancer. Our work may also contribute to address questions concerning the evolution of complex behaviours and to help design robust and adaptive robots

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

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    Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

    Quantifying criticality, information dynamics and thermodynamics of collective motion

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    Active matter consists of self-propelled particles whose interactions give rise to coherent collective motion. Well-known examples include schools of fish, flocks of birds, swarms of insects and herds of ungulates. On the micro-scale, cells, enzymes and bacteria also move collectively as active matter, inspiring engineering of artificial materials and devices. These diverse systems exhibit similar collective behaviours, including gathering, alignment and quick propagation of perturbations, which emerge from relatively simple local interactions. This phenomenon is known as self-organisation and is observed in active matter as well as in many other complex collective phenomena, including urban agglomeration, financial crises, ecosystems dynamics and technological cascading failures. Some open challenges in the study of self-organisation include (a) how the information processing across the collective and over time gives rise to emergent behaviour, (b) how to identify the regimes in which different collective behaviours exist and their phase transitions, and (c) how to quantify the thermodynamics associated with these phenomena. This thesis aims to investigate these topics in the context of active matter, while building a rigorous theoretical framework. Specifically, this thesis provides three main contributions. Firstly, the question of how to formally measure information transfer across the collective is addressed and applied to a real system, i.e., a school of fish. Secondly, general relations between statistical mechanical and thermodynamical quantities are analytically derived and applied to a model of active matter, resulting in the formulation of the concept of “thermodynamic efficiency of computation during collective motion”. This concept is then extended to the domain of urban dynamics. Thirdly, this thesis provides a rigorous quantification of the non-equilibrium entropy production associated with the collective motion of active Brownian particles
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