Roombots extended: Challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture

Hauser, Simon; Mutlu, Mehmet; Léziart, Pierre‐Alexandre; Khodr, Hala; Bernardino, Alexandre; Ijspeert, Auke Jan

doi:10.1016/j.robot.2020.103467

Cited by 46 publications

(21 citation statements)

References 64 publications

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“…Within the domain of rigid robots there have been a large variety of such modular robotic technologies. Recent examples of these include M-blocks [98], SMORES [20], Soldercubes [82], and Roombots [36]. Realizing soft modular or self-assembling robots is less common, largely due to the inherent difficulties in fabricating and controlling continuum bodies.…”

Section: Modular and Distributed Systemsmentioning

confidence: 99%

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

et al. 2020

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We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

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Section: Modular and Distributed Systemsmentioning

confidence: 99%

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

et al. 2020

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“…In the literature, several works propose relevant contributions about smart pieces of furniture. In particular, the selfconfigurable modular robots named "Roombots" can provide adaptive and assistive smart pieces of furniture [13]. The extended version of Roombots focused on the difficulties in real hardware, in aspects such as autonomously moving furniture, object manipulation, gripping features and easy-touse interfaces.…”

Section: Smart Furniturementioning

confidence: 99%

Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

2020

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Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.

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“…For example, one can program the robots so that, starting from an initial configuration without any holes, they can self-reconfigure into a line containing all the robots, without ever breaking the connectivity of the system [17,6]. However, it has been shown and proven [7,5] that the number of all possible configurations of modular robots increases drastically as the number of *This work has been supported by the EIPHI Graduate School (contract "ANR-17-EURE-0002") † All authors are with Univ. Bourgogne Franche-Comté, FEMTO-ST Institute, CNRS, 1 cours Leprince-Ringuet, 25200, Montbéliard, France.…”

Section: Introductionmentioning

confidence: 99%

Linear Distributed Clustering Algorithm for Modular Robots Based Programmable Matter

Bassil¹,

Moussa²,

Makhoul³

et al. 2020

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

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Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules which are able to coordinate in order to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay and improving the self-configuration processes that focuses on finding a sequence of reconfiguration actions to convert robots from an initial configuration to a goal one. The main idea is to divide the nodes in an initial shape into some clusters based on the final goal shape in order to reduce the time complexity and enhance the self-reconfiguration tasks. In this paper, we propose a robust clustering approach based on a distributed density-cut graph algorithm to divide the networks into a predefined number of clusters based on the final goal shape. The result is an algorithm with linear complexity that scales to large modular robot systems. We implement and demonstrate our algorithm on a real Blinky Blocks system and evaluate it in simulation on networks of up to 30,000 modules.

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Roombots extended: Challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture

Cited by 46 publications

References 64 publications

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

Linear Distributed Clustering Algorithm for Modular Robots Based Programmable Matter

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