Linear Distributed Clustering Algorithm for Modular Robots Based Programmable Matter

Bassil, Jad; Moussa, Mohamad; Makhoul, Abdallah; Piranda, Benoît; Bourgeois, Julien

doi:10.1109/iros45743.2020.9341032

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Our objective is to propose an efficient distributed clustering algorithm to partition the modules in the initial shape given the number of clusters and the size of each cluster according to the goal shape in order to enhance the self-reconfiguration process. A tree-based density-cut algorithm was proposed in [6] for the same purpose. However, it resulted in arbitrary sized clusters so, we aim to propose a new algorithm to control the number of modules in each cluster which is crucial for self-reconfiguration since a cluster of modules in the initial shape needs to reconfigure into a specific part of the goal shape requiring a fixed number of modules.…”

Section: Fig 2 Clustering Motivationmentioning

confidence: 99%

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Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

Bassil

Makhoul

Piranda

et al. 2023

ACM Trans. Auton. Adapt. Syst.

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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-reconfiguration process that focuses on finding a sequence of reconfiguration actions to convert robots from an initial shape to a goal one. The main idea of clustering is to divide the modules in an initial shape into a number of groups based on the final goal shape in order to enhance the self-reconfiguration process by allowing clusters to reconfigure in parallel. In this work, we prove that the size-constrained clustering problem is NP-complete and we propose a new tree-based size-constrained clustering algorithm called ”SC-Clust”. The idea is to divide a network into a predefined number of clusters constrained by a given number of modules in each cluster based on the final goal shape. The result is an efficient algorithm that scales to large modular robot systems. To show the efficiency of our approach, we implement and demonstrate our algorithm in simulation on networks of up to 30,000 modules and on the Blinky Blocks hardware with up to 144 modules.

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Section: Fig 2 Clustering Motivationmentioning

confidence: 99%

“…In [6] we proposed a fully distributed and adapted version of the DCut algorithm originally proposed by Shao et al…”

Section: Workmentioning

confidence: 99%

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

Bassil

Makhoul

Piranda

et al. 2023

ACM Trans. Auton. Adapt. Syst.

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“…In lattice H-MRSs. based on the distributed density-cut graph algorithm, Bassil et al 24 divided modules into different clusters, and the proposed self-reconfiguration strategy reduced the time complexity. In the design of self-reconfiguration strategies, it is also possible to consider current system configurations as nodes and deformation actions as edges.…”

Section: Introductionmentioning

confidence: 99%

A novel distributed meta-module motion design for modular robotic systems based on grid partition method

Guan,

Wang,

Zhang

et al. 2024

Science Progress

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This article studies a meta-module motion design approach for homogenous modular robotic systems in self-configuration. By utilizing configuration diversity, scalability and unit-substitutability, homogenous modular robotic systems can be a promising approach to life detection and space exploration in the future. Based on the requirements of the potential applications, self-configuration can be considered as the precondition. As similar to swarm robotic systems, the distributed control strategy in which the modular robots are operated in a sequence of motion circles consist of ‘detection’– ‘decision’– ‘execution’ is of great significance. However, there is a limitation to the applicability of previously proposed work on the self-configuration topic, due to the fact that the self-configuration strategy execution suffers from the motion constraints of modular robots. In order to solve the problem, we propose a grid partition method that removes the gap between the locomotion of a single modular robot and the reconfiguration of the whole system. Under the analysis of the grid partition, the meta-module motion design is proposed to realize the distributed self-configuration strategy. We simulated the self-configuration in M-Lattice, a two-dimensional homogenous modular robotic system.

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“…Saving energy in a modular robot can be made by two ways, the first one by reducing the number of movements when it changes its shape from an initial configuration to a final configuration [6]. The second one is to reduce the size of the packets/data transmitted in the system [7]. On the other hand, due to the connector fragility and the system dynamics, a packet may not be successfully transmitted to the destination and this can lead to several dysfunctions.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerance Technique Using Bidirectional Hetero-Associative Memory for Self-Reconfigurable Programmable Matter

Makhoul

Bassil

2023

2023 International Wireless Communications and Mobile Computing (IWCMC)

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Programmable Matter (PM) based on modular robots is a material which can be reprogrammed to have different shapes and to change its physical properties on demand. It can be deployed in several domains and has a variety of applications in construction, surgery, environmental science, space exploration, etc. PM is composed of a big number of limited resources connected robots called modules or particles to form its shape. These modules communicate with each other and move around each other dynamically in order to switch from one configuration to another. Due to the limited resources of modules and the high number of packets that transit within the system, it is very challenging to ensure packet delivery with high reliability. In this paper, we are using a Bidirectional Hetero-Associative Memory (BHAM) networks to improve the reliability and fault tolerance in PM. The idea is to let modules sending packets with smaller size without loosing any information. Furthermore, this model is also capable to remove noise from received packets. The proposed approach is tested on a real programmable matter blinky blocks platform as well as via simulations. We studied two versions of artificial neural networks based on storage capacity. The experimental results show that the studied approach is efficient in reducing the size of packets that transit in the system thus reducing energy consumption and it is capable to detect and remove noise and correct noisy packets.

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Linear Distributed Clustering Algorithm for Modular Robots Based Programmable Matter

Cited by 7 publications

References 14 publications

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

A novel distributed meta-module motion design for modular robotic systems based on grid partition method

Fault Tolerance Technique Using Bidirectional Hetero-Associative Memory for Self-Reconfigurable Programmable Matter

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