Cluster-Based Distributed Self-reconfiguration Algorithm for Modular Robots

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

doi:10.1007/978-3-030-75100-5_29

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…We considered an initial chain shape of the modular robot to be reconfigured into the letter 'S' like shape. The selfreconfiguration algorithm proposed in [6] is used in this simulation. As a matter of fact, the idea is having modules moving from one position to another by disconnecting from the module's interface and joining another one having a free spot ready to fit in.…”

Section: Resultsmentioning

confidence: 99%

A Dynamic ID Assignment Approach for Modular Robots

Assaker

Makhoul²,

Bourgeois³

et al. 2022

Advanced Information Networking and Applications

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In this paper, we present a distributed Id assignment algorithm for modular robots. Our proposed solution supports both the removal and the addition of particles in the system, while maintaining particular characteristics in the logical tree, allowing for fast and efficient inter-module communications. The key goal here is to maintain easily calculated routes between any two particles in the system, with the minimal overhead possible. The idea of "holes" or free IDs is introduced in the system by three main alterations to our previous unique id assignment algorithm [2]. The first being the modification of the unique ID assignment phase. The second being the handling of particles removal from the system. And the third being the handling and initiation of a newly added particle anywhere in the system.

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Section: Resultsmentioning

confidence: 99%

A Dynamic ID Assignment Approach for Modular Robots

Assaker

Makhoul²,

Bourgeois³

et al. 2022

Advanced Information Networking and Applications

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“…Modular robots with lattice architecture consist of modules or submodules with a regular geometric shape, such as square [19][20][21][22] or circular [23][24][25] modules in 2D or cubic [20,[26][27][28][29] or (quasi-)spherical [30][31][32][33][34] modules in 3D, respectively. The uniform module geometry and placement of connectors enable these systems to be organized in a regular grid where each module location corresponds to a discrete position in the lattice.…”

Section: Related Workmentioning

confidence: 99%

PARTS—A 2D Self-Reconfigurable Programmable Mechanical Structure

Gerbl,

Pieber,

Ulrich

et al. 2024

Robotics

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Modular self-reconfigurable robots hold the promise of being capable of performing a wide variety of tasks. However, many systems fall short of either delivering this promised functionality due to constraints in system architecture or validating it on functional hardware prototypes. This paper demonstrates the functional capabilities of the Planar Adaptive Robot with Triangular Structure (PARTS) and documents the versatility of this robot system using a holistic approach that combines simulations and hardware demonstrations on a prototype with nine fabricated modules. PARTS is a two-dimensional modular robot consisting of modules with a shape-shifting triangular geometry capable of forming adaptable space-covering structures. Meta-modules and mesh restructuring techniques are presented as methods for achieving topological self-reconfiguration. The feasibility of these methods is demonstrated by applying them on a simulated reconfiguration example of 62 modules. The paper showcases the versatility of PARTS on the hardware prototype using task-specific configurations, including locomotion using a meta-module and a walker configuration, module-module interaction by establishing a bridge between two separated module clusters, and interaction with the environment using a gripper and supporting structure configuration. The results validate the versatility and emphasize the potential of the system’s design concept, motivating the transfer of the hardware architecture to the third dimension.

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“…The formed clusters can then reconfigure in parallel to form their corresponding part of the goal shape thus, reducing the time and communication required for transforming the configuration from the initial shape to the goal one. For instance, in [40], the authors proposed a Manuscript submitted to ACM cluster-based self-reconfiguration algorithm where clusters of modules in the initial shape reconfigure in parallel to form the goal shape. To show the advantage that clustering can yield to self-reconfiguration, they compared the execution time and communication load while varying the number of clusters.…”

Section: Fig 2 Clustering Motivationmentioning

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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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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Cluster-Based Distributed Self-reconfiguration Algorithm for Modular Robots

Cited by 7 publications

References 12 publications

A Dynamic ID Assignment Approach for Modular Robots

A Dynamic ID Assignment Approach for Modular Robots

PARTS—A 2D Self-Reconfigurable Programmable Mechanical Structure

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

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