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Reconfiguration Algorithms for Cubic Modular Robots with Realistic Movement Constraints

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arxiv 2405.15724 v1 pith:NS3DQVEU submitted 2024-05-24 cs.CG cs.DMcs.RO

Reconfiguration Algorithms for Cubic Modular Robots with Realistic Movement Constraints

classification cs.CG cs.DMcs.RO
keywords modulesmodelrobotsfirstmodularpracticalsecondstructure
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce and analyze a model for self-reconfigurable robots made up of unit-cube modules. Compared to past models, our model aims to newly capture two important practical aspects of real-world robots. First, modules often do not occupy an exact unit cube, but rather have features like bumps extending outside the allotted space so that modules can interlock. Thus, for example, our model forbids modules from squeezing in between two other modules that are one unit distance apart. Second, our model captures the practical scenario of many passive modules assembled by a single robot, instead of requiring all modules to be able to move on their own. We prove two universality results. First, with a supply of auxiliary modules, we show that any connected polycube structure can be constructed by a carefully aligned plane sweep. Second, without additional modules, we show how to construct any structure for which a natural notion of external feature size is at least a constant; this property largely consolidates forbidden-pattern properties used in previous works on reconfigurable modular robots.

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  1. Decentralised self-organisation of pivoting cube ensembles using geometric deep learning

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    Decentralized reinforcement learning with local receptive fields can reconfigure pivoting cube ensembles into target shapes, achieving near-optimal move counts with multiple local message-passing rounds.