Centralised Connectivity-Preserving Transformations by Rotation: 3 Musketeers for all Orthogonal Convex Shapes
Matthew Connor, Othon Michail

TL;DR
This paper presents a centralised method to transform orthogonal convex shapes on a grid via rotations, ensuring connectivity is preserved, with optimal move complexity, advancing understanding of programmable matter transformations.
Contribution
It introduces a generic centralised transformation for orthogonal convex shapes, using a minimal seed, and proves optimal move complexity, addressing a key open problem in shape transformation feasibility.
Findings
Transformations are possible between colour-consistent orthogonal convex shapes.
The method guarantees connectivity preservation throughout the transformation.
The process runs in optimal O(n^2) moves.
Abstract
We study a model of programmable matter systems consisting of devices lying on a 2-dimensional square grid, which are able to perform the minimal mechanical operation of rotating around each other. The goal is to transform an initial shape A into a target shape B. We are interested in characterising the class of shapes which can be transformed into each other in such a scenario, under the additional constraint of maintaining global connectivity at all times. This was one of the main problems left open by Michail et al., JCSS'19. Note that the considered question is about structural feasibility of transformations, which we exclusively deal with via centralised constructive proofs. Distributed solutions are left for future work and form an interesting research direction. Past work made some progress for the special class of nice shapes. We here consider the class of orthogonal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModular Robots and Swarm Intelligence · Advanced Materials and Mechanics · Micro and Nano Robotics
