Centralised transformations of modular robots
A centralised algorithm must transform one connected shape of \(n\) modules on the square grid into another using local moves.
Problem statement and measuresexpand
\(n\) modules (unit squares, the particles of this model) occupy cells of the square grid and form a connected shape; modules in cells that share a side are neighbours, and the shape is connected when every module can be reached from every other through neighbours. A centralised algorithm must transform it into a given target shape with the same number of modules, using only the allowed local moves, keeping the shape connected where required. Moves counts sequential moves; time is parallel time when several moves may happen at once.
Measures: Moves, sequential moves; Time, parallel time.
Rotation and sliding
verifiedRotation and sliding are universal for connected shapes: \(\Theta(n^2)\) moves sequentially and \(O(n)\) parallel time, both optimal.
Assumed (added on the last edge are outlined)
Not assumed
Formal definitions of this setting
- Mechanism: rotation + sliding
- In addition, a module may slide one cell along a line of modules.
What is known
- Theorem 18: transforming a ladder into a line
- holds even without preserving connectivity
- Proposition 15 (arXiv version)
- connectivity is preserved
Results, in order of publication
Introductory and current best; connectivity preserved.
A stronger mechanism; a suspected \(\Omega(n\log n)\) lower bound is left open.
Open questions
Prove the suspected \(\Omega(n\log n)\) lower bound for universal transformations with line pushing Almethen et al. 2020.
Distributed universal transformations with line pushing Almethen et al. 2020.
Notes
With pivoting square modules (facet-connected), universal reconfiguration between any two configurations needs only five extra helper modules and \(O(n^2)\) pivot moves, worst-case optimal Akitaya et al. 2021. Checked against the full texts.
What carries over from neighbouring settings
Read off the order of assumptions, not checked case by case: results may carry extra assumptions (shown on their pages), and asynchronous time is measured in epochs.
Algorithms from weaker settings work here
Lower bounds from stronger settings hold here
No catalogued stronger setting.
Know a better result for this setting, or found a mistake? Suggest a result · Report an error. Accepted contributions are acknowledged.