Selective Self-Assembly using Re-Programmable Magnetic Pixels
Martin Nisser, Yashaswini Makaram, Faraz Faruqi, Ryo Suzuki, Stefanie Mueller
Abstract
This paper introduces a method to generate highly selective encodings that can be magnetically “programmed” onto physical modules to enable them to self-assemble in chosen configurations. We generate these encodings based on Hadamard matrices, and show how to design the faces of modules to be maximally attractive to their intended mate, while remaining maximally agnostic to other faces. We derive guarantees on these bounds, and verify their attraction and agnosticism experimentally. Using cubic modules whose faces have been covered in soft magnetic material, we show how inexpensive, passive modules with planar faces can be used to selectively self-assemble into target shapes without geometric guides. We show that these modules can be easily re-programmed for new target shapes using a CNC-based magnetic plotter, and demonstrate self-assembly of 8 cubes in a water tank.
BibTeX
@inproceedings{iros2022_selectiveselfass,
title = {Selective Self-Assembly using Re-Programmable Magnetic Pixels},
author = {Martin Nisser and Yashaswini Makaram and Faraz Faruqi and Ryo Suzuki and Stefanie Mueller},
booktitle = {IROS 2022},
year = {2022}
}