@inproceedings{806, author = {Melvin Gauci and Radhika Nagpal and Michael Rubenstein}, title = {Programmable Self-Disassembly for Shape Formation in Large-Scale Robot Collectives}, abstract = {
We present a method for a large-scale robot collective to autonomously form a wide range of user-specified shapes. In contrast to most existing work, our method uses a subtractive approach rather than an additive one, and is the first such method to be demonstrated on robots that operate in continuous space. An initial dense, stationary configuration of robots distributively forms a coordinate system, and each robot decides if it is part of the desired shape. Non-shape robots then re- move themselves from the configuration using a single external light source as a motion guide. The subtractive approach allows for a higher degree of motion paral- lelism than additive approaches; it is also tolerant of much lower-precision motion. Experiments with 725 Kilobot robots allow us to compare our method against an additive one that was previously evaluated on the same platform. The subtractive method leads to higher reliability and an order-of-magnitude improvement in shape formation speed.\
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}, year = {2016}, journal = {13th International Symposium on Distributed Autonomous Robotic Systems (DARS)}, language = {eng}, }