Self-assembly for maximum yields under constraints

Michael J. Fox, Jeff S. Shamma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an algorithm that, given any target tree, synthesizes reversible self-assembly rules that provide a maximum yield in the sense of stochastic stability. If the reversibility constraint is relaxed then the same algorithm can be trivially modified so that it converges to a maximum yield almost surely. The proof of correctness in both cases relies on the notion of a completing rule. We examine the conservatism of this technique by considering its implications for the internal states of the system. We show by example that any algorithm that guarantees the existence of a completing rule for all target trees will, for some cases, (1) produce complete assemblies with non-unique internal states, or (2) produce internal states that cannot be recovered from the unlabeled graph.

Original languageEnglish (US)
Title of host publicationIROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Subtitle of host publicationCelebrating 50 Years of Robotics
Pages4770-4775
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11 - San Francisco, CA, United States
Duration: Sep 25 2011Sep 30 2011

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
Country/TerritoryUnited States
CitySan Francisco, CA
Period9/25/119/30/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this