{"id":993,"date":"2024-03-27T12:14:50","date_gmt":"2024-03-27T19:14:50","guid":{"rendered":"https:\/\/labs.engineering.asu.edu\/sops\/?page_id=993"},"modified":"2026-07-20T21:21:51","modified_gmt":"2026-07-21T04:21:51","slug":"emergence","status":"publish","type":"page","link":"https:\/\/labs.engineering.asu.edu\/sops\/emergence\/","title":{"rendered":"Emergence"},"content":{"rendered":"
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Emergence<\/h1>\n\n\n\n
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We utilize tools from statistical physics and Markov chain analysis to investigate how macro-scale behaviors in distributed systems of programmable matter can naturally emerge from local micro-behaviors by individual computational units. When this stochastic approach is combined with the amoebot model from the SOPS project<\/a>, we translate Markov chains defined at a system level into local, distributed algorithms for self-organizing particle systems that are inherently robust \u2014 tolerant of particle faults \u2014 and oblivious, using very little memory and communication when compared to the stateful SOPS algorithms.<\/p>\n\n\n\n

We can then translate distributed, stochastic algorithms for desirable macroscopic behaviors such as clustering, flocking, exploration, or desegregation into the physical mechanics of simple, analog robots. Ultimately, this project will provide a better end-to-end understanding of how microscopic rules can induce macroscopic behaviors, providing a more systematic approach to building and analyzing swarm robotic systems.<\/p>\n<\/div>\n\n\n\n

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Publications<\/h2>\n\n\n\n
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Refereed Journal Papers<\/h3>\n\n\n\n