Title:  Collective Behavior and Task Persistification in Lazy and Minimalist Collective

Committee:

Dr. Dana Randall, CoC, Chair , Advisor

Dr. Magnus Egerstedt, ECE, Co-Advisor

Dr. Seth Hutchinson, ECE

Dr. Ayanna Howard, ECE

Dr. Matthieu Bloch, ECE

Dr. Andrea Richa, Arizona State

Abstract: When individuals in a collective system are constrained in terms of sensing, memory, computation, or power reserves, the design of algorithms to control them becomes challenging. These individual limitations can be due to multiple reasons like the shrinking size of each agent for bulk manufacturing efficiency or enforced simplicity to attain cost efficiency.  Whereas, in some areas like nano-medicine, the nature of the task itself warrants such simplicity. This thesis presents algorithms inspired by biological and statistical physics models to achieve useful collective behavior through simple local physical interactions and, minimalist approaches to persistify tasks for long durations in collectives with limited capabilities and energy reserves. The first part of the thesis presents a system of vibration-driven robots that embodies the features of simplicity described above.   A combination of theory,  experiment,  and simulation is used to study dynamic aggregation behavior in these robots facilitated via short-range physical attraction potentials between agents.  Collectives in a dynamically aggregated state are shown to be capable of transporting objects over relatively long distances in a finite arena. In the rest of the thesis, two different, yet complementary systems are studied and elaborated to highlight the usefulness of distributed inactivity and activity modulation in aiding persistification of tasks in collectives incapable of implementing complicated algorithms to incorporate regular energy replenishing cycles.  To summarize, an approach to achieving dynamic aggregation and related tasks like object transport in a constrained brushbot system is described. Two different artificial and biological collective systems are explored to reveal strategies through which tasks can be persistified without requiring complicated computations, sensing, and memory.