Candidate: Justine Shaw
Date: July 31, 2024
Time: 2:00pm
Location: in-person EIT 3145 and online
Supervisor: Professor Gennaro Notomista
All are welcome!
Abstract:
In this work we present the design and density control of a swarm of bimodal particles that switch their geometric shape, and consequently their motion behavior, between two modes. The particles are designed and 3D printed using layers of thermoplastic polyurethane (TPU) and polylactic acid (PLA) materials. The switching between a closed mode classified as mode 1 and an open mode classified as mode 2 of the geometric shape is achieved thanks to the reaction of the particles to an external stimuli which, in the specific case we considered, consisted of a temperature change. To study the changes in geometric shape, various temperature-based hot and cold programming methods were conducted. To quantify the aggregation of the swarm and control the switching motion we utilize the metric of the Motility-Induced Phase Separation (MIPS) index. To control the particles using the MIPS index, we identify the motion model of the swarm as a stochastic system having different parameters for each mode. We experimentally validate the noise parameters that directly affect the motion of the particles thus affecting the MIPS index, and allowing us to control the swarm aggregation state. Simulations were conducted to characterize this switching behavior using the identified noise parameters. The results of closed-loop control simulations demonstrate how, by switching between the two identified particle motion modes, a desired swarm aggregation level can be achieved. This research highlights how the simplicity of hardware design of a single agent can achieve aggregations for swarms which enable various environmental sensing tasks to be achieved.