Dense Networks of Bacteria Propelled Micro-Robotic Swarms

Abstract

We develop a new computational framework and physical platform for modeling, analyzing, and designing dense networks of micro-robotic swarms. The physical platform is based on a bio-hybrid micro-robotic approach, where attached bacteria are used as on-board actuators. The bacteria propelled micro-robots are controlled through passive and active steering mechanisms, and we demonstrate through an experimental study that chemoattractant gradients are an effective means for passively steering the bacteria propelled micro-robots. A statistical physics model is developed to characterize the dynamic behavior of the dense network of the micro-robotic swarms. The swarms are modeled through a collection of interacting random walks, which obey volume exclusion rules. It is shown that the volume exclusion rules and the long- and short-range microscopic interactions between swarm agents have a significant impact on the overall swarm dynamics. Through the use of our statistical physics model and physical platform, we work towards developing a formal stochastic framework to accurately characterize micro-robotic swarm dynamics, with the future goal of using this new Cyber Physical System design in future medical diagnosis and targeted drug delivery applications.

Award ID: 1135850