Fabricating Physical and Robotic Systems to Investigate Collective Behavior

Abstract

The motion of particles has always been fascinating for scientists. We, as scientists, try to decode the mysteries of motion of massive objects like black holes, stars, and planets to objects nearly void of mass such as photons, electrons, and molecules. Such studies have developed our understanding of nature and unravelled the mysteries around it. The insights developed from these studies not only help us compute the end state but also predict the evolution of a system. Understanding motion helps us engineer different applications in various fields of science. One such mystery that remains unearthed is understanding the motion of active matter systems. Active matter refers to entities such as bacteria, cells, and artificial agents that consume energy from their environment to produce motion. An active matter system is composed of a group of entities which interact with each other and the environment to produce various collective motions. The output of an active matter system is said to be out of equilibrium as active matter constantly consumes energy from the environment. The complex class of such physical systems is still not fully understood. Studying active matter systems helps us uncover the principles governing the collective behaviour of living or non-living systems, which have broader implications in physics, biology, and engineering. Active matter system is studied using a combination of experimental and theoretical approaches. Researchers observe the motion of individual particles and their interactions with other particles in their vicinity. After the observations are made, a mathematical model is fitted to define the characteristics of motion of active matter particles. A computer simulation is often used to approve the theories crafted for such systems. Conventional methods of conducting active matter studies are tedious to set up, time-consuming to produce reliable data, inflexible, and non-scalable. In this thesis, we aim to provide another technique to validate theories and fabricate custom active matter systems. We suggest using physical robotic systems to simulate an active matter system. In this thesis, we break down the various characteristics that an active matter system showcases and sequentially embed those characteristics in our robotic system. We created robots that can be programmed to emulate different types of motion an active particle performs. We created a network through which the robots could effectively communicate with each other. The same network setup also helps to store the data collected by sensors mounted on robots during the runtime of an experiment. The sensors mounted on the robot make it interactive with its physical environment. We created algorithms where the robot can be influenced by its environment and move towards a physical source present in the environment. The major achievement of this thesis is producing a robotic system which can behave as an active matter system. The system is flexible and can be modified and programmed to behave as different active matter systems. After the system's initial setup, experiments can be repeated faster to generate more data. Experiments could be easily tweaked to understand the influence of various parameters on the system's output. Our system can be scaled up to perform experiments with a larger number of particles, the only limitations being imposed by the space for the experiment and the cost of fabricating multiple particles.iv We believe that robotics will emerge as an influential field of technology in the 21st century. Apart from its applications, robots can be utilized as a tool to investigate various natural phenomena, such as active matter systems. By analysing such systems, we could develop algorithms that could be used in other fields of science. Developing such methodologies would be pathbreaking in understanding the emergent properties we observe from systems composed of many interacting bodies.