| dc.description.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. | en_US |
