2 research outputs found
Active particles using reinforcement learning to navigate in complex motility landscapes
As the length scales of the smallest technology continue to advance beyond
the micron scale it becomes increasingly important to equip robotic components
with the means for intelligent and autonomous decision making with limited
information. With the help of a tabular Q-learning algorithm, we design a model
for training a microswimmer, to navigate quickly through an environment given
by various different scalar motility fields, while receiving a limited amount
of local information. We compare the performances of the microswimmer, defined
via time of first passage to a target, with performances of suitable reference
cases. We show that the strategy obtained with our reinforcement learning model
indeed represents an efficient navigation strategy, that outperforms the
reference cases. By confronting the swimmer with a variety of unfamiliar
environments after the finalised training, we show that the obtained strategy
generalises to different classes of random fields
Network topology of interlocked chiral particles
Self-assembly of chiral particles with an L-shape is explored by Monte-Carlo
computer simulations in two spatial dimensions. For sufficiently high packing
densities in confinement, a carpet-like texture emerges due to the interlocking
of L-shaped particles, resembling a distorted smectic liquid crystalline layer
pattern. From the positions of either of the two axes of the particles, two
different types of layers can be extracted, which form distinct but
complementary entangled networks. These coarse-grained network structures are
then analyzed from a topological point of view. We propose a global charge
conservation law by using an analogy to uniaxial smectics and show that the
individual network topology can be steered by both confinement and particle
geometry. Our topological analysis provides a general classification framework
for applications to other intertwined dual networks.Comment: 11 pages, 11 figure