Tech Briefs Magazine - April 2024 - MD-22

make a flexible carpet-like sheet capable
of undulating. A helical structure
on the underside of the sheet rotates
like a corkscrew to cause the carpet to
undulate and create a travelling wave
on the water.
Professor Sunny Jung (far right) and study coauthors (from left), Jisoo Yuk, Chris Roh and Yicong Fu, watch a
demonstration of their snail-inspired robot. (Image: Jason Koski/Cornell University)
" We were inspired by how this snail
collects food particles at the [water
and air] interface to engineer a device
that could possibly collect microplastics
in the ocean or at a water body's
surface, " said Sunghwan " Sunny " Jung,
Professor and Director of Graduate
Studies in the Department of Biological
and Environmental Engineering in the
College of Agriculture and Life Sciences
(CALS). Jung is senior author of a study,
" Optimal free-surface pumping by an
undulating carpet, " which published in
Nature Communications.
The prototype, modified from an existing
design, would need to be scaled
up to be practical in a real-world setting.
The researchers used a 3D printer to
Analyzing the motion of the fluid
was key to this research. " We needed
to understand the fluid flow to characterize
the pumping behavior, " Jung
said. The fluid-pumping system based
on the snail's technique is open to
the air. The researchers calculated
that a similar closed system, where
the pump is enclosed and uses a tube
to suck in water and particles, would
require high energy inputs to operate.
On the other hand, the snail-like
open system is far more efficient. For
example, the prototype, though small,
runs on only 5 volts of electricity
while still effectively sucking in water,
Jung added.
Due to the weight of a battery and
motor, the researchers may need to attach
a floatation device to the robot to
keep it from sinking, Jung said.
For more information, contact Becka
Bowyer at rpb224@cornell.edu; 607220-4185.
AI
Model to Streamline Operations in a Robotic
Warehouse
By breaking an intractable problem into smaller chunks, a new deep-learning technique identifies
the optimal areas for thinning out traffic in a warehouse.
Massachusetts Institute of Technology, Cambridge, MA
G
etting warehouse robots to and
from their destinations efficiently
while keeping them from crashing
into each other is no easy task. It is
such a complex problem that even
the best path-finding algorithms
struggle to keep up with the breakneck
pace of e-commerce or manufacturing.
In a sense, these robots are
like cars trying to navigate a crowded
city center. So, a group of MIT researchers
who use AI to mitigate traffic
congestion applied ideas from that
domain to tackle this problem.
They built a deep-learning model
that encodes important information
about the warehouse, including the
22
robots, planned paths, tasks, and obstacles,
and uses it to predict the best
areas of the warehouse to decongest
to improve overall efficiency.
Their technique divides the warehouse
robots into groups, so these
smaller groups of robots can be decongested
faster with traditional algorithms
used to coordinate robots.
In the end, their method decongests
the robots nearly four times faster
than a strong random search
method.
A group of MIT researchers who use AI to mitigate traffic
congestion applied ideas from that domain to tackle the problem
of multiple robots in a warehouse setting. (Image: iStock)
In addition to streamlining warehouse
operations, this deep learning
approach could be used in other complex
planning tasks, like computer
Motion Design, April 2024

Tech Briefs Magazine - April 2024

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