Tech Briefs Magazine - April 2024 - MD-23

chip design or pipe routing in large
buildings.
" We devised a new neural network architecture
that is actually suitable for
real-time operations at the scale and
complexity of these warehouses. It can
encode hundreds of robots in terms of
their trajectories, origins, destinations,
and relationships with other robots, and
it can do this in an efficient manner that
reuses computation across groups of robots, "
said Cathy Wu, the Gilbert W.
Winslow Career Development Assistant
Professor in Civil and Environmental
Engineering (CEE), and a member of a
member of the Laboratory for
Information and Decision Systems
(LIDS) and the Institute for Data,
Systems, and Society (IDSS).
From a bird's eye view, the floor of a
robotic e-commerce warehouse looks a
bit like a fast-paced game of " Tetris. "
When a customer order comes in, a
robot travels to an area of the warehouse,
grabs the shelf that holds the requested
item, and delivers it to a human
operator who picks and packs the item.
Hundreds of robots do this simultaneously,
and if two robots' paths conflict as
they cross the massive warehouse, they
might crash.
Traditional search-based algorithms
avoid potential crashes by keeping one
robot on its course and replanning a
trajectory for the other. But with so
many robots and potential collisions, the
problem quickly grows exponentially.
" Because the warehouse is operating
online, the robots are replanned about
every 100 milliseconds. That means that
every second, a robot is replanned 10
times. So, these operations need to be
very fast, " Wu said.
Because time is so critical during replanning,
the MIT researchers use machine
learning to focus the replanning
on the most actionable areas of congestion
- where there exists the most potential
to reduce the total travel time of
robots.
Wu and Yan built a neural network
architecture that considers smaller
groups of robots at the same time. For
instance, in a warehouse with 800 robots,
the network might cut the warehouse
floor into smaller groups that
contain 40 robots each.
Then, it predicts which group has the
most potential to improve the overall
solution if a search-based solver were
Motion Design, April 2024
23
used to coordinate trajectories of robots
in that group.
An iterative process, the overall algorithm
picks the most promising robot
group with the neural network, decongests
the group with the search-based
solver, then picks the next most promising
group with the neural network, and
so on.
The neural network can reason about
groups of robots efficiently because it
captures complicated relationships that
exist between individual robots. For example,
even though one robot may be
far away from another initially, their
paths could still cross during their trips.
The technique also streamlines computation
by encoding constraints only once,
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Tech Briefs Magazine - April 2024

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