Transcriber: H Maria Castro Reviewer: Denise RQ
So you just heard my name is Nicole
and what I'd like is to hear all of your names.
So, on the count of 3, can you tell me your name?
Ready? 1, 2, 3!
(audience responding)
You guys are so compliant, that's awesome.
So, if I was trying to hear if there was another Nicole here,
how would I solve that problem?
I want to tell you how that problem
is an analogue for a problem that I'm trying to solve now
that took me to a bat cave in China
and will hopefully help us control teams of underwater vehicles one day.
I'm an engineer, and I study distributed systems.
Distributed systems are systems that are made up of many elements
that interact to accomplish a task together.
Here's an example.
If I have a problem, I can have one computer solve it,
or I can have a lot of computers solve it all together.
And if they work together,
presumably, they'll be able to solve the problem faster.
But the question becomes: how do I control their interactions?
How do they talk to each other?
I can have one guy be in charge and tell everyone what to do,
and then bring the answers together and tell them to me,
or I can make their interactions more complex.
And the cool thing is, if I design these interactions well,
the system can be resilient to the failure of one of the computers.
So if one guy dies, the job still gets done.
And this is really nice.
As an engineer, we call this robustness.
So systems like this are really robust to failure.
So we use distributed systems
to make solutions for a lot of different problems.
For example, wireless sensor networks that you can see here
would monitor an environment like a forest,
or teams of underwater vehicles
that would explore the ocean and build maps.
I study how to control systems like these.
But I don't do it from an entirely engineering or mathematical perspective.
I like to take inspiration from systems
that are already really good at doing this type of collaborative behavior.
Which are animal groups.
I'm sure you're familiar with fish schools and bird flocks,
they move in these really cool patterns.
And the interesting feature about them
is that there's not one guy in charge.
They just look at the guys next to them,
and they make these simple interactions based on this local information.
But you get this over-arching or global pattern that just emerges.
And among these groups,
there's a group that I say is actually even more singular, more unique.
And that group is bat swarms.
Bats are unique for a lot of reasons.
They're mammals, and they fly.
The uniqueness that I'm interested in is in their sensing.
Many species of bats use eco-location or sonar
to sense the world around them.
They make a sound, they listen for the echo,
and they build a map of their environment,
just based on the echoes and obstacles.
But when the bats live in a large group - which many species of bats do;
they'll live in a colony of a million members,
which is super-impressive -
when they use this type of sensing in a large group,
it becomes a much more complicated problem.
Here's an example of one bat eco-locating,
shifted down so you can hear it.
(Bat eco-locating sounds)
It's pretty straightforward.
Here's like 15 bats.
(Bats eco-locating sounds)
So it gets intense pretty quickly.
And if you look at this picture, there's well more than 15, right?
So, often in a colony of a million members, the question is,
how do you know the echo of your sound is the echo that you're hearing?
It's like the question of how do I know if I hear "Nicole"
in a group of a bunch of other names.
How do you solve that problem?
That problem is called jamming, and that's a problem that plagues bats,
or should plague bats,
and also any engineered system that uses sensing like bats,
like sonar for those underwater vehicles.
We call this type of sensing active sensing.
So it's sensing that relies on generating a signal,
it interacts with the world around, and it comes back to you.
So, sonar does this with sound, lidar does it with light,
radar does it with radio frequency.
This is in direct contrast with a passive sensing like vision,
where you take in a signal like light from the world around you.
So engineered systems with active sensing suffer from jamming,
so a lot of times the solutions, like a distributed system that uses a sensing,
you would just have everyone behave independently,
you wouldn't have any interaction.
But from what the bats do, and we see in the laboratory,
it seems like they're actually interacting.
So what we see is that when bats are being jammed,
they actually have these behaviors to accommodate the jamming.
So they'll change their frequency to better distinguish the sounds,
they'll just be quiet for a while.
What's really interesting is when they're flying together in pairs through a maze,
they seem to eco-locate less on average,
so that tell us they may actually be collaborating
to build those maps of the world around them.
That's something that could be really cool
to implement in an engineered system,
because it could save us energy on sensing,
and saving energy is huge, right?
So what I'm trying to do with my research group
is to understand what the bats do
to inspire control algorithms for groups of engineered systems
that would use this type of sensing.
Here's how we do it. We have a couple different perspectives.
First, we wondered what the bats do.
So we had to figure out a way to understand that,
because you can't talk to them, you can't ask them,
"Are you interacting?"
So we went to this bat cave in China, which is beautiful,
with a couple of thousand bats that live in it,
and we filmed them as they fly out using thermal or infrared light.
The question we wanted to answer was, "Are they even interacting?"
This is not necessarily known.
I wasn't quite sure that they were interacting,
but when we filmed them going through this obstacle course that we built,
we found that information flows from the guy in front to the guy behind,
or it seems to flow this way,
just by mathematically analyzing their trajectories.
So it says, "OK, it looks like they're interacting."
So now we can build bat-inspired rules
into mathematical models of bats that would move together.
And we can study these numerically.
What we found by studying these models is that bats,
when they interact together, are able to better avoid obstacles,
using interaction then behaving independently.
So it tells us that the question is well posed,
this is a good thing to try and do.
So now we're building robotic systems that have custom sonar sensors
that allow this type of interception or sensing signals
and we want to build some bat-inspired rules into their interaction
and be able to test hypotheses about engineered systems.
So what we really want to do is take inspiration from the bats
and then use it to inspire solutions in an engineered system
like teams of underwater vehicles.
When you have teams of underwater vehicles
to go collect climate data or ocean data,
they go out for a really long time,
so saving energy on sensing would be really huge
in terms of getting much larger data sets.
But the cool thing about taking inspiration
from a biological system, a bio-inspired system,
is that we actually get the opportunity
to learn something about the bats
when we study the engineering system or the mathematical model.
And this can allow us to interact both with the bats
and the engineered system at the same time.
For example, I don't know if you know,
but wind turbines kill bats disproportionately
as compared to other animals, and we're not really sure why.
And so if we can learn about how the bats interact with each other,
the opportunity is there to actually engineer systems
that are good for both the humans and the animals that inspire us as well.
Thank you.
(Applause)