
Robotic Airplane Flies In Tight Spaces  - gagan2020
http://mashable.com/2012/08/12/robotic-airplane/
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makmanalp
What's cool here other than the fact that it seems to react really quickly is
that it has no universal conception of location, like GPS.

My limited understanding of the topic is thus:

For localizing, they use a particle filter, which is basically a method that
helps you figure out latent variables (in this case location) based on
multiple observations.

Using the data, it creates a model of how the aircraft moves. In each "tick",
you make a prediction about where the aircraft will be in the future (let's
say, based on how fast you know the motor goes and which way the rudders are
tilted etc). Then, you actually compare it to the data you got from your
sensors (in this case a laser rangefinder) and update your model. Thus, your
model is better.

The more traditional formulation of this is the Kalman Filter, which is
everywhere in classical controls systems. I think the particle filter is just
simpler for large numbers of variables whereas for Kalman filters, complexity
increases exponentially.

edit:

Another way to look at it is that this is how robots deal with the "real
world" where sensors are noisy and slightly off, actuators are unreliable and
can't produce smooth and constant output etc. Instead of trying to guess all
these factors, it automagically accounts for these on the fly by looking at
how the robot behaves and how you expected it to behave.

edit2:

Corrections abound! Read replies below!

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jayzee
The difference is not really about the number of variables but the dynamics of
the plant...

The classical formulation of Kalman filters assumes gaussian distributions for
the variables. This makes the computations much faster since the posterior
distributions is a few matrix operations away. This works fine when the model
for the plant is linear (since a linear transformation of a gaussian variable
is gaussian). In situations where the plant model is not linear estimates can
go awry quickly.

In the case of particle filters you sample the prior distribution, simulate
the non-linear plant with these initial conditions and then construct the
posterior distribution by incorporating the measurements. This process is
computationally more expensive than classical Kalman filtering but is more
accurate for non-linear plants.

~~~
beambot
Obligatory link to "Probabilistic Robotics" -- generally considered the #1
reference on the subject, written by top roboticists (Thrun, Burgard, and
Fox): [http://www.amazon.com/Probabilistic-Robotics-Intelligent-
Aut...](http://www.amazon.com/Probabilistic-Robotics-Intelligent-Autonomous-
Agents/dp/0262201623)

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Matt_Cutts
Source video here: <http://www.youtube.com/watch?v=VUeKKvKEvYI>

~~~
tptacek
Thank you, this is much better.

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chaz
Original story from MIT News Office w/ video:
[http://web.mit.edu/newsoffice/2012/autonomous-robotic-
plane-...](http://web.mit.edu/newsoffice/2012/autonomous-robotic-plane-flies-
indoors-0810.html)

~~~
solutionyogi
This is a much better link and admin should change the story link to this one.

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Schwolop
The big "what stopping this from being mainstream" part of this is the need
for a prior map. It's absolutely analogous to Google's autonomous vehicles
needing to be manually driven through the area in which they're planning to
later operate autonomously.

This is great work, but it's not onboard SLAM, only onboard localisation. All
up, it's great to see more of the autonomous ground vehicle work becoming
small and lightweight enough to go on aerial vehicles. Traditionally the low
payload capacity has been a showstopper for UAVs, and laser range finders are
often heavy, hence why so many UAVs have used vision-only localisation
techniques.

~~~
makmanalp
For us civilians, slam is Simultaneous Localization and Mapping, which is the
equivalent of walking around and exploring an unknown area and committing it
to memory (mapping) while also still maintaining your own bearings in there
(localization).

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confluence
Some relevant videos on autonomous catastrophic flight recovery by Rockwell
Collins:

Continuous AI flight after blowing off one wing:

<http://www.youtube.com/watch?v=xN9f9ycWkOY>

Continuous AI flight after catastrophic wing loss (showing manual/AI
difference):

<http://www.youtube.com/watch?v=dGiPNV1TR5k>

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d0vs
<http://news.ycombinator.com/item?id=4367650>

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sliverstorm
I am always so happy to see a navigational demonstration that doesn't rely on
external positioning, e.g. GPS. Having tried both sides (dead
reckoning/sensing and external positioning), I have come to feel like so many
GPS-based projects are essentially trivial GPS demos.

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rogerbinns
What happens when it hits a dead end? At least a helicopter can stop and
return on exactly the track it came in on.

Presumably one of the reasons for the prebuilt map is so that it doesn't enter
a dead end, which some parking garages have.

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niels
On a sidenote I'd like to add that rc flying is a ton of fun. Especially if
you couple it with FPV (first person view) cams, goggles etc...

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stcredzero
That plane thinks it's Han Solo! (Never tell me the odds!)

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eps
Cool, but I'd trim "genius" from the title.

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fluorescentLAMP
If we can put a rover the size of a VW beetle on Mars, we can definitely put a
couple of these on it too.

