Self-Learning Helicopter Uses Neural Network

model helicopter attached to boom

Though this project uses an RC helicopter, it’s merely a vessel to demonstrate a fascinating machine learning algorithm developed by two Cornell students – [Akshay] and [Sergio]. The learning environment is set up with the helicopter at its center, attached to a boom. The boom restricts the helicopter’s movement down to one degree of motion, so that it can only move up from the ground (not side to side or front to back).

The goal is for the helicopter to teach itself how to get to a specific height in the quickest amount of time. A handful of IR sensors are used to tell the Atmega644 how high the helicopter is. The genius of this though, is in the firmware. [Akshay] and [Sergio] are using an evolutionary algorithm adopted from Floreano et al, a noted author on biological inspired artificial intelligences. The idea is for the helicopter to create random “runs” and then check the data. The runs that are closer to the goal get refined while the others are eliminated, thus mimicking evolutions’ natural selection.

We’ve seen neural networks before, but nothing like this. Stay with us after the break, as we take this awesome project and narrow it down so that you too can implement this type of algorithm in your next project.

 

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Neural networks control a toy car

After taking the Stanford Machine Learning class offered over the Internet last year, [David Singleton] thought he could build something really cool. We have to admit that he nailed it with his neural network controlled car. There’s not much to the build; it’s just an Android phone, an Arduino and a toy car. The machine learning part of this build really makes it special.

A neural network takes a whole bunch of inputs and represents them as a node in a network. Each node in [Davids]‘s input layer corresponds to a pixel retrieved from his phone’s camera. All the inputs of the input layer are connected to 64 nodes in the ‘hidden layer’. The nodes in the hidden layer are connected to the four output nodes, namely left, right, forward and reverse.

After training the network and weighting all the connections, [David] got a toy car to drive around a track. Weird, but it works. All the code is up on github, so feel free to take a look behind the inner machinations of a neural net. Of course, you could check out the video of [David]‘s car in action after the break.

EDIT: We originally credited [icebrain] as the author. Our bad, and we hope [David] doesn’t hate us now.

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MegaUpload captcha cracking in JavaScript

megaupload-the-leading-online-storage-and-file-delivery-service

This was certainly the last thing we expected to see today. [ShaunF] has created a Greasemonkey script to bypass the captcha on filehosting site Megaupload. It uses a neural network in JavaScript to do all of the OCR work. It will auto submit and start downloading too. It’s quite a clever hack and is certainly helped by the simple 3 character captcha the site employs. Attempting to do the same thing with ReCAPTCHA has proven much more difficult.

UPDATE: [John Resig] explained of how it works.

[via Waxy]