Kalman filter keeps your bot balanced

If you’re looking to improve the stability of your self balancing robot you might use a simple horrifying equation like this one. It’s part of the journey [Lauszus] took when developing a sensor filtering algorithm for his balancing robot. He’s not breaking ground on new mathematical ideas, but trying to make it a bit easier for the next guy to use a Kalman filter. It’s one method of suppressing noise and averaging data from the sensors commonly used in robotic applications.

His robot uses a gyroscope and accelerometer to keep itself upright on just two wheels. The combination of these sensors presents an interesting problem in that accelerometer input is most accurate when sampled over longer periods, and a gyroscope is the opposite. This filter takes those quirks into account, while also factoring out sensor noise. Despite the daunting diagram above, [Lauszus] did a pretty go job of breaking down the larger function and showing us where to get the data and how to use it in microcontroller code.

Self Balancing Robot with Wii parts

[Moser] is looking to build a quadrocopter sometime in the future, without plunking down a good chunk of change for a kit model. Looking for a good place to start he figured why not work on the control system. Thinking that the balance of the flying platform of doom would be similar to working out a self balancing robot he spent a couple days and made his self balancing robot.

Armed with a plan, and a logic analyzer, he went out and got a  Wii Motion Plus, which is an inexpensive three axis gyroscope, and a nunchuck which features an accelerometer which both can be found in just about any strip mall. After fiddling for a day getting the Wii nunchuck and motion plus to play nice all it took was a little more time to code up the self balancing routines.

And while its not perfect, all its going to take is a little tweaking and maybe some faster servo motors to get things up to top notch.

Join us after the break for a couple quick videos.

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