With interest and accessibility to both wearable tech and virtual reality approaching an all-time high, three students from Cornell University — [Daryl Sew, Emma Wang, and Zachary Zimmerman] — seek to turn your body into the perfect controller.
That is the end goal, at least. Their prototype consists of three Kionix tri-axis accelerometer, gyroscope and magnetometer sensors (at the hand, elbow, and shoulder) to trace the arm’s movement. Relying on a PC to do most of the computational heavy lifting, a PIC32 in a t-shirt canister — hey, it’s a prototype! — receives data from the three joint positions, transmitting them to said PC via serial, which renders a useable 3D model in a virtual environment. After a brief calibration, the setup tracks the arm movement with only a little drift in readings over a few minutes.
Continue reading “Your Arm Is The Ideal Controller”
There aren’t too many sports named for the sound that is produced during the game. Even though it’s properly referred to as “table tennis” by serious practitioners, ping pong is probably the most obvious. To that end, [Nekojiru] built a ping pong ball juggling robot that used those very acoustics to pinpoint the location of the ball in relation to the robot. Not satisfied with his efforts there, he moved onto a visual solution and built a new juggling rig that uses computer vision instead of sound to keep a ping pong ball aloft.
The main controller is a Raspberry Pi 2 with a Pi camera module attached. After some mishaps with the planned IR vision system, [Nekojiru] decided to use green light to illuminate the ball. He notes that OpenCV probably wouldn’t have worked for him because it’s not fast enough for the 90 fps that’s required to bounce the ping pong ball. After looking at the incoming data from this system, an algorithm extracts 3D information about the ball and directs the paddle to strike the ball in a particular way.
If you’ve ever wanted to get into real-time object tracking, this is a great project to look over. The control system is well polished and the robot itself looks almost professionally made. Maybe it’s possible to build something similar to test [Nekojiru]’s hypothesis that OpenCV isn’t fast enough for this. If you want to get started in that realm of object tracking, there are some great projects that make use of that piece of software as well.
How often do you see problems that need fixing? How often do you design your own solutions to them — even if they won’t be implemented at scale? Seeing that many of the municipal parking lots in his native Sri Lanka use a paper ticketing system which is prone to failure, [Shazin Sadakath] whipped up his own solution: an efficient RFID tag logging system.
Continue reading “Faulty Parking Meter Tracking System? RFID To The Rescue!”
The next giant leap for mankind is to the stars. While we are mostly earthbound — for now — that shouldn’t stop us from gazing upwards to marvel at the night sky. In saying that, if you’re an amateur astrophotographer looking to take long-exposure photos of the Milky Way and other stellar scenes, [Anthony Urbano] has devised a portable tracking setup to keep your photos on point.
When taking pictures of the night sky, the earth’s rotation will cause light trails during long exposures. Designed for ultra-portability, [Urbano’s] rig uses an Arduino UNO controlled Sanryusha P43G geared stepper motor coupled to a camera mounting plate on a small tripod. The setup isn’t designed for anything larger than a DSLR, but is still capable of taking some stellar pictures.
Continue reading “A Compact Star Tracking Tripod”
NASA has been tracking bright meteoroids (“fireballs”) using a distributed network of video cameras pointed upwards. And while we usually think of NASA in the context of multi-bazillion dollar rocket ships, but this operation is clearly shoe-string. This is a hack worthy of Hackaday.
The basic idea is that with many wide-angle video cameras capturing the night sky, and a little bit of image processing, identifying meteoroids in the night sky should be fairly easy. When enough cameras capture the same meteoroid, one can use triangulation to back out the path of the meteoroid in 3D, estimate its mass, and more. It’s surprising how many there are to see on any given night.
You can watch the videos of a meteoroid event from any camera, watch the cameras live, and even download the meteoroid’s orbital parameters. We’re bookmarking this website for the next big meteor shower.
The work is apparently based on [Rob Weryk]’s ASGARD system, for which the code is unfortunately unavailable. But it shouldn’t be all that hard to hack something together with a single-board computer, camera, and OpenCV. NASA’s project is limited to the US so far, but we wonder how much more data could be collected with a network of cameras all over the globe. So which ones of you are going to take up our challenge? Build your own version and let us know about it!
Between this project and the Radio Meteor Zoo, we’re surprised at how much public information there is out there about the rocky balls of fire that rain down on us every night, and will eventually be responsible for our extinction. At least we can be sure we’ll get it on film.
[Chris Gunawardena] is still holding his breath on Valve and Facebook surprising everyone by open sourcing their top secret VR prototypes. They have some really clever ways to track the exact location and orientation of the big black box they want people to strap to their faces. Until then, though, he decided to take his own stab at the 3D tracking problems they had to solve.
While they used light to perform the localization, he wanted to experiment with using electromagnetic fields to perform the same function. Every phone these days has a magnetometer built in. It’s used to figure out which way is up, but it can also measure the local strength of magnetic fields.
Unfortunately to get really good range on a magnetic field there’s a pesky problem involving inverse square laws. Some 9V batteries in series solved the high current DC voltage source problem and left him with magnetic field powerful enough to be detected almost ten centimeters away by his iPhone’s magnetometer.
As small as this range seems, it ended up being enough for his purposes. Using the existing math and a small iOS app he was able to perform rudimentary localization using EM fields. Pretty cool. He’s not done yet and hopes that a more sensitive magnetometer and a higher voltage power supply with let him achieve greater distances and accuracy in a future iteration.
Sometimes, a person has a reason to track a target. A popular way to do this these days is with a camera, a computer, and software to analyze the video. But, that lends itself more to automated systems, like sentries. What if you want to be able to target something by “painting” it with a laser?
That’s exactly what [Jeremy Leaf] wanted to do, and the results are pretty impressive. He was able to track a .06 milliwatt laser at 2 meters. His design does this using three photodiodes in order to determine the position of a laser spot using triangulation.
Once the location of the laser spot has been determined, it can either simply be reported or it can be tracked. Tracking is achieved with a gimbal setup which updates quickly and accurately. Of course, it can only track the laser if the laser has something to be projected upon. If you need to track something in open 3D space, there are alternatives that would be better suited to the task.
Continue reading “Infrared Targeting On a Small Scale”