Before 3D printers, there was LEGO. And the little bricks are still useful for putting something together on the quick. Proof is YouTuber [Matthias Wandel]’s awesome bottle cap shooter build that uses rudimentary DIY computer vision to track you and then launch a barrage of plastic pieces at you.
This is an amazing project that has a bit of something for everyone. Lets start with the LEGO. [Matthias Wandel] starts with making a crossbow designed launcher and does an awesome job with showing us how it works in a video. The mechanism is an auto reloading and firing system that can be connected to a stepper motor. Next comes the pan and tilt mechanism which allows the turret to take better aim at moving targets: more LEGO and stepper motors.
The target tracker uses color matching in a program that curiously uses no OpenCV. It compares consecutive frame and then filters out red objects – the largest red dot is it. Since using a fisheye lens on the Raspbery Pi camera adds distortion, [Matthias Wandel] uses a jig made with more Legos to calibrate the image.
The final testing involved having his own child walk around the room being hunted but the autonomous machine. Kids do love toys even if they are trying to shoot bottle caps at them.
Want more Lego inspiration? Check out the Lego Quadcopter Mod and the Lego Tank with the ESP8266.
Continue reading “Wandel Weaponizes Waste with Lego and a Raspberry Pi”
Given how long humans have been warming themselves up, you’d think we would have worked out all the kinks by now. But even with central heating, and indeed sometimes because of it, some places we frequent just aren’t that cozy. In such cases, it often pays to heat the person, not the room, but that can be awkward, to say the least.
Hacking polymath [Matthias Wandel] worked out a solution to his cold shop with this target-tracking infrared heater. The heater is one of those radiant deals with the parabolic dish, and as anyone who’s walked past one on demo in Costco knows, they throw a lot of heat in a very narrow beam. [Matthias] leveraged a previous project that he whipped up for offline surveillance as the core of the project. Running on a Raspberry Pi with a camera, the custom software analyzes images and locates motion across the width of a frame. That drives a stepper that swivels a platform for the heater. The video below shows the build and the successful tests; however, fans of [Matthias] should prepare themselves for a shock as he very nearly purchases a lazy susan to serve as the base for the heater rather than building one.
We’re never disappointed by [Matthias]’ videos, and we’re always impressed by his range as a hacker. From DIY power tools to wooden logic circuits to his recent Lego chocolate engraver, he always finds ways to make things interesting.
Continue reading “Raspberry Pi Tracks Humans, Blasts Them With Heat Rays”
What’s more disruptive to the drone first-person view (FPV) experience than dropouts in your video feed when you’re in the middle of a race? Probably nothing, and there’s probably also not much you can do about it. Or is there? Might a simple tracker based on RSSI help keep your video signal locked in?
Honestly, we’re not sure it would, but we think it’s pretty nifty to see [FlyerFpv]’s tracker following his drone around. The idea is simple and uses the full-diversity FPV receiver he already has. Diversity receivers constantly monitor signal strength from multiple antennas to determine which one to listen to, which improves reception quality. [FlyerFpv] sends the RSSI outputs to analog inputs on an Arduino which drives a servo to keep the signals as close to each other as possible. The Arduino and the DC-DC converter needed to power it fit nicely inside the receiver case with no modifications, which is a nice touch. With a 3D-printed servo mount and some fancy directional antennas, the setup keeps pretty good track of his drone now. See it in action below.
Sure, the response could be snappier, and we’d love to see another receiver and servo added to track pitch as well as yaw. For a first pass, we think it’s great, but [FlyerFpv] should enjoy it while he can in case AI takes over our flying fun soon.
Continue reading “Super Simple, Super Cheap FPV Drone Tracking”
People who exercise with fitness trackers have a digital record of their workouts. They do it for a wide range of reasons, from gathering serious medical data to simply satisfying curiosity. When fitness data includes GPS coordinates, it raises personal privacy concerns. But even with individual data removed, such data was still informative enough to spill the beans on secretive facilities around the world.
This past weekend, [Nathan Ruser] announced on Twitter that Strava’s heatmap also managed to highlight exercise activity by military/intelligence personnel around the world, including some suspected but unannounced facilities. More worryingly, some of the mapped paths imply patrol and supply routes, knowledge security officers would prefer not to be shared with the entire world.
This is an extraordinary blunder which very succinctly illustrates a folly of Internet of Things. Strava’s anonymized data sharing obsfucated individuals, but didn’t manage to do the same for groups of individuals… like the fitness-minded active duty military personnel whose workout habits are clearly defined on these heat maps. The biggest contributor (besides wearing a tracking device in general) to this situation is that the data sharing is enabled by default and must be opted-out:
“You can opt-out of contributing your anonymized public activity data to Strava Metro and the Heatmap by unchecking the box in this section.” —Strava Blog, July 2017
We’ve seen individual fitness trackers hacked and we’ve seen people tracked through controlled domains before, but the global scope of [Nathan]’s discovery puts it in an entirely different class.
[via Washington Post]
Things rarely go well when humans mix with wildlife. The problems are exacerbated in the suburbs, where bears dine on bird feeders and garbage cans, raccoons take up residence in attics, and coyotes make off with the family cat. And in the suburbs, nuisance wildlife can be an intractable problem because the options for dealing with it are so limited.
Not to be dissuaded in the battle to protect his roses, [dlf.myyta] built this motion-activated sentry gun to apply some watery aversion therapy to marauding deer. Shown in action below against a bipedal co-conspirator, the sentry gun has pretty much what you’d expect under the hood — Raspberry Pi, NoIR camera, a servo for aiming and a solenoid valve to control the water. OpenCV takes care of locating the intruders and swiveling the nozzle to center mass; since the deer are somewhat constrained by a fence, there’s no need to control the nozzle’s elevation. Everything is housed nicely in a plastic ammo can for portability and waterproofing. Any target that stands still for more than three seconds gets a hosing; we assume this is effective, but alas, no snuff films were provided.
We’re not sure if [dlf.myyta]’s code can discern friend from foe, and in this litigious world, hosing the neighbor’s kid could be a catastrophe. Perhaps version 2.0 can include image recognition for target verification.
Continue reading “Auto-Tracking Sentry Gun Gives Deer a Super Soaking”
If you’re in the mood to track satellites, it’s a relatively simple task to look up one of a multitude of websites that can give you a list of satellites visible from your location. However, if you’re interested in using satellites to communicate with far-flung friends, you might be interested in this multi-point satellite tracker.
[Stephen Downward VA1QLE] developed the tracker to make it easier to figure out which satellites would be simultaneously visible to people at different locations on the Earth’s surface. This is useful for amateur radio, as signals can be passed through satellites with ham gear onboard (such as NO-44), or users can even chat over defunct military satellites.
[Stephen] claims the algorithm is inefficient, but calculations are made in a matter of a few seconds, so we’re not complaining. While it was originally designed for just two stations, it works with a near-infinite number of points. [Stephen] recommends verifying the tracks with another tool once calculated to ensure accuracy. The tool is accessible here, and the code is up on GitHub for your perusal.
Perhaps now you need a cost-effective satellite-tracking antenna? [Paul] has you covered.
One of the companion technologies in the developing field of augmented reality is gesture tracking. It’s one thing to put someone in a virtual or augmented world, but without a natural way to interact inside of it the user experience is likely to be limited. Of course, gestures can be used to control things in the real world as well, and to that end [Sarah]’s latest project uses this interesting human interface device to control a drone.
The project uses a Leap Motion sensor to detect and gather the gesture data, and feeds all of that information into LabVIEW. A Parrot AR Drone was chosen for this project because of a robust API that works well with this particular software suite. It seems as though a lot of the grunt work of recognizing gestures and sending commands to the drone are taken care of behind-the-scenes in software, so if you’re looking to do this on your own there’s likely to be quite a bit more work involved. That being said, it’s no small feat to get this to work in the first place and the video below is worth a view.
To some, gestures might seem like a novelty technology with no real applications, but they do have real-world uses for people with disabilities or others with unusual workflow that require a hands-free approach. So far we’ve seen hand gesture technologies that drive cars, help people get around in the physical world, and even play tetris.
Continue reading “Drone Takes Off With a Flick of the Wrist”