What is this world coming to when you can’t even enjoy sitting in your living room without some jamoke flying a drone in through the window? Is nothing sacred? Won’t someone think of the children?
Apparently [Drew Pilcher] did, and the result is this anti-drone sentry gun. It’s a sturdily built machine – one might even say it’s overbuilt. The gimbal is made from machined steel pieces, and the swivels are a pair of Sherline stepper-controlled rotary tables with 1/40 of a degree accuracy selling for $400 each. Riding atop that is a Nerf rifle, which is cocked by a stepper-actuated linear slide, as well as a Kinect for object tracking. The tracking app is a little rough – just OpenCV hacked onto the Kinect SDK – but good enough for testing. The gun tracks as smoothly as one would expect given the expensive hardware, and the auto-cocking feature works well if a bit slowly. Based as it is on Nerf technology, this sentry is only indicated for the control of the micro-drones seen in the snuff video below, but really, anyone afflicted by indoor infestations of Phantoms or Mavics has bigger problems to worry about.
Over-engineered? Perhaps, but it’s better than letting the menace of indoor drones go unanswered. And it’s far from the first sentry gun we’ve seen, targeting everything from cats to squirrels using lasers, paintballs, and even plain water.
Continue reading “Well-Built Sentry Gun Addresses The Menace Of Indoor Micro-UAVs”
It’s one thing to know that your device is leaking electromagnetic interference (EMI), but if you really want to solve the problem, it might be helpful to know where the emissions are coming from. This heat-mapping EMI probe will answer that question, with style. It uses a webcam to record an EMI probe and the overlay a heat map of the interference on the image itself.
Regular readers will note that the hardware end of [Charles Grassin]’s EMI mapper bears a strong resemblance to the EMC probe made from semi-rigid coax we featured recently. Built as a cheap DIY substitute for an expensive off-the-shelf probe set for electromagnetic testing, the probe was super simple: just a semi-rigid coax jumper with one SMA plug lopped off and the raw end looped back and soldered. Connected to an SDR dongle, the probe proved useful for tracking down noisy circuits.
[Charles]’ project takes that a step further by adding a camera that looks down upon the device under test. OpenCV is used to track the probe, which is moved over the DUT manually with the help of an augmented reality display that helps track coverage, with a Python script recording its position and the RF power measurements. The video below shows the capture process and what the data looks like when reassembled as an overlay on top of the device.
Even if EMC testing isn’t your thing, this one seems like a lot of fun for the curious. [Charles] has kindly made the sources available on GitHub, so this is a great project to just knock out quickly and start mapping.
Continue reading “Camera Sees Electromagnetic Interference Using An SDR And Machine Vision”
Thermal cameras are one of those tools that we all want, but just can’t justify actually buying. You don’t really know what you would do with one, and when even the cheap ones are a couple hundred dollars, it’s a bit out of the impulse buy territory. So you just keeping waiting and hoping that eventually they’ll drop to the price that you can actually own one yourself.
Well, today might be the day you were waiting for. While it might not be the prettiest build, we think you’ll agree it can’t get much easier than what [vvkuryshev] has put together. His build only has two components: a Raspberry Pi and a thermal camera module he picked up online for about $80 USD. There isn’t even any wiring involved, the camera fits right on the Pi’s GPIO header.
Of course, you probably wouldn’t be seeing this on Hackaday if all he had to do was just buy a module and solder it to the Pi’s header. As with most cheap imported gadgets, the GY-MCU90640 module that [vvkuryshev] bought came with some crusty Windows software which wasn’t going to do him much good on the Raspberry Pi. But after going back and forth a bit with the seller, he was able to get some documentation for the device that put him on the right track to writing a Python script which got it working under Linux.
The surprisingly simple Python script reads a frame from the camera four times a second over serial and run it through OpenCV. It even adds some useful data like the minimum and maximum temperatures in the frame to the top of the image. Normally the script would output to the Pi’s primary display, but if you want to use it remotely, [vvkuryshev] says he’s had pretty good luck running it over VNC. In fact, he says that with a VNC application on your phone you could even use this setup on the go, though the setup is a bit awkward for that in its current incarnation.
This isn’t the first DIY thermal camera build we’ve seen, and it isn’t even the first one we’ve seen that leveraged a commercially available imaging module. But short of buying a turn-key camera, we don’t see how it could get any easier to add heat vision to your bag of tricks.
Sorting out a mountain of screws and other workbench detritus by hand is a task that only appeals to a select few of us. [AdrienR] is not one of those people. He believes the job is better suited to a robot, so he built an intelligent and good-looking machine that does just that.
[Adrien]’s sorting bot is capable of organizing a hodgepodge of parts quickly and effectively. He simply scatters the parts on the light box work surface, illuminates it, and takes a picture with a downward-facing web cam. An algorithm studies the parts and their positions using OpenCV image processing, and sends the triangulation back to the arm so it can pick and place the parts into laser cut boxes using a home brew electromagnet.
[Adrien] calls this a work in progress. He plans to control it with a Raspberry Pi so it can be a standalone unit, and will probably move the parts boxes to the outside curve. Drop yourself past the break to see it sort.
If delta robots are more your sort, this one has balls. Colored balls.
Continue reading “This Light-Up Sorter Is A Bright Idea”
If IKEA made ball-balancing PID robots, they’d probably look like this one.
This [Johan Link] build isn’t just about style. A look under the hood reveals not the standard, off-the-shelf microcontroller development board you might expect. Instead, [Johan] designed and built his own board with an ATmega32 to run the three servos that control the platform. The entire apparatus is made from a dozen or so 3D-printed parts that interlock to form the base, the platform, and the housing for the USB webcam that’s perched on an aluminum tube. From that vantage point, the camera’s images are analyzed with OpenCV and the center of the ball is located. A PID loop controls the three servos to center the ball on the platform, or razzle-dazzle it a little by moving the ball in a controlled circle. It’s quite a build, and the video below shows it in action.
We’ve seen a few balancing platforms before, but few with such style. This Stewart platform comes close, and this juggling platform gets extra points for closing the control loop with audio feedback. And for juggling, of course.
Continue reading “High-Style Ball Balancing Platform”
Robots, as we currently understand them, tend to run on electricity. Only in the fantastical world of Futurama do robots seek out alcohol as both a source of fuel and recreation. That is, until [Les Wright] and his beer seeking robot came along. (YouTube, video after the break.)
A Raspberry Pi 3 provides the brains, with an Intel Neural Compute stick plugged in as an accelerator for neural network tasks. This hardware, combined with the OpenCV image detection software, enable the tracked robot to identify objects and track their position accordingly.
That a beer bottle was chosen is merely an amusing aside – the software can readily identify many different object categories. [Les] has also implemented a search feature, in which the robot will scan the room until a target bottle is identified. The required software and scripts are available on GitHub for your perusal.
Over the past few years, we’ve seen an explosion in accelerator hardware for deep learning and neural network computation. This is, of course, particularly useful for robotics applications where a link to cloud services isn’t practical. We look forward to seeing further development in this field – particularly once the robots are able to open the fridge, identify the beer, and deliver it to the couch in one fell swoop. The future will be glorious!
Continue reading “Robot Can’t Take Its Eyes Off The Bottle”
Most people are familiar with the idea that machine learning can be used to detect things like objects or people, but for anyone who’s not clear on how that process actually works should check out [Kurokesu]’s example project for detecting pedestrians. It goes into detail on exactly what software is used, how it is configured, and how to train with a dataset.
The application uses a USB camera and the back end work is done with Darknet, which is an open source framework for neural networks. Running on that framework is the YOLO (You Only Look Once) real-time object detection system. To get useful results, the system must be trained on large amounts of sample data. [Kurokesu] explains that while pre-trained networks can be used, it is still necessary to fine-tune the system by adding a dataset which more closely models the intended application. Training is itself a bit of a balancing act. A system that has been overly trained on a model dataset (or trained on too small of a dataset) will suffer from overfitting, a condition in which the system ends up being too picky and unable to usefully generalize. In terms of pedestrian detection, this results in false negatives — pedestrians that don’t get flagged because the system has too strict of an idea about what a pedestrian should look like.
[Kurokesu]’s walkthrough on pedestrian detection is great, but for those interested in taking a step further back and rolling their own projects, this fork of Darknet contains YOLO for Linux and Windows and includes practical notes and guides on installing, using, and training from a more general perspective. Interested in learning more about machine learning basics? Don’t forget Google has a free online crash course to get you up to speed.