Automated Dice Tester Uses Machine Vision To Ensure A Fair Game

People take their tabletop games very, very seriously. [Andrew Lauritzen], though, has gone far above and beyond in pursuit of a fair game. The game in question is Star War: X-Wing, a strategy wargame where miniature pieces are moved according to rolls of the dice. [Andrew] suspected that commercially available dice were skewing the game, and the automated machine-vision dice tester shown in the video after the break was the result.

The rig is a very clever design that maximizes the data set with as little motion as possible. The test chamber is a box with clear ends that can be flipped end-for-end by a motor; walls separate the chamber into four channels to test multiple dice on each throw, and baffles within the channels assure randomization. A webcam is positioned below the chamber to take a snapshot of each “throw”, which is then analyzed in OpenCV. This scheme has the unfortunate effect of looking at the dice from the table’s perspective, but [Andrew] dealt with that in true hacker fashion: he ignored it since it didn’t impact the statistics he was interested in.

And speaking of statistics, he generated a LOT of them. The 62-page report of results from his study is an impressive piece of work, which basically concludes that the dice aren’t fair due to manufacturing variability, and that players could use this fact to cheat. He recommends pooled sets of dice to eliminate advantages during competitive play. 

This isn’t the first automated dice roller we’ve seen around these parts. There was the tweeting dice-bot, the Dice-O-Matic, and all manner of electronic dice throwers. This one goes the extra mile to keep things fair, and we appreciate that.

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Well-Built Sentry Gun Addresses The Menace Of Indoor Micro-UAVs

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.

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Camera Sees Electromagnetic Interference Using An SDR And Machine Vision

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.

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The Easiest Thermal Camera Build You’ll Ever See

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.

This Light-Up Sorter Is A Bright Idea

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.

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High-Style Ball Balancing Platform

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.

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Robot Can’t Take Its Eyes Off The Bottle

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!

 

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