These days everyone’s excited about transparent OLED panels, but where’s the love for the classic Nokia 5110 LCD? As the prolific [Nick Bild] demonstrates in his latest creation, all you’ve got to do is peel the backing off the the late 90s era display, and you’ve got yourself a see-through cyberpunk screen for a couple bucks.
View through the modified LCD.
In this case, [Nick] has attached the modified display to a pair of frames, and used an Adafruit QT Py microcontroller to connect it to the ESP32 powered ESP-EYE development board and OV2640 camera module. This lets him detect QR codes within the wearer’s field of vision and run a TensorFlow Lite neural network right on the hardware. Power is provided by a 2000 mAh LiPo battery running through an Adafruit PowerBoost 500.
The project, intended to provide augmented reality reminders for medical professionals, uses the QR codes to look up patient and medication information. Right now the neural network is being used to detect when the wearer has washed their hands, but obviously the training model could be switched out for something different as needed. By combining these information sources, the wearable can do things like warn the physician if a patient is allergic to the medication they’re currently looking at.
For readers that might not spend their free time watching spools of PLA slowly unwind, The Spaghetti Detective (TSD) is an open source project that aims to use computer vision and machine learning to identify when a 3D print has failed and resulted in a pile of plastic “spaghetti” on the build plate. Once users have installed the OctoPrint plugin, they need to point it to either a self-hosted server that’s running on a relatively powerful machine, or TSD’s paid cloud service that handles all the AI heavy lifting for a monthly fee.
Unfortunately, 73 of those cloud customers ended up getting a bit more than they bargained for when a configuration flub allowed strangers to take control of their printers. In a frank blog post, TSD founder Kenneth Jiang owns up to the August 19th mistake and explains exactly what happened, who was impacted, and how changes to the server-side code should prevent similar issues going forward.
TSD allows users to remotely manage and monitor their printers.
For the record, it appears no permanent damage was done, and everyone who was potentially impacted by this issue has been notified. There was a fairly narrow window of opportunity for anyone to stumble upon the issue in the first place, meaning any bad actors would have had to be particularly quick on their keyboards to come up with some nefarious plot to sabotage any printers connected to TSD. That said, one user took to Reddit to show off the physical warning their printer spit out; the apparent handiwork of a fellow customer that discovered the glitch on their own.
According to Jiang, the issue stemmed from how TSD associates printers and users. When the server sees multiple connections coming from the same public IP, it’s assumed they’re physically connected to the same local network. This allows the server to link the OctoPrint plugin running on a Raspberry Pi to the user’s phone or computer. But on the night in question, an incorrectly configured load-balancing system stopped passing the source IP addresses to the server. This made TSD believe all of the printers and users who connected during this time period were on the same LAN, allowing anyone to connect with whatever machine they wished.
New code pushed to the TSD repository limits how many devices can be associated with a single IP.
The mix-up only lasted about six hours, and so far, only the one user has actually reported their printer being remotely controlled by an outside party. After fixing the load-balancing configuration, the team also pushed an update to the TSD code which puts a cap on how many printers the server will associate with a given IP address. This seems like a reasonable enough precaution, though it’s not immediately obvious how this change would impact users who wish to add multiple printers to their account at the same time, such as in the case of a print farm.
While no doubt an embarrassing misstep for the team at The Spaghetti Detective, we can at least appreciate how swiftly they dealt with the issue and their transparency in bringing the flaw to light. This is also an excellent example of how open source allows the community to independently evaluate the fixes applied by the developer in response to a discovered flaw. Jiang says the team will be launching a full security audit of their own as well, so expect more changes getting pushed to the repository in the near future.
We were impressed with TSD when we first covered it back in 2019, and glad to see the project has flourished since we last checked in. Trust is difficult to gain and easy to lose, but we hope the team’s handling of this issue shows they’re on top of things and willing to do right by their community even if it means getting some egg on their face from time to time.
We love depth-sensing cameras and every neat hack they enabled, but this technological novelty has yet to break through to high volume commercial success. So it was sad but not surprising when CRN reported that Intel has decided to wind down their RealSense product line.
As of this writing, one of the better confirmations for this report can be found on the RealSense SDK GitHub repository README. The good news is that core depth-sensing RealSense products will continue business as usual for the foreseeable future, balanced by the bad news that some interesting offshoots (facial authentication, motion tracking) will be declared “End of Life” immediately and phased out over the next six months.
This information tells us while those living out on the bleeding edge will have to scramble, there is no immediate crisis for everyone else, whether they be researchers, hobbyists, or product planners. But this also means there will be no future RealSense cameras, kicking off many “What’s Next?” discussions in various communities. Like this thread on ROS (Robot Operating System) Discourse.
Three popular alternatives offer distinctly different tradeoffs. The “Been Around The Block” name is Occipital, with their more expensive Structure Pro sensor. The “Old Name, New Face” option is Microsoft Azure Kinect, the latest non-gaming-focused successor to the gaming peripheral that started it all. And let’s not forget OAK-D as the “New Kid On The Block” that started with a crowdfunding campaign and building an user community by doing things like holding contests. Each of these will appeal to a different niche, and we’ll keep our eye open in the future. Let’s see if any of them find the success that eluded the original Kinect, Google’s Tango, and now Intel’s RealSense.
Farming has been undergoing quite a revolution in the past few years. Since World War 2, most industrial farming has relied on synthetic fertilizer, large machinery, and huge farms with single crops. Now there is a growing number of successful farmers bucking that trend with small farms growing many crops and using natural methods of fertilizing that don’t require as much industry. Of course even with these types of farms, some machinery is still nice to have, so this farmer has been developing an open-source automated farming robot.
The robot is known as Acorn and is the project of [taylor] who farms in California. The platform is powered by an 800 watt solar array feeding a set of supercapacitors for energy storage. It uses mountain bike wheels and tires fitted with electric hub motors which give it four wheel drive and four wheel steering to make it capable even in muddy fields. The farming tools, as well as any computer vision and automation hardware, can be housed under the solar panels. This prototype uses an Nvidia Jetson module to handle the heavy lifting of machine learning and automation, with a Raspberry Pi to handle the basic operation of the robot, and can navigate itself around a farm using highly precise GPS units.
While the robot’s development is currently ongoing, [taylor] hopes to develop a community that will build their own versions and help develop the platform. Farming improvements like this are certainly needed as more and more farmers shift from unsustainable monocultures to more ecologically friendly methods involving multiple simultaneous crops, carbon sequestration, and off-season cover crops. It’s certainly a long row to hoe but plenty of people are already plowing ahead.
We’ll state right up front that it’s a really, really bad idea to let a robotic archer shoot an apple off of your head. You absolutely should not repeat what you’ll see in the video below, and if you do, the results are all on you.
That said, [Kamal Carter]’s build is pretty darn cool. He wisely chose to use just about the weakest bows you can get, the kind with strings that are basically big, floppy elastic bands that shoot arrows with suction-cup tips and are so harmless that they’re intended for children to play with and you just know they’re going to shoot each other the minute you turn your back no matter what you told them. Target acquisition is the job of an Intel RealSense depth camera, which was used to find targets and calculate the distance to them. An aluminum extrusion frame holds the bow and adjusts its elevation, while a long leadscrew and a servo draw and release the string.
With the running gear sorted, [Kamal] turned to high school physics for calculations such as the spring constant of the bow to determine the arrow’s initial velocity, and the ballistics formula to determine the angle needed to hit the target. And hit it he does — mostly. We’re actually surprised how many on-target shots he got. And yes, he did eventually get it to pull a [William Tell] apple trick — although we couldn’t help but notice from his, ahem, hand posture that he wasn’t exactly filled with self-confidence about where the arrow would end up.
[T-Kuhn]’s Octo-Bouncer platform has learned some new tricks since we saw it last. If you haven’t seen it before, this device uses computer vision from a camera mounted underneath its thick, clear acrylic platform to track a ball in 3D space, and make the necessary (and minute) adjustments needed to control the ball’s movements with a robotic platform in real time.
We loved the Octo-Bouncer’s mesmerizing action when we saw it last, and it’s only gotten better. Not only is there a whole new custom ball detection algorithm that [T-Kuhn] explains in detail, there are also now visualizations of both the ball’s position as well as the plate movements. There’s still one small mystery, however. Every now and again, [T-Kuhn] says that the ball will bounce in an unexpected direction. It doesn’t seem to be a bug related to the platform itself, but [T-Kuhn] has a suspicion. Since contact between the ball and platform is where all the control comes from, and the ball and platform touch only very little during a bounce, it’s possible that bits of dust (or perhaps even tiny imperfections on the ball’s surface itself) might be to blame. Regardless, it doesn’t detract from the device’s mesmerizing performance.
Design files and source code are available on the project’s GitHub repository for those who’d like a closer look. It’s pretty trippy watching the demonstration video because there is so much going on at once; you can check it out just below the page break.
Over the last year or so, we’ve been checking in on the progress [Andrew Consroe] has been making with his incredible CNC scroll saw project. While we were already impressed with his first prototype version, he somehow manages to keep pushing the envelope forward with each new upgrade, and we’re always excited when one of his progress reports hits the inbox.
Recently he’s been struggling with the fact that the considerable flexing of the scroll saw’s ultra-thin blade introduces positional errors while cutting. To combat this, he’s developed an ingenious sensor that can track the movement of the blade in two dimensions without actually touching it. Utilizing the Raspberry Pi HQ camera, a 3D printed framework, and some precisely placed mirrors, [Andrew] says his optical sensor is able to determine the blade’s position to within 10 microns.
In the video below [Andrew] goes over how his “Split Vision Periscope” works, complete with some ray traced simulations of what the Pi camera actually sees when it looks through the device. After experimenting with different lighting setups, the final optical configuration presents the camera with two different perspectives of the saw blade set on a black background. That makes it relatively easy to pick out the blade using computer vision, and turn that into positional information.
The periscope arrangement is particularly advantageous here as it allows the camera and lens to be placed under the work surface and well away from the actual cutting, though we’re interested in seeing how it fares against the dust and debris that will inevitably be produced as the saw cuts. While he hit all of his design goals, [Andrew] does note that his mirrors do leave some room for improvement; but considering he hand cut them out of old hard drive platters we think the results are more than acceptable.
