Automate The Farm With Acorn

July 1, 2021 by Bryan Cockfield 40 Comments

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.

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Machine-Vision Archer Makes You The Target, If You Dare

June 30, 2021 by Dan Maloney 6 Comments

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.

[Kamal] says he drew inspiration both from [Mark Rober]’s dart-catching dartboard and [Shane Wighton]’s self-dunking basketball hoop for this build. We’d say his results put in him good standing with the skill-optional sports community.

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Robotic Ball-Bouncing Platform Learns New Tricks

June 30, 2021 by Donald Papp 11 Comments

[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.

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Optical Sensor Keeps Eye On Wandering Saw Blade

May 24, 2021 by Tom Nardi 17 Comments

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.

An incredible amount of progress has been made since the first time we saw the CNC scroll saw, and we’re eager to see this new sensor fully integrated into the next version of [Andrew]’s impressive long-term project.

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Omnibot From The 80s Gets LED Matrix Eyes, Camera

May 18, 2021 by Donald Papp 8 Comments

[Ramin assadollahi] has been busy rebuilding and improving an Omnibot 5402, and the last piece of hardware he wanted to upgrade was some LED matrix eyes and a high quality Raspberry Pi camera for computer vision. An Omnibot was something most technical-minded youngsters remember drooling over in the 80s, and when [ramin] bought a couple of used units online, he went straight to the workbench to give the vintage machines some upgrades. After all, the Omnibot 5402 was pretty remarkable for its time, but is capable of much more with some modern hardware. One area that needed improvement was the eyes.

The eyes on the original Omnibot could light up, but that’s about all they were capable of. The first upgrade was installing two 8×8 LED matrix displays to form what [ramin] calls Minimal Expressive Eyes (MEE), powered by a Raspberry Pi. With the help of a 3D-printed adapter and some clever layout, the LED matrix displays fit behind the eye plate, maintaining the original look while opening loads of new output possibilities.

Adding a high quality Raspberry Pi camera with wide-angle lens was a bit more challenging and required and extra long camera ribbon connector, but with the lens nestled just below the eyes, the camera has a good view and isn’t particularly noticeable when the eyes are lit up. Having already upgraded the rest of the hardware, all that remains now is software work and we can’t wait to see the results.

Two short videos of the hardware are embedded below, be sure to give them a peek. And when you’re ready for more 80s-robot-upgrading-action, check out the Hero Jr.

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Cheat At Cornhole With A Bazillion-Dollar Robot

April 4, 2021 by Bryan Cockfield 19 Comments

While the days of outdoor cookouts may be a few months away for most of us, that certainly leaves plenty of time to prepare for that moment. While some may spend that time perfecting recipies or doing various home improvement projects during their remaining isolation time, others are practicing their skills at the various games played at these events. Specifically, this group from [Dave’s Armory] which have trained a robot that helps play the perfect game of cornhole. (Video, embedded below.)

While the robot in question is an industrial-grade KUKA KR-20 robot with a hefty price tag of $32,000 USD, the software and control system that the group built are fairly accessible for most people. The computer vision is handled by an Nvidia Jetson board, a single-board computer with extra parallel computing abilities, which runs OpenCV. With this setup and a custom hand for holding the corn bags, as well as a decent amount of training, the software is easily able to identify the cornhole board and instruct the robot to play a perfect game.

While we don’t all have expensive industrial robots sitting around in our junk drawer, the use of OpenCV and an accessible computer might make this project a useful introduction to anyone interested in computer vision, and the group made the code public on their GitHub page. OpenCV can be used for a lot of other things besides robotics as well, such as identifying weeds in a field or using a Raspberry Pi for facial recognition.

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Science Officer…Scan For Elephants!

March 17, 2021 by Al Williams 29 Comments

If you watch many espionage or terrorism movies set in the present day, there’s usually a scene where some government employee enhances a satellite image to show a clear picture of the main villain’s face. Do modern spy satellites have that kind of resolution? We don’t know, and if we did we couldn’t tell you anyway. But we do know that even with unclassified resolution, scientists are using satellite imagery and machine learning to count things like elephant populations.

When you think about it, it is a hard problem to count wildlife populations in their habitat. First, if you go in person you disturb the target animals. Even a drone is probably going to upset timid wildlife. Then there is the problem with trying to cover a large area and figuring out if the elephant you see today is the same one as one you saw yesterday. If you guess wrong you will either undercount or overcount.

The Oxford scientists counting elephants used the Worldview-3 satellite. It collects up to 680,000 square kilometers every day. You aren’t disturbing any of the observed creatures, and since each shot covers a huge swath of territory, your problem of double counting all but vanishes.

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