Hackaday Prize 2023: Computer Vision Guides This Farm Mower

It’s a problem common to small-scale mixed agriculture worldwide, that of small areas of grass and weeds that need mowing. If you have a couple of sheep and enough electric fence there’s one way to do it, otherwise, if you rely on machinery, there’s a lot of hefting and pushing a mower in your future. Help is at hand, though, thanks to [Yuta Suito], whose pylon-guided mower is a lightweight device that mows an area defined by a set of orange traffic cones. Simply set the cones around the edge of the plot, place the mower within them, and it does the rest.

At its heart is a computer vision system that detects the cones and estimates distance from them by their perceived size. It mows in a spiral pattern by decreasing the cone height at which it turns, thus covering the whole area set out. Inside is a Raspberry Pi doing the heavy lifting, and because it’s designed for farmland rather than lawns, it has an adaptive track system to deal with obstacles. In its native Japan there is an ageing rural population, so it is particularly suitable for being operated by an older person. See it in action in the video below the break.

A robotic mower aimed at farms is certainly unusual here, but we’ve seen a lot of more conventional lawnmowers.

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3D Printed Robot Wants To Be Your Pet

Robots are cool. Robots you build yourself are cooler, especially ones that use stuff you have lying around already. Snoopy is a new open-source robot that uses an Arduino as a brain but with a 3D printed body and a short list of parts that can probably be sourced from the junk drawer. It’s still being developed, but it looks like a cool project heading in the right direction to produce an interesting robot.

It’s based on a new robot software platform called Kaia.ai that is built on top of the Robot Operating System 2 (ROS2), but with a more friendly and beginner-focused interface. Currently, the Snoopy project includes enough to get up and running with a printed frame and the electronics to install an Arduino running ROS2 that controls it. That’s an excellent place to start if you want to get into robotics, but without diving straight into the technical challenges of working with real-time operating systems.

It is also interesting that the previous project from the creator (called Kiddo) fell into the complexity trap, where you keep adding features and create an overly complex design that is a pain to build. Hopefully the designers have learned from Kiddo and will keep Snoopy simple.

We’ve covered plenty of other robot projects here at Hackaday, from ones that venture into nuclear reactors to ones that write your thank-you notes for you or give you hugs. We’ve even looked at how to give your robots a personality. Combine all those together with Snoopy and you could build a hugging, compassionate robot that has nice handwriting and can repair a nuclear reactor. And if you do, write it up and send it to our tips line!

Helping Robots Learn By Letting Them Fail

The [MIT Technology Review] has just released its annual list of the top innovators under the age of 35, and there are some interesting people on this list of the annoyingly accomplished at a young age. Like [Lerrel Pinto], an associate professor of computer science at NY University. His work focuses on teaching robots how to do things in the home by failing.

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Open Source Rover Gets An Update For Easier Building

Once upon a time, NASA-JPL put out a design for an open-source rocker-bogie rover. It was an impressive and capable thing, albeit a little expensive and difficult to build. Now, the open source community has dived in and refreshed the design, making it cheaper and more accessible than ever before.

Many parts of the original design have either become prohibitively expensive, gone out of stock, or been discontinued entirely. The new version, developed by the community that formed around the project, focuses on using off-the-shelf parts to bring costs down. Where the original design could cost as much as $3000 to build, the new model slashes that bill almost in half. It also eliminates any need for anything custom fabricated, with no machined or 3D printed parts required.

Other optimizations include cutting the rover’s head out from the basic model, as it’s not necessary for a great deal of applications. There is also better fluid and dust ingress protection, and improved serviceability. The entire rover model can also be loaded in OnShape for those desiring to inspect it or make their own modifications.

Parts lists are on GitHub for those desiring to build their own. Alternatively, check out the original design to learn more. Video after the break.

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Machine Learning Robot Runs Arduino Uno

When we think about machine learning, our minds often jump to datacenters full of sweating, overheating GPUs. However, lighter-weight hardware can also be used to these ends, as demonstrated by [Nikodem Bartnik] and his latest robot.

The robot is charged with autonomously navigating a simple racetrack delineated by cardboard barriers. The robot is based on a two-wheeled design with tank-style steering. Controlled by an Arduino Uno, the robot uses a Slamtec RPLIDAR sensor to help map out its surroundings. The microcontroller is also armed with a Bluetooth link and an SD card for storage.

The robot was first driven around the racetrack multiple times under manual control, all the while collecting LIDAR data. This data was combined with control inputs to help create a data set that could be used to train a machine learning model. Feature selection techniques were used to refine down the data points collected to those most relevant to completing the driving task. [Nikodem] explains how the model was created and then refined to drive the robot by itself in a variety of race track designs.

It’s a great primer on machine learning techniques applied to a small embedded platform.

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Big 3D Printed Hand Uses Big Servos, Naturally

[Ivan Miranda] isn’t afraid to dream big, and hopes to soon build a 3D printed giant robot he can ride around on. As the first step towards that goal, he’s built a giant printed hand big enough to hold a basketball.

The hand has fingers with several jointed segments, inspired by those wooden hand models sold as home decor at IKEA. The fingers are controlled via a toothed belt system, with two beefy 11 kg servos responsible for flexing each individual finger joint. A third 25 kg servo flexes the finger as a whole. [Ivan] does a good job of hiding the mechanics and wiring inside the structure of the hand itself, making an attractive robot appendage.

As with many such projects, control is where things get actually difficult. It’s one thing to make a robot hand flex its fingers in and out, and another thing to make it move in a useful, coordinated fashion. Regardless, [Ivan] is able to have the hand grip various objects, in part due to the usefulness of the hand’s opposable thumb. Future plans involve adding positional feedback to improve the finesse of the control system.

Building a good robot hand is no mean feat, and it remains one of the challenges behind building capable humanoid robots. Video after the break.

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Next-Gen Autopilot Puts A Robot At The Controls

While the concept of automotive “autopilots” are still in their infancy, pretty much any aircraft larger than an ultralight will have some mechanism to at least hold a fixed course and altitude. Typically the autopilot system is built into the airplane’s controls, but this new system replaces the pilot themselves in a manner reminiscent of the movie Airplane.

The robot pilot, known as PIBOT, uses both AI and robotics technology to fly the airplane without altering the aircraft. Unlike a normal autopilot system, this one can be fed the aircraft’s manuals in natural language, understand them, and use that information to fly the airplane. That includes operating any of the aircraft’s cockpit controls, not just the control column and pedal assembly. Supposedly, the autopilot can handle everything from takeoff to landing, and operate capably during heavy turbulence.

The Korea Advanced Institute of Science and Technology (KAIST) research team that built the machine hopes that it will pave the way for more advanced autopilot systems, and although this one has only been tested in simulators so far it shows enormous promise, and even has certain capabilities that go far beyond human pilots’ abilities including the ability to remember a much wider variety of charts. The team also hopes to eventually migrate the technology to the land, especially military vehicles, although we’ve seen how challenging that can be already.