We Dig This LEGO Excavator Conversion

July 4, 2017 by Lewin Day 16 Comments

[Frank] was lucky enough to score a bucket wheel excavator LEGO set as a birthday present, and we won’t lie – we’re jealous. However, out of the box, the kit is somewhat limited; there is only one motor to animate the entire machine and it can’t be fully remote controlled. But don’t worry — [Frank] set out to change that (Google Translation).

The first part of the build was to add motors to control the different functions of the excavator. One motor was added for each of the two tracks to allow the machine to drive forwards, backwards, and turn. Two more motors were added to raise and lower the digging buckets, and spin the tower. Finally, the original motor was left in place to turn the conveyor.

With that done, [Frank] then used a Raspberry Pi 3 to control all the hardware, being sure to house the new electronics in LEGO for an original look. The Raspberry Pi might be a lot of muscle to simply control a few motors, but it made it quick and easy for [Frank] to implement a Wiimote as a controller over Bluetooth. You can check out a couple demo videos in his most recent update.

It’s a great project, and we’d love to see the Raspberry Pi put to good use by allowing control over the Internet so we can dig in the sand over lunch breaks. We’ve seen some great LEGO hacks before, like this method of modifying cheap gear motors to work with LEGO parts.

Australian Raspberry Pi Tutorials

July 2, 2017 by Al Williams 20 Comments

There’s a new and very detailed video tutorial about the Raspberry Pi available from the Australian firm Core Electronics. There are 30 videos and 5 chapters in total. A few of the introduction videos are short, but the detail videos range from 3 to 16 minutes.

The instructor [Michael] starts out at the very beginning — loading NOOBS on the Pi — and then moves on to Python, shell scripting, and building GUI applications with TkInter. It also covers using Particle Pi for IoT applications that integrate with IFTTT.

We do realize that most people reading Hackaday have probably used a Raspberry Pi at least once or twice. However, we also know that we all get asked to recommend material for beginners, or — in some cases — we are using material to teach classes in schools or hackerspaces.

Continue reading “Australian Raspberry Pi Tutorials” →

Hackaday Prize Entry: Elephant AI

June 22, 2017 by John Baichtal 9 Comments

[Neil K. Sheridan]’s Automated Elephant Detection System was a semi-finalist in last year’s Hackaday Prize. Encouraged by his close finish, [Neil] is back at it with a refreshed and updated Elephant AI project.

The purpose of Elephant AI is to help humans and elephants coexist by eliminating contact between the two species. What this amounts to is an AI that can herd elephants. For this year’s project, [Neil] did away with the RF communications and village base stations in favor of 4G/3G-equipped, autonomous sentries equipped with Raspberry Pi computers with Go Pro cameras.

The main initiative of the project involves developing a system able to classify wild elephants visually, by automatically capturing images and then attempting to determine the elephant’s gender and age. Of particular importance is the challenge of detecting and controlling bull elephants during musth, a state of heightened aggressiveness that causes bulls to charge anyone who comes near. Musth can be detected visually, thanks to secretions called temporin that appear on the sides of the head. If cameras could identify bull elephants in musth and somehow guide them away from humans, everyone benefits.

This brings up another challenge: [Neil] is researching ways to actually get elephants to move away if they’re approaching humans. He’s looking into nonlethal techniques like audio files of bees or lions, as well as ping-pong balls containing chili pepper.

Got some ideas? Follow the Elephant AI project on Hackaday.io.

Testing The Outernet Dreamcatcher SDR

June 20, 2017 by Al Williams 8 Comments

What do you get when you cross an ARM-based Linux PC and an RTL-SDR? Sounds like the start of a joke, but the answer is Outernet’s Dreamcatcher. It is a single PCB with an RTL-SDR software defined radio, an L-band LNA, and an Allwinner A13 processor with 512MB of RAM and a 1 GHz clock speed. The rtl-sdr site recently posted a good review of the $99 board.

We’ll let you read the review for yourself, but the conclusion was that despite some bugs, the board was no more expensive than pulling the parts together separately. On the other hand, if you uses, for example, a Raspberry Pi 3, you might expect more support and more performance.

Despite the L-band hardware, there is a bypass antenna jack that allows you to receive other frequencies. There’s also two SD slots, one to boot from and another for storage. Several pieces of software had trouble running on the somewhat sluggish CPU, although some software that is optimized for the particular processor used fared better. You can read the details in the review.

The board is interesting, although unless you have a special packaging problem, you are probably as well off to combine a Pi and a dongle, as we have seen so many times before. If you have more horsepower you can even make the Pi transmit, although we’d suggest some filtering if you were going to do that for real.

Btrfs For The Pi

June 18, 2017 by Al Williams 19 Comments

File systems are one of those things that typical end users don’t think much about. Apparently, [seaQueue] isn’t a typical end user. He’s posted some instructions on how to run an alternate file system–btrfs–on the Raspberry Pi.

The right file system can make a big difference when it comes to performance and maintainability of any system that deals with storage. Linux, including most OSs for the Raspberry Pi, uses one of the EXT file systems. These are battle-hardened and well understood. However, there are other file systems, many of which have advanced features superior to the default file system for some applications.

Btrfs, often pronounced “butter eff ess”, begin life at Oracle and was born from an idea in an IBM paper. It offers advanced features like pooling, snapshots, and the ability to fuse multiple devices into one logical device. One notable feature the file system offers is copy-on-write. That means file copies can share common blocks as long as they stay common. Compression is available, as is seeding a file system with read-only storage, which could be very useful in some embedded systems. You can also configure several types of RAID using nothing but btrfs. You can see a video presentation about features of btrfs below.

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Hackaday Prize Entry: Automated Wildlife Recognition

June 14, 2017 by Donald Papp 11 Comments

Trail and wildlife cameras are commonly available nowadays, but the Wild Eye project aims to go beyond simply taking digital snapshots of critters. [Brenda Armour] uses a Raspberry Pi to not only take photos of wildlife who wander into the camera’s field of view, but to also automatically identify and categorize the animals seen using a visual recognition API from IBM via the Node-RED infrastructure. The result is a system that captures an image when motion is detected, sends the image to the visual recognition API, and attempts to identify any wildlife based on the returned data.

The visual recognition isn’t flawless, but a recent proof of concept shows promising results with crows, a cat, and a dog having been successfully identified. Perhaps when the project is ready to move deeper into the woods, elements from these solar-powered networked birdhouses (which also use the Raspberry Pi) could help cut some cords.

DIY Raspberry Neural Network Sees All, Recognizes Some

June 14, 2017 by Steven Dufresne 36 Comments

As a fun project I thought I’d put Google’s Inception-v3 neural network on a Raspberry Pi to see how well it does at recognizing objects first hand. It turned out to be not only fun to implement, but also the way I’d implemented it ended up making for loads of fun for everyone I showed it to, mostly folks at hackerspaces and such gatherings. And yes, some of it bordering on pornographic — cheeky hackers.

An added bonus many pointed out is that, once installed, no internet access is required. This is state-of-the-art, standalone object recognition with no big brother knowing what you’ve been up to, unlike with that nosey Alexa.

But will it lead to widespread useful AI? If a neural network can recognize every object around it, will that lead to human-like skills? Read on. Continue reading “DIY Raspberry Neural Network Sees All, Recognizes Some” →