Bringing MATLAB to a Vacuum Near You

The essence of hacking is modifying something to do a different function. Many of us learned as kids, though, that turning the family TV into an oscilloscope often got you into trouble.

These days, TVs are flat and don’t have high voltage inside, but there’s always the family robot, often known as a Roomba. Besides providing feline transportation, these little pancake-shaped robots also clean floors.

If you don’t want to evict the cat and still get a robust domestic robot platform for experimentation, about $200 will get you a Roomba made to be hacked — the iRobot Create 2. [Gstatum] has a tutorial for using a Raspberry Pi and MATLAB to get one quickly running and even doing basic object recognition using the Pi’s camera.

The code even interfaces with Twitter. The impressive part is the code fits on about a page. This isn’t, however, completely autonomous. It uses a connected phone’s sensor’s so that the phone’s orientation controls the robot’s motion, but the robot does use sensors to prevent driving into walls or falling off a cliff. It also can detect being picked up and uses the Pi’s camera to detect a green flag.

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Swarm of Servos Plays this Robotic Glockenspiel

It’s the happiest sounding instrument in the marching band, and it’s got the best name to boot. It’s the glockenspiel, and if this robotic glockenspiel has anything to say about it, the days of human glockenspielists are numbered.

In its present prototype form, [Averton Engineering]’s “Spielatron” looks a little like something from a carousel calliope or an animatronic pizza restaurant band. Using a cast-off glockenspiel from a school music room as a base, the Spielatron uses four mallets to play all the notes. Each key is struck by a mallet secured to a base made of two servos. For lack of more descriptive mallet terminology, these servos provide pan and tilt so the mallet can strike the proper keys. The video below shows the Spielatron’s first recital.

An Arduino runs the servos and a MIDI interface; unfortunately, this version can’t play chords and is a little limited on note length, but upgrades are on the way. We’ve seen a robotic glockenspiel before with a similar design that might have some ideas for increasing performance. But if you’re looking for a more sublime sound, check out this dry ice-powered wind chime.

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Reanimating Boney the Robot Dog

[Divconstructors] cashed in after Halloween and picked up a skeleton dog prop from the Home Depot, for the simple and logical purpose of turning it into a robot.

The first step was to cut apart the various body parts, followed by adding bearings to the joints and bolting in a metal chassis fabricated from 1/8″ aluminum stock. This is all pretty standard stuff in the Dr. Frankenstein biz. For electronics he uses a Mega with a bark-emitting MP3 shield on top of it. Separately, a servo control board manages the dozenish servos — not to mention the tail-wagging stepper.

[Divconstructors] actually bought two skeletons, one to be his protoype and the other to be the nice-looking build. However, we at Hackaday feel like he might have missed an opportunity: As any necromancer can tell you, a freakish combination of two skeletons beats out two normal skeletons any night of the week. Also, two words for you to consider: cyberdog ransomeware. We imagine you don’t really feel ransomware until there’s the family robodog ready to test out its high-torque jaw servos on your flesh. Of course if he were a real dog we could either remotely control him with a hot dog, or just give him a talking collar.

Prototyping, Making A Board For, And Coding An ARM Neural Net Robot

[Sean Hodgins]’s calls his three-part video series an Arduino Neural Network Robot but we’d rather call it an enjoyable series on prototyping, designing a board with surface mount parts, assembling it, and oh yeah, putting a neural network on it, all the while offering plenty of useful tips.

In part one, prototype and design, he starts us out with a prototype using a breadboard. The final robot isn’t on an Arduino, but instead is on a custom-made board built around an ARM Cortex-M0+ processor. However, for the prototype, he uses a SparkFun SAM21 Arduino-sized board, a Pololu DRV8835 dual motor driver board, four photoresistors, two motors, a battery, and sundry other parts.

Once he’s proven the prototype works, he creates the schematic for his custom board. Rather than start from scratch, he goes to SparkFun’s and Pololu’s websites for the schematics of their boards and incorporates those into his design. From there he talks about how and why he starts out in a CAD program, then moves on to KiCad where he talks about his approach to layout.

Part two is about soldering and assembly, from how he sorts the components while still in their shipping packages, to tips on doing the reflow in a toaster oven, and fixing bridges and parts that aren’t on all their pads, including the microprocessor.

In Part three he writes the code. The robot’s objective is simple, run away from the light. He first tests the photoresistors without the motors and then writes a procedural program to make the robot afraid of the light, this time with the motors. Finally, he writes the neural network code, but not before first giving a decent explanation of how the neural network works. He admits that you don’t really need a neural network to make the robot run away from the light. But from his comparisons of the robot running using the procedural approach and then the neural network approach, we think the neural network one responds better to what would be the in-between cases for the procedural approach. Admittedly, it could be that a better procedural version could be written, but having the neural network saved him the trouble and he’s shown us a lot that can be reused from the effort.

In case you want to replicate this, [Sean]’s provided a GitHub page with BOM, code and so on. Check out all three parts below, or watch just the parts that interest you.

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An Interview with Alex Williams, Grand Prize Winner

Alex Williams pulled off an incredible engineering project. He developed an Autonomous Underwater Vehicle (AUV) which uses a buoyancy engine rather than propellers as its propulsion mechanism and made the entire project Open Source and Open Hardware.

The design aims to make extended duration missions a possibility by using very little power to move the vessel. What’s as remarkable as the project itself is that Alex made a goal for himself to document the project to the level that it is fully reproducible. His success in both of these areas is what makes the Open Source Underwater Glider the perfect Grand Prize winner for the 2017 Hackaday Prize.

We got to sit down with Alex the morning after he won to talk about the project and the path he took to get here.

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Gorgeous Engineering Inside Wheels of a Robotic Trail Buddy

Robots are great in general, and [taylor] is currently working on something a bit unusual: a 3D printed explorer robot to autonomously follow outdoor trails, named Rover. Rover is still under development, and [taylor] recently completed the drive system and body designs, all shared via OnShape.

Rover has 3D printed 4.3:1 reduction planetary gearboxes embedded into each wheel, with off the shelf bearings and brushless motors. A Raspberry Pi sits in the driver’s seat, and the goal is to use a version of NVIDA’s TrailNet framework for GPS-free navigation of paths. As a result, [taylor] hopes to end up with a robotic “trail buddy” that can be made with off-the-shelf components and 3D printed parts.

Moving the motors and gearboxes into the wheels themselves makes for a very small main body to the robot, and it’s more than a bit strange to see the wheel spinning opposite to the wheel’s hub. Check out the video showcasing the latest development of the wheels, embedded below.

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Real-Life Electronic Neurons

All the kids down at Stanford are talking about neural nets. Whether this is due to the actual utility of neural nets or because all those kids were born after AI’s last death in the mid-80s is anyone’s guess, but there is one significant drawback to this tiny subset of machine intelligence: it’s a complete abstraction. Nothing called a ‘neural net’ is actually like a nervous system, there are no dendrites or axions and you can’t learn how to do logic by connecting neurons together.

NeruroBytes is not a strange platform for neural nets. It’s physical neurons, rendered in PCBs and Molex connectors. Now, finally, it’s a Kickstarter project, and one of the more exciting educational electronic projects we’ve ever seen.

Regular Hackaday readers should be very familiar with NeuroBytes. It began as a project for the Hackaday Prize all the way back in 2015. There, it was recognized as a finalist for the Best Product, Since then, the team behind NeuroBytes have received an NHS grant, they’re certified Open Source Hardware through OSHWA, and there are now enough NeuroBytes to recreate the connectome of a flatworm. It’s doubtful the team actually has enough patience to recreate the brain of even the simplest organism, but is already an impressive feat.

The highlights of the NeuroBytes Kickstarter include seven different types of neurons for different sensory systems, kits to test the patellar reflex, and what is probably most interesting to the Hackaday crowd, a Braitenberg Vehicle chassis, meant to test the ideas set forth in Valentino Braitenberg’s book, Vehicles: Experiments in Synthetic Psychology. If that book doesn’t sound familiar, BEAM robots probably do; that’s where the idea for BEAM robots came from.

It’s been a long, long journey for [Zach] and the other creators of NeuroBytes to get to this point. It’s great that this project is now finally in the wild, and we can’t wait to see what comes of it. Hopefully a full flatworm connectome.