Drone racing is nifty as heck, and a need all races share is a way to track lap times. One way to do it is to use transponders attached to each racer, and use a receiver unit of some kind to clock them as they pass by. People have rolled their own transponder designs with some success, but the next step is ditching add-on transponders entirely, and that’s exactly what the Delta 5 Race Timer project does.

The open-sourced design has a clever approach. In drone racing, each aircraft is remotely piloted over a wireless video link. Since every drone in a race already requires a video transmitter and its own channel on which to broadcast, the idea is to use the video signal as the transponder. As a result, no external hardware needs to be added to the aircraft. The tradeoff is that using the video signal in this way is trickier than a purpose-made transponder, but the hardware to do it is economical, accessible, and the design is well documented on GitHub.

The hardware consists of RX508 video receiver PCBs modified slightly to enable them to communicate over SPI. Each RX508 is attached to its own Arduino, which takes care of low-level communications. The Arduinos are themselves connected to a Raspberry Pi over I2C, allowing the Pi high-level control over the receivers while it serves up a web-enabled user interface. As a bonus, the Pi can do much more than simply act as a fancy stopwatch. The races themselves can be entirely organized and run through the web interface. The system is useful enough that other projects using its framework have popped up, such as the RotorHazard project by [PropWashed] which uses the same hardware design.

While rolling one’s own transponders is a good solution for getting your race on, using the video transmission signal to avoid transponders entirely is super clever. The fact that it can be done with inexpensive, off the shelf hardware is just icing on the cake.

Machine learning is starting to come online in all kinds of arenas lately, and the trend is likely to continue for the forseeable future. What was once only available for operators of supercomputers has found use among anyone with a reasonably powerful desktop computer. The downsizing isn’t stopping there, though, as Microsoft is pushing development of machine learning for embedded systems now.

The Embedded Learning Library (ELL) is a set of tools for allowing Arduinos, Raspberry Pis, and the like to take advantage of machine learning algorithms despite their small size and reduced capability. Microsoft intended this library to be useful for anyone, and has examples available for things like computer vision, audio keyword recognition, and a small handful of other implementations. The library should be expandable to any application where machine learning would be beneficial for a small embedded system, though, so it’s not limited to these example applications.

There is one small speed bump to running a machine learning algorithm on your Raspberry Pi, though. The high processor load tends to cause small SoCs to overheat. But adding a heatsink and fan is something we’ve certainly seen before. Don’t let your lack of a supercomputer keep you from exploring machine learning if you see a benefit to it, and if you need more power than just one Raspberry Pi you can always build a cluster to get your task done just a little bit faster, too.

Thanks to [Baldpower] for the tip!

A Raspberry Pi Zero (W) and Arduino are very different animals, the prior has processing power and connectivity while the latter has some analog to digital converters (ADCs) and nearly real-time reactions. You can connect them to one another with a USB cable and for many projects that will happily wed the two. Beyond that, we can interface this odd couple entirely through serial, SPI, I2C, and logic-level signaling. How? Through a device by [cburgess] that is being called an Arduino shield that supports a Pi0 (W). Maybe it is a cape which interfaces with Arduino. The distinction may be moot since each board has a familiar footprint and both of them are found here.

Depending on how they are set up and programmed, one can take control over the other, or they could happily do their own thing and just exchange a little information. This board is like a marriage counselor between a Raspberry Pi and an Arduino. It provides the level-shifting so they don’t blow each other up and libraries so they can speak nicely to one another. If you want to dig a bit deeper into this one, design files and code examples are on available.

Perhaps we’ll report on this board at the heart of a pinball machine retrofit, a vintage vending machine restoration, or maybe a working prop replica from the retro bar in Back to the Future II.

When [314Reactor] got a robot car kit, he knew he wanted to add some extra things to it. At about the same time he was watching a Star Trek episode that featured exocomps — robots that worked in dangerous areas. He decided to use those fictional devices to inspire his modifications to the car kit. Granted, the fictional robots were intelligent and had a replicator. So you know he won’t make an actual working replica. But then again, the ones on the TV show didn’t have all that either.

A Raspberry Pi runs Tensorflow using the standard camera.  This lets it identify objects of interest (assuming it gets them right) and sends the image back to the operator along with some identifying information. The kit already had an Arduino onboard and the new robot talks to it via a serial port. You can see a video about the project, below.

The design is complicated a bit by the fact that the original kit uses a Bluetooth adapter to send and receive serial commands from a mobile device. However, the controller software with the kit, though, allows for extra buttons, so the Arduino can receive command and send them to the Pi.

The code for the robot — known as Scorpion — is available on GitHub. The extra commands relate to the camera and also some servos that move pincers to mimic the TV robot. Images return to the operator via the Telegram cloud service.

We have to admit, the Scorpion isn’t quite the same as an exocomp. But we can see the influence on the design. It wasn’t smart enough to identify itself in the mirror, so we don’t think it achieved sentience.

We’ve seen smart robots using Tensorflow before. If you prefer, you can always try OpenCV.

It’s said that beauty and art can be found anywhere, as long as you look for it. The latest art project from [dmitry] both looks in unassuming places for that beauty, and projects what it sees for everyone to view. Like most of his projects, it’s able to produce its artwork in a very unconventional way. This particular project uses water as a lens, and by heating and cooling the water it produces a changing image.

The art installation uses a Peltier cooler to periodically freeze the water that’s being used as a lens. When light is projected through the frozen water onto a screen, the heat from the light melts the water and changes the projected image. The machine uses an Arduino and a Raspberry Pi in order to control the Peliter cooler and move the lens on top of the cooler to be frozen. Once frozen, it’s moved again into the path of the light in order to show an image through the lens.

[dmitry] intended the project to be a take on the cyclical nature of a substance from one state to another, and this is a very creative and interesting way of going about it. Of course, [dmitry]’s work always exhibits the same high build quality and interesting perspective, like his recent project which created music from the core samples of the deepest hole ever drilled.

  We were glued to our screens last month as NASA successfully landed the InSight module on Mars. (Bet you were, too.) What an amazing sight a Martian sunrise turns out to be! Now, we’ve got the bug. The bigtime Space Bug. Accordingly, our final Humble Bundle ebook deal of […]

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The Fifth Annual DNA Lounge Cocktail Robotics Grand Challenge is this Sunday, bringing out the best competitors in the art of robotics and bartending.

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The post Robot Bartenders Compete In Mixology and Microcontrollers appeared first on Make: DIY Projects and Ideas for Makers.

The Fifth Annual DNA Lounge Cocktail Robotics Grand Challenge is this Sunday, bringing out the best competitors in the art of robotics and bartending.

Read more on MAKE

The post Robot Bartenders Compete In Mixology and Microcontrollers appeared first on Make: DIY Projects and Ideas for Makers.

There’s an old saying that the cobbler’s children have no shoes. Sometimes we feel that way because we stay busy designing things for other people or for demos that we don’t have time to just build something we want. [Blue Blade Fish] wanted to build an Arduino-based aquarium controller. He’s detailed the system in (so far) 14 videos and it looks solid.

This isn’t just a simple controller, either. It is a modular design with an Arduino Mega and a lot of I/O for a serious fish tank. There are controls for heaters, fans, lights, wave makers and even top-off valves. The system can simulate moonlight at night and has an LCD display and keys. There’s also an Ethernet port and a Raspberry Pi component that creates a web interface, data storage, and configures the system. Even fail safes have been designed into the system, so you don’t boil or freeze expensive fishes. No wonder it took 14 videos!

We really liked the moonlight which has 32 LEDs with custom switching and a shift register. In front of the LEDs is — surprise — a picture of the moon. We wondered how it would be with a 3D-printed lilthophane which might diffuse the LEDs more. Because of the shift register, it is simple to control all the lights with just a few pins.

Apparently, giving fish an idea of the ambient lighting outside is important to people who keep fish. Because of the safeguards, a bad software bug isn’t going to boil his fish. Despite the looks, apparently neither will this upcycled fish tank.

In European medieval folklore, a practitioner of magic may call for assistance from a familiar spirit who takes an animal form disguise. [Alex Glow] is our modern-day Merlin who invoked the magical incantations of 3D printing, Arduino, and Raspberry Pi to summon her familiar Archimedes: The AI Robot Owl.

The key attraction in this build is Google’s AIY Vision kit. Specifically the vision processing unit that tremendously accelerates image classification tasks running on an attached Raspberry Pi Zero W. It no longer consumes several seconds to analyze each image, classification can now run several times per second, all performed locally. No connection to Google cloud required. (See our earlier coverage for more technical details.) The default demo application of a Google AIY Vision kit is a “joy detector” that looks for faces and attempts to determine if a face is happy or sad. We’ve previously seen this functionality mounted on a robot dog.

[Alex] aimed to go beyond the default app (and default box) to create Archimedes, who was to reward happy people with a sticker. As a moving robotic owl, Archimedes had far more crowd appeal than the vision kit’s default cardboard box. All the kit components have been integrated into Archimedes’ head. One eye is the expected Pi camera, the other eye is actually the kit’s piezo buzzer. The vision kit’s LED-illuminated button now tops the dapper owl’s hat.

Archimedes was created to join in Google’s promotion efforts. Their presence at this Maker Faire consisted of two tents: one introductory “Learn to Solder” tent where people can create a blinky LED badge, and the other tent is focused on their line of AIY kits like this vision kit. Filled with demos of what the kits can do aside from really cool robot owls.

Hopefully these promotional efforts helped many AIY kits find new homes in the hands of creative makers. It’s pretty exciting that such a powerful and inexpensive neural net processor is now widely available, and we look forward to many more AI-powered hacks to come.