The Perfect Desktop Kit For Experimenting With Self Driving Cars

When we think about self-driving cars, we normally think about big projects measured in billions of dollars, all funded by major automakers. But you can still dive into this world on a smaller scale, as [jmoreno555] demonstrates.

The build consists of a small RC car—an HSP 94123, in fact. It’s got a simple brushed motor inside, driven by a conventional speed controller, and servo-driven steering. A Raspberry Pi 4 is charged with driving the car, but it’s not alone. It’s outfitted with a Google Coral USB stick, which is a machine learning accelerator card capable of 4 trillion operations per second. The car also has a Wemos D1 onboard, charged with interfacing distance sensors to give the car a sense of its environment. Vision is courtesy of a 1.2-megapixel camera with a 160-degree lens, and a stereoscopic camera with twin 75-degree lenses. Software-wise, it’s early days yet. [jmoreno555] is exploring the use of Python and OpenCV to implement basic lane detection and other self driving routines, while using Blender as a simulator.

The real magic idea, though, is the treadmill. [jmoreno555] realized that one of the frustrations of working in this space is in having to chase a car around a test track. Instead, the use of a desktop treadmill allows the car to be programmed and debugged with less fuss in the early stages of development.

If you’re looking for a platform to experiment with AI and self-driving, this could be an project to dive in to. We’ve covered some other great builds in this space, too. Meanwhile, if you’ve cracked driving autonomy and want to let us know, our tipsline is always standing by!

Two assembled 1 dollar TinyML boards

$1 TinyML Board For Your “AI” Sensor Swarm

You might be under the impression that machine learning costs thousands of dollars to work with. That might be true in many cases, but there’s more to machine learning than you might think. For instance, what if you could shower anything with a network of cheap machine-learning-enabled sensors? The 1 dollar TinyML project by [Jon Nordby] allows you to do just that. These tiny boards host an STM32-like MCU, a BLE module, lithium ion power circuitry, and some nice sensor options — an accelerometer, a pair of microphones, and a light sensor.

What could you do with these sensors? [Jon] has talked a bit about a few commercial and non-commercial applications he’s worked on in his ML career, and tells us that the accelerometer alone lets you do human presence detection, sleep tracking, personal activity monitoring, or vibration pattern sensing, for a start. As for the sound input, there’s tasks ranging from gunshot or clapping detection, to coffee roasting process tracking, voice and speech detection, and surely much more. Just a few years ago, we’ve seen machine learning used to comfort a barking dog while its owner is away.

Bottom line is, you ought to get a few of these in your hands and start playing with ML. You still might need a bit of beefier hardware to train your code, but it gets that much easier once you have a network of sensors waiting for your command. Plus, since it’s an open source project, you’ll have a much easier time adding on any additional capabilities your particular application might need.

These boards are pretty cost-optimized, which makes it possible for you to order a couple dozen without breaking the bank. The $1 target is BOM cost, especially if you opt to not include one of the pricier sensors. You can assemble these boards yourself, or get them assembled at a fab of your choice for barely a cost increase. As for software, they will work with the emlearn framework.

Everything is on GitHub — from KiCad sources to Jupyter notebooks. As for Hackaday.io, there are five worklogs of impressive insight — the microphone worklog alone will teach you about microphone amplification in low-power conditions while keeping the cost low. Not as price-constrained and want to try on some image processing tasks? Here’s a beautiful Pi Pico ArduCam board with a camera and a TFT screen.

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Hackaday Links: April 21, 2024

Do humanoid robots dream of electric retirement? Who knows, but maybe we can ask Boston Dynamics’ Atlas HD, which was officially retired this week. The humanoid robot, notable for its warehouse Parkour and sweet dance moves, never went into production, at least not as far as we know. Atlas always seemed like it was intended to be an R&D platform, to see what was possible for a humanoid robot, and in that way it had a heck of a career. But it’s probably a good thing that fleets of Atlas robots aren’t wandering around shop floors or serving drinks, especially given the number of hydraulic blowouts the robot suffered. That also seems to be one of the lessons Boston Dynamics learned, since Atlas’ younger, nimbler replacement is said to be all-electric. From the thumbnail, the new kid already seems pretty scarred and battered, so here’s hoping we get to see some all-electric robot fails soon.

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Reggaeton-Be-Gone Disconnects Obnoxious Bluetooth Speakers

If you’re currently living outside of a Spanish-speaking country, it’s possible you’ve only heard of the music genre Reggaeton in passing, if at all. In places with large Spanish populations, though, it would be more surprising if you hadn’t heard it. It’s so popular especially in the Carribean and Latin America that it’s gotten on the nerves of some, most notably [Roni] whose neighbor might not do anything else but listen to this style of music, which can be heard through the walls. To solve the problem [Roni] is now introducing the Reggaeton-Be-Gone. (Google Translate from Spanish)

Inspired by the TV-B-Gone devices which purported to be able to turn off annoying TVs in bars, restaurants, and other places, this device can listen to music being played in the surrounding area and identify whether or not it is hearing Reggaeton. It does this using machine learning, taking samples of the audio it hears and making decisions based on a trained model. When the software, running on a Raspberry Pi, makes a positive identification of one of these songs, it looks for Bluetooth devices in the area and attempts to communicate with them in a number of ways, hopefully rapidly enough to disrupt their intended connections.

In testing with [Roni]’s neighbor, the device seems to show promise although it doesn’t completely disconnect the speaker from its host, instead only interfering with it enough for the neighbor to change locations. Clearly it merits further testing, and possibly other models trained for people who use Bluetooth speakers when skiing, hiking, or working out. Eventually the code will be posted to this GitHub page, but until then it’s not the only way to interfere with your neighbor’s annoying stereo.

Thanks to [BaldPower] and [Alfredo] for the tips!

A Badge For AI-Free Content – 100% Human!

These days, just about anyone with a pulse can fall on a keyboard and make an AI image generator spurt out some kind of vaguely visual content. A lot of it is crap. Some of it’s confusing. But most of all, creators hate it when their hand-crafted works are compared with these digital extrusions from mathematical slop. Enter the “not by AI” badge.

Screenshot from https://notbyai.fyi/business

Basically, it’s exactly what it sounds like. A sleek, modern badge that you slap on your artwork to tell people that you did this, not an AI. There are pre-baked versions for writers (“written by human”), visual artists (“painted by human”), and musicians (“produced by human”). The idea is that these badges would help people identify human-generated content and steer away from AI content if they’re trying to avoid it.

It’s not just intended to be added to individual artworks. Websites that have “at least 90%” of content created by humans are invited to host the badge, along with apps, too. This directive reveals an immediate flaw—the badge would easily confuse someone if they read the 10% of content by AI on a site wearing the badge. There’s also nothing stopping people from slapping the badge on AI-generated content and simply lying to people.

You might take a more cynical view if you dig deeper, though. The company is charging for various things, such as a monthly fee for businesses that want to display the badges.

We’ve talked about this before when we asked a simple question—how do you convince people your artwork was made by a human? We’re not sure we’ve yet found the answer, but this badge program is at least trying to do something about the issue. Share your human thoughts in the comments below.

Two researchers, a white woman and dark-skinned man look at a large monitor with a crystal structure displayed in red and white blocks.

AI On The Hunt For Better Batteries

While certain dystopian visions of the future have humans power the grid for AIs, Microsoft and Pacific Northwest National Laboratory (PNNL) set a machine learning system on the path of better solid state batteries instead.

Solid state batteries are the current darlings of battery research, promising a step-change in packaging size and safety among other advantages. While they have been working in the lab for some time now, we’re still yet to see any large-scale commercialization that could shake up the consumer electronics and electric vehicle spaces.

With a starting set of 32 million potential inorganic materials, the machine learning algorithm was able to select the 150 most promising candidates for further development in the lab. This smaller subset was then fed through a high-performance computing (HPC) algorithm to winnow the list down to 23. Eliminating previously explored compounds, the scientists were able to develop a promising Li/Na-ion solid state battery electrolyte that could reduce the needed Li in a battery by up to 70%.

For those of us who remember when energy materials research often consisted of digging through dusty old journal papers to find inorganic compounds of interest, this is a particularly exciting advancement. A couple more places technology can help in the sciences are robots doing the work in the lab or on the surgery table.

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Paddling Help From Electric-Assisted Kayak

Electric-assisted bicycles, or ebikes, are fundamentally changing the way people get around cities and towns. What were once sweaty, hilly, or difficult rides have quickly turned into a low-impact and inexpensive ways around town without foregoing all of the benefits of exercise. [Braden] hoped to expand this idea to the open waters and is building what he calls the ebike of kayaking, using the principles of electric-assisted bicycles to build a kayak that helps you get where you’re paddling without removing you completely from the experience.

The core of the project is a brushless DC motor originally intended a hydrofoil which is capable of providing 11 pounds (about 5 kg) of thrust. [Braden] has integrated it into a 3D-printed fin which attaches to the bottom of his inflatable kayak. The design of the fin took a few iterations to get right, but with a working motor and fin combination he set about tuning the system’s PID controller in a tub before taking it out to the open water. With just himself, the battery, and the motor controller in the kayak he’s getting about 14 miles of range with plenty of charge left in the battery after the trips.

[Braden]’s plans for developing this project further will eventually include a machine learning algorithm to detect when the rider is paddling and assist them, rather than simply being a throttle-operated motor as it exists currently. On a bicycle, strapping a sensor to the pedals is pretty straightforward, but we expect detecting paddling to be a bit more of a challenge. There are even more details about this build on his personal project blog. We’re looking forward to seeing the next version of the project but if you really need to see more boat hacks in the meantime be sure to check out [saveitforparts]’s boat which foregoes sails in favor of solar panels.

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