This year marks the anniversary of the most popular selling home computer ever, the Commodore 64, which made its debut in 1982. Note that I am saying “home computer” and not personal computer (PC) because back then the term PC was not yet in use for home computer users.
Some of you have probably not heard of Commodore, which is kind of sad, though there is a simple reason why — Commodore is no longer around to maintain its legacy. If one were to watch a documentary about the 1980s they may see a picture of an Apple computer or its founders but most likely would not see a picture of a Commodore computer in spite of selling tens of millions of units.
To understand the success of the C64 I would first back up and talk about the fabled era of home computers which starts with understanding the microprocessor of the day, the venerable 6502. Check out the video and follow along below.
Robotic mowers are becoming a common sight in some places, enabled by the cost of motors and the needed control electronics being much lower, thanks to the pace of modern engineering. But, in many cases, they still appear to be really rather dumb, little more than a jacked up bump-and-go with a spinning blade. [Clemens Elflein] has taken a cheap, dumb mower and given it a brain transplant based around a Raspberry Pi 4 paired up with a Raspberry Pi Pico for the real time control side of things. [Clemens] is calling this OpenMower, with the motivation to create an open source robot mower controller with support for GPS navigation, using RTK for extra precision.
The donor robot was a YardForce Classic 500, and after inspection of the control PCB, it looks like many other robot mower models are likely to use the same controller and thus be compatible with the openmower platform. A custom mainboard houses the Pi 4 and Pico, an ArduSimple RTK GPS module (giving a reported navigational accuracy of 1 cm,) as well as three BLDC motor drivers for the wheels and rotor. Everything is based on modules, plugging into the mainboard, reducing the complexity of the project significantly. For a cheap mower platform, the Yardforce unit has a good build quality, with connectors everywhere, making OpenMower a plug and play solution. Even the user interface on top of the mower was usable, with a custom PCB below presenting some push buttons at the appropriate positions.
OpenMower mainboard
Motor control is courtesy of the xESC project, which provides FOC motor control for low cost, interfacing with the host controller via a serial link. This is worth looking into in its own right! On the software side of things, [Clemens] is using ROS, which implements the low level robot control, path planning (using code taken from Slic3r) as well a kinematics constraints for object avoidance. The video below, shows how simple the machine is to operate — just drive it around the perimeter of lawn with a handheld controller, and show it where obstacles such as trees are, and then set it going. The mower is even capable of mowing multiple lawns, making the journey between them automatically!
Neural networks have become a hot topic over the last decade, put to work on jobs from recognizing image content to generating text and even playing video games. However, these artificial neural networks are essentially just piles of maths inside a computer, and while they are capable of great things, the technology hasn’t yet shown the capability to produce genuine intelligence.
Cortical Labs, based down in Melbourne, Australia, has a different approach. Rather than rely solely on silicon, their work involves growing real biological neurons on electrode arrays, allowing them to be interfaced with digital systems. Their latest work has shown promise that these real biological neural networks can be made to learn, according to a pre-print paper that is yet to go through peer review. Continue reading “Researchers Build Neural Networks With Actual Neurons”→
We’ve gotten used to the GPIO-available functions of Raspberry Pi computers remaining largely the same over the years, which is why it might have flown a little bit under the radar: the Raspberry Pi 4 has six SPI controllers, six I2C controllers, and six UARTs – all on its 40-pin header. You can’t make use of all of these at once, but with up to four different connections wired to a single pin you can carve out a pretty powerful combination of peripherals for your next robotics, automation or cat herding project.
The datasheet for these peripherals is pleasant to go through, with all the register maps nicely laid out – even if you don’t plan to work with the register mappings yourself, the maintainers of your preferred hardware enablement libraries will have an easier time! And, of course, these peripherals are present on the Compute Module 4, too. It might feel like such a deluge of interfaces is excessive, however, it lets you achieve some pretty cool stuff that wouldn’t be possible otherwise.
Having multiple I2C interfaces helps deal with various I2C-specific problems, such as address conflicts, throughput issues, and mixing devices that support different maximum speeds, which means you no longer need fancy mux chips to run five low-resolution Melexis thermal camera sensors at once. (Oh, and the I2C clock stretching bug has been fixed!) SPI interfaces are used for devices with high bandwidth, and with a few separate SPI ports, you could run multiple relatively high-resolution displays at once, No-Nixie Nixie clock style.
As for UARTs, the Raspberry Pi’s one-and-a-half UART interface has long been an issue in robotics and home automation applications. With a slew of devices like radio receivers/transmitters, LIDARs and resilient RS485 multi-drop interfaces available in UART form, it’s nice that you no longer have to sacrifice Bluetooth or a debug console to get some fancy sensors wired up to your robot’s brain. You can enable up to six UARTs. Continue reading “Did You Know That The Raspberry Pi 4 Has More SPI, I2C, UART Ports?”→
Join us on Wednesday, January 19 at noon Pacific as we kick off the 2022 Hack Chat season with the Electromyography Hack Chat with hut!
It’s one of the simplest acts most people can perform, but just wiggling your finger is a vastly complex process under the hood. Once you consciously decide to move your digit, a cascade of electrochemical reactions courses from the brain down the spinal cord and along nerves to reach the muscles fibers of the forearm, where still more reactions occur to stimulate the muscle fibers and cause them to contract, setting that finger to wiggling.
The electrical activity going on inside you while you’re moving your muscles is actually strong enough to make it to the skin, and is detectable using electromyography, or EMG. But just because a signal exists doesn’t mean it’s trivial to make use of. Teasing a usable signal from one muscle group amidst the noise from everything else going on in a human body can be a chore, but not an insurmountable one, even for the home gamer.
To make EMG a little easier, our host for this Hack Chat, hut, has been hard at work on PsyLink, a line of prototype EMG interfaces that can be used to detect muscle movements and use them to control whatever you want. In this Hack Chat, we’ll dive into EMG in general and PsyLink in particular, and find out how to put our muscles to work for something other than wiggling our fingers.
The festive season is often as good a reason as any to get out the tools and whip up a fun little project. [Simon] wanted a little tchotchke to give out for the holidays, so they whipped up a Christmas tree PCB that’s actually Arduino-compatible.
O’ Christmas Tree, on PCB…
It’s a forward-looking project, complete with USB-C connector, future-proofing it for some time until yet another connector standard comes along. When plugged in, like many similar projects, it blinks some APA102 LEDs in a festive way. The PCB joins in on the fun, with white silkscreen baubles augmented by golden ones created by gaps in the soldermask.
An ATTiny167 is the brains of the operation, using the Micronucleus bootloader in a similar configuration to the DigiSpark Pro development board. It relies on a bit-banged low-speed USB interface for programming, but the functionality is largely transparent to the end user. It can readily be programmed from within the Arduino IDE.
It’s not an advanced project by any means, but is a cute giveaway piece which can make a good impression in much the same way as a fancy PCB business card. It could also serve as an easy tool for introducing new makers to working with addressable LEDs. Meanwhile, if you’ve been cooking up your own holiday projects in the lab, don’t hesitate to drop us a line!
Music visualizers were all the rage back in era of Winamp and Windows Media Player. They’re even cooler when they don’t just live on your computer screen, though, as [Emily Velasco’s] latest project demonstrates.
The build consists of two mannequin arms on a board mounted on the wall. The arms were sourced for just $5 from a Sears that went out of business, and originally fastened to the mannequin thanks to magnets inside. Thus, putting two steel plates on the board allowed the arms to be attached, and they can be freely arranged as [Emily] sees fit.
The ESP32-based Pixelblaze LED controller serves as the brains of the operation, controlling LEDs mounted inside the arms themselves. Using a dedicated controller makes working with addressable LEDs a cinch. As a further bonus, the board serves up a web interface, allowing patterns to be changed without having to hook up a cable to the device. Meanwhile, a sensor board inside the arms uses a microphone to enable the light show to react to sound and music.
It’s one of the more obscure uses for an old mannequin, but definitely one that appeals to our love of everything that flickers and glows. It’s a build very much up [Emily’s] alley; as a prolific maker, she loves to build weird and wonderful creations, as shared during her talk at the 2019 Hackaday Superconference. Video after the break.
