Robotic Coffee Comes To Brooklyn, But Will It Stay?

Robots are cool. Everyone knows it, and [Eater NY] highlights a coffee shop with a robotic server opening in Brooklyn. While robots able to prepare and serve drinks or food is not new, it isn’t every day a brick-and-mortar café with a robot behind the counter opens up. But expensive automation isn’t the only puzzle piece needed to make a location work.

A robotic coffee shop (like a robotic burger joint) certainly offers novelty, but can it sustain itself beyond that?

As one example, the linked article above points out that the city of New York prohibits entirely cashless businesses. Establishments must accept cash payments, and it’s unclear how the touchscreen-driven system would comply with that requirement.

There are also many tasks involved in running even a modest establishment — loading, cleaning, and maintaining for example — that can’t be realistically taken care of by an immobile robot barista. It’s unclear to what extent the robotic coffee shop will employ human staff, but it’s clear that human involvement is something that isn’t going be eliminated any time soon.

Some of you may remember the robotic burger joint that our own Brian Benchoff managed to check out, and many of his same observations come to mind. The robot burger was perhaps ahead of its time (its single location is listed as closed on Google maps with no recent activity) but maybe the robot coffee place can make it work. Still, expensive automation is only one piece of a system, and the ability to crank out a drink per minute 24/7 might not actually be the missing link.

Tiny Microcontroller Uses Real-Time Operating System

Most of the computers we interact with on a day-to-day basis use an operating system designed for flexibility. While these are great tools for getting work done or scrolling your favorite sites, they have a weakness when it comes to interacting quickly with a real-world environment. For these kinds of low-latency, high-reliability systems you may want to turn to something like freeRTOS which is optimized for this kind of application and which [Parikshit Pagare] has used to build his home automation system.

This build is based around an ESP32 for which freeRTOS, designed specifically for embedded systems, is uniquely suited. There are several channels built in capable of monitoring temperature, functioning as a smoke alarm, and sensing whether someone is at the front door. All of these are reported to a small OLED screen but are also updated on an Android app as well, which happens nearly instantaneously thanks to the real-time operating system. There are a number of user-controllable switches as well that are capable of turning lights or fans on and off.

For a home automation system, it’s one of the most low-cost and fully-featured we’ve seen and if you’re still having trouble coming across a Raspberry Pi as they sort out supply issues, something like this might make an excellent substitute at a fraction of the price. If you’re looking to expand even beyond this build, one of the gold standards for ESP32-based automation design is this build from [Marcus] which not only demonstrates how to build a system like this but goes into great detail on the ESPHome environment.

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Pi Pico Calculates Water Usage

Modern WiFi-enabled microcontrollers have made it affordable and easy to monitor everything from local weather information to electricity usage with typically no more than a few dollars worth of hardware and a little bit of programming knowledge. Monitoring one’s own utility data can be a little bit more difficult without interfering with the metering equipment, but we have seen some clever ways of doing this over the years. The latest is this water meter monitoring device based on a Raspberry Pi Pico.

The clever thing here isn’t so much that it’s based on the tiniest of Raspberry Pis, but how it keeps track of the somewhat obscured water flow information coming from the meter. Using a magnetometer placed close to the meter, the device can sense the magnetic field created as water flows through the meter’s internal sensors. The magnetic field changes in a non-obvious way as water flows through it, so the program has to watch for specific peaks in the magnetic field. Each of these specific waveforms the magnetometer detects counts to 0.0657 liters of water, which is accurate for most purposes.

For interfacing with a utility meter, this is one of the more efficient and elegant hacks we’ve seen in a while. There have, of course, been other attempts to literally read the meter using web cams and computer vision software, but the configuration for these builds is much more complex than something like this. You can interface with plenty of utility meters other than water meters, too, regardless of age.

Automating The Most Analog Of HVAC Equipment

Burning wood, while not a perfect heating solution, has a number of advantages over more modern heating appliances. It’s a renewable resource, doesn’t add carbon to the atmosphere over geologic time scales like fossil fuels do, can be harvested locally using simple tools, and it doesn’t require any modern infrastructure to support it. That being said, wood stoves aren’t something that are very high-tech and don’t lend themselves particularly well to automation as a result, at least with the exception of this wood stove from [jotulf45v2].

While this doesn’t automate the loading or direct control of a modern pellet stove, it does help [jotulf45v2] know when the best times are for loading more wood into the stove and helps keep the stove in the right temperature range to avoid the dangerous formation of creosote on the inside of his chimney caused by low temperature burns. Two temperature sensors, one on the stovetop and the other on the stove pipe, monitor the stove exhaust temperature. They feed data to a Node-RED system running on a Raspberry Pi which automatically notifies the user by text message when certain stove temperatures are reached.

For anyone heating with wood, tools like this are indispensable to help avoid spending an otherwise unnecessary amount of time getting a fire up to temperature quickly without over-firing the stove. Modern pellet stoves have some more modern conveniences like this built in, but many of the perks of using cord wood are lost with these devices. There are plenty of other ways to heat with wood too; take a look at this custom wood boiler which serves as a hot water heater.

A grey smartphone sits inside a sleeve made of light brown wood veneer and a black felt interior.

Wooden Smartphone Sleeve Keeps You On Task

Smartphones are amazing tools, but sometimes they can be an equally amazing time suck. In an effort to minimize how much precious time goes down the drain, [Lance Pan and Zeynep Kirmiziyesil] decided to make a functional and beautiful smartphone sleeve to keep you on task.

Most modern smartphones have some form of Do Not Disturb mode available, but having the phone visible can still be an invitation for distraction. By tucking the phone into an accessible but less visible sleeve, one can reduce the visual trigger to be on the phone while keeping it handy in the even of an emergency.

Once in the sleeve, the NFC tag sandwiched between the felt and wood veneer triggers an automation to put the phone into Do Not Disturb mode. This hack looks like something that you could easily pull off in an afternoon and looks great which is always a winning combination in our book.

To see some more focus-oriented hacks, checkout the Pomodachi or this Offline E-Paper Typewriter.

All Aboard The Garbage Express

Cog railways are a somewhat unusual way of train locomotion, typically only installed when a train needs to climb steep terrain. Any grade above about 10% needs the extra traction since the friction between the wheels and rails won’t be enough to push the train forward or keep it from falling backwards. Even without a steep hill to climb, sometimes a cog railway is necessary for traction as [Max Maker] discovered while building a train for his garbage cans.

The build started out as a way to avoid having to wheel his seven waste bins to the curb every month. Originally he built a more standard railway with a simple motor to drive the train, but he quickly realized that there wasn’t enough grip even when using plastic wheels, even though this track follows fairly flat terrain. Since the rail is built out of steel he quickly welded up a rack-and-pinion system to one of the rails. The build goes through many iterations before he finally settles on a design that solves the problem, and it includes several other features as well such as remote control and a spring-loaded automatic charger for the train at its station in the back yard.

While we always appreciate the eccentricity of those who would automate a relatively simple task that only happens once a month, [Max Maker] hopes to build this into a commercial product aimed at the elderly or disabled who would really benefit from a reliable, semi-automatic system that takes their trash bins to the curb for them. And, if your system only involves a single trash can, there are other ways of automating the task of taking the garbage to the curb.

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Shopping Cart Does The Tedious Work For You

Thanks to modern microcontrollers, basic home automation tasks such as turning lights on and off, opening blinds, and various other simple tasks have become common DIY projects. But with the advent of artificial intelligence and machine learning the amount of tasks that can be offloaded to computers has skyrocketed. This shopping cart that automates away the checkout lines at grocery stores certainly fits into this category.

The project was inspired by the cashierless Amazon stores where customers simply walk into a store, grab what they want, and leave. This is made possible by the fact that computers monitor their purchases and charge them automatically, but creator [kutluhan_aktar] wanted to explore a way of doing this without a fleet of sensors and cameras all over a store. By mounting the hardware to a shopping cart instead, the sensors travel with the shopper and monitor what’s placed in the cart instead of what’s taken from a shelf. It’s built around the OpenMV Cam H7, a microcontroller paired with a camera specifically designed for these types of tasks, and the custom circuitry inside the case also includes WiFi connectivity to make sure the shopping cart can report its findings properly.

[kutluhan_aktar] also built the entire software stack from the ground up and trained the model on a set of common products as a proof-of-concept. The idea was to allow smaller stores to operate more efficiently without needing a full suite of Amazon hardware and software backing it up, and this prototype seems to work pretty well to that end. If you want to develop a machine vision project on your own with more common hardware, take a look at this project which uses the Raspberry Pi instead.