[Chris Mullins] wanted to automate opening and closing the slats of mini blinds in his apartment, and came up with a system to do it as a fun project. Manually opening and closing the slats means twisting a rod. Seems straightforward to automate that, but as usual when having to work around something that already exists, making no permanent alterations, complications arose.
The blinds are only 1 inch wide, leaving little room for mounting any sort of hardware. While there is a lot of prior art when it comes to automating blinds, nothing he found actually fit the situation [Chris] had, so he rolled his own.
The rod that is normally twisted to control the blinds is removed, and the shaft of a stepper motor takes its place. [Chris]’ mounting solution is made to fit blinds with narrow 1 inch tracks (existing projects he found relied on 2 inch tracks) and the 3D printed mount is fully adjustable, so the 28BYJ stepper motor can be moved into exactly the right position. Speaking of the stepper motor, the 28BYJ motor is unipolar but the A4988 driver he wanted to use is for bipolar steppers only. Luckily, cutting a trace on the motor’s PCB is all it takes to turn a unipolar motor into bipolar.
To drive the motor and provide wireless functionality, the whole thing works with a Wemos D1 ESP8266, an A4988 stepper driver, and a buck converter. While it worked fine as a one-off on a perfboard, [Chris] used the project as an opportunity to learn how to make a PCB using KiCad; the PCB project is here on GitHub and the ESP8266 runs the ESPHome firmware. Be sure to check out the project page on his blog for all the details; [Chris] links to all the resources there, and covers everything from a bill of materials to walking through configuration of ESPHome with integration into the open-source Home Assistant project.
Looking to control natural light but blinds aren’t your thing? Maybe consider automated curtains.
In the fast food industry, speed is everything. The concept has never just been about cooking quickly. Players in this competitive space spend huge fortunes every year on optimizing every aspect of the experience, from ordering, to queueing, to cleaning up afterwards. And while fast food restaurants are major employers worldwide, there’s always been a firm eye cast over the gains that automation has to offer.
Flipping Burgers
In the West, fast food most commonly brings burgers to mind. Preparing a quality burger requires attention to the grade of meat, fat content, as well as the preparation steps before it hits the grill. Then it’s all about temperature and time, and getting just the right sear to bring out the natural flavors of the beef. While a boutique burger joint will employ a skilled worker to get things just right, that doesn’t fly for fast food. Every order needs to be preparable by whichever minimum-wage worker got the shift, and be as repeatable as possible across entire countries, or even the world, to meet customer expectations.
Flippy ROAR (Robot On A Rail) at work on the fryers in a White Castle in Chicago.
In their efforts to improve efficiency, White Castle have taken the bold step of installing a robotic burger flipper, imaginitively named Flippy. Built by Miso Robotics, the robot hangs from a ceiling rail to minimise the space taken up in the kitchen area. Based on a Fanuc robot arm, the system uses artificial intelligence to manage kitchen resources, Flippy is capable of managing both the grill and fryers together to ensure fries don’t get cold while the burgers are still cooking, for example. Currently undergoing a trial run in Chicago, White Castle has ambitions to roll the technology out to further stores if successful.
We’ve seen other robotic burger systems before, too. In late 2018, our own [Brian Benchoff] went down to check out Creator, which cooks and assembles its burgers entirely by machine. Despite suspicions about the business model, Creator have persisted until the present day with their unique blend of technology and culinary arts. Particularly impressive were their restaurant modifications in the face of COVID-19. The restaurant received an overhaul, with meals being robotically prepared directly in a take-out box with no human contact. Take-out meals are double-bagged and passed to customers through an airlock, with a positive-pressure system in the restaurant to protect staff from the outside world.
Pizzabots
Pizza is a staple food for many, with high demand and a stronger dependence on delivery than other fast food options. This has led to the industry exploring many avenues for automation, from preparation to order fulfillment.
In terms of outright throughput, Zume were a startup that led the charge. Their system involves multiple robots to knead dough, apply sauce and place the pie in the oven. Due to the variable nature sizes and shapes of various toppings, these are still applied by humans in the loop. Capable of turning out 120 pizzas per hour, a single facility could compete with many traditional human-staffed pizza shops. They also experimented with kitchens-on-wheels that use predictive algorithms to stock out trucks that cook pizzas on the way to the customer’s door. Unfortunately, despite a one-time $4 billion USD valuation, the startup hit a rocky patch and is now focusing on packaging instead.
Picnic aim to make lots of pizza, fast. Their business model involves working directly with existing restaurants, rather than creating their own fast-food brand from scratch.
Picnic have gone further, claiming an output rate of up to 300 twelve-inch pies an hour. The startup aims to work with a variety of existing pizza restaurants, rather than striking out as their own brand. One hurdle to overcome is the delivery of a prepared pizza into the oven. There are many varieties and kinds of pizza oven used in commercial settings, and different loading techniques are required for each. This remains an active area of development for the company. The company has a strong focus on the emerging ghost kitchen model, where restaurants are built solely to fulfill online delivery orders, with no dining area.
Domino’s is one of the largest pizza companies in the world, and thus far have focused their efforts on autonomous delivery. The DRU, or Domino’s Robotic Unit, was launched to much fanfare, promising to deliver pizzas by a small wheeled robotic unit. Equipped with sensors to avoid obstacles and GPS navigation, the project has not entered mainstream service just yet. However, between this and the multitude of companies exploring drone delivery, expect to see this become more of a thing in coming years.
Despite the marketing sizzle, the DOM Pizza Checker does not project holograms.
A more immediate innovation from Domino’s has been the DOM Pizza Checker. With customer complaints about pizza quality plaguing the chain, the pizza checker is an AI-powered visual system. It’s responsible for determining if the correct pizza has been made, with the right toppings and good distribution. An impressive practical use of AI imaging technology, it sounds an alarm if the pizza isn’t up to scratch, prompting it to be remade. However, it has come under scrutiny as a potential method to harass franchisees and workers. Additionally, the limitations of the system mean that Domino’s are still perfectly capable of turning out a bad pizza on occasion.
Other Efforts
One of the most visible examples of fast food automation is the widespread adoption of order kiosks by McDonalds, which kicked off in earnest in 2015. The majority of stores in the US now rely on these to speed up the ordering process, while also enabling more customization for customers with less fuss. Over-the-counter ordering is still possible at most locations, but there’s a heavy emphasis on using the new system.
McDonald’s automated beverage dispenser will be a familiar sight to many. Considered a great help when it works, and a great hindrance when it jams, spills, or simply shuts down.
In general, online ordering and delivery has become the norm, where ten years ago, the idea of getting McDonalds delivered was considered magical and arcane. This writer made seven attempts to take advantage of an early version of the service in China in 2015, succeeding only once, largely due to a lack of understanding of addresses written in non-Latin characters. However, due to the now-ubiquitous nature of services like Ubereats, Postmates, and Menulog, it’s simple for any restaurant to largely automate their ordering and fulfillment process, and reach customers at a distance from their brick-and-mortar locations.
Other efforts are smaller in scope, but contribute to great efficiency gains back-of-house. McDonalds and other chains have widely adopted automated beverage systems. Capable of automatically dispensing cups and the requisite fluids, they take instructions directly from the digital ordering system and take the manual labor out of drink preparation. They’re also great at slightly underfilling the cups, in a way that any human would consider incredibly rude.
Conclusion
Robots in the fast-food kitchen stand to reduce or eliminate tedious, repetitive work. Robots don’t get sick, and less human labour means fewer rostering hassles. It seems to be a foregone conclusion that more automation is on the way, and while some startups may falter, others will surely succeed. Your next meal may just yet be entirely prepared by a robot, even if it’s still delivered by a tired grad student on a moped. Come what may!
Giving a 3D printer the ability to remove its own prints means that it can crank out part after part automatically, without relying on a human operator between jobs. [Damien Weber] has done exactly that to his Prusa MK3/S printer, with what he calls the Chain Production Add-on.
[Damien]’s approach is one we haven’t quite seen before. When printing is complete, a fan cools the part then an arm (with what looks like utility knife blades attached at an angle) swings up and behind the bed. The arm zips forward and scoops the print off the bed, dumping the finished part in the process. It’s all made from 3D printed parts, aluminum extrusion and hardware, two stepper motors, and a driver PCB. The GitHub repository linked above holds all the design files, but there is also a project page on PrusaPrinters.org.
Not quite sure how it all works? Watch it in action in the video embedded below.
Smart homes come with a lot of perks, not least among which is the ability to monitor the goings-on in your home, track them, and make trends. Each piece of monitoring equipment, such as sensors or cameras, is another set of wires that needs to be run and another “thing” that needs to be maintained on your system. There are sometimes clever ways of avoiding sensors, though, while still retaining the usefulness of having them.
In this build, [squix] uses existing sensors for electricity metering that he already had in order to alert him when his oven is pre-heated. The sensor is a Shelly 3EM, and the way that it interfaces with his home automation is by realizing that his electric oven will stop delivering electricity to the heating elements once it has reached the desired temperature. He is able to monitor the sudden dramatic decrease in electricity demand at his house with the home controller, and use that decrease to alert him to the fact that his oven is ready without having to install something extra like a temperature sensor.
While this particular sensor may only be available in some parts of Europe, we presume the idea would hold out across many different sensors and even other devices. Even a small machine learning device should be able to tell what loads are coming on at what times, and then be programmed to perform functions based on that data.
Tending to a garden is usually a rewarding endeavor, as long as there is good soil to work with. If there isn’t, it can either get frustrating quickly having to deal with soils like sand or hard clay, or it can get expensive by having to truck in compost each year. Alternatively, it’s possible to set up systems of growing plants that don’t need any soil at all, although this requires an automated system otherwise known as hydroponics to manage water and nutrients sent to the plants.
This setup by [Kyle] is unique in that it uses his own open-source software which he calls Mycodo to control the hydroponic system. It is loaded onto a Raspberry Pi 4 (which he notes can now be booted from a USB drive instead of an SD card) which controls all of the peripherals needed for making sure that the water has the correct amount of nutrients and chemical composition.
The build is much more than just a software control panel, though. [Kyle] walks through every part of setting up a small hydroponic system capable of effectively growing 15-20 plants indoors. He grows varieties of lettuce and basil, but this system can work for many more types of plants as well. With just slight variations, a similar system can not only grow plants like these, but fish as well.
I will be the first to admit it. This is almost not — at least not specifically — a Linux article. The subject? An automation tool for Chrome or Firefox. But before you hit the back button, hear me out. Sure, this Chrome plugin started out as a tool to automatically test web pages and automate repetitive tasks in the browser. However, it can extend that power to all programs on your computer. So, in theory, you can use it to graphically build macros that can interact with desktop applications in surprisingly sophisticated ways. In theory, anyway; there are a few problems.
The program has a few different names. Most documentation says UI Vision RPA, although there are some references to Kantu, which appears to be an older name. RPA is an acronym for Robotic Process Automation, which is an industry buzz word.
Let’s take it for a spin and see what it’s all about.
Hackers always have their eye out for a good deal, so when [Gadget Reboot] saw a good price on square stickers he had to pull the trigger. There was just one problem: his logo is a circle. He could have rectified the problem with a pair of scissors on a lazy afternoon, but we think building an elaborate circle cutting machine was a much better use of his time.
But this project isn’t just for the Giotto wannabes. Even if you don’t find yourself in need of an infinite supply of perfect paper circles, the video after the break provides an excellent case study in getting stepper motors to do your bidding; whatever that might be. [Gadget Reboot] walks the viewer through the design and construction of a dual stepper motor controller that could be used for a multitude of tasks.
With an onboard Arduino Pro Micro, OLED display, and rotary encoder, this controller just needs some custom software to make your CNC dreams come true. [Gadget Reboot] is even using low-cost optical endstops in this build, which are a great non-contact way of making sure your machine doesn’t go out of bounds. That’s particularly important when the machine happens to be wielding a razor blade.
The rod that is normally twisted to control the blinds is removed, and the shaft of a stepper motor takes its place. [Chris]’ mounting solution is made to fit blinds with narrow 1 inch tracks (existing projects he found relied on 2 inch tracks) and the 3D printed mount is fully adjustable, so the 28BYJ stepper motor can be moved into exactly the right position. Speaking of the stepper motor, the 28BYJ motor is unipolar but the A4988 driver he wanted to use is for bipolar steppers only. Luckily, cutting a trace on the motor’s PCB is all it takes to turn a unipolar motor into bipolar.
