[James Bruton] is an impressive roboticist, building all kinds of robots from tracked, exploring robots to Boston Dynamics-esque legged robots. However, many of the robots are proof-of-concept builds that explore machine learning, computer vision, or unique movements and characteristics. This latest build make use of everything he’s learned from building those but strives to be useful on a day-to-day basis as well, and is part of the beginning of a series he is doing on building a Really Useful Robot. (Video, embedded below.)
While the robot isn’t quite finished yet, his first video in this series explores the idea behind the build and the construction of the base of the robot itself. He wants this robot to be able to navigate its environment but also carry out instructions such as retrieving a small object from a table. For that it needs a heavy base which is built from large 3D-printed panels with two brushless motors with encoders for driving the custom wheels, along with a suspension built from casters and a special hinge. Also included in the base is an Nvidia Jetson for running the robot, and also handling some heavy lifting tasks such as image recognition.
As of this writing, [James] has also released his second video in the series which goes into detail about the mapping and navigation functions of the robots, and we’re excited to see the finished product. Of course, if you want to see some of [James]’s other projects be sure to check out his tracked rover or his investigations into legged robots.
Home automation is a fine goal but typically remains confined to lights, blinds, and other things that are relatively stationary and/or electrical in nature. There is a challenge there to be certain, but to really step up your home automation game you’ll need to think outside the box. This automated garbage can that can take itself out, for example, has all the home automation street cred you’d ever need.
The garbage can moves itself by means of a scooter wheel which has a hub motor inside and is powered by a lithium battery, but the real genius of this project is the electronics controlling everything. A Raspberry Pi Zero W is at the center of the build which controls the motor via a driver board and also receives instructions on when to wheel the garbage can out to the curb from an Nvidia Jetson board. That board is needed because the creator, [Ahad Cove], didn’t want to be bothered to tell his garbage can to take itself out or even schedule it. He instead used machine learning to detect when the garbage truck was headed down the street and instruct the garbage can to roll itself out then.
The only other thing to tie this build together was to get the garage door to open automatically for the garbage can. Luckily, [Ahad]’s garage door opener was already equipped with WiFi and had an available app, unbeknownst to him, which made this a surprisingly easy part of the build. If you have a more rudimentary garage door opener, though, there are plenty of options available to get it on the internets.
Every beginning standard needs a test case, and OSK’s is a simple one. A bowl that tracks what you eat. While a simple concept, the way in which the data is shared, tracked, logged, and communicated is the real goal.
The current demo uses a Nvidia Jetson Nano as its processing center. This $100 US board packs a bit of a punch in its weight class. It processes the video from a camera held above the bowl of fruit, suspended by a scale in a squirrel shaped hangar, determining the calories in and calories out.
It’s an interesting idea. One wonders how the IoT boom might have played out if there had been a widespread standard ready to go before people started walling their gardens.
At first sight, [Kyle]’s Elroy lamp is simply an attractive piece of modern-styled interior furnishing; its clean lines, wood grain, and contemporary patterning being an asset to the room. But when he pulls out his phone, things change. Because this lamp hides a secret: at its heart may be a standard LED bulb, but the shade conceals four LCD screens driven by an Nvidia Jetson. These can be controlled through a web app to display a variety of textures, completing the effect.
This is not however simply a set of laptop screens bolted to a lampshade. The screens started life in laptops sure enough, but have since had their reflective backing removed to create a transparent LCD panel. Then an appropriate diffuser had to be found, which after much experimentation became a composite including more than one textured paper. Finally the whole was enclosed in an attractive wooden lamp frame and became part of the furniture. We like it, both as an aesthetically pleasing lamp and as a genuine departure from the norm.
Thanks to the wonders of neural networks and machine learning algorithms, it’s now possible to do things that were once thought to be inordinately difficult to achieve with computers. It’s a combination of the right techniques and piles of computing power that make such feats doable, and [Robert Bond’s] ant zapping project is a great example.
The project is based around an NVIDIA Jetson TK1, a system that brings the processing power of a modern GPU to an embedded platform. It’s fitted with a USB camera, that is used to scan its field of view for ants. Once detected, thanks to a little OpenCV magic, the coordinates of the insect are passed to the laser system. Twin stepper motors are used to spin mirrors that direct the light from a 5 mW red laser, which is shined on the target. If you’re thinking of working on something like this we highly recommend using galvos to direct the laser.
Such a system could readily vaporize ants if fitted with a more powerful laser, but [Robert] decided to avoid this for safety reasons. Plus, the smell wouldn’t be great, and nobody wants charred insect residue all over the kitchen floor anyway. We’ve seen AIs do similar work, too – like detecting naughty cats for security reasons.
Telepresence is one of those futuristic buzzwords that’s popped up a few times over the decades; promising the ability to attend a meeting in New York City and another in Tokyo an hour later, all without having to leave the comfort of your home or office. This is the premise of Double Robotics’ Double 3, its most recent entry in this market segment, as the commercial counterpoint to more DIY offerings.
Looking like a tablet perched on top of a Segway, the built-in dual 13 megapixel cameras allow the controller to get a good look at their surroundings, while the 6 beamforming microphones should theoretically allow one to pick up any conversation in a meeting or on the work floor.
Battery life is limited to 4 hours, and it takes 2 hours to recharge the built-in battery. Fortunately one can just hop over to another, freshly charged Double 3 if the battery runs out. Assuming the $3,999 price tag doesn’t get in the way of building up a fleet of them, anyway.
Probably the most interesting aspect of the product is its self-driving feature, which has resulted in a whole range of sensors and cameras (Intel RealSense D430 stereo vision depth sensors) being installed. To handle the processing of this sensor data, the system is equipped with an NVidia Jetson TX2 ARM board, running Ubuntu Linux, which also renders the mixed-reality UI for the user with way points and other information.
Currently Double Robotics accepts sign-ups for the private beta of the Double 3 API, which would give developers access to the sensor data and various autonomous features of Double 3’s hardware. Co-founder of Double Robotics, [Marc DeVidts] stated to Hackaday that he is looking forward to seeing what people can build with it. Hopefully this time people will not simply take the thing for a joyride, like what happened with a predecessor of the Double 3.
Found yourself with a shiny new NVIDIA Jetson Nano but tired of having it slide around your desk whenever cables get yanked? You need a stand! If only there was a convenient repository of options that anyone could print out to attach this hefty single-board computer to nearly anything. But wait, there is! [Madeline Gannon]’s accurately named jetson-nano-accessories repository supports a wider range of mounting options that you might expect, with modular interconnect-ability to boot!
A device like the Jetson Nano is a pretty incredible little System On Module (SOM), more so when you consider that it can be powered by a boring USB battery. Mounted to NVIDIA’s default carrier board the entire assembly is quite a bit bigger than something like a Raspberry Pi. With a huge amount of computing power and an obvious proclivity for real-time computer vision, the Nano is a device that wants to go out into the world! Enter these accessories.
At their core is an easily printable slot-and-tab modular interlock system which facilitates a wide range of attachments. Some bolt the carrier board to a backplate (like the gardening spike). Others incorporate clips to hold everything together and hang onto a battery and bicycle. And yes, there are boring mounts for desks, tripods, and more. Have we mentioned we love good documentation? Click into any of the mount types to find more detailed descriptions, assembly directions, and even dimensioned drawings. This is a seriously professional collection of useful kit.