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.
We live in an exciting time of machine intelligence. Over the past few months, several products have been launched offering neural network processors at a price within hobbyist reach. But as exciting as the hardware might be, they still need software to be useful. Nvidia was not content to rest on their impressive Jetson hardware and has created a software framework to accelerate building robots around them. Anyone willing to create a Nvidia developer account may now play with the Isaac Robot Engine framework.
Isaac initially launched about a year ago as part of a bundle with Jetson Xavier hardware. But the $1,299 developer kit price tag pushed it out of reach for many of us. Now we can buy a Jetson Nano for about a hundred bucks. For those familiar with Robot Operating System (ROS), Isaac will look very familiar. They both aim to make robotic software as easy as connecting common modules together. Many of these modules called GEMS in Isaac were tailored to the strengths of Nvidia Jetson hardware. In addition to those modules and ways for them to work together, Isaac also includes a simulator for testing robot code in a virtual world similar to Gazebo for ROS.
While Isaac can run on any robot with an Nvidia Jetson brain, there are two reference robot designs. Carter is the more expensive and powerful commercially built machine rolling on Segway motors, LIDAR environmental sensors, and a Jetson Xavier. More interesting to us is the Kaya (pictured), a 3D-printed DIY robot rolling on Dynamixel serial bus servos. Kaya senses the environment with an Intel RealSense D435 depth camera and has Jetson Nano for a brain. Taken together the hardware and software offerings are a capable and functional package for exploring intelligent autonomous robots.
It is somewhat disappointing Nvidia decided to create their own proprietary software framework reinventing many wheels, instead of contributing to ROS. While there are some very appealing features like WebSight (a browser-based inspect and debug tool) at first glance Isaac doesn’t seem fundamentally different from ROS. The open source community has already started creating ROS nodes for Jetson hardware, but people who work exclusively in the Nvidia ecosystem or face a time-to-market deadline would appreciate having the option of a pre-packaged solution like Isaac.
Today, Nvidia released their next generation of small but powerful modules for embedded AI. It’s the Nvidia Jetson Nano, and it’s smaller, cheaper, and more maker-friendly than anything they’ve put out before.
The Jetson Nano follows the Jetson TX1, the TX2, and the Jetson AGX Xavier, all very capable platforms, but just out of reach in both physical size, price, and the cost of implementation for many product designers and nearly all hobbyist embedded enthusiasts.
The Nvidia Jetson Nano Developers Kit clocks in at $99 USD, available right now, while the production ready module will be available in June for $129. It’s the size of a stick of laptop RAM, and it only needs five Watts. Let’s take a closer look with a hands-on review of the hardware.
In our modern connected age, our devices have become far more powerful and useful when they could draw upon resources of a global data network. The downside of a cloud-connected device is the risk of being over-reliant on computers outside of our own control. The people who brought a Jibo into their home got a stark reminder of this fact when some (but not all) Jibo robots gave their owners a farewell message as their servers are shut down, leaving behind little more than a piece of desktop sculpture.
Jibo launched their Indiegogo crowdfunding campaign with the tagline “The World’s First Social Robot For The Home.” Full of promises of how Jibo will be an intelligent addition to a high tech household, it has always struggled to justify its price tag. It cost as much as a high end robot vacuum, but without the house cleaning utility. Many demonstrations of a Jibo’s capabilities centered around its voice control, which an Amazon Echo or Google Home could match at a fraction of the price.
If you see one of these orphans in your local thrift store for a few bucks, consider adopting it. You could join the group hoping for something interesting down the line, but you’re probably more interested in its hacking potential: there is a Nvidia Jetson inside good for running neural networks. Probably a Tegra K1 variant, because Jibo used the Jetson TK1 to develop the robot before launch. Jibo has always promised a developer SDK for the rest of us to extend Jibo’s capabilities, but it never really materialized. The inactive Github repo mainly consists of code talking to servers that are now offline, not much dealing directly with the hardware.
Jibo claimed thousands were sold and, if they start becoming widely available inexpensively, we look forward to a community working to give new purpose to these poor abandoned robots. If you know of anyone who has done a teardown to see exactly what’s inside, or if someone has examined upgrade files to create custom Jibo firmware, feel free to put a link in the comments and help keep these robots out of e-waste.
If you want to experiment with power efficient neural network accelerators but rather work with an officially supported development platform, we’ve looked at the Jetson TK1 successors TX1 and TX2. And more recently, Google has launched one of their own, as has our friends at Beaglebone.
The engineers and product designers at [moovel lab] have created the Open Data Cam – an AI camera platform that can identify and count objects as they move through its field of view – along with an open source guide for making your own.
Step one: get out your ruler and utility knife. In this world of ubiquitous 3D-printers they’ve taken a decidedly low-tech approach to the project’s enclosure: a cut, folded, and zip-tied plastic box, with a cardboard frame inside to hold the electronic bits. It’s “splash proof” and certainly cheap to make, but we’re a little worried about cooling and physical protection for the electronics inside, as they’re not exactly cheap and rugged components.
So what’s inside? An Nvidia Jetson TX2 board, a LiPo battery with some charging circuitry, and a standard webcam. The special sauce, however, is the software, which is available on GitHub. [Moovel lab]’s engineers have put together a nice-looking wifi-accessible mobile UI for marking the areas where you’d like the software to identify and tally objects. The actual object detection and identification tasks are performed by the speedy YOLO neural network, a task the Nvidia board’s GPU is of course well suited for.
As the Open Data Cam’s unblinking glass eye gazes upon our urban environments, it will log its observations in an ancient and mysterious language: CSV. It’s up to you, human, to interpret this information and use it for good.
A summary video and build time lapse are embedded after the break.
[Mark] is using 26 Arducams, arranging them in a rhombicuboctahedron configuration, which consists of three rings of 8 cameras with each ring controlled by a Beaglebone; the top and bottom rings are angled at 45 degrees, while the center ring looks straight out. The top and bottom cameras are controlled by a fourth Beaglebone, which also serves to communicate with the Nvidia Jetson TX1 that runs everything. Together, these cameras can see in all directions at once, with enough overlap for provide a seamless display for viewers.
In the image to the right, [Mark] is testing out his software for getting the various cameras to work together. The banks of circles and the dots and lines connecting to them represent the computer’s best guess on how to seamlessly merge the images.
If you want to check out the project in person, [Mark] will be showing off the Quamera at the Dover Mini Maker Faire this August. In the meantime, to learn more about the Jetson check out our thorough overview of the board.