[Junglist] correctly points out that agricultural robotics is fast on its way to being the next big thing (TM) and presents his easy to build ArrBot platform so others can get hacking fast.
The frame is built out of the same brackets and aluminum tubing used to add handrails to stairwells on buildings. Not only is this a fast way to do it, the set-up can be guaranteed to be sturdy since hand rails are often literally standing between life and death. The high ground clearance allows for all sorts of sensors and devices to be mounted while still being able to clear the plants below.
For motion hub motors driven by an ODrive were re-purposed for the task. He explored turning the wheels as well, but it seems like differential steer and casters works well for this set-up. ROS on an Nividia Jetson runs the show and deals with the various sensors such as a stereoscopic camera and IMU.
We’re excited to see what hacks people come up with as research in this area grows. (Tee-hee!) For example, [Junglist] wants to see the effect of simply running a UV light over a field rather than spraying with pesticides or fungicides would have.
Every appliance business wants to be the one that invents the patented, license-able, and profitable standard that all the other companies have to use. Open Source Kitchen wants to beat them to it.
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.
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.
When shopping online, there’s plenty of great deals out there on modern graphics hardware. Of course, if you’re like [Dawid] and bought a GTX1050 Ti for $48 from Wish, you probably suspect it’s too good to be true. Of course, you’d be correct.
[Dawid] notes from the outset that the packaging the card ships in is unusual. While it’s covered in NVIDIA and GeForce branding, there’s no note of the model number or even the overarching series. The card is loosely packed in bubblewrap, free to bounce around in transit. Upon installation, the card reports itself as a GTX1050 Ti, but refuses to properly work with NVIDIA drivers and routinely causes a Blue Screen of Death.
Upon disassembly, it becomes apparent that the card is merely a poorly manufactured GTS450 Revision 2, over five generations older than the card it was advertised as. Thanks to the mismatch between the actual hardware and what the card reports as, the drivers are unable to properly work with the card.
For those that have been scammed, there is some hope. [Phil] has had experience with several of these cards, which similarly misreport their actual hardware. To correct this, the cards need to have their BIOS flashed to reflect reality, but the fake cards don’t work with NVIDIA’s NVFlash tool. Instead, they must be flashed manually using an EEPROM programmer. Once the cards are flashed with an appropriate BIOS, they can be used with the proper drivers and will function properly, albeit with much less performance than was advertised.
It’s an interesting insight into the state of online shopping platforms, and the old adage remains true – if it’s too good to be true, it probably is. Plus, hacking GPUs can often have great results. Video after the break.
Continue reading “Fake Graphics Cards And How To Fix Them”
Nvidia’s GeForce Experience (GFE) is the companion application for the Nvidia drivers, keeping said drivers up to date, as well as adding features around live streaming and media capture. The application runs as two parts, a GUI, and a system service, using an HTTP API to communicate. [David Yesland] from Rhino Security Labs decided to look into this API, searching for interesting, undocumented behavior, and shared the results on Sunday the 2nd.
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.
The current wave of excitement around machine learning kicked off when graphics processors were repurposed to make training deep neural networks practical. Nvidia found themselves the engine of a new revolution and seized their opportunity to help push frontiers of research. Their research lab in Seattle will focus on one such field: making robots smart enough to work alongside humans in an IKEA kitchen.
Today’s robots are mostly industrial machines that require workspaces designed for robots. They run day and night, performing repetitive tasks, usually inside cages to keep squishy humans out of harm’s way. Robots will need to be a lot smarter about their surroundings before we could safely dismantle those cages. While there are some industrial robots making a start in this arena, they have a hard time justifying their price premium. (Example: financial difficulty of Rethink Robotics, who made the Baxter and Sawyer robots.)
So there’s a lot of room for improvement in this field, and this evolution will need a training environment offering tasks of varying difficulty levels for robots. Anywhere from the rigorous structured environment where robots work well today, to a dynamic unstructured environment where robots are hopelessly lost. Lab lead Dr. Dieter Fox explained how a kitchen is ideal. A meticulously cleaned and organized kitchen is very similar to an industrial setting. From there, we can gradually make a kitchen more challenging for a robot. For example: today’s robots can easily pick up a can with its rigid regular shape, but what about a half-full bag of flour? And from there, learn to pick up a piece of fresh fruit without bruising it. These tasks share challenges with many other tasks outside of a kitchen.
This isn’t about building a must-have home cooking robot, it’s about working through the range of challenges shared with common kitchen tasks. The lab has a lot of neat hardware, but its success will be measured by the software, and like all research, published results should be reproducible by other labs. You don’t have a high-end robotics lab in your house, but you do have a kitchen. That’s why it’s not just any kitchen, but an IKEA kitchen, to take advantage of the fact they are standardized, affordable, and available around the world for other robot researchers to benchmark against.
Most of us can experiment in a kitchen, IKEA or not. We have access to all the other tools we need: affordable AI hardware from Google, from Beaglebone, and from Nvidia. And we certainly have no shortage of robot arms and manipulators on these pages, ranging from a small laser-cut MeArm to our 2018 Hackaday Prize winner Dexter.