It’s been a few years since the RTL-SDR TV Tuner dongle blew up the world of amateur radio; it’s a simple device that listens in on digital television frequencies, but it’s one of those tools that’s just capable enough to have a lot of fun. Now, we have a transmitting dongle. It’s only being used to transmit live HDTV from a Pi, but that in itself is very interesting and opens up a lot of possible builds.
The key piece of hardware for this build is a UT-100C DVB-T modulator. It’s a $169 USB dongle capable of transmitting between 1200-1350 MHz, and with a special edition of OpenCaster it’s possible to transmit over-the-air TV. There’s no amplifier, so you won’t be sending TV very far, but it does work.
On the Raspberry Pi side of the build, the standard camera captures H.264 video with raspivid, which is converted to a DVB compliant stream using ffmpeg. These are well-worn bits of software in the Raspberry Pi world, and OpenCaster takes care of the rest.
While this seems like the perfect solution to completely overbuilt quadcopters, keep in mind transmitting on the 23cm band does require a license. Transmitting in the UHF TV bands is a bad idea.
In the world of drones, quadcopters, and unmanned aerial vehicles, the community has pretty much settled on AVR microcontrollers for the low end, and ARM for the high performance boards. If the FAA doesn’t screw things up, there will soon be another market that requires even more computational power, and Navio, the autopilot shield for the Pi, is just the thing for it.
Where high end multicopter and autopilot boards like the OpenPilot Revolution use ARM micros, there’s a small but demanding segment of the hobby that needs even more processing power. Think of something like the Outback Challenge, where fixed-wing drones search the desert for a lost mannequin autonomously. You’re going to need OpenCV for that, and that means Linux.
Navio is a shield for the Raspberry Pi, complete with a barometric pressure sensor, gyros, accelerometer, and compass, and GPS. It’s designed to run a more real-time version of Linux, and has the ability to do some interesting telemetry configurations – putting a 3G modem on the Navio isn’t much of a problem, and since it’s a Raspi, doing image processing of a downward facing camera is just a matter of writing the code.
The Navio team is currently running an Indiegogo campaign, with the baseline version available for $145. That’s pretty close to the price of the OpenPilot Revolution. There’s also a version upgraded with the U-blox NEO-6T that allows for on-board processing of raw GPS data.
Name one other podcast where you can hear about heavy 3D-printed drones, DIY semiconductors, and using licorice to block laser beams. Throw in homebrew relays, a better mouse trap, and logic analyzers, and you’ll certainly be talking about Elliot Williams and Al Williams on Hackaday Podcast 235.
There’s also contest news, thermoforming, and something that looks a little like 3D-printed Velcro. Elliot and Al also have their semi-annual argument about Vi vs. Emacs. Spoiler alert: they decided they both suck.
Missed any of their picks? Check out the links below, and as always, tell us what you think about this episode in the comments!
When building autonomous airborne vehicles like drones or UAVs, saving a little bit of weight goes a long way, literally. Every gram saved means less energy needed to keep the aircraft aloft and ultimately more time in the air, but unmanned vehicles often need to compromise some on weight in order to carry increased computing abilities. Thankfully this one carries a dizzying quantity of computer power for an absolute minimum of weight, and has some clever design considerations to improve its performance as well.
The advantage of this board compared to other similar offerings is that it is built to host a Raspberry Pi Compute Module 4, while the rest of the flight controllers are separated out onto a single circuit board. This means that the Pi is completely sandboxed from the flight control code, freeing up computing power on the Pi and allowing it to run a UAV-specific OS like OpenHD or RubyFPV. These have a number of valuable tools available for unmanned flight, such as setting up a long range telemetry and camera links. The system itself supports dual HD camera input as well as additional support for other USB devices, and also includes an electronic speed controller mezzanine which has support for quadcopters and fixed wing crafts.
Separating non-critical tasks like cameras and telemetry from the more important flight controls has a number of benefits as well, including improved reliability and simpler software and program design. And with a weight of only 30 grams, it won’t take too much cargo space on most UAVs. While the flight computer is fairly capable of controlling various autonomous aircraft, whether it’s a multi-rotor like a quadcopter or a fixed wing device, you might need a little more computing power if you want to build something more complicated.
When we think of robotics, the first thing that usually comes to mind for many of us is some sort of industrial arm that’s bolted to the floor, or perhaps a semi-autonomous rover trudging its way across the dusty Martian landscape. While these two environments are about as different as can be, the basic “rules” are pretty much the same. Being on firm ground ground gives the robot a clear understanding of its position and orientation, which greatly simplifies tasks such as avoiding collisions or interacting with nearby objects.
But what happens when that reference point goes away? How does a robot navigate when it’s flying through open space or hovering in mid-air? That’s just one of the problems that fascinates Nick Rehm, who stopped by to host this week’s Aerial Robotics Hack Chat to talk about his passion for flying robots. He’s currently an aerospace engineer at Johns Hopkins Applied Physics Laboratory, where he works on the unique challenges faced by autonomous flying vehicles such as the detection and avoidance of mid-air collisions, as well as the development of vertical take-off and landing (VTOL) systems. But before he had his Master’s in Aerospace Engineering and Rotorcraft, he got started the same way many of us did, by playing around with DIY projects.
In fact, regular Hackaday readers will likely recall seeing some of his impressive builds. His autonomous ekranoplan designed to follow a target using computer vision graced the front page in April. Back in 2020, we took a look at his recreation of SpaceX’s Starship prototype, which used a realistic arrangement of control surfaces and vectored thrust to perform the spacecraft’s signature “Belly Flop” maneuver — albeit with RC motors and propellers instead of rocket engines. But even before that, Nick recalls asking his mother for permission to pull apart a Wii controller so he could use its inertial measurement unit (IMU) in a wooden-framed tricopter he was working on.
Discussing some of these hobby builds leads the Chat towards Nick’s dRehmFlight project, a GPLv3 licensed flight control package that can run on relatively low-cost hardware, namely a Teensy 4.0 microcontroller paired with the GY-521 MPU6050 IMU. The project is designed to let hobbyists easily experiment with VTOL craft, specifically those that transition between vertical and horizontal flight profiles, and has powered the bulk of Nick’s own flying craft.
Moving onto more technical questions, Nick says one of the most difficult aspects when designing an autonomous flying vehicle is getting your constraints nailed down. What he means by that is having a clear goal of what the craft needs to do, and critically, how long it needs to do it. How far does the craft need to be able to fly? How fast? Does it need to loiter at the target location, and if so, for how long? The answers to these questions will largely dictate the form of the final vehicle, and are key to determining if it’s worth implementing the complexity of transitioning from VTOL to fixed-wing horizontal flight.
But according to Nick, the biggest challenge in aerial robotics is onboard state estimation. That is, the ability for the craft to know its position and orientation relative to the ground. While high-performance computers have gotten lighter and sensors have improved, he says there’s still no substitute for having a ground-based tracking system. He mentions that those fancy demonstrations you’ve seen with drones flying in formation and working collaboratively towards a task will almost certainly have an array of motion capture cameras tucked off to the side. This makes for an impressive show, but greatly limits the practical application of these drone swarms.
Nick’s custom Raspberry Pi 4-powered quadcopter lets him test autonomous flight techniques.
So what does the future of aerial robotics look like? Nick says open source projects like ArduPilot and PX4 are still great choices for hobbyists, but sees promise in newer platforms which pair the traditional autopilot with more onboard computing power, such as Auterion’s Skynode. More powerful flight controllers can enable techniques such as simultaneous localization and mapping (SLAM), which uses 3D scans of the environment to help the robot orient itself. He’s also very interested in technologies that enable autonomous flight in GPS-denied environments, which is critical for robotic craft that need to operate indoors or in situations where satellite navigation is unavailable or unreliable. In light of the incredible success of NASA’s Ingenuity helicopter, we imagine these techniques will also play an invaluable role in the future airborne exploration of Mars.
We want to thank Nick for hosting this week’s Aerial Robotics Hack Chat, which turned out to be one of the fastest hours in recent memory. His experience as both an avid hobbyist and a professional in the field provided exactly the sort of insight the Hackaday community looks for, and his gracious offer to keep in touch with several of those who attended the Chat to further discuss their projects speaks to how passionate he is about this topic. We expect to see great things from Nick going forward, and would love to have him join us again in the future to see what he’s been up to.
The Hack Chat is a weekly online chat session hosted by leading experts from all corners of the hardware hacking universe. It’s a great way for hackers connect in a fun and informal way, but if you can’t make it live, these overview posts as well as the transcripts posted to Hackaday.io make sure you don’t miss out.
When you think of high-performance computing powered by NVIDIA hardware, you probably think of applications leveraging the capabilities of the company’s graphics cards. In many cases, you’d be right. But naturally there are situations where the traditional combination of x86 computer and bolt-on GPU simply isn’t going to cut it; try packing a modern gaming computer onto a quadcopter and let us know how it goes.
For these so-called “edge computing” situations, NVIDIA offers the Jetson line of ARM single-board computers which include a scaled-down GPU that gives them vastly improved performance for machine learning applications than something like the Raspberry Pi. Today during their annual GPU Technology Conference (GTC), NVIDIA announced the immediate availability of the Jetson AGX Orin Developer Kit, which the company promises can deliver “server-class AI performance” in a package small enough for use in IoT or robotics.
As with the earlier Jetsons, the palm-sized development kit acts as a sort of breakout board for the far smaller module slotted into it. This gives developers access to the full suite of the connectivity and I/O options offered by the Jetson module in a desktop-friendly form that makes prototyping the software side of things much easier. Once the code is working as intended, you can simply pop the Jetson module out of the development kit and install it in your final hardware.
NVIDIA is offering the Orin module in a range of configurations, depending on your computational needs and budget. At the high end is the AGX Orin 64 GB at $1599 USD; which offers a 12-core ARM Cortex-A78AE processor, 32 GB of DDR5 RAM, 64 GB of onboard flash, and a Ampere GPU with 2048 CUDA cores and 64 Tensor cores, which all told enables it to perform an incredible 275 trillion operations per second (TOPS).
At the other end of the spectrum is the Orin NX 8 GB, a SO-DIMM module that delivers 70 TOPS for $399. It’s worth noting that even this low-end flavor of the Orin is capable of more than double the operations per second as 2018’s Jetson AGX Xavier, which until now was the most powerful entry in the product line.
The Jetson AGX Orin Developer Kit is available for $1,999 USD, and includes the AGX Orin 64 GB module. Interestingly, NVIDIA says the onboard software is able to emulate any of of the lower tier modules, so you won’t necessarily have to swap out the internal modules if your final hardware will end up using one of the cheaper modules. Of course the inverse of that is even folks who only planned on using the more budget-friendly units either have to shell out for an expensive dev kit, or try to spin their own breakout board.
Why? Getting a drone that can fly a path and even return home when the battery is low, signal is lost, or on command, is simple enough. Just go to your favorite retailer, search “gps drone” and you can get away for a shockingly low dollar amount. This is possible because GPS receivers have become cheap, small, light, and power efficient. While all of these inexpensive drones can fly a predetermined path, they usually do so by flying over any obstacles rather than around.
[Nick Rehm] has envisioned a quadcopter that can do all of the things a GPS-enabled drone can do, without the use of a GPS receiver. [Nick] makes this possible by using algorithms similar to those used by Google Maps, with data coming from a typical IMU, a camera for Computer Vision, LIDAR for altitude, and an Intel RealSense camera for detection of position and movement. A Raspberry Pi 4 running Robot Operating System runs the autonomous show, and a Teensy takes care of flight control duties.
What we really enjoy about [Nick]’s video is his clear presentation of complex technologies, and a great sense of humor about a project that has consumed untold amounts of time, patience, and duct tape.
![[Nick Rehm] explains the workings of a gps-less self guided drone](https://hackaday.com/wp-content/uploads/2021/10/gpsless-drone-featured.jpg?w=600&h=450)
