RC Ekranoplan Uses LIDAR To Fly In Ground Effect

Ekranoplans are a curious class of vehicle; most well known for several Soviet craft designed to operate at sea, flying just above the waves in ground effect. [rctestflight] had accidentally come across the ground effect flight regime himself years ago, and decided it was time to build an ekranoplan of his own.

I want to see little ekranoplans in at least three top 10 pop film clips by summer’s end. Please and thank you.

While ground-effect flight can be quite stable for a heavy, human-scale craft, the smaller RC version suffered more from minor perturbations from the wind and such. Thus, a Pixracer autopilot was installed, and combined with a small LIDAR device to accurately measure altitude above the ground. With some custom tweaks to the Ardupilot firmware, the craft was able to cleanly fly along barely a foot off the ground.

The final effect is almost mesmerizing; it appears as if the craft is hovering via some heretofore unknown technology rather than just flying in the usual sense. It’s still sensitive to breezes and sudden drops in the terrain lead to a temporary escape from the ground effect region, but the effect is nonetheless impressive. It’s a nerve wracking video at times, though, with quite a few near misses with traffic and children. Regardless of the nature of your experimental craft, be cognisant of your surroundings. We’ve seen [rctestflight]’s Ardupilot experiments before, too. Video after the break.

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New Part Day: Onion Tau LiDAR Camera

The Onion Tau LiDAR Camera is a small, time-of-flight (ToF) based depth-sensing camera that looks and works a little like a USB webcam, but with  a really big difference: frames from the Tau include 160 x 60 “pixels” of depth information as well as greyscale. This data is easily accessed via a Python API, and example scripts make it easy to get up and running quickly. The goal is to be an affordable and easy to use option for projects that could benefit from depth sensing.

When the Tau was announced on Crowd Supply, I immediately placed a pre-order for about $180. Since then, the folks at Onion were kind enough to send me a pre-production unit, and I’ve been playing around with the device to get an idea of how it acts, and to build an idea of what kind of projects it would be a good fit for. Here is what I’ve learned so far.

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Lidar House Looks Good, Looks All Around

A lighthouse beams light out to make itself and its shoreline visible. [Daniel’s] lighthouse has the opposite function, using lasers to map out the area around itself. Using an Arduino and a ToF sensor, the concept is relatively simple. However, connecting to something that rotates 360 degrees is always a challenge.

The lighthouse is inexpensive — about $40 — and small. Small enough, in fact, to mount on top of a robot, which would give you great situational awareness on a robot big enough to support it. You can see the device in action in the video below. Continue reading “Lidar House Looks Good, Looks All Around”

Shhh… Robot Vacuum Lidar Is Listening

There are millions of IoT devices out there in the wild and though not conventional computers, they can be hacked by alternative methods. From firmware hacks to social engineering, there are tons of ways to break into these little devices. Now, four researchers at the National University of Singapore and one from the University of Maryland have published a new hack to allow audio capture using lidar reflective measurements.

The hack revolves around the fact that audio waves or mechanical waves in a room cause objects inside a room to vibrate slightly. When a lidar device impacts a beam off an object, the accuracy of the receiving system allows for measurement of the slight vibrations cause by the sound in the room. The experiment used human voice transmitted from a simple speaker as well as a sound bar and the surface for reflections were common household items such as a trash can, cardboard box, takeout container, and polypropylene bags. Robot vacuum cleaners will usually be facing such objects on a day to day basis.

The bigger issue is writing the filtering algorithm that is able to extract the relevant information and separate the noise, and this is where the bulk of the research paper is focused (PDF). Current developments in Deep Learning assist in making the hack easier to implement. Commercial lidar is designed for mapping, and therefore optimized for reflecting off of non-reflective surface. This is the opposite of what you want for laser microphone which usually targets a reflective surface like a window to pick up latent vibrations from sound inside of a room.

Deep Learning algorithms are employed to get around this shortfall, identifying speech as well as audio sequences despite the sensor itself being less than ideal, and the team reports achieving an accuracy of 90%. This lidar based spying is even possible when the robot in question is docked since the system can be configured to turn on specific sensors, but the exploit depends on the ability to alter the firmware, something the team accomplished using the Dustcloud exploit which was presented at DEF CON in 2018.

You don’t need to tear down your robot vacuum cleaner for this experiment since there are a lot of lidar-based rovers out there. We’ve even seen open source lidar sensors that are even better for experimental purposes.

Thanks for the tip [Qes]

Alfred Jones Talks About The Challenges Of Designing Fully Self-Driving Vehicles

The leap to self-driving cars could be as game-changing as the one from horse power to engine power. If cars prove able to drive themselves better than humans do, the safety gains could be enormous: auto accidents were the #8 cause of death worldwide in 2016. And who doesn’t want to turn travel time into something either truly restful or alternatively productive?

But getting there is a big challenge, as Alfred Jones knows all too well. The Head of Mechanical Engineering at Lyft’s level-5 self-driving division, his team is building the roof racks and other gear that gives the vehicles their sensors and computational hardware. In his keynote talk at Hackaday Remoticon, Alfred Jones walks us through what each level of self-driving means, how the problem is being approached, and where the sticking points are found between what’s being tested now and a truly steering-wheel-free future.

Check out the video below, and take a deeper dive into the details of his talk.

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The Ground Beneath Your Feet: SuperAdobe Construction

Homes in different parts of the world used to look different from each other out of necessity, built to optimize for the challenges and benefits of local climate. When residential climate control systems became commonplace that changed. Where a home in tropical south Florida once required very different building methods (and materials) compared to a home in the cold mountains of New England, essentially identical construction methods are now used for single-family homes in any climate. The result is inefficient and virtually indistinguishable housing from coast to coast, regardless of climate. As regions throughout the world are facing increasingly dire housing shortages, the race is on to find solutions that are economical and available to us right now.

The mission of CalEarth, one of the non-profits that Hackaday has teamed up with for this year’s Hackaday Prize, is to address that housing shortage by building energy-efficient homes out of materials already available in the areas that they will be built. CalEarth specializes in building adobe, or earth, homes that have a large thermal mass and an inexpensive bill of materials. Not only does this save on heating and cooling costs, but transportation costs for materials can be reduced as well. Some downside to this method of construction are increased labor costs and the necessity of geometric precision of the construction method, both of which are tackled in this two-month design challenge.

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Jetson Nano Robot

[Stevej52] likes to build things you can’t buy, and this Jetson Nano robot falls well within that category. Reading the project details, you might think [Stevej52] drinks too much coffee. But we think he is just excited to have successfully pulled off the Herculean task of integrating over a dozen hardware and software modules. Very briefly, he is running Ubuntu and ROS on the PC and Nano. It is all tied together with Python code, and is using Modbus over IP to solve a problem getting joystick data to the Nano. We like it when existing, standard protocols can be used because it frees the designer to focus more on the application. Modbus has been around for 40 years, has widespread support in many languages and platforms.

This is an ongoing project, and we look forward to seeing more updates and especially more video of it in action like the one found below. With the recent release of a price-reduced Jetson Nano, which we covered last week, this might be an excellent project to take on.

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