Autonomous Ground Effect Vehicle Demonstrator Aims To Speed Up Maritime Shipping

October 10, 2021 by Danie Conradie 35 Comments

Ground effect vehicles, or ekranoplans, have the advantage of being more efficient than normal aircraft and faster than boats, but so far haven’t been developed beyond experimental prototypes. Fortunately, this doesn’t stop companies from trying, which has led to a collaboration between [ThinkFlight] and [rctestflight] to create a small-scale demonstrator for the Flying Ship Company.

The Flying Ship Company wants to use unmanned electric ekranoplans as high-speed marine cargo carriers that can use existing maritime infrastructure for loading and unloading. For the scale model, [rctestflight] was responsible for the electronics and software, while [ThinkFlight] built the airframe. As with his previous ekranoplan build, [ThinkFlight] designed it in XFLR5, cut the parts from foam using a CNC hot wire cutter (which we still want a better look at), and laminated it with Kevlar for strength. One of the challenges of ground effect vehicles is that the center of pressure will shift rearward as they leave a ground effect, causing them to pitch up. To maintain control when moving into and out of ground effect, these crafts often use a large horizontal stabilizer high up on the tail, out of ground effect.

A major feature of this demonstrator is automatic altitude control using a LIDAR sensor mounted on the bottom. This was developed by [rctestflight] using a simple foam board ekranoplan and [Think Flighs]’s previous airframe, with some custom code added to ArduPilot. It works very well on smooth, calm water, but waves introduce a lot of noise into the LIDAR data. It looks like they were able to overcome this challenge, and completed several successful test flights in calm and rough conditions.

The final product looks good, flies smoothly, and is easy to control since the pilot doesn’t need to worry about pitch or throttle control. It remains to be seen if The Flying Boat will overcome the challenges required to turn it into a successful commercial craft, and we will be following the project closely.

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RC Ekranoplan Uses LIDAR To Fly In Ground Effect

April 29, 2021 by Lewin Day 33 Comments

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

March 18, 2021 by Donald Papp 40 Comments

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

December 20, 2020 by Al Williams 10 Comments

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

November 27, 2020 by Inderpreet Singh 5 Comments

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

November 24, 2020 by Mike Szczys 44 Comments

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

October 27, 2020 by Bryan Cockfield 25 Comments

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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