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”
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]
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.
Continue reading “Alfred Jones Talks About The Challenges Of Designing Fully Self-Driving Vehicles”
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.
Continue reading “The Ground Beneath Your Feet: SuperAdobe Construction”
[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.
Continue reading “Jetson Nano Robot”
Moore’s law may have reached its physical limit for transistor density, but plenty of other technologies are still on that familiar path of getting smaller and smaller as time passes. It looks like LIDAR is no exception to this trend either. This project from [Owen] shows a fully-functional LIDAR system for about $20 and built almost entirely on top of an ESP32.
The build uses a Time-Of-Flight IR laser range sensor controlled by the ESP32, and the sensor is much smaller than even the ESP32’s footprint so it takes up very little extra space. To get it to function as a LIDAR system instead of just a simple rangefinder it does need a motor in order to rotate itself to see its entire space. Besides its small form factor and low cost, it also has a handy user interface that can run anywhere an HTML5 browser can run, making the use of the system easy and straightforward as well. All of the code is available on the project’s GitHub page.
We wouldn’t expect a system like this to be driving an autonomous car anytime soon, it’s update rate is far too slow, but its intent for small robots and even as an educational demo for learning LIDAR is second to none. If you do need a little more power in a LIDAR system but still don’t want to break the bank, we featured this impressive setup a few weeks ago.
For any project there’s typically a trade-off between quality and cost,as higher quality parts, more features, or any number of aspects of a project can drive its price up. It seems as though [iliasam] has managed to avoid this paradigm entirely with his project. His new LIDAR system knocks it out of the park on accuracy, sampling, and quality, and somehow manages to only cost around $114 in parts.
A LIDAR system works by sending out many pulses of light in different directions, measuring the reflections of that light as it returns. LIDAR systems therefore improve with higher frequency pulses and faster control electronics for both the laser output and the receiving data. This system manages to be accurate to within a few centimeters and works up to 25 meters all while operating at 15 scans per second. The key was a high-powered laser module which can output up to 75 watts for extremely short times. More details can be found at this page (Google Translate from Russian).
Another bonus from this project is that [iliasam] has made everything available from his GitHub page including hardware specifications, so as long as you have a 3D printer this won’t take long to produce either. There’s even detailed breakdowns of how the laser driving circuitry works, and how there are safety features built in to keep anyone’s vision from accidentally getting damaged. Needless to say, this isn’t just a laser rangefinder module but if you want to see how you can repurpose those, [iliasam] can show you that as well.