It’s 5 pm, the sun is slowly setting on the Leipzig conference center, and although we’re only halfway through the first day, there’s a ton that you should see. We’ll report some more on the culture of the con later — for now here’s just the hacks. Continue reading “34C3: The First Day Is A Doozy”
If you are blind or your vision is impaired, moving around in a new space can be a harrowing experience. A cane helps, but only samples one point at a time, and can’t help that much above a certain height. The Digital White Cane is a haptic feedback device that uses Time of Flight components to detect surrounding objects.
The Digital White Cane uses a type of LIDAR known as Time of Flight (ToF) sensing. Rather than a point by point scan by a laser, ToF sensors capture an entire scene with each pulse. These sensors are actually somewhat new and designed for the latest generation of robotics and hand detection for soap dispensers. The good news is that they’re small and cheap, just what you want for a wearable.
The sensors allow detection of objects within 2m (about 6 feet) from all directions. Haptic feedback allows the wearer to determine where the object is around the wearer. Because it’s head-mounted, it detects objects at head height as well as floor height. A Teensy LC is used as the main processor and is connected to the ToF sensors as well as small motor board for the haptic feedback.
This project has a lot of potential to help people with vision impairment and is a great entry into the 2017 Hackaday Prize. Check out the video after the break to see it in action. If you’re looking for some more applications of this small, cheap ToF sensor, check out this cat food dispenser, and here’s a ball-balancing robot – both pretty cool projects in their own right.
[Blecky]’s entry to the Hackaday Prize is MappyDot, a tiny board less than a square inch in size that holds a VL53L0X time-of-flight distance sensor and can measure distances of up to 2 meters.
MappyDot is more than just a breakout board; the ATMega328PB microcontroller on each PCB provides filtering, an easy to use I2C interface, and automatically handles up to 112 boards connected in a bus. The idea is that one or a few MappyDots can be used by themselves, but managing a large number is just as easy. By dotting a device with multiple MappyDots pointing in different directions, a device could combine the readings to gain a LiDAR-like understanding of its physical environment. Its big numbers of MappyDots [Blecky] is going for, too: he just received a few panels of bare PCBs that he’ll soon be laboriously populating. The good news is, there aren’t that many components on each board.
It’s great to see open sourced projects and tools in which it is clear some thought has gone into making them flexible and easy to use. This means they are easier to incorporate into other work and helps make them a great contestant for the Hackaday Prize.
What’s the best way to image a room? A picture? Hah — don’t be so old-fashioned! You want a LIDAR rig to scan the space and reconstruct it as a 3D point map in your computer.
Hot on the heels of [Saulius Lukse]’s scanning thermometer, he’s replaced the thermal camera on their pan/tilt setup with a time-of-flight (TOF) camera — a Garmin LIDAR — capable of 500 samples per second and end up scanning their room in a mere fifteen minutes. Position data is combined with the ranging information to produce a point cloud using Python. Open that file in a 3D manipulation program and you’ll be treated to a sight like this:
Self-driving cars are, apparently, the next big thing. This thought is predicated on advancements in machine vision and cheaper, better sensors. For the machine vision part of the equation, Nvidia, Intel, and Google are putting out some interesting bits of hardware. The sensors, though? We’re going to need LIDAR, better distance sensors, more capable CAN bus dongles, and the equipment to tie it all together.
This is the cheapest LIDAR we’ve ever seen. The RPLIDAR is a new product from Seeed Studios, and it’s an affordable LIDAR for everyone. $400 USD gets you one module, and bizarrely $358 USD gets you two modules. Don’t ask questions — this price point was unheard of a mere five years ago.
Basically, this LIDAR unit is a spinning module connected to a motor via a belt. A laser range finder is hidden in the spinny bits and connected to a UART and USB interface through a slip ring. Mount this LIDAR unit on a robot, apply power, and the spinny bit does its thing at about 400-500 RPM. The tata that comes out includes distance (in millimeters), bearing (in units of degrees), quality of the measurement, and a start flag once every time the head makes a revolution. If you’ve never converted polar to cartesian coordinates, this is a great place to start.
Although self-driving cars and selfie drones are the future, this part is probably unsuitable for any project with sufficient mass or velocity. The scanning range of this LIDAR is only about 6 meters and insufficient for retrofitting a Toyota Camry with artificial intelligence. That said, this is a cheap LIDAR that opens the door to a lot of experimentation ranging from small robots to recreating that one Radiohead video.
[adam] is a caver, meaning that he likes to explore caves and map their inner structure. This is still commonly done using traditional tools, such as notebooks (the paper ones), tape measure, compasses, and inclinometers. [adam] wanted to upgrade his equipment, but found that industrial LiDAR 3D scanners are quite expensive. His Hackaday Prize entry, the Open LIDAR, is an affordable alternative to the expensive industrial 3D scanning solutions out there.
![The 3D scan of a small cave near Louisville (source: [caver.adam's] Sketchfab repository)](https://hackaday.com/wp-content/uploads/2016/06/bildschirmfoto-2016-06-27-um-20-02-11.png)
The gimbal he designed for this task uses stepper motors to aim an SF30-B laser rangefinder. An Arduino controls the movement and lets the eye of the sensor scan an object or an entire environment. By sampling the distance readings returned by the sensor, a point cloud is created which then can be converted into a 3D model. [adam] plans to drive the stepper motors in microstepping mode to increase the resolution of his scanner. We’re looking forwards to see the first renderings of 3D cave maps captured with the Open LIDAR.
I start each day checking out the new and updated projects over on Hackaday.io. Each day one can find all manner of projects – from satellites to machine vision to rockets. One type of project which is always present are robots- robot arms, educational ‘bots, autonomous robots, and mobile robots. This week’s Hackaday.io had a few great robot projects show up on the “new and updated” page, so I’m using the Hacklet to take a closer look.
We start with [Jack Qiao] and Autonomous home robot that does things. [Jack] is building a robot that can navigate his home. He’s learned that just creating a robot that can get itself from point A to point B in the average home is a daunting task. To make this happen, he’s using the Simultaneous Localization and Mapping (SLAM) algorithm. He’s implementing SLAM with the help of Robotic Operating System (ROS). The robot started out as a test mule tethered to a laptop. It’s evolved to a wooden base with a mini ITX motherboard. Mapping data comes in through a Kinect V2, which will soon be upgraded to a Neato XV-11 LIDAR system.
Next up is [Tyler Spadgenske] with TyroBot. TyroBot is a walking robot with some lofty goals, including walking a mile in a straight line without falling down. [Tyler’s] inspiration comes from robots such as Bob the Biped and Zowi. So far, TyroBot consists of legs and feet printed in PLA. [Tyler] is going to use a 32 bit processor for [TyroBot’s] brain, and wants to avoid the Arduino IDE at any cost (including writing his own IDE from scratch). This project is just getting started, so head on over to the project page and watch TyroBot’s progress!
Next is [Mike Rigsby] with Little Friend. Little Friend is a companion robot. [Mike] found that robots spend more time charging batteries than interacting. This wouldn’t do for a companion robot. His solution was to do away with batteries all together. Little Friend is powered by super capacitors. An 8 minute charge will keep this little bot going for 75 minutes. An Arduino with a motor shield controls Little Friend’s DC drive motors, as well as two animated eyes. If you can’t tell, [Mike] used a tomato as his inspiration. This keeps Little Friend in the cute zone, far away from the uncanny valley.
Finally we have the walking robot king, [Radomir Dopieralski], with Logicoma-kun. For the uninitiated, a Logicoma is a robot tank (or “logistics robot”) from the Ghost in the Shell series. [Radomir] decided to bring these cartoon tanks to life – at least in miniature. The bulk of Logicoma-kun is built carefully cut and sculpted acrylic sheet. Movement is via popular 9 gram servos found all over the internet. [Radomir] recently wrote an update outlining his new brain for Logicoma-kun. An Arduino Pro Mini will handle servo control. The main computer will be an ESP8266 running Micropython. I can’t wait to see this little ‘bot take its first steps.
If you want more robotic goodness, check out our brand new mobile robot list! Did I miss your project? Don’t be shy, just drop me a message on Hackaday.io. That’s it for this week’s Hacklet, As always, see you next week. Same hack time, same hack channel, bringing you the best of Hackaday.io!
