If you’ve been keeping up with augmented and virtual reality news, you’ll remember that spacial haptic feedback devices aren’t groundbreaking new technology. You’ll also remember, however, that a professional system is notoriously expensive–on the order of several thousand dollars. Grad students [Jonas], [Michael], and [Jordi] and their professor [Eva-Lotta] form the design team aiming to bridge that hefty price gap by providing you with a design that you can build at home.
A quick terminology dive: a spacial haptic device is a physical manipulator that enables exploration of a virtual space through force feedback. A user grips the “manipulandum” (the handle) and moves it within the work area defined by the physical design of the device. Spacial Haptic Devices have been around for years and serve as excellent tools for telling their users (surgeons) what something (tumor) “feels like.”
In our case, this haptic device is a two-link, two-joint system grounded on a base station and providing force feedback with servo motors and tensioned wire ropes. The manipulator itself supports 3-degree-of-freedom movement of the end-effector (translations, but no rotations) which is tracked with encoders placed on all joints. To enable feedback, joints are engaged with cable-drive transmissions.
The design team isn’t new to iterative prototyping. Hailing from CS235, a Stanford course aimed to impart protoyping techniques to otherwise non-tinkerers, the designers have drawn numerous techniques from this course to deliver a fully functional and reproducible setup. In fact, it’s clear that the designers have a strong understanding of their system’s physics, and they capitalize on a few tricks that don’t immediately jump out to us as intuitive. For instance, rather than rigidly fixing their cable to the motor shaft, they simply wrap the cable around the shaft a mere 5 turns such that the force of friction greatly exceeds the threshold amount that would otherwise cause slipping. They also choose plywood–not necessarily because of its price–but more so because of its function as a stiff, layered composite that makes it ideal “lever arm material” for rigidly transferring forces.
For a full breakdown of their design, take a look at their conference paper (PDF) where they evaluate their design techniques and outline the forward kinematics. They’ve also provided a staggeringly comprehensive bill of materials (Google Spreadsheet). Finally, as justifiably open source hardware, they’ve packaged their control software and CAD models into a github repository so that you too can jump into the world of quality force feedback simulation without shelling out the twenty thousand dollars for a professional system.
Many tablets come with some sort of triaxial magnetic sensor but as [Andrea] and [Ian]’s demo shows, they are only capable of passing along the aggregate vector of all magnetic forces. If one had multiple magnetic objects, the sensor is not able to provide much useful information.
Their solution is a mix of software and hardware. Each object is given a magnet that rotates at a different known speed. This creates complex sinusoidal magnetic fields that can be mathematically isolated with bandpass filters. This also gives them distance to each object. The team added an Arduino with a magnetometer for reasons unexplained, perhaps the ones built into tablets are not sufficient?
The demo video below shows off what is under the hood and some new input mechanics for simple games, sketching, and a logo turtle. Their hope is that this opens the door to all manner of tangible devices.
If you’ve ever wanted your own self-driving car, this is your chance. [Sebastian Thrun], co-lecturer (along with the great [Peter Norvig]) of the Stanford AI class is opening up a new class that will teach everyone who enrolls how to program a self-driving car in seven weeks.
The robotic car class is being taught alongside a CS 101 “intro to programming” course. If you don’t know the difference between an interpreter and a compiler, this is the class for you. You’ll learn how to make a search engine from scratch in seven weeks. The “Building a Search Engine” class is taught by [Thrun] and [David Evans], a professor from the University of Virginia. The driverless car course is taught solely by [Thrun], who helped win the 2005 DARPA Grand Challenge with his robot car.
In case you’re wondering if this is going to be another one-time deal like the online AI class, don’t worry. [Thrun] resigned as a tenured professor at Stanford to concentrate on teaching over the Internet. He’s still staying at Stanford as an associate professor but now he’s spending his time on his online university, Udacity. It looks like he might have his hands full with his new project; so far, classes on the theory of computation, operating systems, distributed systems, and computer security are all planned for 2012.
In a little more than a month, tens of thousands of people around the world will attend a class on Artificial Intelligence at Stanford. Registration for this class is still open for both class ‘tracks’. The “basic” track is simply watching lectures and answering quizzes, or a slightly more advanced version of MIT OpenCourseware or Khan Academy. The “advanced” track is the full class, requires homework and exams, and aspires to Stanford difficulty.
With thousands of people taking this class, there’s bound to be a few study groups popping up around the web. The largest ones we’ve seen are /r/aiclass on Reddit and the stack overflow style aiqus. The most common reply to ‘what language should I learn from this class?’ is Python, although there’s an online code repo that has the text’s working code in Lisp, Java, C++ and C#.
If AI doesn’t float your boat, there are two more classes being taught from Stanford this fall: machine learning and introduction to databases. Any way you look at it, you’re getting to take a class from one of the preeminent instructors in the field for free. Do yourself a favor and sign up.
Thanks to everyone who sent this in. You can stop now.
Those brainy folks over at Stanford are working on an open source digital camera. This is an effort to advance what they call “computational photography”. Basically they’re looking to combine some of the functionality of Photoshop or Gimp right into the camera. One example they discuss is utilizing an algorithm to even out the light levels from one side of the picture to the other. Another trick they’ve already accomplished in the lab is increasing the resolution of full motion video. They take a full resolution photo once every few frames and use the computing power of the camera to incorporate that information into the low-res frames around it.
We like the idea of being able to get at the firmware that runs on our digital cameras. Going with open source would certainly provide that access, but cost will be an issue. The Stanford team hopes to produce a model of what they now call Frankencamera that sells for “less than $1000″.
Stanford’s autonomous helicopter group has made some impressive advancements in the field of autonomous helicopter control, including inverted hovering and performing aerobatic stunts. The group uses reinforcement learning to teach its control system various maneuvers and has been very successful in doing so. One of their latest achievements was teaching the bot the emergency landing technique autorotation. Autorotation is used when a helicopter’s engine fails or is disengaged and works by changing the collective pitch to use the airflow from descent to rotate the blades. The group has more flight demonstrations on their YouTube channel.