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(via Clifford Pickover)
The Raspberry Pi is a great device to experiment on and since it's easy to swap out operating systems on a whim, it's fun to run old ones that you don't have a lot of use for. To that end, here are a few of our favorites.
Many climbers may not even know what a “dirtbag” is, let alone a swami belt, and this is part of the problem. There are some strong, psyched, and promising young climbers who learned or are learning to climb in one of the 889 gyms in America, who might check Webster’s for the word dirtbag and find this: “A dirty, unkempt, or contemptible person.” Arguably, aspects of this short explanation might be true, but here’s a better and more accurate take from Urban Dictionary: “A person who is committed to a given (usually extreme) lifestyle to the point of abandoning employment and other societal norms in order to pursue said lifestyle. Dirtbags can be distinguished from ‘hippies’ by the fact that dirtbags have a specific reason for living communally and generally non-hygienically; dirtbags seek to spend all of their moments climbing.”
When I started climbing at 21, my mentor Sean “Stanley” Leary, who was already an accomplished climber and dirtbag, told me outlandish stories of Yosemite Valley, a mecca not just for climbing, but for dirtbagging, a place where the best climbers lived in their cars (or in caves!), survived on next to nothing, and climbed full-time. Full-time! A seed was planted.
First ascents were made, speed records were broken, and climbing gods were born and lived in that dirtbag bastion! I don’t think many of those epic feats would have been possible without the unlimited climbing our alternative lifestyle provided.
And then, as the 2000s rolled in, things started to change in small, but measurable, increments. Rangers began harassing car campers in The Center. Dirtbags were getting busted and ticketed in their secret caves, so they scurried to every corner of the Valley. We would still meet at the Yosemite Lodge Cafeteria for coffee or in El Cap Meadow to smoke weed, but without The Center, our sense of community was increasingly splintered. Even Camp 4 was a no-go with its more and more strictly enforced two-week camping limit. As years went by, and in spite of increasing ranger-induced challenges, I continued to spend most of the year lurking in Yosemite. There was the occasional new dirtbag on the scene, but it was clear that the party was losing steam.
It’s sad; I learned so much as a dirtbag. Toiling on epic in-a-day ascents of El Cap gave me a tremendous work ethic. Living a simple life in the dirt in such a beautiful place inspired a deep love and respect for the natural world. With little money ever to my name, I learned the value of thrift and conservation. While now, I do have more than a thousand dollars in my bank account and an actual roof over my head, I still live life by the dirtbag ethos that collecting experiences is more important than amassing wealth and material objects. I hope Yosemite’s waning dirtbag population isn’t the canary in the coal mine.
And then there’s the change in venue of where most modern climbers pick up the sport. The majority of climbers now learn in gyms, disconnected from climbing history. To be clear, I’m not bagging on gyms. Heck, I’ve never been stronger than I am now, living in Boulder and climbing in one regularly, but I do hope that we can connect the gym culture to the deeper thread of climbing history. It’s easy to have respect for your forefathers when you literally walk in their footsteps. In Yosemite, giants like Chuck Pratt, Warren Harding, and Royal Robbins spent chunks of their lives sleeping in the dirt and putting up iconic first ascents on El Cap and Half Dome.
Now you can climb 5.13 without ever going outside. You don’t necessarily need to dedicate your entire life to climbing to get really strong, especially as the majority of climbers turn to bouldering and sport climbing. You don’t learn dirtbag culture in the climbing gym, and it seems that some of the environmental ethics and etiquette that are part and parcel of dirtbagging are getting lost as well.
The Internet has changed the way people climb, too. “You don’t have to hang out in Camp 4 to find partners any more,” my friend and dirtbag stalwart James Lucas half-jokes, “You can go on Mountain Project and find beta and a partner for any climb you want to do!” In an era where many people’s social lives and community exist wholly in the virtual world, climbing is suffering from the same over-arching problem.
Ivo Ninov by El Cap Bridge cleaning cams before setting the speed record on Native Son (5.9 A4) on El Cap, with Ammon McNeely.
End rant. I’ll stop whining and outline something to feel optimistic about. Social norms have a way of ebbing and flowing. Dirtbagging hasn’t flatlined just yet, and the beauty and passion that so many of us find in climbing may be enough to draw in the next generation. That’s where I hope to make a difference. I’m not here to say that every climber should quit their job and move to Yosemite, or start sleeping in their Saturn wagon, but I am here to say that it can change your life.
Consider Alex Honnold. He learned to climb in a gym in Sacramento, and somehow found his way to Yosemite where he dirtbagged in proud style. Slowly but surely, he became one of the greatest climbers the world has ever seen; his simple, meager existence allowed him the time to perfect his big wall skills. The Nose speed record and Half Dome free-solo are only a couple on his endless tick list of notable achievements. I can say confidently that Alex’s life would look a lot different if he hadn’t dropped out of college and made that leap of faith to live in his van and follow his dreams.
Do you have a deferred climbing dream? Do you have a crappy job that makes you miserable? Do you have fantasies of climbing rock every day? Is the only time you find joy and passion in your life when the weekend rolls around and you get to hit the rock? Then you might have what it takes to keep the dirtbag dream alive. Maybe this beautiful, unruly thing has some life in it yet. //
Cedar Wright is a professional climber and contributing editor for Climbing. He still only showers about once a week or so.
Helen: Today we’re delighted to have a guest post from 17-year-old student Arne Baeyens, aka Robotanicus, who has form in designing prize-winning robots. His latest, designed for the line-following challenge of a local competition, is rather impressive. Over to Arne…
Two months ago, the 24th of May, I participated in the RoboCup Junior competition Flanders, category ‘Advanced Rescue’. With a Raspberry Pi, of course – I used a model B running Raspbian. Instead of using reflectance sensors to determine the position of the line, I used the Pi Camera to capture a video stream and applied computer vision algorithms to follow the line. My robot wasn’t the fastest but I obtained the third place.
A short video of the robot in action:
In this category of the RCJ competition the robot has to follow a black line and to avoid obstacles. The T-junctions are marked by green fields to indicate the shortest trajectory. The final goal is to push a can out of the green field.
This is not my first robot for the RCJ competition. In 2013 I won the competition with a robot with the Dwengo board as control unit. It used reflectance and home-made colour sensors. The Dwengo board uses the popular pic18f4550 microcontroller and has amongst other functionalities a motor driver, a 2×16 char screen and a big, 40pin extension connector. The Dwengo board is, like the RPi, designed for educational purposes, with projects in Argentina and India.
As the Dwengo board is a good companion for the Raspberry Pi, I decided to combine both boards in my new robot. While the Pi does high-level image processing, the microcontroller controls the robot.
The Raspberry Pi was programmed in C++ using the OpenCV libraries, the wiringPi library (from Gordon Henderson) and the RaspiCam openCV interface library (from Pierre Raufast and improved by Emil Valkov). I overclocked the Pi to 1GHz to get a frame rate of 12 to 14 fps.
Using a camera has some big advantages: first of all, you don’t have that bunch of sensors mounted close to the ground that are interfering with obstacles and deranged by irregularities. The second benefit is that you can see what is in front of the robot without having to build a swinging sensor arm. So, you have information about the actual position of the robot above the line but also on the position of the line in front, allowing calculation of curvature of the line. In short, following the line is much more controllable. By using edge detection rather than greyscale thresholding, the program is virtually immune for shadows and grey zones in the image.
If the line would have had less hairpin bends and I would have had a bit more time, I would have implemented a speed regulating algorithm on the base of the curvature of the line. This is surely something that would improve the performance of the robot.
I also used the camera to detect and track the green direction fields at a T-junction where the robot has to take the right direction. I used a simple colour blob tracking algorithm for this.
A short video of what the robot thinks:
Please note that in reality the robot goes a little bit slower following the line.
Different steps of the image processing
Image acquired by the camera (with some lines and points already added):
The RPi converts the colour image to a greyscale image. Then the pixel values on a horizontal line in the image are extracted and put into an array. This array is visualized by putting the values in a graph (also with openCV):
From the first array, a second is calculated by taking the difference from two successive values. In other words, we calculate the derivative:
An iterating loop then searches for the highest and lowest value in the array. To have the horizontal relative position of the line in the array, the two position values—on the horizontal x axis in the graphed image—are averaged. The position is put in memory for the next horizontal scan with a new image. This makes that the scan line does not have to span the whole image but only about a third of it. The scan line moves horizontally with the centre about above the line.
But this is not enough for accurate tracking. From the calculated line position, circles following the line are constructed, each using the same method (but with much more trigonometry calculations as the scan lines are curved). For the second circle, not only the line position but also the line angle is used. Thanks to using functions, adding a circle is a matter of two short lines of code.
The colour tracking is done by colour conversion to HSV, thresholding and then blob tracking, like explained in this excellent video. The colour tracking slows the line following down by a few fps but this is acceptable.
As seen in the video, afterwards all the scan lines and some info points are plotted on the input image so we can see what the robot ‘thinks’.
After the Raspberry Pi has found the line, it sends the position data and commands at 115,2 kbps over the hardware serial port to the Dwengo microcontroller board. The Dwengo board does some additional calculations, like taking the square root of the proportional error and squaring the ‘integral error’ (curvature of the line). I also used a serial interrupt and made the serial port as bug-free as possible by receiving each character separately. Thus, the program does not wait for the next character while in the serial interrupt.
The Dwengo board sends an answer character to control the data stream. The microcontroller also takes the analogue input of the SHARP IR long range sensor to detect the obstacles and scan for the container.
In short, the microcontroller is controlling the robot and the Raspberry Pi does an excellent job by running the CPU intensive line following program.
There’s a post on the forum with a more detailed technical explanation – but you will find the most important steps below.
Both devices are interconnected by two small boards—one attaches to the RPi and the other to the Dwengo board—that are joined by a right angle header connection. The first does with some resistors the logic level converting (the Dwengo board runs on 5V), the latter board also has two DC jacks with diodes in parallel for power input to the RPi. To regulate the power to the Pi, I used a Turnigy UBEC that delivers a stable 5.25V and feeds it into the Pi by the micro USB connector. This gives a bit more protection to the sensitive Pi. As the camera image was a bit distorted I added a 470uF capacitor to smooth things out. This helped. Even though the whole Pi got hot, the UBEC stayed cold. The power input was between 600 and 700mA at around 8.2 volts.
Last year, I almost missed the first place as the robot only just pushed the can out of the field. Not a second time! Having this in thought, I constructed two 14cm long arms that could be turned open by two 9g servos. With the two grippers opened, the robot spans almost 40 centimetres. Despite this, the robot managed—to everyone’s annoyance—‘to take its time before doing its job’, as can be seen in the video.
Building the robot platform
To build the robot platform I followed the same technology as the year before (link, in Dutch). I made a design in SketchUp, then converted it to a 2D vector drawing and finally lasercutted it at FabLab Leuven. However, the new robot platform is completely different in design. Last year, I made a ‘riding box’ by taking almost the maximum dimensions and mounting the electronics somewhere on or in it.
This time, I took a different approach. Instead of using an outer shell (like insects have), I made a design that supports and covers the parts only where necessary. The result of this is not only that the robot looks much better, but also that the different components are much easier to mount and that there is more space for extensions and extra sensors. The design files can be found here: Robot RoboCup Junior – FabLab Leuven.
3D renders in SketchUp:
On the day of the RCJ competition I had some bad luck as there wasn’t enough light in he competition room. The shutter time of the camera became much longer. As a consequence, the robot had much more difficulties in following sharp bends in the line. However, this problem did not affect the final outcome of the competition.
Maybe I should have mounted some LEDs to illuminate the line…
It's one thing to note the unusually hot weather we've been having and then slather on some suncream. But when a climate expert unceremoniously concedes that the planet is "f**ked", it's probably time to get seriously worried.
Did you know that Facebook, Apple, or Google don't know your password? They just don't store it in their databases so hackers can't retrieve it in case they manage to break in. But if you're the only person who actually knows your password... how the hell do they know you are typing the right password to let you in?
Even when stuffed full of laptops, textbooks, and other less-than-soft items, a backpack still makes for a great makeshift pillow. But imagine how comfy napping in the library would be with Ryan Frayne's Windcatcher Air Pakk that unzips and automatically inflates like a tiny air mattress.
Despite countless ways for us to share information online, business cards are somehow still a popular way to pass on contact info. So instead of trying to replace them, the creators of the swivelCard simply found a way to make business cards better with a built-in USB port that lets you share everything from your resume, to a photo gallery, to a video—and not just your name, email address, and cellphone number.
About 2.5 billion years ago, microbes began making a poison that would cause one of the largest mass extinctions on Earth. The few organisms that could handle this poison flourished, going on to become our ancestors. The poison? It was oxygen. It's a wonder that oxygen levels didn't keep rising until Earth became uninhabitable, though, and a new study suggests we have an ancient worm tunneling through the sea floor to thank.
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