Don’t Make Your Battlebot Out Of A Pumpkin

It’s that time of year again. The nights are getting longer and the leaves are turning. The crisp fall air makes one’s thoughts turn to BattleBots: pumpkin-skinned BattleBots.

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Kids these days can’t even draw without a computer

If you’re asking yourself, “could a laser-cut plywood bot, sheathed in a pumpkin, stand up against an all-metal monster”, you haven’t seen BattleBots before. Besides the hilarious footage (see video embedded below), a lot of the build is documented, from making a CAD model of a pumpkin to laser-cutting the frame, to “testing” the bot just minutes before the competition. (That has to be a good idea!)

The footage of the pumpkinbot’s rival, Chomp, is equally cool. We love that the hammer weapon is accelerated so quickly that Chomp actually lifts in the air, just as Newton would have predicted. We’re not sure if the fire weapon is good for anything but show, and facing plywood pumpkinbots, but we love the effect.

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A Glimpse Into The Mind Of A Robot Vacuum Cleaner

What’s going through the mind of those your autonomous vacuum cleaning robots as they traverse a room? There are different ways to find out such as covering the floor with dirt and seeing what remains afterwards (a less desirable approach) or mounting an LED to the top and taking a long exposure photo. [Saulius] decided to do it by videoing his robot with a fisheye lens from near the ceiling and then making a heatmap of the result. Not being satisfied with just a finished photo, he made a video showing the path taken as the room is being traversed, giving us a glimpse of the algorithm itself.

Looking down on the room and robot
Looking down on the room and robot

The robot he used was the Vorwerk VR200 which he’d borrowed for testing. In preparation he cleared the room and strategically placed a few obstacles, some of which he knew the robot wouldn’t get between. He started the camera and let the robot do its thing. The resulting video file was then loaded into some quickly written Python code that uses the OpenCV library to do background subtraction, normalizing, grayscaling, and then heatmapping. The individual frames were then rendered into an animated gif and the video which you can see below.

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A Robot Arm From The Cupboard

Building a simple robot arm is a lot more straightforward than it used to be.  If you have a laser cutter, or a bit of cash and don’t mind waiting for postage, there are inexpensive kits like the MeArm. If you have a 3D printer, there are any number of 3D-printed designs for you to tackle. What if you need to satisfy your urge to build a robot arm really quickly, and you don’t have a laser cutter or 3D printer? You’ve got a pile of servos from that remote-control project, how can you make the rest?

If you are [roboteurs], you raid the stationery cupboard, and create an arm using rubber bands, paper clips, and binder clips. The binder clips grip the servo arms and hold the whole thing together, the rubber bands provide extra attachment , and the paper clips are bent to form the jaws. It’s not the prettiest or perhaps the most capable of arms, but it undeniably is an arm, and we’d doubt it could be done any more cheaply.

In this particular case, the arm serves as a demonstration piece for [roboteurs]’ Printabots Maker Kit for people without a 3D printer. It uses their controller board, but there is no reason why it could not be used with any other board capable of driving servos.

We’ve covered innumerable robot arms over the years, This one may be the cheapest, but another contender might be this cardboard arm. None of them, however, are as cool as this steam-powered Armatron toy.

TensorFlow Robot Recognizes Objects

Children can do lots of things that robots and computers have trouble with. Climbing stairs, for example, is a tough thing for a robot. Recognizing objects is another area where humans are generally much better than robots. Kids can recognize blocks, shapes, colors, and extrapolate combinations and transformations.

Google’s open-source TensorFlow software can help. It is a machine learning system used in Google’s own speech recognition, search, and other products. It is also used in quite a few non-Google projects. [Lukas Biewald] recently built a robot around some stock pieces (including a Raspberry Pi) and enlisted TensorFlow to allow the robot to recognize objects. You can see a video of the device, below.

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Simulate Your Robot Before You Build It

[Nurgak] shows how one can use some of the great robotic tools out there to simulate a robot before you even build it. To drive this point home he builds the tutorial off of the easily 3D printable and buildable Robopoly platform.

The robot runs on Robot Operating System at its core. ROS is interesting because of its decentralized and input/output agnostic messaging system. For example, if you leave everything alone but swap out the motor output from actual motors to a simulator, you can see how the robot would respond to any arbitrary input.

[Nurgak] uses another piece of software called V-REP to demonstrate this. V-REP is a simulation suite for robotics and has a few ROS nodes built in. So in order to make a simulated line-following robot, [Nurgak] tells V-REP to send a simulated camera image to the decision making node of the robot in ROS. It then sends the movement messages back to V-REP which drives the pretend robot around.

He runs through a few more examples, proving that it’s entirely possible to become if not a roboticist, at least a really good AI programmer without ever dropping the big money on parts to build a robot.

Amazon Offers $2.5M To Make Alexa Your Friend

Amazon has unveiled the Alexa Prize, a $2.5 Million purse for the first team to turn Alexa, the voice service that powers the Amazon Echo, into a ‘socialbot’ capable of, “conversing coherently and engagingly with humans on popular topics for 20 minutes”.

The Alexa Prize is only open to teams from colleges or universities, with the winning team taking home $500,000 USD, with $1M awarded to the team’s college or university in the form of a research grant. Of course, the Alexa Prize grants Amazon a perpetual, irrevocable, worldwide, royalty-free license to make use of the winning socialbot.

It may be argued the Alexa Prize is a competition to have a chat bot pass a Turning Test. This is a false equivalency; the Turing Test, as originally formulated, requires a human evaluator to judge between two conversation partners, one of which is a human, one of which is a computer. Additionally, the method of communication is text-only, whereas the Alexa Prize will make use of Alexa’s Text to Speech functionality. The Alexa Prize is not a Turing Test, but only because of semantics. If you generalize the phrase, ‘Turing Test’ to mean a test of natural language conversation, the Alexa Prize is a Turing Test.

This is not the first prize offered for a computer program that is able to communicate with a human in real time using natural language. Since 1990, the Loebner Prize, cosponsored by AI god Marvin Minsky, has offered a cash prize of $100,000 (and a gold medal) to the first computer that is indistinguishable from a human in conversation. Since 1991, yearly prizes have been awarded to the computer that is most like a human as part of the competition.

For any team attempting the enormous task of developing a theory of mind and consciousness, here are a few tips: don’t use Twitter as a dataset. Microsoft tried that, and their chatbot predictably turned racist. A better idea would be to copy Hackaday and our article-generating algorithm. Just use Markov chains and raspberry pi your way to arduino this drone.

Line Follower with No Arduino

There’s hardly a day that passes without an Arduino project that spurs the usual salvo of comments. Half the commenters will complain that the project didn’t need an Arduino. The other half will insist that the project would be better served with a much larger computer ranging from an ARM CPU to a Cray.

[Will Moore] has been interested in BEAM robotics — robots with analog hardware instead of microcontollers. His latest project is a sophisticated line follower. You’ve probably seen “bang-bang” line followers that just use a photocell to turn the robot one way or the other. [Will’s] uses a hardware PID (proportional integral derivative) controller. You can see a video of the result below.

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