In his everlasting quest to replace physical skill with technology, [Shane] of [Stuff Made Here] has taken aim at the game of eight-ball pool. Using a combination of computer vision and mechatronics, he created a robotic pool system that can allow a physical game of pool over the internet, or just beat human players. See the video after the break.
Making a good pool shot requires three discrete steps. First, you need to identify the best shot, then figure out how exactly to strike the balls to achieve the desired results, and finally physically execute the shot accurately. [Shane’s] goal was to automate all these steps. For the physical part, he built a pool cue with a robotic tip which only requires the user to place in approximately the right position, while a pneumatic piston mounted on a Stewart platform does the rest. A Stewart platform is a triangular plate mounted with six reciprocating rods, which gives it the required freedom of motion. The rods’ bases are attached to a set of cranks actuated by tension cables pulled by servos mounted at the rear-end of the cue. An adjustable air system allows the power of the shot to be adjusted as required.
A camera mounted is mounted over the table and connected to computer vision software to gather the required position information. Fiducials on the corners of the table and the cue tip allow the position of the pockets, balls, and cue to be accurately determined, and theoretically should allow the robot to take the perfect shot. Getting this to work in reality quickly turned into a very frustrating experience. After many hours of debugging, [Shane] tracked the error to a tiny forgotten test function that was introducing 5-10 mm of position error, and 2 of the six servos in the cue not performing up to spec. To determine the vertical positioning of the cue, an IMU and fixed height foot were added. [Shane] also added an overhead projector to overlay all required information directly on the table. Continue reading “Robotic Pool Cue Can Be Your Friend Or Your Foe”→
This [Johan Link] build isn’t just about style. A look under the hood reveals not the standard, off-the-shelf microcontroller development board you might expect. Instead, [Johan] designed and built his own board with an ATmega32 to run the three servos that control the platform. The entire apparatus is made from a dozen or so 3D-printed parts that interlock to form the base, the platform, and the housing for the USB webcam that’s perched on an aluminum tube. From that vantage point, the camera’s images are analyzed with OpenCV and the center of the ball is located. A PID loop controls the three servos to center the ball on the platform, or razzle-dazzle it a little by moving the ball in a controlled circle. It’s quite a build, and the video below shows it in action.
We’ve seen a few balancing platforms before, but few with such style. This Stewart platform comes close, and this juggling platform gets extra points for closing the control loop with audio feedback. And for juggling, of course.
Breaking into the world of auto racing is easy. Step 1: Buy an expensive car. Step 2: Learn how to drive it without crashing. If you’re stuck at step 1, and things aren’t looking great for step 2 either, you might want to consider going with a virtual Porsche or Ferrari and spending your evenings driving virtual laps rather than real ones.
The trouble is, that can get a bit boring after a while, which is what this DIY motion simulator platform is meant to address. In a long series of posts with a load of build details, [pmvcda] goes through what he’s come up with so far on this work in progress. He’s building a Stewart platform, of the type we’ve seen before but on a much grander scale. This one will be large enough to hold a race car cockpit mockup, which explains the welded aluminum frame. We were most interested in the six custom-made linear actuators, though. Aluminum extrusions form the frame holding BLDC motor, and guide the nut of a long ball screw. There are a bunch of 3D-printed parts in the actuators, each of which is anchored to the frame and to the platform by simple universal joints. The actuators are a little on the loud side, but they’re fast and powerful, and they’ve got a great industrial look.
[David Brown]’s entry for The Hackaday Prize is a design for a tool that normally exists only as an expensive piece of industrial equipment; out of the reach of normal experimenters, in other words. That tool is a 6-axis micro manipulator and is essentially a small robotic actuator that is capable of very small, very precise movements. It uses 3D printed parts and low-cost components.
The manipulator consists of six identical actuators, each consisting of a single piece of SLS 3D printed nylon with a custom PCB to control a motor and read positional feedback. The motor moves the central pivot point of the 3D printed assembly, which in turn deflects the entire piece by a small amount. By anchoring one point and attaching the other, a small amount of highly controllable movement can be achieved. Six actuators in total form a Gough-Stewart Platform for moving the toolhead.
Interestingly, this 6-Axis Micro Manipulator is a sort of side project. [David] is interested in creating his own digital UV exposer, which requires using UV laser diodes with fiber optic pig tails attached. In an industrial setting these are created by empirically determining the optimal position of a fiber optic with regards to the laser diode by manipulating it with a micro manipulator, then holding it steady while it is cemented in place. Seeing a distinct lack of micro manipulators in anything outside of lab or industrial settings, and recognizing that there would be applications outside of his own needs, [David] resolved to build one.
[Abhishek] describes Peeqo as a “personal desktop robotic assistant” that looks like “the love child of an Amazon Echo and a Disney character.” We’re not sure about that last part — we’re pretty sure [Bender Bending Rodriquez] would fail a paternity suit on this one. Just look at that resemblance.
Whatever Peeqo’s parentage may be, it’s a pretty awesome build, and from the look of [Abhishek]’s design notes, he put a lot of thought into it, and a lot of work too. The build log reveals 3D-printed parts galore, custom-etched PC boards, and a hacked Raspberry Pi to both listen for voice commands and play responses in the form of animated GIFs on Peeqo’s ‘face’. The base has six modified RC servos to run the Gough-Stewart platform that lets Peeqo emote, and the head contains pretty much all the electronics. Beyond the hardware, a ton of programming went into giving Peeqo the ability to communicate through head gestures and GIFs that make sense for the required response.
Whether it’s bopping along to the tunes on your playlist or motivating you to lay off the social media with [Will Ferrell]’s flaming angry eyes, Peeqo looks like a ton of fun to build and use. Conveniently enough, [Abhishek] has shared all his files so you can build one too.
These days, our automobiles sport glittering consoles adorned with dials and digits to keep us up-to-date with our car’s vitals. In the future, though, perhaps we just wont need such vast amounts of information at our fingertips if our cars are driving themselves around. No information? How will we tell the car what to do? On that end, [Felix] has us covered with Stewart, a tactile gesture-input interface for the modern, self driving car.
Stewart is a 6-DOF “Stewart Interface” capable of both gesture input and haptic-output. Gesture input enables the car’s passenger to deliver driving suggestions to the car. The gentle twist of a wrist can signal an upcoming turn at the next intersection; pulling back on Stewart’s head “joystick style” signals a “whoa–slow down, there, bub!” Haptic output via 6 servos pushes around Stewart’s head in the car’s intended direction. If the passenger agrees with the car, she can let Stewart gesture itself in the desired direction; if she disagrees; she can veto the car’s choices by moving her hand directly against Stewart’s current output gesture. Overall, the interface unites the intentions of the car and the intentions of the passenger with a haptic device that makes the connection feel seamless!
We know we’re not supposed to comment on the “how” with art projects–but we’re engineers–and this one makes us giddy with delight. We’re imagining those rc car shock absorbers dramatically dampening the jittery servos and giving the user a nice resistive feel. Interconnects are laser cut acrylic, and the shell is a smoothly contoured 3d print. We’ve seen Stewart Interfaces before, but nothing with the look-and-feel of a sleek design feature on its way to being dropped into the cockpit of our future self-driving cars.
The selfie: pop culture’s most frivolous form of self-expression is also probably one of the most human acts you could find yourself doing in a day. Everyone is guilty of snapping a quick pic from time to time with the expectation that it will leave an impression on those who see it. All of the implications surrounding why we do this support our deep-seated need to sculpt an identity for ourselves using others as the hammer and chisel. So, consider how upside-down the world would feel if you caught a robot posing for a shot in the mirror? What about one whose sole function was to take selfies and post them? If this breaks your mind a little, that was the intention. This #selfie robot by artists [Radamés Ajna] and [Thiago Hersan] is the first development in a larger body of work called “memememe”, which is meant to comment on our culture’s obsession with the trending, selfing nature of social media. This specific project explores the relationship between conversation and identity in a situation where there is no second party.
Hardware-wise, the #selfie bot is a Stewart platform made from six servo motors and a few pieces of carefully measured pushrod connected with swivel-ball-links. An android phone is mounted on the end effector which acts functionally as the robot’s face and eyes. To make it self-aware in a sense, [Ajna] and [Hersan] created their own recognition software with Open CV using a collection of sample images of various phones as reference points. As soon as the robot recognizes itself in the mirror as indicated by specific words flashing on its screen, it takes a picture, immediately uploading it to its own tumblr account. [Ajna] and [Hersan] have a nice description of their process on the project’s Instructable’s page which you can check out to see how they used Haar Cascades to create their custom object recognition. Additionally, if you’d fancy building your own robot to covertly place in your living room to snap pictures of other phones, you could check out their code on github.