We all know that version one of a project is usually a stinker, at least in retrospect. Sure, it gets the basic idea into concrete form, but all it really does is set the stage for a version two. That’s better, but still not quite there. Version three is where the magic all comes together.
At least that’s how things transpired on [Shane Wighton]’s quest to build the perfect basketball robot. His first version was a passive backboard that redirected incoming shots based on its paraboloid shape. As cool as the math was that determined the board’s shape, it conspicuously lacked any complicated systems like motors and machine vision — you know, the fun stuff. Version two had all these elaborations and grabbed off-target shots a lot better, but still, it had a limited working envelope.
Enter version three, seen in action in the video below. Taking a page from [Mark Rober]’s playbook, [Shane] built a wickedly overengineered CoreXY-style robot to cover his shop wall. Everything was built with the lightest possible materials to keep inertia to a minimum and ensure the target ends up in the right place as quickly as possible. [Shane] even figured out how to mount the motor that tilts the backboard on the frame rather than to the carriage. A Kinect does depth-detection duty on the incoming ball — or the builder’s head — and drains pretty much every shot it can reach.
[Shane] has been doing some great work automating away the jobs of pro athletes. In addition to basketball, he has tackled both golf and baseball, bringing explosive power to each. We’re looking forward to versions two and three on both of those builds as well.
Continue reading “Third Time’s A Charm For This Basketball-Catching Robot”
A few weeks ago, [Shane Wighton] created a basketball backboard which made it impossible to miss a shot even remotely close to the hoop. As a passive device, though, the backboard had its limits. Shots with tremendous velocity wouldn’t go in, and (like most backboards) it was missing facial recognition software. So he got to work on a second version which solves those issues, and takes a more active role in the game.
This version is flat, and looks largely unassuming until a game begins. The flat backboard is mechanized and includes a camera, so incoming shots can be analyzed in real-time while the backboard is moved into a position to direct the ball into the net. Or, since it does include facial recognition, the backboard can always send the ball away from the hoop, ensuring that [Shane] always wins basketball games no matter how many shots his opponent takes.
If you didn’t get a chance to see the original, we featured that a while back, and it’s truly a wonder when you learn about how much analysis went into creating the shape. The new version is even more impressive, doing all of that math in real time, and we can’t wait to see what [Shane] comes up with next.
Continue reading “Robotic Basketball Hoop V2”
With none of the major leagues in any team sport currently meeting, sports fans have a huge void that has to be filled with something. For [Shane Wighton], the machine shop is the place to go when sports let you down, and the result is this basketball backboard that lets you sink every shot every time.
When we first saw this, we thought for sure it would be some overly complicated motorized affair that would move the hoop to catch the basketball, sort of like the dart-catching dartboard we featured some time ago. And while that would be awesome and somebody should totally build that so we can write it up, [Shane]’s hoop dream is a lot simpler mechanically, even if the math needed to determine the proper shape for the backboard was complex. He wrote software to simulate throws from hundreds of positions to determine the shape for the board, which ends up looking like a shallow elliptic paraboloid. The software created a mesh that was translated into CNC tool paths in Fusion 360, and the backboard was carved from blocks of softwood.
The first tests were disappointing; instead of landing every shot, the board seemed to be actively denying them. [Shane] had to puzzle over that for a while before realizing that he didn’t account for the radius of the ball, which means the centroid never actually contacts the backboard. Rather than recalculate and create a new backboard, he just shifted the hoop out from the backboard by a ball radius. With that expedient in place, the setup performed exactly as calculated.
[Shane] may have taken the long road to hoops glory, but we appreciate the effort and the math lesson. And the fact that this ends up being the same shape as some antennas is a plus.
Continue reading “A Basketball Hoop That Never Lets You Brick”
When tossing something into the rubbish bin, do you ever concoct that momentary mental scenario where you’re on a basketball court charging the net — the game’s final seconds ticking down on the clock — making a desperate stretch and flicking some crumpled paper perfectly into the basket only for no one to notice your awesome skills? Well, now you can show off how good you are at throwing out garbage.
Well, not strictly garbage. The genesis of this IoT basketball hoop was in fact an inflatable ball on [Brandon Rice]’s desk that he felt would be more fun to fidget with if he could keep score. The hoop and backboard were laser cut on his Epilog cutter, and sport a Particle Photon to track and upload his running point tally to the Internet. An Arduino and IR sensor detect objects passing through the hoop — ultrasound proved to be too slow to keep up with [Rice]’s shots.
Continue reading “The Internet Of Three-Pointers”
People spend years of their lives practicing on the courts to get the kind of accuracy that this robot achieves. It is able to shoot freethrows thanks to stereoscopic camera analysis of the target. We know what you’re thinking; big deal, it knows the distances which makes the calculations easy. That’s not the case, look a bit closer in the image above. The basket itself is mounted on a robotic platform and creates a randomly moving target. It looks like shots are only taken when the basket is stationary. But still, that means the system is able to calculate accurate throws when the basket is not only at varying distances, but also when it is not directly in front and not square to the arm of the robot. The accuracy relies on analyzing the square on the backboard of the basket. Because two cameras give different perspectives, edge and corner detection of both images allow the system to extrapolate the location of the target.
After the fold there’s a video of this robot being demonstrated to the public. Apparently the yellow-armed-monster isn’t suitable for public consumption because the developers have covered it with the body of a plush seal.
Continue reading “This Robot Will School You At Freethrows”