The coolest part about Zizzy is the 3D printable pneumatic artificial muscles. Project creator, [Michael Roybal] said it took over a year of development to arrive at the design.
The muscles are hollow bellows printed out of Ninjaflex with carefully calibrated settings. A lot of work must have gone into the design to make sure that they were printable. After printing the muscles are painted with a mixture of fabric glue and MEK solvent. If all is done correctly the bellows should be able to hold 20 PSI without any problem.
This results in a robot with very smooth and precise movement. It has none of the gear noise and can also give when it collides with a user, a feature typically found only in very expensive motor systems. If [Michael] can find a quiet compressor system the robot will be nearly silent.
There is a significant constituency among hackers and makers for whom it is not the surroundings in which the drink is served or the character of the person serving it that is important, but the quality of its preparation. Not for them the distilled wit and wisdom of a bartender who has seen it all, instead the computer-controlled accuracy of a precisely prepared drink. They are the creators of bartending robots, and maybe some day all dank taverns will be replaced with their creations.
Drinkro is a bartending robot built by the team at [Synchro Labs]. It uses a Raspberry Pi 3 and a custom motor controller board driving a brace of DC peristaltic liquid pumps. that lift a variety of constituent beverages into the user’s glass. There is a multi-platform app through which multiple thirsty drinkers can place their orders, and all the source code and hardware files can be found in GitHub repositories. The robot possesses a fairly meagre repertoire of vodka and only three mixers, but perhaps it will be expanded with more motor driver and pump combinations.
This collaboration between ETH and the Disney empire’s research arm is a ultra-light robot that can roll across horizontal surfaces and also transition and climb walls.
The robot has four wheels with one steerable set, but its secret sauce is the two propellers gimbaled on its back. Using these propellers it can move itself across the ground, but also, when approaching a wall, provide enough thrust to overcome the gravity vector.
Naturally, the lighter the robot, the less force will be needed to keep it on the wall. That’s why the frame is made from carbon fiber corrugated sandwich panels. The motors, batteries, and controllers are all also light and small.
We liked how the robot was, apparently, using its propellers to provide additional stability even while on the ground. There is a video after the break, and more information can also be found on the Disney Research webpage.
For humans, moving our arms and hands onto an object to pick it up is pretty easy; but for manipulators, it’s a different story. Once we’ve found the object we want our robot to pick up, we still need to plan a path from our robot hand to the object all the while lugging the remaining limbs along for the ride without snagging them on any incoming obstacles. The space of all possible joint configurations is called the “joint configuration space.” Planning a collision-free path through them is called path planning, and it’s a tricky one to solve quickly in the world of robotics.
These days, roboticists have nailed out a few algorithms, but executing them takes 100s of milliseconds to compute. The result? Robots spend most of their time “thinking” about moving, rather than executing the actual move.
It’s worth asking: why is this problem so hard? How did hardware make it faster? There’s a few layers here, but it’s worth investigating the big ones. Planning a path from point A to point B usually happens probabilistically (randomly iterating to the finishing point), and if there exists a path, the algorithm will find it. The issue, however, arises when we need to lug our remaining limbs through the space to reach that object. This feature is called the swept volume, and it’s the entire shape that our ‘bot limbs envelope while getting from A to B. This is not just a collision-free path for the hand, but for the entire set of joints.
Encoding a map on a computer is done by discretizing the space into a sufficient resolution of 3D voxels. If a voxel is occupied by an obstacle, it gets one state. If it’s not occupied, it gets another. To compute whether or not a path is OK, a set of voxels that represent the swept volume needs to be compared against the voxels that represent the environment. Here’s where the FPGA kicks in with the speed bump. With the hardware implementation, voxel occupation is encoded in bits, and the entire volume calculation is done in parallel. Nifty to have custom hardware for this, right?
We applaud the folks at Duke University for getting this up-and-running, and we can’t wait to see custom “robot path-planning chips” hit the market some day. For now, though, if you’d like to sink your teeth into seeing how FPGAs can parallelize conventional algorithms, check out our linear-time sorting feature from a few months back.
There’s a theory that the fear of scurrying things is genetic. Likewise, a similar theory arose about the tendency for humans to find helpless things cute. After all, our useless babies do best in a pest free environment. This all could explain why we found this robotic roach to be both a little cute and a little creepy.
This robot looks like a ladybug going through its rebellious teen phase. It runs on six hook shaped legs which allow it to traverse a wider array of surfaces than wheels would, at the expense of speed and higher vibrations. The robot does a very convincing, if wobbly, scurry across the surface of its test table.
It also has a secret attack in the form of a single Rockem Sockem Robot arm located on its belly. With a powerful burst, the arm can launch the robot up a few feet to a higher surface. If the robot lands on its wheels the researchers high-five. If the robot lands on its back, it can use its ,”wings,” to flip itself right-side-up again.
The resulting paper (PDF file) has a nice description of the robot and its clever jumping mechanism. At least if these start multiplying like roaches, hackers will never short for tiny motors for their projects. Video after the break.
Watching robots doing sports is pretty impressive from a technical viewpoint, although we secretly smile when we compare these robots’ humble attempts to our own motoric skills. Now, a new robot named Robomintoner seeks to challenge human players, and it’s already darn good at badminton.
For all their joking about “reinventing the wheel”, the team behind Ourobot made a very cool robot (German, automatic translation here). The team, at the University of Applied Sciences in “Bielefeld, Germany“, built their wheel out of twelve segments, each with its own servo motor, a 3D-printed case, and a pressure sensor mounted on the outside of the wheel. The latter, plus some clever programming, allows the robot wheel to vary its circular gate and climb up over obstacles automatically.
There are a bunch of interesting constraints in designing the control for this bot. The tracks on the ground, naturally, have to adjust their relative angles so that they lie each flat on the surface, even if that surface isn’t itself flat or level. The segments in the air are unconstrained, but the sum of all the servos’ interior angles has to add up to 1800 degrees, and these angles control where its center of gravity is.