Teslaphoresis; better drones; robotic consensus.
Reconfigured Tesla coil electrifies materials
In a development that could set a clear path toward scalable assembly of nanotubes from the bottom up, Rice University researchers have discovered that the strong force field emitted by a Tesla coil causes carbon nanotubes to self-assemble into long wires, a phenomenon they call Teslaphoresis.
Rice chemist Paul Cherukuri led the team that developed the system that works by remotely oscillating positive and negative charges in each nanotube, causing them to chain together into long wires. The specially designed Tesla coil even generates a tractor beam-like effect as nanotube wires are pulled toward the coil over long distances.
This force-field effect on matter had never been observed on such a large scale, and the phenomenon is believed to be unknown to Nikola Tesla, who invented the coil in 1891 with the intention of delivering wireless electrical energy.
The researchers reminded that electric fields have been used to move small objects, but only over ultrashort distances, but with Teslaphoresis, they’ve been able to massively scale up force fields to move matter remotely.
They discovered that the phenomenon simultaneously assembles and powers circuits that harvest energy from the field. For example, in one experiment, nanotubes assembled themselves into wires, formed a circuit connecting two LEDs and then absorbed energy from the Tesla coil’s field to light them.
They realized a redesigned Tesla coil could create a powerful force field at distances far greater than anyone imagined. The team observed alignment and movement of the nanotubes several feet away from the coil.
Nanotubes were a natural first test material, given their heritage at Rice, where the HiPco production process was invented but the researchers envision many other nanomaterials can be assembled as well.
Paving the way for better drones
As pigeons can currently outclass any aerial robot’s flight, Stanford University engineering professor David Lentink plans to use a new wind tunnel to learn the magic of bird flight and apply it to building better aerial robots.
While most people look at a pigeon and see a common bird, Lentink watches a pigeon dart around a building and land perfectly in its roost, he sees the future of robotic flight.
Lentink has been studying birds in flight for years, with an eye toward applying the tricks birds use to navigate changing conditions in the real world to design better aerial robots.
Most of the insights he and his colleagues have gained so far have resulted from painstaking study, involving calculations of wing force dynamics inspired by footage captured in the wild, but now with the construction of one of the most advanced bird wind tunnels in the world, the researchers hope to reveal even more of the magic of bird flight.
Robot control algorithm factors in moving obstacles
MIT researchers remind that planning algorithms for teams of robots fall into two categories: centralized algorithms, in which a single computer makes decisions for the whole team; and decentralized algorithms, in which each robot makes its own decisions based on local observations.
With centralized algorithms, if the central computer goes offline, the whole system falls apart. Decentralized algorithms handle erratic communication better, but they’re harder to design, because each robot is essentially guessing what the others will do.
Most research on decentralized algorithms has focused on making collective decision-making more reliable and has deferred the problem of avoiding obstacles in the robots’ environment.
That said, the MIT team has developed a decentralized planning algorithm for teams of robots that factors in not only stationary obstacles, but moving obstacles, as well. They said the algorithm also requires significantly less communications bandwidth than existing decentralized algorithms, but preserves strong mathematical guarantees that the robots will avoid collisions.
In simulations involving squadrons of mini-helicopters, the decentralized algorithm came up with the same flight plans that a centralized version did, the researchers added.