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Unmanned vehicles built the bridge themselves

24.09.2015

New projects for the use of drones in construction appear almost every day. Not too long ago, for example, Japanese engineering firm Komatsu launched a system called Smart Construction, which combines cloud-based computing services provided by artificial intelligence. According to the company, the system will connect the three main components of a modern construction site: people, computers and drones.

One of the leading European organizations involved in the development of construction drones is the Institute for Dynamic Systems and Control of the Federal Institute of Technology in Zurich (Switzerland). A few months ago, the institute's researchers announced their plans to build tensile structures using unmanned aerial vehicles. They have just announced the first significant success in this direction - the construction by robots of a rope bridge capable of supporting the weight of an adult.

To do this, a group of quadcopters equipped with motorized cable reels autonomously flew back and forth between two bases, building a bridge using a synthetic cable made from Dyneema material. From fencing suits and tow ropes to bulletproof vests, blankets and even blackboards, this lightweight and durable ultra-high molecular weight polyethylene has found wide applications. The weight of the rope is only 7 g per meter, and the rope with a diameter of 4 mm is able to withstand a load of 1300 kg.

The only part of the construction, made by hand, steel supports. All the rest was done by quadcopters, which knitted knots, links and braid from 120 m of cable, creating a bridge of nine sections with a total length of 7,4 m. Before construction began, the drones made several test flights for training. But as they worked, they corrected their trajectory using a motion-capture system that monitored the position of the vehicles and took into account the force exerted on the vehicles as the tether deployed.

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Transistors that work 10 times faster than brain synapses 03.08.2022

Researchers at the Massachusetts Institute of Technology have developed programmable transistors that operate 10 times faster than brain synapses. The technology used for analog machine learning provides not only high data processing speed, but also good energy efficiency.

The working mechanism of the device is to electrochemically insert a tiny ion, a proton, into an insulating oxide to modulate its electronic conductivity. Scientists used a powerful electric field to accelerate protons and put ion transistors into nanosecond mode of operation.

The researchers note that the secret of new devices in the use of inorganic phosphorus silicate glass. It provides ultra-fast movement of protons, since it contains many nanometer-sized pores, the surfaces of which provide diffusion paths for elementary particles. It can withstand very strong pulsed electric fields.

The action potential in biological cells rises and falls on a time scale of milliseconds as a potential difference of approximately 0,1 volts is limited by the stability of water. In our work, we apply up to 10 volts through a special, nano-thick, hard glass film that produces protons without damaging it. And the stronger the field, the faster the ion devices work.

In the human brain, learning occurs by strengthening and weakening connections between synapses. Deep neural networks use a similar strategy when the weights of the nodes (neurons) are programmed with learning algorithms. When using processors, increasing and decreasing the electrical conductivity of proton resistors provides analog machine learning.

Conductivity is controlled by the movement of protons. To increase conductivity, more protons are pushed into the channel in the resistor, and to decrease conductivity, protons are expelled. This is achieved by using an electrolyte (similar to a battery) that produces protons but blocks electrons. Increasing the speed of protons significantly speeds up the process of machine learning.

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