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New Artificial Intelligence System Helps Drones Land More Quickly

Deep neural networks are capable of automatic learning, which makes them ideally suited for repetitive tasks

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Researchers have developed an Artificial Intelligence (AI)-based system that can help drones to land more safely and quickly, while gobbling up less power.

The system, dubbed the “Neural Lander,” is a learning-based controller that tracks the position and speed of the drone, and modifies its landing trajectory and rotor speed accordingly to achieve the smoothest possible landing.

“This project has the potential to help drones fly more smoothly and safely, especially in the presence of unpredictable wind gusts, and eat up less battery power as drones can land more quickly,” said Soon-Jo Chung, Professor at California Institute of Technology (Caltech) in the US.

Landing multi-rotor drones smoothly is difficult. Complex turbulence is created by the airflow from each rotor bouncing off the ground as the ground grows ever closer during a descent.

This turbulence is not well understood nor is it easy to compensate for, particularly for autonomous drones.

That is why takeoff and landing are often the two trickiest parts of a drone flight. Drones typically wobble and inch slowly toward a landing until power is finally cut, and they drop the remaining distance to the ground.

Microsoft has announced a unique partnership with China-based DJI -- the world's biggest drone company -- where DJI will create a new software development kit (SDK) for Windows 10 PCs.
Representational image. Pixabay

The new system developed by researchers at Caltech uses a deep neural network to help autonomous drones “learn” how to land more safely and quickly.

Deep neural networks (DNNs) are AI systems that are inspired by biological systems like the brain.

The “deep” part of the name refers to the fact that data inputs are churned through multiple layers, each of which processes incoming information in a different way to tease out increasingly complex details.

Deep neural networks are capable of automatic learning, which makes them ideally suited for repetitive tasks.

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To make sure that the drone flies smoothly under the guidance of the deep neural networks, the team employed a technique known as spectral normalisation, which smoothens out the neural net’s outputs so that it doesn’t make wildly varying predictions as inputs/conditions shift.

“With less error, the Neural Lander is capable of a speedier, smoother landing and of gliding smoothly over the ground surface,” said Yisong Yue, Assistant Professor of Computing and Mathematical Sciences at Caltech. (IANS)

Next Story

Researchers Develop Artificial Skin for Robots

Scientists Develop a system combining Artificial skin with control algorithms to Create first Autonomous Humanoid Robot

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Scientists have developed a system combining artificial skin with control algorithms and used it to create the first autonomous humanoid robot. (Representational Image). Pixabay

 Researchers have developed a system combining artificial skin with control algorithms and used it to create the first autonomous humanoid robots with full-body artificial skin.

The artificial skin developed by Professor Gordon Cheng and his team from Technical University of Munich in Germany, consists of hexagonal cells about the size of a two-euro coin (i.e. about one inch in diameter).

According to the study published in the journal Proceedings of the IEEE, each is equipped with a microprocessor and sensors to detect contact, acceleration, proximity and temperature.

Such artificial skin enables robots to perceive their surroundings in much greater detail and with more sensitivity.

Robots
Research has now succeeded in applying artificial skin to a human-size autonomous robot. Pixabay

This not only helps them to move safely. It also makes them safer when operating near people and gives them the ability to anticipate and actively avoid accidents.

According to the study, the biggest obstacle in developing robot skin has always been computing capacity.

Human skin has around five million receptors. Efforts to implement continuous processing of data from sensors in artificial skin soon run up against limits.

Previous systems were quickly overloaded with data from just a few hundred sensors.

To overcome this problem using a neuroengineering approach, researchers do not monitor the skin cells continuously, but rather with an event-based system.

This reduces the processing effort by up to 90 per cent.

With an Event-based approach, research has now succeeded in applying skin to a human-size autonomous robot not dependent on any external computation.

The H-1 robot is equipped with 1,260 cells (with more than 13,000 sensors) on its upper body, arms, legs and even the soles of its feet. This gives it a new “bodily sensation”.

For example, with its sensitive feet, H-1 is able to respond to uneven floor surfaces and even balance on one leg.

Robots
Artificial skin enables Robots to perceive their surroundings in much greater detail and with more sensitivity. Pixabay

With its special skin, the H-1 can even give a person a hug safely. That is less trivial than it sounds – robots can exert forces that would seriously injure a human being. During a hug, two bodies are touching in many different places.

“This might not be as important in industrial applications, but in areas such as nursing care, robots must be designed for very close contact with people,” Cheng explained.

“Our system is designed to work trouble-free and quickly with all kinds of robots,” he said.

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“Now we’re working to create smaller skin cells with the potential to be produced in larger numbers,” he added. (IANS)