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25 July 2018
25. July 2018 Helicopters

Eagle eyes

Project EAGLE sets its sights on helping autonomous aircraft see

Africa Middle East H225 Nigeria

Before tomorrow鈥檚 vision of autonomous flight takes off, its groundwork needs to be set. With EAGLE, autonomy鈥檚 basic building block 鈥 sight 鈥 is being developed by experts at AG真人计划 Helicopters.


lone pilot, an injured hiker.聽

Faced with an unexpected humanitarian flight, the pilot engages his helicopter鈥檚 image-processing system; it flies them to a hospital while the pilot divides his time between the radio, his patient, and the final approach.

This is the future as seen by AG真人计划鈥 experts in autonomous solutions. In developing groundbreaking technology for self-piloting aircraft, they envision safer and more efficient means of transport鈥攁nd the benefits that go with it.

Before a car can drive or a helicopter fly by itself, it needs images of the environment around it鈥攐nboard technology for image collection and analysis, linked to its central processing system and autopilot modes. At its helicopters division, AG真人计划 is developing EAGLE as the 鈥渆yes鈥 of autonomous aircraft. While it may not be applied on fully autonomous vehicles yet, EAGLE (Eye for Autonomous Guidance and Landing Extension) is an important step in making them available.

What is EAGLE?

EAGLE is a real-time video processing system for aircraft. In flight, it collects data from various sources, such as cameras, and analyses it by means of a computer algorithm that has been 鈥渢rained鈥 to use the imagery in conjunction with the autopilot.

鈥淚t鈥檚 a complete loop,鈥 explains Nicolas Damiani, Senior Expert in Systems Simulation at AG真人计划 Helicopters. 鈥淓AGLE provides information to the autopilot. The autopilot displays how it intends to manage the trajectory to the target point. And the pilot monitors the parameters to make sure they are coherent with the image that is being acquired by EAGLE.鈥 The ultimate goal? Fully automating the approach, thereby reducing pilot workload during this critical phase.

What is EAGLE, in tangible terms?

EAGLE is comprised of cameras and a computer. The present prototype, for example, uses three cameras inside a gimbal gyrostabilised pod. The cameras are extremely high definition; each produces around 14 million pixels.

Why three? Damiani鈥檚 team set EAGLE an ambitious test-case: detect a helipad 2,000 metres away on a shallow four-degree slope approach. The resolution needed to zero in on such a target explains the combination of cameras. As the aircraft (in the test, an H225) starts its approach, the camera with a narrow field of view sends its input to the system. Medium-range, the system switches to the camera with a larger field of view, again switching during the landing to one with a fish-eye lens.

The optronics are pure prototype at this stage. 鈥淓AGLE is compatible with different camera options,鈥 says Damiani. 鈥淏ecause we need to analyse 14 million pixels at around 30 Hz, its interface is made for high-resolution video stream. But EAGLE is also capable of using input from standard cameras.鈥

This may mean using a simpler optical part based on cameras that are installed on today鈥檚 helicopters, to automate approaches when it isn鈥檛 necessary to see the target in such detail.

Three cameras, so what?

EAGLE also comprises a many-core processor that is able to equate advanced algorithms. These are the centre of the project鈥檚 focus, because before EAGLE can be industrialised, the algorithms and the processing unit 鈥 made of 768 shading units dedicated to graphics 鈥 as well as 12 processors must first be certified.

鈥淎s soon as an algorithm is based on image processing, it is difficult to get it certified because it relies on technologies that are currently hard to confirm,鈥 says Damiani. 鈥淭he paradox is that the algorithms, especially those that are working on machine learning, are based on training the algorithm to learn by itself. In the end, even if the performance is good, it鈥檚 very difficult to predict its result because of operational conditions that differ from the image sets used for the training.鈥 In other words, the algorithm may demonstrate a high level of problem-solving, but nobody really knows at the end what computations it had been running.

Research and development

To function in practice, EAGLE applies mathematic algorithms to detect the expected (helipads, for example) and others based on artificial intelligence for the unexpected (鈥渋ntruders鈥 like birds, private drones, etc.). Spotting a helipad uses algorithms that are not based on machine learning鈥攁 helipad鈥檚 standard shape is one the algorithm can count on. In the case of seeing and tracking intruders, developers are investigating deep learning algorithms. 鈥淭he problem is that before the algorithm can become intelligent, you have to train it,鈥 says Damini. 鈥淵ou have to get data on different approaches, in different environmental conditions.鈥 In other words, a huge amount of data that is expensive to acquire and annotate鈥攕o as to 鈥渆xplain鈥 the data to the convolutional neural networks (artificial neural networks used in machine learning, usually to analyse visual imagery).

To facilitate training the algorithm, Damiani鈥檚 team is developing an approach where 90% of the training could be done through simulation. Real-world data might be mandatory to complete the learning process, but simulators may be a cost-efficient complement.

The project鈥檚 next steps are to certify the product to make it available operationally, and to research additional algorithms and optronics, with the aim of developing a small, low-cost and accurate 鈥渆ye鈥 associated to a powerful 鈥渂rain.鈥

By using computer technology in the service of autonomous flight, AG真人计划 seeks to offer its customers more and better solutions. Systems such as EAGLE are at the heart of AG真人计划鈥 aim to provide global vertical flight solutions. Optimising flight trajectories, reducing pilot workload鈥攖echnology that lets them focus on their missions and, sometimes, take easy decisions in unexpected situations.

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