MIT Researchers Develop VR Testing Ground to Securely Coach Autonomous Drones

Coaching self-directed drones to soar around composite indoor settings predictably means collapses, not to point out a continuous cycle of replacement and repair. To deal with this, the MIT engineers developed a VR training system that enables the drones to observe virtual images while hovering around a physically vacant test facility.

MIT Researchers Develop VR Testing Ground to Securely Coach Autonomous Drones

Named “Flight Goggles,” the team stated in a blog post that the novel system can “considerably decrease the number of collapses that drones experience in genuine coaching episodes,” and also function as a virtual testbed for varied settings that would otherwise need lengthy setup time and physical barriers.

Developed primarily to function in the new drone-testing facility of MIT in Building 31, the innovative Flight Goggles system incorporates an image rendering program, a motion capture system, and several aboard electronics comprising IMUs, and custom-made circuit boards that incorporate a powerful implanted supercomputer.

The later bits are leashed onto the drone through a 3D-printed carbon fiber and nylon-supported frame. This drone seen is stated to autonomously soar at an utmost pace of 6.7 m/s, or about 15 mph/24 kph. The team can introduce any figure of photorealistic VR settings and send out it at 90 frames/second to the drone as it is hovering through the vacant facility.

As the setting is vastly adaptable, non-static obstacles such as individuals can be set up as well into the training regime of the autonomous drone. Karaman envisages a system wherein the drone test facility of  MIT is divided into 2 parts—1 for the drone to hover in its VR settings and another kept for humans wearing motion-capture suits, efficiently placing in a person into the virtual pathway of a drone without physical threat to the individual.

In another study published recently by researchers from the University of Bologna and ETH Zurich stated to find a means to optimize the bite-sized memory and power limitations of drone using DroNet—a featherlight residual convolutional neural network architecture.

About: Ankit Kadam

Author & Contributor As one of the lead news writers on Share Tech News, Ankit’s specialization lies in the science and technology domains. His passion for the latest developments in cloud technology, connected devices, nanotechnology, and virtual reality, among others, shines through in the most recent industry coverage he provides. Ankit’s take on the impact of digital technologies across the Smartphones, Software & Android/iOS Apps domains gives his writing a fresh and modern outlook.