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Read StoryAssistant Professor Nitin Sanket’s research is inspired by a simple but powerful question: How do natural flyers succeed where machines fail? Sanket recently received a $704,908 three-year National Science Foundation grant for his project “Sound Navigation: Enabling Tiny Robots to Find Their Way Through Smoke, Dust, and Darkness.”
“I have always been fascinated by how nature’s expert flyers like insects and birds are able to effortlessly weave through tough obstacle courses while hunting prey,” he says. “Our robots, though very complex, are no match for these biological flyers. This led me to ponder how we can draw inspiration from nature to build better autonomous aerial robots.”
For more than a decade, Sanket worked with vision-based systems, the standard in aerial autonomy. But darkness, fog, smoke, snow, or dust renders cameras nearly useless. Sound, on the other hand, doesn’t suffer from these problems. Bats use ultrasound to navigate complex environments, and that inspired Sanket to explore how robots might do the same.
The project will focus on enabling tiny aerial robots, smaller than 100 millimeters and weighing less than 100 grams, to navigate without relying on vision. Instead, Sanket’s team will develop sound-based sensing systems, giving drones a form of bat-inspired echolocation.
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