Researchers at Worcester Polytechnic Institute (WPI) in Massachusetts, taking inspiration from the way birds and bats navigate complex environments, are creating sound-based navigation systems for small aerial robots.
According to its developers, this system seeks to enable drones to operate in smoke, dust and darkness where traditional cameras and light sensors tend to fail, thereby advancing robotic perception for search, rescue and hazardous environment missions.
To overcome the challenges of operating in conditions such as fog, smoke or total darkness, the team is examining bio-inspired echolocation, which draws from how bats use ultrasonic sound waves to sense their surroundings.
As part of the project, the researchers are developing tiny airborne robots capable of navigating independently according to sound rather than visual cues. The robots are expected to be smaller than 100 millimeters and weigh less than 100 grams.
To address the challenge of noisy propellers and limited ultrasound resolution, metamaterials that minimize sound interference were used. Specifically, the team altered material geometry so that these structures control how sound waves are reflected — much like how foam absorbs noise.
Taking inspiration from how humans cup their ears or bats adjust ear shapes, the team is working to create systems that better capture and emit low-power sound for navigation. Alternative propulsion methods, such as flapping-wing mechanisms, are also being investigated to improve performance and reduce acoustic interference in compact aerial robots.
“This work will enable rapid deployment of robots in challenging environments such as disaster zones or smoke-filled areas. It’s about creating tools that support protection, prevention, and preservation in a cost-effective, scalable, and deployable way,” the researchers explained in a statement.
The team suggests that the technology could one day be used in applications such as disaster monitoring, hazardous environment inspection and environmental protection, where traditional vision-based navigation is unreliable.
