Tornyol Develops 40-Gram Autonomous Drones That Hunt Mosquitoes Mid-Air Using Doppler Ultrasound and Sound Detection

American startup Tornyol, backed by Y Combinator, has successfully tested a miniature autonomous drone weighing around 40 grams that can chase and collide with flying insects without chemicals. In its first public demonstration on July 14, the prototype tracked and intercepted a moth inside a controlled indoor facility using external motion-capture equipment. The company aims to reduce mosquito-control costs by a factor of 100 by deploying swarms of ten drones to clear one square kilometer. Future versions will rely entirely on onboard sensors, including ultrasonic emitters and microphone arrays, to detect the Doppler shift created by mosquito wingbeats and distinguish species and sex. Engineers plan to move all computation to the drone itself within weeks and eventually form autonomous swarms capable of navigating urban environments. Although the current test still depended on external infrared tracking and a ping-pong ball as a stand-in target, Tornyol views the flight as the first verified aerial interception toward a scalable, non-chemical mosquito eradication system.

securitylab_nTranslated from Russian

Read at source

American startup Tornyol, supported by the Y Combinator accelerator, has unveiled a miniature autonomous drone designed to hunt mosquitoes in mid-air without the use of chemicals. The 40-gram aircraft is intended to locate targets by the sound of their wingbeats, calculate an interception trajectory, and physically collide with the insect. During the first successful test conducted on 14 July inside a closed facility, the prototype autonomously pursued and struck a flying moth, marking what founder Alex Toussaint called the company’s first “aerial defeat of a target.”

The demonstration still relied on external infrastructure: a motion-capture system tracked infrared markers on the drone while a table-tennis ball served as a proxy for the insect’s position. A ground computer processed the sensor data and issued flight commands. Within the next several weeks, Tornyol plans to migrate all processing to the drone’s onboard electronics. The final design will incorporate ultrasonic transducers similar to automotive parking sensors and smartphone-style microphone arrays.

Once airborne, the drone will emit ultrasonic pulses and analyze returning echoes. The characteristic frequency shift caused by rapidly beating mosquito wings—the Doppler effect—will allow onboard algorithms to detect, classify, and even determine the species and sex of the target. After identification, the aircraft will plot an obstacle-avoiding route and deliver a precise physical strike. The company ultimately envisions coordinated swarms of dozens of such drones patrolling entire urban districts.

Founders Alex Toussaint and Clovis Piedallu claim the technology could cut the cost of mosquito control by a factor of 100 compared with existing methods. According to their estimates, a formation of only ten drones would be sufficient to clear mosquitoes from one square kilometer. Although these performance figures have not yet been validated in real-world city environments, the startup continues to refine both the sensing suite and the autonomous navigation stack.

Despite the promising indoor result, significant engineering challenges remain. Engineers must still prove that the tiny platforms can reliably differentiate mosquitoes from other insects, operate safely near humans, and maintain effectiveness when flying outdoors amid wind, obstacles, and variable lighting. Tornyol’s work therefore represents an early but concrete step toward chemical-free, drone-based vector control.

Meridian digestThe week's most consequential security stories, distilled into one briefing.See the digest

More in Other

Mail.ru Reduces Lifetime 1TB Cloud Storage Promo to Standard 8GB Quota for Select Users
Kimi K3 Open Model Surpasses Closed AI Leaders with Million-Token Context and 896 Experts
Innostage Completes Functional Testing of PT NGFW 1.10.3 Across 156 Criteria