New spinning drone hides in plain sight

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New spinning drone hides in plain sight

'Phantom Twist' harnesses motion blur to nearly vanish in flight

EMBARGOED UNTIL 12:01 A.M. EDT (U.S.) ON THURSDAY, JULY 16, 2026

• Like a propeller, new drone spins too fast for the human eye to see clearly
• It's about 10 times less visually perceptible than a conventional quadcopter
• AI designed and optimized the drone, determining ideal placement of each component to make it harder to see

EVANSTON, Ill. - By exploiting the quirks of human vision, Northwestern University engineers have designed a drone that nearly disappears right before the eyes.

For years, researchers have tried to design invisible drones and robots using camouflage, transparent materials or light-bending optical systems. But the Northwestern team instead used a concept called "motion blur" - the same effect that makes fast-spinning fans and propellers seem to disappear.

Called the "Phantom Twist," the drone spins up to 25 times per second, which is too fast for the human eye to see clearly. While it isn't completely invisible, it morphs into a ghostly smudge that seamlessly blends into the background. The work eventually could lead to drones that monitor wildlife, survey the environment and inspect infrastructure with less visual disruption.

The Northwestern team will present this work at 2:30 p.m. AEST (12:30 a.m. EDT) on Thursday, July 16 at Robotics: Science and Systems 2026 in Sydney, Australia. The talk, "Computational Design of a Low-Visibility UAV Using Human-Aligned Perceptual Metric," is part of the session "Robot & Sensor Design."

"Most efforts to hide drones focus on making them look like their surroundings," said Northwestern's Michael Rubenstein, who led the work. "Instead, we asked whether we could design the drone itself around the way humans perceive motion. This idea of low visibility through persistent motion is something few people have explored."

An expert in robotics design, Rubenstein is an associate professor of computer science and mechanical engineering at Northwestern's McCormick School of Engineering, where he is a member of the Center for Robotics and Biosystems. Northwestern coauthors include Emma Alexander, an assistant professor of computer science at McCormick; Sam Kriegman, an assistant professor of computer science, mechanical engineering and chemical and biological engineering at McCormick; and David Matthews, a postdoctoral researcher in Kriegman's lab. The study's co-first authors are Jingxian Wang, a Ph.D. graduate from Rubenstein's lab, and Chen Yu, a Ph.D. student in Kriegman's lab.

Hiding in plain sight

Whether monitoring nesting birds, surveying wetlands or inspecting aging infrastructure, drones often alter natural behavior simply because people or animals notice them. The disruption can cause wildlife to scatter and people to behave differently. A drone that's harder to see, on the other hand, could perform the same tasks while blending into its surroundings.

While most attempts to hide drones focus on changing how they look, Northwestern's engineers instead changed how people - and many animals - see them. Unlike a typical quadcopter with four separate rotors, Phantom Twist has one motor and one propeller. The propeller spins in one direction, and the rest of the drone spins in the opposite direction.

"For a typical quadrotor drone, the propellers are spinning, but the robot is stationary," Rubenstein said. "So, you still see its body. For our drone, the whole thing is rotating, so there are no stationary parts."

Searching for the unseen

To design the drone, the Northwestern team first used a computational model to generate roughly 20,000 drone configurations capable of stable flight. Then, they used artificial intelligence (AI) and optimization algorithms to repeatedly rearrange the drones' major components, including a motor, propeller, circuit board, counterweight and batteries.

After sifting through many different configurations, the algorithms determined the ideal placement of the drone's components to minimize its visibility from virtually every viewing angle while allowing for stable flight.

After selecting promising candidates, the engineers simulated each drone spinning in flight and overlaid those images a hundred real-world backgrounds. Then, they used a perception model that approximates human vision to determine how noticeable each design appeared. Designs that blended into their surroundings received lower visibility scores. The team selected the 500 lowest-scoring designs and applied the optimization algorithm, which repeatedly adjusted the positions of components to further minimize those scores.

"The design process was fully automated," Rubenstein said. "Then, when we were confident that a drone met all our criteria, we built it."

Tricking the eyes

The resulting design spread the components around the drone - at different heights and different angles with lots of space in between - to prevent them from visually overlapping when spinning. So, when everything blurs together, the drone becomes a faint, semi-transparent cloud rather than a distinct shape.

According to the visibility metric, the optimized drone is about 10 times less visually perceptible than a conventional quadcopter.

"The human eye takes time to accumulate signals, roughly analogous to the exposure time of a camera," said Alexander, an expert in computer vision. "When an object spins quickly, we perceive it as blurring out and losing distinct features. Because this new drone is almost entirely transparent, its few opaque components are visually averaged with the background for an overall appearance of a slight haze."

The new drone still has several limitations, the researchers noted. The propeller makes audible noise, and the drone's wires and support rods are still somewhat visible. Rubenstein said his team plans to design future iterations with more transparent materials or quieter propulsion to make the Phantom Twist even less noticeable.

The study was supported by the National Science Foundation.

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Michael Rubenstein

Corresponding author

Associate professor of computer science and mechanical engineering

• [email protected]