Inspired by the eyes of mantis shrimp, a research team at the University developed a camera that can be used to help self-driving cars identify objects other cameras struggle to detect.

Viktor Gruev, associate professor in Engineering and leader of the research team, said the team’s camera can identify an object three times farther away than what other cameras on self-driving cars can detect and at a much cheaper price. For multiple decades, Gruev worked with marine biologists to learn about the eyes of mantis shrimp.

Missael Garcia, postdoctoral researcher in Gruev’s research team, said the camera mimics the eye systems of the shrimp, which allows it to get clear, detailed images in rainy, foggy and overly bright driving conditions.

The team first tested the camera on people driving cars in weather conditions where regular cameras didn’t work well. Gruev said he and the team members will test their cameras on self-driving cars next.

Because of their unique eye systems, mantis shrimp can navigate more effectively and identify prey and predators in murky water more accurately than other shrimp.

“It makes me think about this little creature that we don’t pay much attention about it, that we must cook it or have it in restaurant,” Gruev said. “They are actually one of the best predators in shallow water.”

Gruev hopes to use the team’s camera on everyday cars. He said it can replace regular cameras because it has an advantage when detecting pedestrians and other cars on the road.

“I think that would be a humongous impact, a really big impact on our society,” he said.

Gabriel Popescu, professor in Engineering, said he talked with Gruev about using the camera in his research. He hopes to use the camera as an attachment on microscopes to observe micro-scale objects in medical research, which could potentially be helpful in cancer diagnostics and prognosis.

Gruev said he and the team will continue researching and improving the camera. He said he hopes to establish a research center to apply the technology to other fields, including robotics and machine learning.

Gruev believes understanding how nature functions can help scientists build better devices.

“We can learn some very important lessons from nature, and we should never overlook those lessons because they hold the key of building the next generation of devices (so) that we can have lifelong impacts in society,” Gruev said.

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