In 2010, Dan Quinn was awarded an Outstanding Student Award from the University of Virginia Engineering School. From there, he went on to Princeton University, earning a Ph.D. in Mechanical and Aerospace Engineering. In 2016, he returned to the University of Virginia, this time in a professional capacity as an Assistant Professor of Mechanical and Aerospace Engineering.

With access to a state of the art lab and dedicated students, Dan has been developing autonomous systems with fluid interaction. As described on the University of Virginia’s website, “Assistant Professor Dan Quinn seeks to invent and inspire new technologies that rely on the efficient interaction of fluids (e.g. air, water, blood) and structures (e.g. vehicles, medical devices, energy harvesters). These research projects are highly multidisciplinary as they involve circuit design, nano-manufacturing, biology and control theory.” One of Dan’s main projects involves developing a drone the can maneuver through water mimicking the natural movements of a fish.

Aquatic drones are not a new concept, but Dan seems to have solved one of the biggest challenges that aquatic drone designers face. Finding a way to get the drone to autonomously speed up or slow down the same way a fish controls its speed. By flexing and relaxing their fins, fish can seamlessly transition their speed. The exact biological science behind how a fish accomplishes this has been elusive to scientists. Dan’s goal was to develop a drone that can speed through the water to cover distances and then slow down to inspect and collect data at a designated spot.

Autonomous underwater drones are limited by the fact that they can only operate at a single set speed because they can not change the rigidity of their fins. As Dan explains, “Having one tail stiffness is like having one gear ratio on a bike. You’d only be efficient at one speed. It would be like biking through San Francisco with a fixed-gear bike; you’d be exhausted after just a few blocks.” Applying a mathematical equation, Dan and postdoctoral researcher Qiang Zhong discovered that they needed to tune the drone’s tail to stiffen automatically while swimming. The ratio of when to stiffen should be squared by swimming speed.

“To test our theory, we built a fishlike robot that uses a programmable artificial tendon to tune its own tail stiffness while swimming in a water channel,” Qiang said. “What happened is that suddenly our robot could swim over a wider range of speeds while using almost half as much energy as the same robot with a fixed-stiffness tail. The improvement was really quite remarkable.” A drone that can autonomously mimic the way fish naturally swim has countless applications. They can efficiently cover wide ranges and collect data. They can also blend in with an environment so as not to spook animals.

The first drone that Dan and Qiang designed resembles a tuna, a fish known for its speed and agility. With the success they found in tuning tail rigidity, the duo is looking at other species to mimic. They have thought of scaling up to make a drone that mimics a dolphin and even scaling down to make a drone reminiscent of a tadpole. “I don’t think we’ll run out of projects anytime soon,” Dan said. “Every aquatic animal we’ve looked at has given us new ideas about how to build better swimming robots. And there are plenty more fish in the sea.”

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