缅北强奸

缅北强奸's Angela Schoellig named to MIT's list of Innovators Under 35

Engineering researcher recognized by MIT Technology Review
Professor Angela Schoellig demonstrates a drone that can land on water to take environmental samples (photo by Tyler Irving)

Assistant Professor Angela Schoellig of the University of Toronto Institute for Aerospace Studies (UTIAS) has been named one of the world鈥檚 top .

The Faculty of Applied Science & Engineering's Schoellig works on control theory and applying machine learning to drones, autonomous vehicles and other robots.

鈥淚t鈥檚 an honour,鈥 says Schoellig of the publication's recognition. 鈥淭o be named among this group of luminaries comes as a wonderful surprise. It鈥檚 also great for my students and postdoctoral researchers, because it鈥檚 really about their work as well.鈥

As a child, Schoellig was captivated by mathematics. During her master鈥檚 degree, she studied mathematical models that could describe everything from the chemical reactions in a living cell to the movements of birds. When she learned that mathematical algorithms could also be used to program robots, she was hooked.

鈥淚 really wanted to do something where you directly see the impact and the result,鈥 she says.

As a PhD student at ETH Zurich, where she worked under the supervision of robotics pioneer and 缅北强奸 engineering alumnus Raffaello D鈥橝ndrea, Schoellig and her colleagues worked on software that could enable flying robots to execute , as well as . But she quickly discovered that the algorithms alone weren鈥檛 enough.

鈥淭he model that we had would not provide enough information for the robot to do the task,鈥 she says. 鈥淵ou need to use the data from previous runs to improve the task execution, which gets you closer to how humans learn things.鈥

In other words, she needed to apply machine learning to robots.

Machine learning, a form of artificial intelligence, has become a common part of our lives 鈥 it鈥檚 what enables smartphones to recognize voice commands and computer programs to recognize faces in photos. But teaching a robot is a very different challenge from teaching a computer.

鈥淚f a computer doesn鈥檛 recognize a face, that鈥檚 not a big deal,鈥 says Schoellig. 鈥淏ut if a robot makes a mistake, it could crash. While you can train a computer on millions of photos, getting that amount of data for a robot is very expensive and difficult.鈥

One of Schoellig鈥檚 biggest challenges is designing algorithms that are flexible enough to enable robots to experiment but rigid enough to ensure that they will be safe while they are learning the new task.

Another major challenge Schoellig works on is handling dynamic environments, where conditions change over time. One of her current projects aims to enable autonomous drones to make deliveries to remote locations, such as communities in Canada鈥檚 north. These drones would need to adapt to changing wind speeds and light conditions that make navigation difficult.

Schoellig also has projects in the mining and environmental monitoring sectors, such as developing a drone that can land on water and take samples to track pollution levels. She is even looking at the possibility of using drones to deliver automated external defibrillators (AEDs) to treat patients suffering cardiac arrest.

For Schoellig, smarter robots have the potential to make our lives better and easier.

鈥淲e hear a lot about how robots will replace humans, but that鈥檚 not how I see it,鈥 she says. 鈥淗umans have always built tools to help them to advance, from a simple hammer to a computer. We couldn鈥檛 predict in the 1970s how computers would change our society. I think we are at a similar point with robotics, and I鈥檓 excited to see what creative ideas will emerge.鈥

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