Robots in reality: Robots for real-world challenges

MIT News
Nov 28, 2011

Nicholas Roy, an MIT associate professor of aeronautics and astronautics
Photo: Dominick Reuter

Consider the following scenario: A scout surveys a high-rise building that’s been crippled by an earthquake, trapping workers inside. After looking for a point of entry, the scout carefully navigates through a small opening. An officer radios in, “Go look down that corridor and tell me what you see.” The scout steers through smoke and rubble, avoiding obstacles and finding two trapped people, reporting their location via live video. A SWAT team is then sent to lead the workers safely out of the building.

Despite its heroics, though, the scout is impervious to thanks. It just sets its sights on the next mission, like any robot would do.

In the not-too-distant future, such robotics-driven missions will be a routine part of disaster response, predicts Nicholas Roy, an MIT associate professor of aeronautics and astronautics. From Roy’s perspective, robots are ideal for dangerous and covert tasks, such as navigating nuclear disasters or spying on enemy camps. They can be small and resilient — but more importantly, they can save valuable manpower.

The key hurdle to such a scenario is robotic intelligence: Flying through unfamiliar territory while avoiding obstacles is an incredibly complex computational task. Understanding verbal commands in natural language is even trickier.

Both challenges are major objectives in Roy’s research — and with both, he aims to design machine-learning systems that can navigate the noise and uncertainty of the real world. He and a team of students in the Robust Robotics Group, in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), are designing robotic systems that “do more things intelligently by themselves,” as he puts it.

For instance, the team is building micro-aerial vehicles (MAVs), about the size of a small briefcase, that navigate independently, without the help of a global positioning system (GPS). Most drones depend on GPS to get around, which limits the areas they can cover. In contrast, Roy and his students are outfitting quadrotors — MAVs propelled by four mini-chopper blades — with sensors and sensor processing, to orient themselves without relying on GPS data.

“You can’t fly indoors or quickly between buildings, or under forest canopies stealthily if you rely on GPS,” Roy says. “But if you put sensors onboard, like laser range finders and cameras, then the vehicle can sense the environment, it can avoid obstacles, it can track its own position relative to landmarks it can see, and it can just do more stuff.”
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