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The robot was programmed to stop momentarily if a person passed by. But the researchers noticed that the robot would often freeze in place, overly cautious, long before a person had crossed its path. If this took place in a real manufacturing setting, such unnecessary pauses could accumulate into significant inefficiencies.
The team traced the problem to a limitation in the robot's trajectory alignment algorithms used by the robot's motion predicting software. While they could reasonably predict where a person was headed, due to the poor time alignment the algorithms couldn't anticipate how long that person spent at any point along their predicted path -- and in this case, how long it would take for a person to stop, then double back and cross the robot's path again.
Members - MIT - Team - Solution - Algorithm
Now, members of that same MIT team have come up with a solution: an algorithm that accurately aligns partial trajectories in real-time, allowing motion predictors to accurately anticipate the timing of a person's motion. When they applied the new algorithm to the BMW factory floor experiments, they found that, instead of freezing in place, the robot simply rolled on and was safely out of the way by the time the person walked by again.
"This algorithm builds in components that help a robot understand and monitor stops and overlaps in movement, which are a core part of human motion," says Julie Shah, associate professor of aeronautics and astronautics at MIT. "This technique is one of the many way we're working on robots better understanding people."
Shah - Colleagues - Lead - Student - Przemyslaw
Shah and her colleagues, including project lead and graduate student Przemyslaw "Pem" Lasota, will present their results this month at the Robotics: Science and Systems conference in Germany.
To enable robots to predict human movements, researchers typically borrow algorithms from music and speech processing. These algorithms are designed to align two complete...
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