Rapidly moving containers without dropping, spilling or damaging them is tough enough for humans, let alone robots. Now, Ken Goldberg, professor of industrial engineering and operations research and of electrical engineering and computer sciences; postdoctoral researcher Jeff Ichnowski; and their team at UC Berkeley’s AUTOLAB have published Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT).
In their paper, presented in May at the 2022 International Conference on Robotics and Automation, the UC Berkeley team solves this challenge for robots transporting open-top containers and fragile objects in settings like warehouses and hospitals. Watch a video to see how GOMP-FIT makes this possible — and solves what the researchers call “the rushing sommelier” problem, where a waiter must quickly serve wine to customers.
On Friday, Goldberg’s team will present a second paper, Grasp-Optimized Motion Planning for Suction Transport (GOMP-ST), at the 2022 Workshop on the Algorithmic Foundations of Robotics. This algorithm helps robots maintain a suction cup grasp during high-speed pick-and-place operations used in warehouses and logistics centers.
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