Researchers from Northeastern University, Massachusetts, are working to eliminate the stiff, jerky motions in robotic arms to make them deft enough to gently pick up an egg or sturdy enough to stack dinner plates.
According to the project, its findings could one day allow doctors to remotely perform surgery on a distant battlefield or help bomb disposal experts safely remove an explosive device.
In the above video an engineer can be seen wearing a C-shaped gripping claw attached to his right hand while a nearby robotic arm mimicked his exact movements. The researchers said this showed the promise of hydraulic technology designed to be low friction.
The researcher lowered and raised his arm, swept it left and right, and bent it at the wrist, resulting in smooth actions copied in tandem by the robotic arm. What was not readily apparent was how the human operator was able to feel the same forces as the mechanical arm when it closed on an object, allowing the user to get a sense of textured surfaces.
The Northeastern project involves building remote-controlled robot arms that do not have heavy motors traditionally installed in the wrist joints. Instead, they are placed in the base of the machine.
“With no motors in the arm, they are much lighter than a traditional arm,” said Peter Whitney, assistant professor of mechanical and industrial engineering at Northeastern. “So now if you have a lighter arm, it’s much easier to move it around.”
The engineering advancement has the potential to overcome a fundamental obstacle humans face when controlling robots remotely, understanding the environment the machine is in.
“It’s hard to perceive exactly where the robot is, relative to the environment—whether it’s touching something or not, or how or how hard it is touching an object,” explained Whitney, whose research is focused on the design of robots, the materials they are made of, and how they are operated and controlled.
“These are all factors that can influence how we can get good performance, but also maintain safety,” he added.
Researchers now can study machine learning with real-time information that tells how much force is being applied. “So, when we try to grasp an object or manipulate an object, we can actually make use of those contact forces, similar to how human muscles sense forces such as how heavy something is,” noted Whitney.
