首页|Studies from Kyushu Institute of Technology Add New Findings in the Area of Robo tics (Motion Evaluation of Variable-Stiffness Link Based on Shape-Memory Alloy a nd Jamming Transition Phenomenon)

Studies from Kyushu Institute of Technology Add New Findings in the Area of Robo tics (Motion Evaluation of Variable-Stiffness Link Based on Shape-Memory Alloy a nd Jamming Transition Phenomenon)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented i n a new report. According to news originating from Fukuoka, Japan, by NewsRx cor respondents, research stated, "In rapidly aging societies, the application of ro bots has spread from industry to nursing and social welfare." Financial supporters for this research include Japan Society For The Promotion o f Science. Our news editors obtained a quote from the research from Kyushu Institute of Tec hnology: "As the designs of industrial and non-industrial robots are different, numerous robot components with various shapes and stiffness are required for dif ferent tasks. In this study, we attached a variable-stiffness link based on a sh ape-memory alloy (SMA) and the jamming transition phenomenon to a robot arm and evaluated its pick-and-place motion for various objects with different shapes an d weights. The link can be fixed in an arbitrary shape and then restored to its initial shape via the shape memory effect. The objects were picked up and moved by a prototype link, which consisted of four SMA wires inserted in the jamming m echanism." According to the news editors, the research concluded: "We compared two states o f the link, namely with and without deformation of the link into a shape (the ce nterline and the cross section) to suit the target object using a mold. Experime nts confirmed that changing and fixing the link shape to suit the target object increased both positioning accuracy and weight capacity."

Kyushu Institute of TechnologyFukuokaJapanAsiaEmerging TechnologiesMachine LearningNano-robotRobotRoboti cs

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.6)