arm design
A Fast and Model Based Approach for Evaluating Task-Competence of Antagonistic Continuum Arms
Fan, Bill, Roulier, Jacob, Olson, Gina
Soft robot arms have made significant progress towards completing human-scale tasks, but designing arms for tasks with specific load and workspace requirements remains difficult. A key challenge is the lack of model-based design tools, forcing advancement to occur through empirical iteration and observation. Existing models are focused on control and rely on parameter fits, which means they cannot provide general conclusions about the mapping between design and performance or the influence of factors outside the fitting data. As a first step toward model-based design tools, we introduce a novel method of analyzing whether a proposed arm design can complete desired tasks. Our method is informative, interpretable, and fast; it provides novel metrics for quantifying a proposed arm design's ability to perform a task, it yields a graphical interpretation of performance through segment forces, and computing it is over 80x faster than optimization based methods. Our formulation focuses on antagonistic, pneumatically-driven soft arms. We demonstrate our approach through example analysis, and also through consideration of antagonistic vs non-antagonistic designs. Our method enables fast, direct and task-specific comparison of these two architectures, and provides a new visualization of the comparative mechanics. While only a first step, the proposed approach will support advancement of model-based design tools, leading to highly capable soft arms.
- North America > United States > Massachusetts > Hampshire County > Amherst (0.14)
- North America > United States > Massachusetts > Norfolk County > Needham (0.04)
- Asia > China > Shandong Province > Qingdao (0.04)
Digital world-beater Arm needs a helping hand from Boris Johnson John Naughton
Last September, Nvidia, the American manufacturer of graphics processing chips, and the Japanese company SoftBank announced an agreement under which Nvidia would acquire the British chip designer Arm from SoftBank for $40bn. Since SoftBank had acquired Arm in 2016 for $32bn, you could say that a 25% profit on a five-year investment isn't to be sneezed at, especially if industry mutterings about SoftBank's crackpot investment strategy and Arm's internal difficulties with its China-based operation are to be believed. But even if one were foolish enough to sympathise with SoftBank's desire to climb out of the hole it had dug for itself, the idea that Arm should be sold to a US chip manufacturer is so daft that even Boris Johnson's administration had begun to smell a rat. And so on Monday it announced that the secretary of state for digital, culture, media and sport was "intervening in the sale on national security grounds", based on advice received "from officials across the investment security community". To which decision the only possible response is: what took him so long?
- Information Technology > Hardware (0.72)
- Information Technology > Artificial Intelligence > Machine Learning (0.51)
- Information Technology > Communications > Mobile (0.50)
The Making Of Sophia: Hardware Engineering for Arms and Hands - Hanson Robotics
Social robots living and working with humans must be able to navigate environments never designed for use by robots. Arms and hands are a vital tool for robots to manipulate common everyday objects, like door handles, pens, keyboards, or switches, not to mention giving humans handshakes or high-fives! Hands are also an essential means of communicating between people. They can be used as nonverbal shortcuts, like giving a thumbs up, or as a means to express a variety of different emotions. By having a human-like hand design, social robots can take advantage of these many nonverbal cues humans use to communicate.