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DTSTAMP:20240506T200013Z
UID:A7ACEF46-26CE-4122-99F3-574AB07D8647
DTSTART;TZID=America/New_York:20240501T190000
DTEND;TZID=America/New_York:20240501T200000
DESCRIPTION:The vast majority of robots in use today operate in very struct
 ured environments\, e.g.\, in factory assembly lines\, and possess only th
 ose limited motion capabilities required to perform specific tasks. While 
 these robots can outperform humans in terms of speed\, strength\, and accu
 racy for these tasks\, they are no match for the dexterity of human motion
 . Part of a human's inherent advantage over industrial robots is due to th
 e large number of degrees of freedom in the human body. Articulated\, i.e.
 \, jointed\, motion systems that possess more degrees of freedom than the 
 minimum required to perform a specified task are referred to as kinematica
 lly redundant. In an effort to mimic the dexterity of biological systems\,
  researchers have built a number of kinematically redundant robotic system
 s\, e.g.\, anthropomorphic arms\, multi-fingered hands\, dual-arm manipula
 tors\, and walking machines. While these systems vary in their appearance 
 and intended applications\, they all require motion control strategies tha
 t coordinate large numbers of joints to achieve the high degree of dexteri
 ty possible with redundant systems. This talk will discuss the issues that
  arise when designing such strategies\, frequently drawing on the use of t
 he singular value decomposition\, including the characterization of redund
 ancy\, the quantification of dexterity\, and the development of efficient 
 and numerically stable motion control algorithms that simultaneously optim
 ize multiple criteria.\n\nSpeaker(s): Dr. Maciejewski\n\nVirtual: https://
 events.vtools.ieee.org/m/411361
LOCATION:Virtual: https://events.vtools.ieee.org/m/411361
ORGANIZER:
SEQUENCE:44
SUMMARY:KINEMATICALLY REDUNDANT ROBOTS: THE PROMISE OF HUMAN-LIKE DEXTERITY
URL;VALUE=URI:https://events.vtools.ieee.org/m/411361
X-ALT-DESC:Description: <br /><p><span style="font-size: 11.0pt\; font-fami
 ly: 'Times New Roman'\,serif\; mso-fareast-font-family: Calibri\; mso-fare
 ast-theme-font: minor-latin\; mso-ansi-language: EN-US\; mso-fareast-langu
 age: EN-US\; mso-bidi-language: AR-SA\;">The vast majority of robots in us
 e today operate in very structured environments\, e.g.\, in factory assemb
 ly lines\, and possess only those limited motion capabilities required to 
 perform specific tasks. While these robots can outperform humans in terms 
 of speed\, strength\, and accuracy for these tasks\, they are no match for
  the dexterity of human motion. Part of a human's inherent advantage over 
 industrial robots is due to the large number of degrees of freedom in the 
 human body. Articulated\, i.e.\, jointed\, motion systems that possess mor
 e degrees of freedom than the minimum required to perform a specified task
  are referred to as kinematically redundant. In an effort to mimic the dex
 terity of biological systems\, researchers have built a number of kinemati
 cally redundant robotic systems\, e.g.\, anthropomorphic arms\, multi-fing
 ered hands\, dual-arm manipulators\, and walking machines. While these sys
 tems vary in their appearance and intended applications\, they all require
  motion control strategies that coordinate large numbers of joints to achi
 eve the high degree of dexterity possible with redundant systems. This tal
 k will discuss the issues that arise when designing such strategies\, freq
 uently drawing on the use of the singular value decomposition\, including 
 the characterization of redundancy\, the quantification of dexterity\, and
  the development of efficient and numerically stable motion control algori
 thms that simultaneously optimize multiple criteria.</span></p>
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