首页|Researchers from University of Toronto Describe Findings in Robotics (Moss: Monocular Shape Sensing for Continuum Robots)
Researchers from University of Toronto Describe Findings in Robotics (Moss: Monocular Shape Sensing for Continuum Robots)
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NSTL
IEEE
Data detailed on Robotics have been presented. According to news reporting from Mississauga, Canada, by NewsRx journalists, research stated, "Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge." Financial support for this research came from CGIAR. The news correspondents obtained a quote from the research from the University of Toronto, "Embedded shape sensing has high hardware complexity and cost, while vision-based methods require stereo setup and struggle to achieve real-time performance. This letter proposes a novel eye-to-hand monocular approach to continuum robot shape sensing. Utilizing a deep encoder-decoder network, our method, MoSSNet, eliminates the computation cost of stereo matching and reduces requirements on sensing hardware. In particular, MoSSNet comprises an encoder and three parallel decoders to uncover spatial, length, and contour information from a single RGB image, and then obtains the 3D shape through curve fitting. A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0.91 mm, or 0.36% of robot length) and real-time (70 fps) shape sensing on real-world data."
MississaugaCanadaNorth and Central AmericaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsUniversity of Toronto
NETL
NSTL
IEEE
