首页|Reports on Robotics Findings from University of Texas Austin Provide New Insight s (A Biomechanics-aware Robot-assisted Steerable Drilling Framework for Minimall y Invasive Spinal Fixation Procedures)

Reports on Robotics Findings from University of Texas Austin Provide New Insight s (A Biomechanics-aware Robot-assisted Steerable Drilling Framework for Minimall y Invasive Spinal Fixation Procedures)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news reporting originating in Austin, Texas, by NewsRx jo urnalists, research stated, “In this paper, we propose a novel biomechanicsawar e robot-assisted steerable drilling framework with the goal of addressing common complications of spinal fixation procedures occurring due to the rigidity of dr illing instruments and implants.” Financial support for this research came from National Institutes of Health (NIH ) - USA. The news reporters obtained a quote from the research from the University of Tex as Austin, “This framework is composed of two main unique modules to design a ro botic system including (i) a Patient- Specific Biomechanics-aware Trajectory Sele ction Module used to analyze the stress and strain distribution along an implant ed pedicle screw in a generic drilling trajectory (linear and/or curved) and obt ain an optimal trajectory; and (ii) a complementary semi-autonomous robotic dril ling module that consists of a novel Concentric Tube Steerable Drilling Robot (C T-SDR) integrated with a seven degree-of-freedom robotic manipulator. This semi- autonomous robot-assisted steerable drilling system follows a multi-step drillin g procedure to accurately and reliably execute the optimal hybrid drilling traje ctory (HDT) obtained by the Trajectory Selection Module.”

AustinTexasUnited StatesNorth and Central AmericaAutonomous RobotBiomechanical EngineeringEmerging Technolog iesMachine LearningRobotRoboticsRobotsUniversity of Texas Austin

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.28)