首页|University of Texas Tyler Researchers Publish Findings in Robotics [Redesign of Leg Assembly and Implementation of Reinforcement Learning for a Multi-Purpose Rehabilitation Robotic Device (RoboREHAB)]
University of Texas Tyler Researchers Publish Findings in Robotics [Redesign of Leg Assembly and Implementation of Reinforcement Learning for a Multi-Purpose Rehabilitation Robotic Device (RoboREHAB)]
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Research findings on robotics are discussed in a new report. According to news reporting out of Tyler, Texas, by NewsRx editors, research stated, “Patients who are suffering from neuromuscular disorders or injuries that impair motor control need to undergo rehabilitation to regain mobility.” Financial supporters for this research include Capstone Senior Design Teams in The Department of Mechanical Engineering At The University of Texas. The news reporters obtained a quote from the research from University of Texas Tyler: “Gait training is commonly prescribed to patients to regain muscle memory. Automated-walking training devices were created to aid this process; while these devices establish accurate ankle-path trajectories, the knee and hip movements are inaccurate. In this work, a redesign of the leg assembly in a multi-purpose rehabilitation robotic device (RoboREHAB) was explored to improve hip- and knee-movement accuracy by adding an extra link and rollers to the assembly. Motion analysis was employed to test feasibility, reinforcement learning was utilized to train the new leg assembly to walk, and the joint motions achieved with the redesign were compared to those achieved by motion-capture (mocap) data. As a key result, the motion analysis showed an improvement in the knee- and hip-path trajectories due to the added roller/joint segment. The redesigned leg assembly, under the reinforcement-learning policy, showed a 5% deviation from the motioncapture joint trajectories with a maximum deviation of 51.177 mm but maintained a similar profile to the mocap trajectory data.”
University of Texas TylerTylerTexasUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningReinforcement LearningRoboticsRobots
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