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Accurate pose measurement is crucial for parallel manipulators (PM). This study designs a novel integrated 6-DOF motion tracking system to achieve precise online pose measurement. However, the presence of geometric errors introduces imperfections in the accuracy of the measured pose. Based on the displacement information of six grating rulers, measurement pose is obtained through forward kinematics. By comparing the measurement results with the actual pose information captured by stereo vision, measurement errors can be obtained. A closed-loop vector-based kinematic model and an error model are established, and then the geometric errors are identified with the least-squares method. Finally, the geometric calibration experiments are conducted, and the results show that the measurement accuracy has significantly improved, with the average position error decreasing from 3.148 mm to 0.036 mm, and the average orientation error is decreased from 0.225° to 0.022°.
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http://dx.doi.org/10.1364/OE.510804 | DOI Listing |
J Biomech Eng
September 2025
Department of Biomedical Engineering and Lerner Research Institute, Cleveland Clinic Foundation, Department of Applied Biomedical Engineering, Cleveland State University, 2111 E 96th Street, Cleveland OH 44106.
To quantify the contributions of specific ligaments to overall joint biomechanics, the principle of superposition has been used for nearly 30 years. This principle relies on a robotic test system to move a biological joint to the same pose before and after transecting a ligament. The difference in joint forces before and after transecting the ligament is assumed to be the transected ligament's tension.
View Article and Find Full Text PDFBioengineering (Basel)
July 2025
School of Exercise and Health, Shanghai University of Sport, 200 Hengren Road, Shanghai 200438, China.
Hallux valgus (HV) is described as a lateral deviation of the great toe at the first metatarsophalangeal joint (MTP), which is a very common foot deformity in the clinic. This deformity extends beyond localized foot mechanics to affect the entire lower extremity kinetic chain, potentially increasing dynamic instability during locomotion. This study aimed to characterize the kinematics of ankle and knee joints during walking in HV patients compared to controls.
View Article and Find Full Text PDFVirtual Real
July 2025
AVRRC, Loughborough University, Epinal Way, Leicestershire, LE11 3TU England, UK.
The advancements in the field of XR devices and systems are interesting from an industrial point of view, as they present new opportunities for improving productivity and operations through-smart tooling, digitally enhanced assembly and maintenance, inspection, remote collaborations, etc. Typically, the XR headsets claim to provide a full 6-DoF tracking, while this may be good enough for consumer or entertainment applications; for an industrial application, we need to determine the exact errors and tolerances of the tracking for practical applications. In this paper, we present our methods and critical measurements from evaluating HTC Vive XR Elite and Magic Leap 2 for full 6-DoF tracking, depth perception accuracy, and drift accumulation over time.
View Article and Find Full Text PDFSensors (Basel)
July 2025
Department of Vehicle Technology, Faculty of Transportation Sciences, Czech Technical University in Prague, Konviktská 20, 110 00 Prague, Czech Republic.
The paper presents the innovative approach to a high-fidelity motorcycle riding simulator based on VR (Virtual Reality)-visualization, equipped with a Gough-Stewart 6-DOF (Degrees of Freedom) motion platform. Such a solution integrates a real-time tension sensor system as a source for highly realistic motion cueing control as well as the servomotor integrated into the steering system. Tension forces are measured at four points on the mock-up chassis, allowing a comprehensive analysis of rider interaction during various maneuvers.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
July 2025
Collecting a large amount of measured configuration data for robots entails high costs and time, which restricts the widespread use of neural networks for robot error prediction and compensation. In this study, a "transfer network" is established by taking the motion transmission characteristics inherent in the ideal kinematic model as prior knowledge and transferring it to a network trained based on the actual poses. Compared with the traditional back propagation (BP) neural network trained by actual poses alone, the transfer network shows significant performance advantages, effectively solving the problems of low prediction accuracy and weak generalization ability in the case of the small-sample measured data.
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