Sho Morita

The University of Tokyo

Papers

2

Total Citations

31

H-Index

2

About

Sho Morita is a robotics researcher whose work focuses on enhancing the absolute positioning accuracy of industrial articulated robots. His primary research areas include kinematic modeling, error compensation, and precision calibration for six-degree-of-freedom (6-DOF) robotic systems. Morita’s major contribution lies in identifying and modeling bidirectional angular positioning deviations of rotary axes—a critical factor often overlooked in traditional Denavit–Hartenberg (D-H) parameter models. By incorporating these deviations into a novel kinematic framework, he has developed methods that significantly improve static volumetric error compensation, enabling robots to achieve higher precision in real-world manufacturing and automation tasks. His most-cited work, “Inclusion of Bidirectional Angular Positioning Deviations in the Kinematic Model of a Six-DOF Articulated Robot for Static Volumetric Error Compensation” (2022), has garnered 29 citations, reflecting its growing impact on the field of robot calibration. This research builds on his earlier foundational study from 2020, which laid the groundwork for identifying these deviations. Morita’s work is particularly notable for bridging the gap between theoretical kinematic models and practical industrial applications, offering engineers a more accurate tool for robot performance optimization. His contributions are essential reading for researchers and students interested in precision robotics, metrology, and advanced manufacturing.

Research Focus

Key Achievements

2
H-Index
2
Papers
31
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Inclusion of Bidirectional Angular Positioning Deviations in the Kinematic Model of a Six-DOF Articulated Robot for Static Volumetric Error Compensation
29 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: The University of Tokyo

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
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