Papers
7
Total Citations
121
H-Index
3
About
Luping Luo’s research centers on advancing robotic manipulation and sensing, with key contributions in force/moment sensor design, energy-efficient trajectory planning, and high-degree-of-freedom (DOF) path planning. His work on a compact 6-axis force/moment sensor with a serial structure for humanoid robot feet (61 citations) has been instrumental in enabling precise force interaction for legged robots, while his mathematical modeling of such sensors provides a systematic foundation for their design. In industrial robotics, Luo developed a Lagrange interpolation-based method for trajectory planning that minimizes energy consumption (44 citations), directly addressing efficiency in manufacturing. For complex, high-DOF articulated robots, he proposed an adaptive rapidly-exploring random tree (RRT) algorithm that selects robot bodies based on task complexity, dramatically improving path planning in high-dimensional spaces. His recent work on dynamic trajectory planning for automatic grinding of large-curved forgings, using adaptive impedance control, demonstrates ongoing innovation in precision manufacturing. With over 120 total citations and a consistent focus on practical, implementable solutions—from sensor modeling to torque-minimized trajectories—Luo’s research bridges foundational theory and real-world robotic applications, making him a notable figure in robotics and automation.
Research Focus
Key Achievements
Top Papers
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- 6Mathematical modeling of a six-axis force/moment sensor2 citations · 2015
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