Papers
6
Total Citations
226
H-Index
5
About
Long Quan is a pioneering researcher whose work spans robotics, control systems, and 3D computer vision. His most influential contribution, "Smooth point-to-point trajectory planning for industrial robots with kinematical constraints based on high-order polynomial curve" (123 citations), provides a foundational method for generating jerk-limited, smooth robot motions, directly impacting industrial automation efficiency. In control systems, his "Tracking differentiator based back-stepping control for valve-controlled hydraulic actuator system" (44 citations) offers a robust solution for precise hydraulic actuation, critical in heavy machinery and aerospace. Quan’s early work on "Image-Based Modeling" (42 citations) laid groundwork for reconstructing 3D scenes from photographs, a technique now central to computer graphics and autonomous navigation. More recently, his "Design and Analysis of a Novel Impact-Resistant Electro-Mechanical Actuator" (2023) addresses a key industry challenge—poor impact resistance in EMAs—by integrating disc springs and hydraulic buffering, promising safer, more durable actuators for aerospace and robotics. With over 200 total citations, Quan’s research bridges theoretical rigor and practical engineering, offering students and researchers robust frameworks for trajectory planning, actuator design, and visual servoing.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Image-Based Modeling42 citations · 2010
- 4
- 5Robot Stereo-hand Coordination for Grasping Curved Parts6 citations · 1998
- 6