Qinhu Chen
Papers
3
Total Citations
64
H-Index
3
About
Qinhu Chen is a robotics researcher whose work focuses on enabling multi-degree-of-freedom (DOF) robots to operate effectively in unstructured, real-world environments. His primary contributions lie in motion planning and inverse kinematics for complex robotic systems, including humanoid robots. Chen’s most cited paper, “A RRT based path planning scheme for multi-DOF robots in unstructured environments” (2024), has garnered 50 citations, demonstrating its impact on practical robot navigation. He further advanced the field with “Division-merge based inverse kinematics for multi-DOFs humanoid robots in unstructured environments” (2022, 8 citations), offering a novel approach to solving kinematic challenges. More recently, Chen has extended his expertise to agricultural robotics with “ET-PatchNet: A low-memory, efficient model for Multi-view Stereo with a case study on the 3D reconstruction of fruit tree branches” (2025, 6 citations), showcasing his ability to apply computer vision and efficient deep learning to real-world problems. His work bridges theoretical algorithms and practical deployment, making him a notable figure in robotics and automation.
Research Focus
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Top Papers
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