Hongjun San
Papers
11
Total Citations
170
H-Index
8
About
Hongjun San is a leading researcher in robotics, with key contributions spanning industrial robot calibration, bionic locomotion, and optimization algorithms. His work addresses fundamental challenges in robot accuracy and adaptability. San’s most cited paper (48 citations) introduces a hybrid BPNN-PSO algorithm for kinematic parameter identification, significantly improving industrial robot precision. He further advanced error compensation for articulated arm coordinate measuring machines (AACMM) using BP neural networks (28 citations), and developed a novel kinematic calibration method linking industrial robots with AACMM probes (13 citations). In bionics, San designed a quadruped robot with an antiparallelogram leg structure, enhancing load-bearing and gait switching via CPG oscillators (12 citations), and studied structural design for a 4-DOF parallel manipulator (8 citations). Notably, he proposed the wave search algorithm (25 citations) as a novel optimization method, and recently explored deep reinforcement learning for multi-robot pathfinding (2025). His work bridges theoretical innovation and practical application, with over 160 total citations, making him a pivotal figure in advancing robotic precision, locomotion, and intelligent control.
Research Focus
Key Achievements
Top Papers
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- 3A novel optimization method: wave search algorithm25 citations · 2024
- 4Structural design and gait research of a new bionic quadruped robot17 citations · 2021
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- 9Error Analysis of a New Five-Degree-of-Freedom Hybrid Robot7 citations · 2023
- 10Trajectory planning and simulation of 5-DOF hybrid robot based on ADAMS2 citations · 2017