Tuochang Wu
Papers
2
Total Citations
6
H-Index
2
About
Tuochang Wu is a robotics researcher specializing in dynamic modeling, control, and human-robot interaction for industrial and humanoid systems. His work addresses critical challenges in high-precision manufacturing and autonomous vehicle operation. Wu’s most cited paper, “An Accurate Dynamic Model Identification Method of an Industrial Robot Based on Double-Encoder Compensation” (2023, 4 citations), introduces a novel approach to overcome inaccuracies caused by friction, link dynamics, and mechanical deformation. By leveraging double-encoder compensation, his method significantly enhances dynamic identification, enabling more reliable robot-based manufacturing. In a second notable contribution, “An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation” (2023, 2 citations), Wu tackles the complex task of enabling a 7-DOF humanoid manipulator to drive a vehicle. He proposes a fuzzy parameter-based admittance control strategy, advancing humanoid behavioral skills for real-world applications. Though early in his career, Wu’s work demonstrates clear impact by addressing fundamental gaps in robot precision and autonomy. His research holds promise for industries requiring high-accuracy automation and for developing robots capable of human-like manipulation in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 2