Huoming Wang
Papers
2
Total Citations
21
H-Index
2
About
Huoming Wang is a robotics researcher whose work focuses on advancing the precision and intelligence of collaborative robots through improved dynamics modeling and parameter identification. His research lies at the intersection of robot control, human-machine interaction, and optimization algorithms. Wang’s major contributions include developing a systematic error compensation strategy using an optimized recurrent neural network for collaborative robot dynamics, a method that enhances control accuracy and safety in human-robot collaboration. His work has been cited over 20 times, with his most notable paper, "A Systematic Error Compensation Strategy Based on an Optimized Recurrent Neural Network for Collaborative Robot Dynamics" (2020), receiving 13 citations. Additionally, his innovative application of an improved artificial fish swarm algorithm for dynamic parameter identification has provided a novel approach to obtaining accurate robot models, achieving 8 citations. Wang’s research is particularly significant for enabling precise control in self-developed six-degree-of-freedom collaborative robots, addressing critical challenges in optimal control and human-machine interaction. His achievements mark him as a promising contributor to the field of collaborative robotics.
Research Focus
Key Achievements
Top Papers
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