Papers
5
Total Citations
61
H-Index
5
About
Zhi Yang is a robotics and computational intelligence researcher whose work centers on redundant robot manipulator control, neural network-based motion planning, and the mathematical equivalence frameworks governing kinematic redundancy resolution. His most influential contributions lie at the intersection of optimization theory and real-time robot control, where he has advanced methods for ensuring precise, repeatable motion in redundant robotic systems. Yang's most cited work (19 citations) rigorously revisits and compares Ma equivalence and Zhang equivalence — two foundational frameworks for analyzing redundancy-resolution schemes at different mathematical levels — providing clearer theoretical grounding for the field. Complementing this, his 2017 study extended these equivalency analyses to minimum-kinetic-energy control with error-feedback incorporation, earning 12 citations and demonstrating his sustained commitment to theoretical refinement. On the applied side, Yang has made notable contributions to neural network-driven motion planning, particularly through linear variational inequality (LVI)-based primal-dual neural networks demonstrated on benchmark platforms including the PA10 and PUMA560 manipulators. His investigations into cyclic motion planning further highlighted subtle pitfalls in extending performance indices, offering important cautionary insights for robotics practitioners. Collectively, his body of work provides both theoretical clarity and practical tools for next-generation intelligent robotic systems.
Research Focus
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