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About

Zenggui Gao is a leading researcher at the intersection of robotics, human-robot collaboration, and intelligent manufacturing. His work centers on developing advanced frameworks for human-robot collaborative assembly, with a particular focus on task allocation, action recognition, and motion planning. Gao's most impactful contributions include a dynamic task allocation framework integrating digital twin technology and an improved genetic algorithm with tabu search (IGA-TS), which has garnered 11 citations since its 2025 publication. He has also pioneered hybrid convolutional neural network approaches for recognizing collaborative actions in assembly tasks, achieving 11 citations for its potential to enhance sustainable manufacturing efficiency. Additionally, Gao's research spans non-holonomic spherical constraint underactuated parallel robotics (20 citations) and motion planning for seven-degree-of-freedom manipulators using deep deterministic policy gradient (DDPG) methods. His work on shape modeling of parallel soft panel continuum robots and spatiotemporal collaborative digital twin structural health monitoring further demonstrates his versatility. With over 60 total citations across his most-cited papers, Gao's research is shaping the future of intelligent, human-centric automation in manufacturing.