Papers
14
Total Citations
148
H-Index
6
About
Jiazhong Xu is a pioneering researcher in robotics and intelligent manufacturing, whose work bridges the gap between advanced control systems and real-world industrial applications. His primary research areas include multi-agent reinforcement learning for scheduling, haptic teleoperation, and robotic trajectory planning. Xu’s most impactful contribution is his 2023 paper on multi-task multi-agent reinforcement learning for real-time scheduling in dual-resource flexible job shops with robots, which has garnered 38 citations and addresses the complex challenge of coordinating multiple robots and machines in dynamic manufacturing environments. He also developed the human-inspired Fans Optimizer for mechanical design optimization (20 citations), and made significant advances in bilateral haptic teleoperation for hexapod robots, enabling semi-autonomous walking and manipulation with force feedback (18 and 17 citations). Xu’s work on industrial robot winding trajectory planning for elbow pipes (18 citations) and high-precision Sin-Cos encoder subdivision systems (7 citations) further demonstrates his versatility. His research on low-speed control of heavy-load transfer robots using Stribeck friction models (6 citations) and local path planning with improved dual covariant Hamiltonian optimization (5 citations) showcases his commitment to solving practical engineering challenges. Xu’s contributions are shaping the future of intelligent robotics and automated manufacturing.
Research Focus
Key Achievements
Top Papers
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- 9Control system design of robotized filament winding for elbow pipe5 citations · 2013
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