Papers
2
Total Citations
11
H-Index
2
About
Wu Zhe is a robotics researcher focused on advancing the motion planning and control of complex robotic systems, particularly redundant manipulators and humanoid cable-driven robots. His major contributions include developing an evolutionary multi-objective trajectory optimization method for redundant robots operating in Cartesian space, which effectively addresses obstacle avoidance during motion—a critical challenge in industrial and service robotics. This work, published in 2022, has garnered 9 citations, highlighting its relevance to researchers tackling safe robot navigation in cluttered environments. More recently, Wu Zhe has pioneered the integration of deep reinforcement learning with decoupling proportional-integral-derivative (PID) control for a novel humanoid cable-driven hybrid robot, designed to mimic the human arm’s structure for applications in service, medical, and rehabilitation fields. This 2024 publication, though early in its citation life (2 citations), represents a significant step toward more adaptive and human-like robotic control. His research bridges optimization, learning, and classical control, offering practical solutions for robots that must operate safely and efficiently alongside humans.
Research Focus
Key Achievements
Top Papers
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- 2