Papers
5
Total Citations
59
H-Index
3
About
Hang Fu is a robotics researcher focused on creating more lifelike and efficient robotic systems, with key contributions in musculoskeletal robots, bio-inspired control, and networked robotic manipulators. Fu’s most cited work, “Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection” (38 citations), addresses a core challenge in humanoid robotics: managing the high redundancy of joints and actuators to achieve human-like precision and flexibility. This work has been foundational for researchers aiming to build robots that mimic human movement. Fu also advances control theory for semi-Markov jump systems, developing asynchronous resource-aware controllers and passivity-based filters that reduce network resource consumption while maintaining stability—critical for applications like robot arms. Additional notable work includes bio-inspired modeling of pneumatic artificial muscles for manipulators and path planning for unmanned ground vehicles using an improved A-star algorithm. With a growing citation record and research spanning from theoretical control to practical robotic systems, Fu is making impactful strides toward the long-standing dream of creating robots with human-like behavior and appearance.
Research Focus
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