Papers
3
Total Citations
11
H-Index
2
About
Xiaobai Wang is a robotics and control systems researcher whose work spans two complementary domains: the adaptive control of space robotics and intelligent path planning for mobile robots. Their early contributions, published in 2010, focused on the challenging problem of controlling free-floating space robots (FFSRs) operating in microgravity environments. These systems lack a fixed base, creating complex dynamic coupling between manipulators and the spacecraft body. Wang addressed this through innovative robust fuzzy compensator-based adaptive controllers capable of handling friction, disturbances, and payload variations — work that has collectively garnered approximately 8 citations and laid meaningful groundwork for autonomous space manipulation research. More recently, Wang's research has evolved toward deep reinforcement learning, with a 2025 paper introducing DPDQN-TER, an enhanced algorithm for mobile robot path planning in dynamic, obstacle-dense environments relevant to industrial measurement and quality inspection tasks. This newer work, already accumulating early citations, reflects Wang's ability to bridge classical control theory with modern machine learning approaches. Across their career, Wang's research consistently addresses real-world robustness challenges in robotic systems operating under uncertainty — a thread connecting space exploration applications to terrestrial industrial automation.
Research Focus
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