Papers
3
Total Citations
83
H-Index
3
About
Shaoyang Hua is a robotics and control systems researcher whose work focuses on autonomous navigation, adaptive control, and intelligent inspection systems. His research spans key areas including simultaneous localization and mapping (SLAM), reinforcement learning for obstacle avoidance, and advanced sliding mode control for robotic platforms. Hua’s most influential work, “The Q-learning obstacle avoidance algorithm based on EKF-SLAM for NAO autonomous walking under unknown environments” (2015), has garnered 48 citations, demonstrating its impact on autonomous robot navigation in unstructured settings. He further advanced the field with his 2019 study on “Adaptive Extended State Observer-Based Nonsingular Terminal Sliding Mode Control for the Aircraft Skin Inspection Robot” (22 citations), which introduced robust control methods for precision inspection tasks. Another notable contribution, “Predictor-based adaptive feedback control for a class of systems with time delay and its application to an aircraft skin inspection robot” (13 citations), tackled the challenging problem of controlling systems with unknown time delays, unmeasurable states, and disturbances—a critical issue for real-world robotic applications. Hua’s work is particularly significant for its practical integration of theoretical control methods with physical robot platforms, advancing the reliability and autonomy of inspection robots in hazardous environments.
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