Bingding Huang
Papers
6
Total Citations
295
H-Index
6
About
Bingding Huang is a robotics and artificial intelligence researcher whose work centers on motion planning, reinforcement learning, and robotic perception systems. His most influential contribution is a comprehensive 2021 review of motion planning algorithms for intelligent robots, which has garnered over 234 citations and has become a key reference for researchers navigating the landscape of traditional planning methods, machine learning approaches, and reinforcement learning techniques. Building on this foundation, Huang has made significant advances in deep reinforcement learning for complex robotic navigation, developing attention-based actor-critic algorithms with prioritized experience replay to tackle the particularly challenging problem of motion planning in dense, dynamic indoor environments — work that demonstrates both theoretical rigor and practical applicability. His research extends beyond simulated environments into real-world medical robotics, including an optical navigation robot-assisted puncture system for lung nodule biopsy that shows promising clinical safety and accuracy. More recently, Huang has expanded his scope to underwater robotics, reviewing tactile sensing and manipulation capabilities for subsea systems. Collectively, his publications reflect a researcher committed to bridging algorithmic innovation with applied robotics across diverse and high-impact domains.
Research Focus
Key Achievements
Top Papers
- 1A review of motion planning algorithms for intelligent robots234 citations · 2021
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- 6A review of motion planning algorithms for intelligent robotics8 citations · 2021