Papers
3
Total Citations
40
H-Index
3
About
Hainan Pan is a robotics researcher specializing in autonomous motion control for tracked robots operating in complex, unstructured environments. His work focuses on integrating geometry-based planning and deep reinforcement learning to enable flipper-equipped tracked robots to traverse rough terrain and perform urban search and rescue tasks without expert teleoperation. Pan’s major contributions include developing a real-time flipper motion planning algorithm that uses geometric reasoning to overcome large obstacles and gaps, and pioneering deep reinforcement learning frameworks that leverage LiDAR data for autonomous flipper control in rescue environments. His most cited paper, “Geometry‐based flipper motion planning for articulated tracked robots traversing rough terrain in real‐time” (2023, 18 citations), demonstrates a practical approach to automating flipper adjustments. Another influential work, “Deep Reinforcement Learning for Flipper Control of Tracked Robots in Urban Rescuing Environments” (2023, 17 citations), advances intelligent operation in complex settings. Most recently, Pan introduced FTR‐Bench (2025), a benchmark for standardizing deep reinforcement learning evaluation in flipper-track robot control, providing a critical tool for the research community. His cumulative work addresses a key gap in autonomous ground robotics, with growing impact as tracked robots become vital in disaster response.
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